National Aeronautics and Space Administration

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Flash drought is characterized by the rapid intensification toward drought conditions and can lead to wide-ranging impacts, including agricultural yield loss, reduction in water resources, moisture stress on ecosystems, and increased risk of wildfires and heatwaves. Unlike conventional (slowly developing) drought, flash drought can rapidly desiccate land surface conditions in only the span of a few weeks and place excessive stress on the environment. Flash droughts present challenges for drought mitigation strategies as they often develop with limited warning, and their characteristics, evolution, and drivers are not well understood. To address some of the challenges related to flash drought development and their associated impacts, this presentation highlights 1) the temporal and spatial evolution of flash drought via case study analysis, 2) a regional and global climatology of flash drought occurrence, and 3) future projections of flash drought risk in a changing climate. These research tasks are addressed by using a combination of reanalysis datasets, satellite observations, and global climate models on local to global scales. While the results from this research highlight key advancements in our understanding of flash droughts, several future research pathways exist to develop monitoring techniques of flash drought, improve the predictability of these events, and untangle the complex interactions between flash drought and socioeconomic impacts.
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In recent years, the severity, frequency and spatial extent of the extreme heat and precipitation events have even surprised climate scientists. The record-shattering 2021 Pacific Northwest(PNW) heatwave that led to deaths in the thousands and promoted wildfires affecting air quality throughout the continent, and the devastating 2022 Pakistan floods that killed over 1700 people and displaced more than 33 million, are two of the extreme examples. In this talk, I’ll discuss the weather and climate conditions that led to the 2021 PNW heat extremes and the 2022 Pakistan extreme precipitation, and their future implications. I’ll also discuss what these two events have in common in terms of their causal mechanisms and the role climate change may have played in both events.
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As temperatures rise due to global warming, fundamental thermodynamics dictate that the hydrologic cycle will intensify in response. Most of this response arises from the exponentially increased propensity for warmer air to “hold” water vapor, which in turn raises the ceiling on how intense both precipitation and evaporation can become. Separately, these effects can account for increases in both heavy precipitation events and extreme drought events at opposite ends of the hydroclimate spectrum. When they occur in rapid succession, however, they give rise to “hydroclimate whiplash”—sudden transitions between extremely wet and extremely dry states that can greatly amplify societal and ecological impacts. In this talk, I will review the recent literature on hydroclimate whiplash on a warming Earth and offer new evidence that large and nearly universal global increases in such volatility should be expected as the Earth continues to warm. Finally, I'll offer thoughts on some of the less obvious downstream consequences of a warmer, more volatile hydroclimate--from wildfires to snowstorms and even co-seismic hazards.
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Water availability is drastically changing and fluctuating the world over including the western U. S., creating challenges for water managers who manage systems that are designed and operated based on assumptions of past variability. More and more, it’s clear that the stationarity paradigm of water management no longer applies. This requires new approaches, providing NASA an unprecedented opportunity to directly benefit water management in the western U. S.. The NASA Western Water Applications Office (WWAO), within the Earth Action Water Resources Program, aims to address the pressing needs and challenges of Western water resource managers through sustained engagement with the community to understand decision contexts, identification of gaps and needs in monitoring and information, implementation of projects that address the needs, and assistance in transitioning successful projects to operational use. This talk provides an overview of NASA’s WWAO, along with a description of activities and highlights of current projects and past successes.
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Join me for an insightful exploration of Earth science data management from the perspective of NASA Headquarters. In this talk, I’ll share personal experiences alongside an historical overview of NASA’s approach to handling Earth Science Data Systems and some perspectives on its future. We’ll discuss the practical challenges of data acquisition and management and distribution, highlighting the role of the system in ensuring effective stewardship of valuable scientific resources. Together, we’ll examine the intersection of technology and innovation in our ongoing journey to explore and better understand our planet.
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Gravity waves are ubiquitous in the Earth’s atmosphere. While they are generated predominantly in the troposphere by e.g., flow over orography and strong convective events in the tropics, their largest impact on the circulation is in the middle atmosphere. There they attain large amplitudes and on breaking/saturation drive important features that modulate atmospheric teleconnections, which are a major source of predictability for surface weather and climate. Such features in the stratosphere include the quasi-biennial oscillation, the polar vortices, and the subtropical jets above the tropopause. Because gravity waves have a broad wavelength spectrum, most models rely on gravity wave parameterizations to simulate their effect on the large-scale flow. However, global models that explicitly resolve gravity waves are now possible due to recent technological advancements. Using ECMWF IFS global simulations at horizontal grid-spacing ranging from 10 km to 1 km, in which gravity waves are partially or fully resolved, this talk elucidates the following questions relating to the representation of gravity waves in the stratosphere: i) At what horizontal grid-spacings do we expect to resolve the whole gravity wave spectrum so that parameterizations of gravity waves are no longer needed? And ii) What can we learn from km-scale models with regards to the parametrization design at lower resolution? Finally, the impact of making the hydrostatic approximation (as is currently done at ECMWF) on resolved gravity waves is discussed.
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It is well-documented that the atmospheric circulation changes in response to increased CO2, although the responses differ among aspects of the circulation, and the causes of these differences are not well understood. More recently, studies have also shown that the atmospheric response can be not only a nonlinear, but also non-monotonic, function of CO2 forcing. We begin by showing that this nonlinearity in the atmospheric circulation response occurs more broadly across the Coupled Model Intercomparison Project (CMIP) Phase 6 archive and occurs in association with a collapse of the Atlantic Meridional Overturning Circulation (AMOC). To illustrate this last point, we then isolate the climate impacts of a weakened AMOC using a unique ensemble of Shared Socioeconomic Pathway (SSP) 2-4.5 integrations performed using the CMIP6 version of the NASA Goddard Institute for Space Studies ModelE (E2.1). In these runs internal variability alone results in a spontaneous bifurcation of the ocean flow, wherein two out of ten ensemble members exhibit an entire AMOC collapse, while the other eight recover at various stages despite identical forcing of each ensemble member and with no externally prescribed freshwater perturbation. We show that an AMOC collapse results in an abrupt northward shift and strengthening of the Northern Hemisphere (NH) Hadley Cell and intensification of the northern midlatitude eddy-driven jet. Comparisons with a set of coupled atmosphere-ocean abrupt CO2 experiments spanning 1-5xCO2 reveal that this response to an AMOC collapse results in a nonlinear shift in the NH circulation moving from 2xCO2 to 3xCO2. Slab-ocean versions of these experiments, by comparison, do not capture this nonlinear behavior. Finally, provided time, we show that the CO2 forcing at which this nonlinearity occurs can be influenced by stratospheric ozone feedbacks. Overall, our results suggest that changes in ocean heat flux convergences associated with an AMOC collapse — while highly uncertain — can result in profound changes in the NH atmospheric circulation.
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The integrated observing system for air quality is made up of ground-based, airborne, and satellite observations that are interpreted with models to provide the best understanding of atmospheric composition. Airborne field campaigns provide the opportunity to exercise the entire observing system. Field campaign modeling work will be presented from prior field campaigns including the NASA SEAC4RS, ATom, and KORUS-AQ missions with the goal of illustrating how multiple observational perspectives are invaluable for improving models. Specific examples will illustrate improvements to emissions inventories, constraints on model oxidation capacity, and missing physical processes that impact simulated surface air quality.
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The January 2022 Hunga Tonga-Hunga Ha'apai eruption was one of the most explosive volcanic events of the modern era, producing a vertical plume that peaked more than 50 km above the Earth. The initial explosion and subsequent plume triggered atmospheric waves that propagated around the world multiple times. A global-scale wave response of this magnitude from a single source has not previously been observed. Here we show the details of this response, using a comprehensive set of satellite and ground-based observations to quantify it from surface to ionosphere. A broad spectrum of waves was triggered by the initial explosion, including Lamb waves propagating at very high phase speeds of 318.2 ± 6 m/s at surface level and between 308 ± 5 to 319 ± 4 m/s in the stratosphere, and gravity waves propagating at 238 ± 3 to 269 ± 3 m/s in the stratosphere. Gravity waves at sub-ionospheric heights have not previously been observed propagating at this speed or over the whole Earth from a single source. Latent heat release from the plume remained the most significant individual gravity wave source worldwide for more than 12 h, producing circular wavefronts visible across the Pacific basin in satellite observations. A single source dominating such a large region is also unique in the observational record. The Hunga Tonga eruption represents a key natural experiment in how the atmosphere responds to a sudden point-source-driven state change, which will be of use for improving weather and climate models.
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Accurate, physically-based precipitation retrieval over global land surfaces is an important goal of the now 10 year-old NASA/JAXA Global Precipitation Measurement Mission (GPM). This is a challenging task for the passive microwave constellation, as the signal over radiometrically warm land surfaces in the microwave frequencies means that the measurements used are indirect, and typically require inferring some type of relationship between an observed scattering signal and precipitation at the surface. GPM, which includes a core satellite with collocated radiometer and dual-frequency radar, along with a constellation of partner radiometers, is an excellent tool for testing and validating improved passive retrievals. The operational GPM passive microwave retrieval scheme, the Goddard Profiling Algorithm (GPROF) is a Bayesian probabilistic scheme, utilizing an a priori database constructed using the GPM core satellite along with radiative transfer to expand to the full constellation. This operational technique, along with the emergence of AI/deep learning technology, makes the quality of the a priori database of utmost importance for the implementation and training of the schemes. For accuracy in the radiative transfer calculations required for creating the databases, emissivity is a key variable. In contrast to the radiometrically cold ocean surface, land emissivity in the microwave is large with highly dynamic variability. An accurate understanding of the instantaneous, dynamic emissivity in terms of the associated surface properties is necessary for a physically based retrieval of precipitation and other geophysical variables over land. This presentation will introduce and discuss various avenues for emissivity retrieval and modeling along with implementations of emissivity information for atmospheric retrieval and modeling applications with a focus on precipitation.
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Clouds play a central role in the global climate system through the modulation of Earth’s energy flows and as a mediator of precipitation. In the Arctic, clouds are also a major player; however, the processes that govern cloud evolution in the Arctic differ from most of the other regions of the globe. Thus, clouds are a key “wildcard” within the Arctic climate system that could have a substantial influence on the Arctic climate system response to anthropogenic forcing. What is the role of clouds within the phenomenon known as Arctic Amplification? This is question does not have a clear answer. Clouds seem to be a center to the important processes driving Arctic Amplification (e.g., the atmospheric response to sea ice loss and airmass transformation), however feedback analysis studies indicate that net cloud feedback in the Arctic is small. This seminar discusses the role of clouds within the Arctic Amplification processes reviewing aspects of what we know about Arctic clouds and the uncertainties that limit our ability to model them. Results from recently published and ongoing work are presented that provide an observationally-based estimate of the cloud-sea ice feedback and evaluate cloud properties within models. The goal of this presentation is to ignite a discussion and new collaborations around the best approaches to resolving uncertainties related to the role of clouds with the Arctic system and how to better represent Arctic clouds in models.
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Some of the most consequential outcomes of global warming for societies and ecosystems are changes in extreme events. Comparing 2000-2019 with 1980-1999, extreme temperature and flood events have more than doubled globally while the number of disastrous storms and droughts has increased by 30-50%. While the nonlinear increase in latent energy with warmer surface air temperature may explain the global increasing trends in weather extremes, credible projections of the regional changes in extreme events and changes in different types of extreme events remain challenging, partly because of model limitations in simulating the extreme events. In this seminar, I will discuss some recent advances in modeling extreme events and their future changes using a hierarchy of models spanning simple conceptual models to computationally intensive global storm-resolving models. Examples including modeling of mesoscale convective systems, atmospheric rivers, and hurricanes will be highlighted.
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Reliable probabilistic forecasts about the potential for warmer, colder, wetter, or drier conditions at a few weeks to several seasons lead are valuable for routine planning and resource management. Many sectors would benefit from these predictions, including emergency management, public health, energy, water management, agriculture, and marine fisheries. The ability to make skillful forecasts on subseasonal (2-4 week) and seasonal (1-12 months) lead-times depends on understanding and harnessing the predictive capabilities of key sources of predictability. I will present results from research which are advancing our predictive capabilities on these timescales through understanding sources of predictability. First, I will demonstrate the current state of subseasonal prediction skill and the benefit of a multi-model ensemble using the national, multi-model, research to operations project called The Subseasonal Experiment (SubX). The SubX models show skill for temperature and precipitation three weeks ahead of time in specific regions. The SubX multi-model ensemble mean is more skillful than any individual model overall. While average skill at individual gridpoints on subseasonal timescales is relatively low, there is potential for more skillful predictions through identification of forecasts of opportunity. However, subseasonal precipitation predictions remain a significant challenge. In the second part of this presentation, I investigate sources of predictability for South-East US (SEUS) precipitation using explainable machine learning. We investigate the predictability of the sign of daily SEUS precipitation anomalies associated with large-scale climate variability where the predictors are perfectly known. Indices of climate phenomena (e.g., NAO, AMO, PDO, ENSO, MJO, etc.) produce neither accurate nor reliable predictions, indicating that the indices themselves are not good predictors. A convolutional neural network using gridded fields as predictors is reliable and more accurate than the index-based models. Using explainable machine learning we identify which variables and gridpoints of the input fields are most relevant for confident and correct predictions. Our results show that the local circulation is most important as represented by maximum relevance of 850hPa geopotential heights and zonal winds to making skillful, high probability predictions. Corresponding composite anomalies identify connections between SEUS precipitation and the El-Ni\~{n}o Southern Oscillation during winter and the Atlantic Multidecadal Oscillation and North American Subtropical High during summer.
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In June 2021, the Pacific Northwest of the US and Canada experienced a heat wave of historical proportions. Many locations broke all time high temperature records, often by several degrees, with severe human and ecological impacts. This presentation will describe and diagnose the meteorological conditions that caused this event to occur and be so severe. We use synoptic analysis and air parcel back trajectories to show that the atmospheric setup was similar to past severe heat waves, only much stronger. In particular, a record breaking ridge of high pressure was critical in driving temperatures to such extreme values. Climate model projections of future changes in ridges in the region will also be explored.
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In this talk I'll outline the challenges and opportunities of using data (from high resolution models as well as observations) to inform the physics within global atmospheric models. I will present some progress we've made in development and data-driven constraint of cloud microphysics parameterizations, as well as results from our work tuning the NASA GISS ModelE Global Climate Model with satellite observations, Bayesian inference, and machine learning. Most of this work leverages perturbed-parameter ensembles (PPEs) with machine learning surrogates (or ensembles of these surrogates), and Markov Chain Monte Carlo sampling for parameter estimation and uncertainty quantification. Despite some notable successes with this approach, many details of this inference workflow remain ad-hoc, and there is need for systematic evaluation and optimization of choices, a goal hampered by the extreme computational cost of global models. Additionally, it is unclear the appropriate way to share information across scales, from laboratory studies through high resolution large-eddy simulations all the way to global climate simulations. I'll summarize current work towards a more systematic calibration workflow, as well as outstanding challenges associated with data-driven constraint of parametric and structural uncertainties in physical models of atmospheric processes.
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Weather forecasters’ operational risk assessment and forecast decision-making environments are complex, dynamic, and increasingly focused on providing the best predictions and decision-support information for high-impact weather risks. New numerical weather prediction (NWP) and artificial intelligence / machine learning (AI/ML) guidance is constantly – and increasingly – being developed to support forecasters’ roles. To support the development and refinement of guidance that is most useful for forecasters, for nearly the last decade, we have been conducting inter- and transdisciplinary research with social, physical, and computational science that is also user-based, meaning that it is driven by data collected with forecasters. This presentation will discuss results from a suite of NOAA- and NSF-funded research projects with National Weather Service forecasters that has focused on (a) eliciting forecasters’ perceptions of and needs for high-resolution ensemble guidance, (b) developing probabilistic timing guidance for winter and fire weather forecasting using, and (c) understanding forecasters’ perspectives on the trustworthiness of AI/ML and guiding development of AI/ML tools accordingly.
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Despite its simple definition (a period of abnormally dry condition), drought is a complex phenomena, affected by a diverse array of physical and biological processes, some of which may become increasingly important with climate change. Climate models can provide a critically important tool for understanding the role of these processes in driving drought variability in the past and future, especially in the context of climate change. In this talk, I will discuss several case studies, demonstrating how we have used climate model experiments to investigate and inform our understanding of past and future drought events in western North America. These include the impact of land degradation on the Dust Bowl drought of the 1930s; how the naturally-occurring 1950s drought would intensify in a warmer world; and the likely increased risk of megadroughts in the future analogous to the recent multi-decadal event in Southwestern North America. These studies highlight the utility of climate models for investigating observed drought events, and the insights they can provide on how anthropogenic processes can influence drought risk and severity.
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Ice sheets are subject to the whims of a variable climate system and the complexity of small-scale glaciological processes. Simulating the effects of these processes in physical ice sheet models is computationally expensive and subject to considerable process uncertainty. In this talk, I explore a hierarchy of stochastic approaches, ranging from the mathematical to the intensely computational, for the problem of capturing variability in a range of processes that force ice sheets. Stochastic approaches recognize that ice sheets integrate the effects of rapidly varying processes, such that these processes can be equivalently represented by their statistics rather than their detailed deterministic dynamics. Stochastic methods constitute a parallel strategy for making progress on the outstanding problems with current deterministic ice sheet modeling and reveal some new problems as well. I discuss how we apply these stochastic modeling methods to two particularly challenging issues: (1) quantifying uncertainty in projections of future ice sheet change, and (2) quantifying the human contribution to past ice sheet change.
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The relative merits of different ocean observation systems (moored buoys, Argo, satellite, XBTs and others) are evaluated by their impact on ocean analyses and subseasonal forecast skill. Several ocean analyses were performed where different ocean observation platforms were withheld from the assimilation in addition to one ocean analysis where all observations were assimilated. These ocean analyses products are then used for initializing a set of subseasonal forecasts to evaluate the impact of different ocean analyses states on the forecast skill. Results from the NASA GMAO and the European Centre for Medium-Range Weather Forecasts (ECMWF) assimilation systems and ensemble prediction systems’ experiments will be presented to highlight changes in the ocean analyses states in the tropical Indian and Pacific Ocean and their impact on the forecast skill from weather to subseasonal timescales. Coupled air-sea interaction processes relevant to weather and intraseasonal variability in the earth’s climate system are inadequately represented in regional and global coupled models. These inaccuracies could be related to either poor parameterization of model physics or insufficient model resolution to resolve the critical processes. New efforts in observations, process understanding and translation into weather and climate models are necessary for improvements in simulation and prediction of the intraseasonal variability and associated weather events. We will discuss the merits of different observation platforms in this context and future observation and model improvement pathways.
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Disturbance, rising atmospheric CO2 and changing climate are rapidly shifting the functional status quo of terrestrial ecosystems, yet the myriad process responses involved confound our ability to resolve the future trajectory of terrestrial ecosystem carbon reservoirs. As a result, the state-of-the-art biogeochemical models fundamentally disagree on the sign and magnitude of the land C sink in coming decades. Repeat observations of the Earth’s terrestrial ecosystems—vegetation states, greenhouse gas exchanges, disturbances and their inextricable links to the water and energy cycles—hold the clues to how ecosystem scale C cycling is responding to a changing environment. Fusing observational knowledge with process modelling is therefore an urgent priority for advancing decadal projections of the land C sink. I will show how using Bayesian inference to constrain biosphere model processes using multi-decadal satellite and ground observation records can provide the necessary quantitative insights on the evolution of the land C sink, carbon-water interactions, the CO2 fertilization effect and the magnitude and sign of terrestrial carbon-climate feedbacks.
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For many years the most effective way to assimilate data into a numerical weather prediction model has been four dimensional variational assimilation (4D-Var). One of the difficulties with 4D-Var has been the development and maintenance of the linear model, approximately tangent linear to the full model, for evolving perturbations. The linear model is particularly problematic for physical parameterisations. We present a new method, the hybrid tangent linear model, which solves most of these long-standing issues. Reference: T.J. Payne, “A Hybrid Differential-Ensemble Linear Forecast Model for 4D-Var”, Monthly Weather Review (2021), doi: https://doi.org/10.1175/MWR-D-20-0088.1.
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This talk will explore the tropical cyclone projections in the CMIP6 models. Standard environmental proxies for tropical cyclone activity, such as potential intensity, genesis indices, and the ventilation index have been calculated in the CMIP6 ensemble. First, I will discuss how the global historical climatological patterns of these environmental proxies in the CMIP6 models compare with the ERA5 reanalysis climatology and show the systematic biases across the CMIP6 models. Then, the expected range of future projections of these proxies will be shown for three future scenarios, namely ssp245, ssp370 and ssp585, for the end of the 21st century. The role of ENSO diversity in the modulation of TC environmental proxies will also be discussed, as model biases in simulating ENSO diversity can lead to significant model differences, both in present and future climates. The role of ENSO diversity in shaping the tropical cyclone-ENSO relationship in present and future climates will be explored. In the last part of our talk, a statistical-downscaling model that generates synthetic tropical cyclones from reanalysis and climate models large-scale fields will be presented. I will show the results obtained when downscaling the CMIP6 models and what we can learn from them.
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High-resolution numerical weather prediction models are a useful tool for obtaining a better understanding of the processes related to cloud and precipitation formation; however, the spatial extent and life-cycle of simulated clouds are quite sensitive to the assumptions made by model parameterization schemes. The main motivation for this work is to increase understanding of the processes leading to convective initiation (CI) through application of object-based evaluations commonly used in geostationary satellite nowcasting studies. By taking advantage of new high temporal resolution data from the GOES-16 Advanced Baseline Imager (ABI) we track and evaluate the life-cycle of CI events produced by high-resolution (500 m) Weather Research and Forecasting (WRF) model simulations employing different microphysical and land surface parameterization schemes. The work presented will focus on a case study in southeastern United States, however, the satellite-based methodology for tracking and analyzing convection is applicable globally. The southeast U.S. poses an especially challenging forecast problem related to convective storm initiation and the upscale development of convective storms during the summer months when synoptic scale forcing is typically weak. Using the object-based methodology, cloud properties derived from individual cloud objects are examined and assessed using infrared brightness temperatures from the 5-min GOES-16 imagery. Cloud objects are tracked over time and related to observed clouds reaching CI to examine the impacts of microphysical and land assumptions leading to CI onset, cloud extent, longevity, and growth rate.
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First half: The early-to mid-Pliocene (5.3–3 Myr), characterized by warmer temperatures and a similar CO2 concentration to that at present, is considered a useful analog for future warming scenarios. Geological evidence suggests that at that time, modern-day desert regions in the South-West US, including Death Valley in California, received higher levels of rainfall and supported large lakes. These wetter conditions have been difficult to reconcile with model projections of 21st-century drying over the same areas. We show that this discrepancy between past evidence and future projections may be due to the models missing an important feedback: Increasing sea surface temperature (SST) due to a weakening of the California coastal upwelling leads to wetter conditions over nearby land, and wetter land leads to a weakening of the wind that forces the upwelling. The mechanism and consequences are discussed. [Work led by Minmin Fu]. Second half: Increases in extreme weather events are an important possible consequence of anthropogenic climate change (ACC), yet it is famously difficult to attribute individual events to ACC. We are motivated by recent attribution studies by the ``World Weather Attribution Project'' (WWAP) based on fitting the observed record to extreme value distribution functions and making the distribution parameters a function of the observed global mean surface temperature (GMST). We re-examine three attribution cases, suggesting modifications that may increase our confidence in the meaningfulness of the attribution results. We test (1) if an extreme value distribution (vs. a normal or log-normal distribution) is required by the data, (2) if the addition of a GMST-dependence of the distribution parameters is justified statistically, and (3) if the errors in the GMST dependence allow a meaningful attribution. We find that the uncertainty in GMST dependence tends to make it difficult to make a confident attribution and that natural variability can lead to a seeming dependence on GMST that does not reflect ACC and may lead to wrong attribution conclusions. [With Peter Sherman and Peter Huybers]
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The primary objective of NOAA's Quantitative Observing System Assessment Program (QOSAP) is to improve quantitative and objective assessment capabilities to evaluate operational and future observation system impacts and trade-offs to assess and to prioritize NOAA’s observing system architecture. Observing System Simulation Experiments (OSSEs) are a vital tool as part of the assessment of future observing systems. The OSSE system used as part of these studies will be discussed, outlining both advantages and caveats for their use. In addition, examples of how the OSSE system has been used to provide guidance on future observing system plans will be provided.
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Across the broader scientific community, rapid advances in machine learning (ML), and deep learning (DL) in particular, have inspired researchers to consider how these tools might enable new science advances that previously would have been unattainable. The appeal in ML usage stems in part from the ability of ML to model complex nonlinear systems, and in part from recent algorithmic and computational advances (such as graphics processing units, i.e., GPUs) that have improved and accelerated DL model training. For the field of subseasonal-to-seasonal (S2S) prediction (timescales of two weeks to two months), skillful prediction of precipitation remains very difficult. Predictability stemming from atmospheric initial conditions is substantially reduced beyond approximately two weeks and the ocean generally does not offer added predictability until a trajectory reaches the seasonal timescale. Imperfect initial conditions and model systematic errors also contribute to the difficulty of deterministic initialized forecasts. Ensemble forecasting has helped assess forecast spread in relation to initial condition errors, but the high cost of running global initialized forecasts precludes the creation of many ensemble members. These challenges motivate the use of ML and DL methods for S2S prediction. Two approaches to S2S prediction research using the Community Earth System Model version 2 (CESM2) will be highlighted: (1) a predictability study and (2) a bias correction approach. The first study focuses on assessing the predictability of North American weather regimes, which are persistent large-scale atmospheric patterns that can imprint on surface anomalous precipitation. Various Earth system components, such as the atmosphere and land, will be used to assess contributions to predictability. The second study focuses on the use of DL models for offline bias correction of S2S forecasts of global precipitation. The DL model architectures include image-to-image approaches (e.g., U-Net), which enables learning of spatial patterns and displacement errors in CESM precipitation fields using various convolutional and pooling layers. We also show how DL methods can be leveraged to create a large ensemble of subseasonal forecasts.
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Atmospheric rivers (ARs), tropical storms (TSs), and mesoscale convective systems (MCSs) are important weather phenomena that often threaten society through heavy precipitation and strong winds. Despite their potentially vital role in global and regional hydrological cycles, their contributions to long-term mean and extreme precipitation have not been systematically explored at the global scale. Using observational and reanalysis data, and NOAA’s Geophysical Fluid Dynamics Laboratory’s new high-resolution global climate model, we quantify that despite their occasional (13%) occurrence globally, AR, TS, and MCS days together account for ∼55% of global mean precipitation and ∼75% of extreme precipitation with daily rates exceeding its local 99th percentile. The model reproduces well the observed percentage of mean and extreme precipitation associated with AR, TS, and MCS days. In an idealized global warming simulation with a homogeneous SST increase of 4 K, the modeled changes in global mean and regional distribution of precipitation correspond well with changes in AR/TS/MCS precipitation. Globally, the frequency of AR days increases and migrates toward higher latitudes while the frequency of TS days increases over the central Pacific and part of the south Indian Ocean with a decrease elsewhere. The frequency of MCS days tends to increase over parts of the equatorial western and eastern Pacific warm pools and high latitudes and decreases over most parts of the tropics and subtropics. The AR/TS/MCS mean precipitation intensity increases by ∼5%/K due primarily to precipitation increases in the top 25% of AR/TS/MCS days with the heaviest precipitation, which are dominated by the thermodynamic component with the dynamic and microphysical components playing a secondary role.
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Surface-based observations, such as those from conventional surface stations, radiosondes, aircraft and weather radar, provide invaluable information in both global and regional assimilation systems. At the Met Office, the Assimilation of Surface-based Observations Group is responsible for carrying out research to improve and develop the assimilation of the wide range of surface-based observations, and to increase their impact in operational numerical weather prediction systems. This seminar will present some of the recent research and operational updates provided by members of the Assimilation of Surface-based Observations Group including: the assimilation of sonde descents, direct assimilation of radar reflectivity, the increased use of roadside sensor data, understanding the impact of aircraft observations and an overview of our upgrade to the observation processing system.
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The US operational global data assimilation system cycles with a six-hourly cadence, which is not frequent enough to handle the rapid error growth associated with fast-moving hurricanes or other storms. This motivates development of an hourly-updating global data assimilation system, but observational data latency can be a barrier. Two methods are presented to overcome this challenge: “catch-up cycles”, in which a 1-hourly system is reinitialized from a 6-hourly system that has assimilated high-latency observations; and “overlapping assimilation windows”, in which the system is updated hourly with new observations valid in the past three hours. The performance of these methods is assessed in a near-operational setup using the Global Forecast System by comparing short-term forecasts to in-situ observations. Methods to control high-frequency noise induced by applying analysis increments every hour are also discussed.
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Air quality managers have long relied on atmospheric chemistry measurements and models to support decision-making. Today an even wider audience of policy, planning, and advocacy organizations are interested in air quality and climate data. By collaborating with these new communities, especially energy, health, and environmental justice. scientists can expand the impact of existing knowledge, data, and tools. The evolution of satellite data for air quality and health applications highlights these opportunities, with lessons learned over the past 10 years through initiatives of the NASA Applied Sciences Program. Atmospheric models play an important role interpreting satellite data, connecting emissions and impacts, and answering “what if?” questions relevant to policy and planning. Advanced regional models, including the EPA Community Multiscale Air Quality (CMAQ) model, are well-suited to policy applications, but may not be appropriate for all user needs. Simpler, reduced-form models, including COBRA, InMAP, and AERMOD, complement complex models to support a wider range of partners and problems. Traditional scientific frameworks are evolving to better support engagement and to expand the benefits of science to new issues and communities. Still, challenges remain, especially for early-career scientists balancing academic milestones with “real-world” engagement and societal impact.
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Climate data records of surface temperature, including that from NASA GISS, show that 2022 is among the warmest years in the modern era, because of greenhouse-gas-induced heating. Over the USA, and the world, NOAA monitoring isolated many tens of record extrema, related to temperatures, precipitation, and severe weather events. This presentation will give a NASA-focused view of 2022 (and early 2023) focused on the unique observations and their impacts on analyses and predictions of many components of the Earth system. The presentation will feature accomplishments using the Goddard Earth Observing System (GEOS) family of global models and assimilation systems, developed and maintained in the GMAO. There will be some focus on the planned evolution of the GEOS system over the next few years, as GMAO provides the global Earth system modeling and analysis infrastructure required to support NASA’s Earth observation mission as it transitions to the ESO era.
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Food insecurity and increased human migration are two major challenges facing society in the coming decades due to a range of factors including climate change, political instability, and economic inequality. To improve society’s responds to these challenges, it is important to advance our understanding of food systems and human migration dynamics as well as our capacity to observe these dynamics. I will present recent research findings using empirical and modeling analyses to understand the structure and response of these systems. Then I will briefly discuss key opportunities to integrate NASA’s Earth Observations into food systems and migration analyses to generate new knowledge and to support stakeholders (e.g., governments, institutions, and individuals) as they work it improve food security and migration outcomes.
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Recent studies have been sparking concerns about the impending arrival of “tipping points” later in the 21st century. This study analyzes observed global surface temperature trends in three target latitudinal regions: the Arctic Circle, Tropics, and the Antarctic Circle. We show that global warming is accelerating unevenly across the planet, with the Arctic warming at more than three times the average rate of our world. We also analyzed the reliability of latitude-dependent surface temperature simulations from a suite of Coupled Model Intercomparison Project Phase 6 (CMIP6) models and their multi-model mean (MMM) by comparing their outputs to observational data sets. We selected the best-performing models based on their statistical abilities to reproduce historical, latitude-dependent values adapted from these data sets. The surface temperature projections were calculated from ensemble simulations of the Shared Socioeconomic Pathway 2–4.5 (SSP2–4.5) by the selected CMIP6 models. We estimate the calendar years of when surface temperatures will increase by 1.5, 2.0, and 2.5°C relative to the preindustrial period, both globally and in the three target regions. Our results reaffirm a dramatic, upward trend in projected surface temperatures, with unprecedented acceleration in the Arctic Circle, which could lead to catastrophic consequences across the Earth. Further studies are necessary to determine the most efficient solutions to reduce global warming acceleration and maintain a low SSP, both globally and regionally.
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Over the past 20+ years there has been remarkable progress in developing a global ocean observing system. Global in-situ observations of essential climate variables in the ice-free ocean to 2000m depth are now routinely available to address the long-term observational requirements of forecast and modeling centers, international research programs, major scientific assessments, and decision-makers. The resulting global observations and products contribute to numerous agencies, and enable a multitude of national and international capabilities aimed at understanding, modeling, and forecasting of the earth system, as well as developing targeted information to better inform society about changes of the earth system and response options. This presentation will highlight recent progress in global ocean observing; describe new capabilities being evaluated, as well as regional and phenomenological interests and activities of GOMO to develop a more comprehensive and co-designed global ocean observing system serving many needs.
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Greenhouse gas emissions have likely contributed to current drought conditions in southwestern North America, which is experiencing one of its driest periods on record. But greenhouse gas emissions have risen steadily since the beginning of the Industrial Revolution: why then, have these drought conditions only emerged recently? In fact, in the latter part of the twentieth century regional soil moisture anomalies were unprecedentedly wet. In this talk, I will present a Bayesian method for detection and attribution that quantifies uncertainties, handles multiple external forcings, and can be used for model evaluation. The evidence suggests that aerosol forcing, aided by the eruption of Pinatubo in 1991, partially counteracted greenhouse gas-driven decreases in soil moisture, delaying the emergence of the current anthropogenically forced drought.
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NEPTUNE (Navy Environmental Prediction sysTem Utilizing a Nonhydrostatic Engine) is the Navy’s next-generation unified modeling and DA system. The initial application will be a global numerical weather prediction (NWP) system, with future planned applications for ensembles, coupled, regional/limited areas and tropical cyclones. NEPTUNE is a three-dimensional spectral element model on a cubed sphere representation of the Earth, and uses the fully compressible, deep atmosphere, non-hydrostatic equation set. NEPTUNE is designed to be highly scalable to meet future exascale computational challenges. The data assimilation system for NEPTUNE is being developed using the JCSDA (Joint Effort for Data assimilation Infrastructure) JEDI system. Our initial goal is a half-degree JEDI-enabled 3DVar, with Hybrid 4DVar being the eventual goal. In order to accommodate our development timeline for NEPTUNE DA, while still supporting ongoing development of other Navy NWP systems, we have opted to adapt numerous components of the current Navy Global Environmental Model (NAVGEM). For NEPTUNE DA, a new flexible model agnostic observation API is being built to link the NAVGEM-based observation decoding, ingest, QC to JEDI. Additional NEPTUNE specific QC and filters are then configured with JEDI yamlfiles. The NEPTUNE model verification will be provided through METplus with visualization provided by the NRL-developed Automated Diagnostics System (ADS) Python 3 enabled software originally developed for NAVGEM. This software packing includes scorecard plotting for RMSE and anomaly correlation scores. Many of the data assimilation observation-based diagnostics are being ported from NAVGEM. These python-enabled components include plots summarizing observation counts (by type) versus time, satellite radiance “radgrams”, and mean and standard deviation innovation plots. In additional, the NEPTUNE Post Processor (NPP) python-based post-processing software for is used for field visualization and analysis.
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While much attention has been given to understanding how anthropogenic radiative forcing influences the mean state of the climate system, far less scrutiny has been paid to how it may modulate naturally occurring modes of variability. In this study, we investigate forced changes to unforced modes of wintertime atmospheric circulation variability and associated impacts on precipitation over the North Pacific and adjacent regions based on the 40-member CESM1 Large Ensemble during 1920-2100. Each simulation is subject to the same radiative forcing protocol but starts from a slightly different initial condition, leading to different sequences of internal variability. Evolving forced changes in the amplitude and spatial character of the leading internal modes of 500 hPa geopotential height variability are determined by applying Empirical Orthogonal Function analysis across the ensemble dimension at each time step. The results show that the leading modes of internal variability intensify and expand their region of influence in response to anthropogenic forcing, with concomitant impacts on precipitation. Linkages between the Pacific and Atlantic, and between the tropics and extra-tropics, are also enhanced in the future. These projected changes are driven partly by teleconnections from amplified ENSO activity and partly by dynamical processes intrinsic to the extra-tropical atmosphere. The marked influence of anthropogenic forcing on the characteristics of internal extratropical atmospheric circulation variability presents fundamental societal challenges to future water resource planning, flood control, and drought mitigation.
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Korea’s second geostationary meteorological satellite, Geo-Kompsat-2A (Geostationary-Korean Multi-Purpose Satellite-2A, GK2A), was successfully launched on December 4, 2018. GK2A generates Atmospheric Motion Vectors (AMVs) every 10 minutes in the full disk area. This data has been disseminated via Global Telecommunication System (GTS) since October 25, 2019. This study evaluates the quality of GK2A AMVs in the Goddard Earth Observing System (GEOS) atmospheric data assimilation system (ADAS). The data shows slow wind speed biases at 200-300 hPa and 600-800 hPa in the northern and southern hemispheres. These biases are caused by observation height assignment errors near jet streams. The Equivalent Blackbody Temperature (EBBT) method of GK2A tends to assign clouds at higher altitude, which mainly causes slow wind speed biases, especially in the lower atmosphere. The IR/WV intercept method of GK2A assigns clouds slightly lower in the atmospheric layers below the altitude of 400 hPa, which causes positive biases. Quality control (QC) criteria to select the most suitable GK2A AMV data for assimilation are presented based on these quality assessments. A new QC criterion utilizing height errors within the GEOS ADAS is introduced to exclude data with slow wind speed biases and large errors. GEOS forecast accuracy is slightly improved after assimilating GK2A AMVs along with other conventional, radiance, and satellite winds which include AMVs made by the Himawari-8 satellite in nearly the same observational area of GK2A. Additionally, the present work shows that GEOS forecasts can be significantly improved, especially in the tropics and southern hemisphere after assimilating GK2A data in the absence of Himawari-8 AMVs. This study demonstrates that GK2A AMV data is a valuable data source to enhance the robustness of GEOS ADAS and that quality control is an important procedure for utilizing AMV data in NWP models.
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As the climate system warms, megafires have become more frequent with devastating effects. A byproduct of these events is the creation of smoke plumes that can rise into the stratosphere and spread across the globe where they reside for many months. To gain a deeper understanding of the plume dynamics, global climate simulations of a megafire were performed at a wide range of grid spacings from 2.0° down to 7 km, including a 7 km nonhydrostatic experiment. The analysis focuses on how the resolved dynamics affects the specification of the plume characteristics such as injection height and black carbon (BC) mass. Prior studies initialize the smoke plume at one or a few grid points and this is shown here to produce severely dissipative dynamics. In order to validate such simulations with observations, enhancements of the plume characteristics to offset the dissipation is necessary. Using a numerically converged simulation, sensitivity tests show that to approximate the observed stratospheric lifetime, a reduction in BC fraction by 50% is necessary for external mixtures. The vorticity dynamics of the plume is also analyzed with a Lagrangian budget to understand the mechanisms responsible for the evolution of a collocated anticyclonic vortex. The results can be distilled down into a simple conceptual model. As the plume rises, the air diverges at the top of the updraft where the largest concentrations of smoke are found. This divergence induces a dilution of the background cyclonic absolute vorticity producing an anticyclonic vortex. Vortex decay occurs from opposite arguments.
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Theory and numerical modeling suggest that as the planet has warmed under rising greenhouse gas concentrations over the last four decades, the water vapor content of the atmosphere should have increased over land, on average, as a result of the increased water vapor holding capacity of a warmer atmosphere. Here, we compare historical near surface humidity trends in the current state-of-the-art ESMs with observation-based datasets of near-surface humidity, including reanalyses and in-situ station-based measurements. We demonstrate a global discrepancy between modeled and observed near surface humidity trends over arid and semi-arid regions of the world in the sense that the near surface humidity in observations over these regions has not increased in the way that ESMs suggest it should have. This points toward a major mis-representation of some aspect of the hydrological cycle, either in the movement of water vapor in the atmosphere or exchange between the land and atmosphere, over arid and semi-arid regions of the world in ESMs and is a problem that must be solved in order to have confidence in projections of the hydroclimate and related aspects, such as temperature extremes, in these regions moving forward.
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The past two decades represent a “Golden Age” for satellite measurements of atmospheric composition. The long-term nature of these records, along with recent developments in modeling and data assimilation, has opened the way for new analyses of the response of the atmosphere to changes in emissions and new understanding of natural variability in composition. In this talk, I will discuss an array of recent results focused on these two topics, from COVID-19-related anomalies in surface air quality to decadal-scale variability in stratospheric trace gases driven by the QBO. I will also present new work addressing ENSO-QBO interactions and their implications for stratospheric transport and the contribution of international emissions to background ozone in the US.
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Exposure to atmospheric aerosols is the leading environmental cause of premature mortality. Atmospheric aerosols can also act to cool or warm the climate and they are the leading source of uncertainty in global climate forcing. Characterizing these environmental impacts requires a deep understanding of aerosol sources and evolution in the atmosphere. This is complicated by the myriad natural and anthropogenic sources of particles which vary around the world. Global models are often used to estimate aerosol climate and health impacts, but, of course, important uncertainties remain. In this talk, I’ll use three examples to describe gaps in our understanding of emissions, chemical formation, and optical properties. I’ll illustrate these by discussing some of my group’s work on bioaerosol, sulfate from DMS, and brown carbon. I’ll also address the implications of the recent strengthening of the WHO air quality guidelines for PM2.5.
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Tomorrow.io is launching the world's first multi-sensor satellite constellation for operational precipitation forecasting. In this talk, we will address our approach to data assimilation. Due to the need for operational speeds with relatively limited resources, we utilize all of the best available technologies in a hybrid technology framework. We present our unique approach for physically- and statistically-based observation operators for satellite radar and radiometer assimilation. We will also show preliminary results of our high-resolution OSSEs using the DYAMOND project.
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The Pacific-North American (PNA) pattern is a large-scale wave-like pressure pattern that forms over the North Pacific and across North America with important implications for precipitation and temperatures. In this work, we examine the optimal growth of the PNA by considering both the role of tropical variability and the mid-latitude circulation. Using linear inverse modeling (LIM), we find that the development and amplification of the PNA largely occurs when modes internal to the atmosphere, including the Madden-Julian Oscillation (MJO), interfere with modes strongly coupled to sea surface temperatures, in particular the El Niño-Southern Oscillation (ENSO). Optimal growth occurs when there is an ENSO event and, concurrently, an MJO event which evolves on weekly timescales. These two tropical phenomena independently force teleconnection patterns that evolve from destructive towards constructive interference as the MJO propagates eastward, leading to rapid growth of a PNA pattern.
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The meteorological enterprise has achieved remarkable success over the last 50 years, in part due to strong coordination from within and outside the federal government. Nevertheless, the United States continues to experience increasing impacts from weather and climate extremes, highlighting the need to further strengthen our coordination approach across an interdisciplinary landscape to ameliorate the most pressing impacts to society. The development of the Interagency Council for Advancing Meteorological Services (ICAMS) represents the first major restructuring and modernization of the Federal meteorological enterprise since the 1960s. In addition to the mandate in the Weather Research and Forecasting Innovation Act of 2017, a 10-year charter was signed in 2020 establishing ICAMS as the formal mechanism by which all relevant Federal departments and agencies coordinate implementation of policy and practices for U.S. global leadership in the meteorological services enterprise. To ensure ICAMS is informed by the best available science, ICAMS coordination is based on an Earth system approach and is inclusive of meteorological science and services from local weather to global climate. Accordingly, the first part of the discussion will focus on an overview of ICAMS, including its history, organizational structure, near term priorities, and goals for the future. Additionally, the holistic nature of ICAMS will be illuminated by exploring high-level scientific examples that connect meteorology and climate science research to service applications, including some of the most pressing climate adaptation issues facing modern society today and in the future.
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This presentation will cover multiple aspects of Earth Radiation Budget (ERB) research and novel ERB observations. First, we will talk about the science goals and objectives of Libera, the recently selected EVC-1 mission. As one of its main goals, Libera aims to provide continuity ERB observations through measuring the broadband shortwave, longwave and total radiances with almost identical characteristics as the Clouds and Earth’s Radiant Energy System (CERES) instruments. Not only does Libera enable critical continuity of the 40-year radiation flux record, but introduces innovative technology to enhance scientific merit and to meet the future needs of smaller and cost-effective observation systems. Libera addresses the need to miniaturize radiometers but also the coincident imagers required for the radiance-to-irradiance conversion. Another innovation is the novel “split- shortwave” radiometer, which enables us to split the shortwave spectrum into nearly identical halves, one that is absorbed by the atmosphere while the other is not – the near-infrared and visible portions of solar radiation. Research possibilities are various with this novel measurement and, for example, will help us to advance the understanding of mechanisms that yield hemispheric symmetry in Earth’s albedo. Secondly, I will discuss different approaches to measure and estimate Earth’s energy imbalance (EEI) including the assessment of contemporary sea level budget using altimetry and GRACE/GRACE-FO observations. Looking into the future, we will present ideas for a potential observing system that measures radiation pressure variations in orbit around Earth, which are proportional to the radiation fluxes entering and exiting our climate system. Initial feasibility simulations are under way to give evidence that this approach may indeed facilitate a high-accuracy measurement of TOA net radiative flux and EEI.
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Assimilation of microwave radiance data has been a significant contributor to the ECMWF atmospheric analysis since the late 1990s. The following two decades have seen a proliferation of microwave radiances assimilated, as the number of sensors and frequencies used have increased. In addition, the gradual move to ‘all-sky’ assimilation has permitted usage of microwave data in meteorologically active areas of the troposphere; these radiances fill in gaps left by other observation types and can significantly improve forecast skill. First, we quantify the impact of microwave sounders (ATMS, AMSU-A, MHS) on forecast skill by comparing observing systems experiments (OSEs) with different sounders actively assimilated. Then the additional benefit of moving from clear-sky to all-sky assimilation is demonstrated for the case of AMSU-A temperature sounding radiances. Lastly, we consider the future of all-sky sounder assimilation, including the potential use of smaller platforms like TROPICS and the possibility of using even more information from heritage sensors.
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Paleoclimate simulations and proxy constraints have been touted as a means to vet CMIP6 simulations for feasibility studies of equilibrium climate sensitivity. Here we use a paleoclimate database of land surface to constrain a perturbed (clouds and convection) parameter ensemble of simulations from GISS-E2.1 as part of preliminary work intended to narrow the equilibrium climate sensitivity of the GISS model based on its performance during past climate. Past climate simulations generally have much larger forcing than that observed in the historical period, with anomalies to modern dominated not by interannual variability, but by forced response to greenhouse gas, orbital, or orographic change. We assess the locations in the world that are best suited for constraining clouds and convection parameters and assess whether these locations align with the current method using satellite retrievals. A pre-print of our paper in review is here: https://eartharxiv.org/repository/view/2748/
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Accurate predictions of the ice sheets' future contribution to sea-level require that models yield simulations that fit observations within observational uncertainty, yet current ice sheet hindcasts show poor agreement with observations. While reanalyses created by blending observations with numerical models using data assimilation techniques have been widely-available for decades, for both the atmosphere (e.g., ERA, MERRA-2) and the ocean (e.g., ECCO), similar products for the ice sheets currently do not exist. Here we will assimilate surface elevation and velocity observations with the Parallel Ice Sheet Model PISM to create a reanalysis of the Greenland Ice Sheet for 1980-2020 (RAGIS-40) based on Ensemble Kalman Filtering/Smoothing. This NASA-funded project is in the early phase of gathering data sets and setting up the assimilation framework, and we hope for a stimulating discussion with NASA's Global Modeling and Assimilation Office.
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Estimates of air-sea CO2 fluxes in the Southern Ocean diverge widely. In this talk, I describe a novel approach to estimating air-sea fluxes, leveraging several aircraft campaigns that measured CO2 in the atmosphere overlying the Southern Ocean. This analysis demonstrates that the Southern Ocean remains a strong sink for CO2. The results are consistent with flux estimates from ship-board pCO2 observations, but show stronger uptake than suggested by recent float-based pH observations.
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The recognition of the importance of airborne transmission of the SARS-CoV-2 virus by respiratory aerosols has spurred several research groups to develop and apply simple mechanistic aerosol models to investigate COVID-19 outbreaks associated with specific events. These studies have highlighted the importance of layered, non-pharmaceutical intervention strategies such as masking, ventilation, and air cleaning to mitigate transmission risk. To date, however, there is a disconnect between these micro-scale mechanistic aerosol models and the macro-scale epidemiological models that have been used to study large-scale COVID-19 disease dynamics. In this talk, I will discuss my recent research to try to close this gap. I will present a Monte Carlo analysis of transmission risk and secondary infections arising in social settings by modeling aerosol transmission of COVID-19 in indoor locations in the United States. I will show that variability of viral load among index cases plays a key role in shaping the variability in transmission risk at these locations. I will further demonstrate that aerosol transmission is consistent with the observation that COVID-19 transmission is overdispersed and will provide a mechanistic explanation for this large-scale characteristic of the pandemic. Implications of this finding with regards to the effectiveness of non-pharmaceutical interventions in curbing the pandemic will be discussed.
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The ocean plays an important role in shaping the pattern and variability of sea ice, as well as its response to ongoing climate change. Ocean transports heat horizontally and vertically contributing to sea ice melting. It also transports freshwater and salinity anomalies that greatly impact the stratification of polar oceans. Part of these transports are achieved by meanders and eddies at meso and submeso-scale and are mitigated by frontal structures. However, resolving these fine-scale circulation features in models requires relatively high horizontal resolutions (order of 10 km or below) which is extremely costly to attain in climate models. The lack of resolution in the current generation of climate models might contribute to explain some of the discrepancies with observations that have been persisting across model generations. In a similar way, discrepancies in the response of sea ice to climate change is expected between models of differing resolutions. Further investigation of the impact of refining ocean resolution in climate models on sea ice and its response to ongoing climate change is needed to advance our knowledge of key processes in polar oceans but also to guide the modelling community as they develop the next generation of climate models. In this talk, I will present two studies which explore the impact of refining horizontal resolutions in the ocean component of climate models on the response of the sea ice to climate change. One study investigates oceanic heat transport into the Arctic and its influence on sea ice. The other study examines the role of freshwater transport on the occurrence of the Weddell Sea polynya in Antarctica. Both studies use climate models with various horizontal resolutions in the ocean ranging from non-eddying (1°-2°) to eddy-rich (0.10°) that are run under climate change scenarios. These studies both show a significant impact of refining the horizontal resolutions on the representation of the response of sea ice to climate change, through the modification of ocean heat pathways (Arctic case), and a better representation of cross-shelf exchanges (Antarctic case).
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A brief overview of climate model development at NASA GISS is focused on parameterization schemes relevant to mixed-phase stratiform cloud processes. Replacement of ModelE
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Anthropogenically-forced changes in the ocean are underway in both its critical role as a carbon sink and marine habitats. Predicting, understanding, and detecting such changes will require quantification of not only the magnitude of the change (anthropogenic signal) but also the natural variability inherent to the climate system (noise). Earth System Models (ESMs) are an essential tool for testing hypothesis about anthropogenic and natural pathways of change, and for quantifying the past, present and future change and variation. The recent availability of Large Ensemble experiments of ESMs, which involve 30-100+ simulations that capture the distribution of stochastic climate variability over time and space, has allowed unprecedented exploration of the contributions and impacts of natural climate variation in the context of global change. This seminar will focus on key insights that Large Ensemble methodologies have provided about ocean chemistry and future ecological vulnerabilities. Specifically, this seminar will present Large Ensemble experiments from multiple ESMs, finding that changes in different components of the ocean carbon cycle occur on vastly different time-scales, ranging from under a decade to over a century, with implications for observing system design and the timeline for mitigation of ecological impacts.
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Observational results are shown from recent tropical oceanic field programs with an emphasis on opportunities for addressing process-oriented predictive understanding, satellite data evaluation, and model evaluation. Experiments include the NOAA ATOMIC, ONR/NOAA PISTON, and NASA SPURS-2 campaigns in the tropical regions of the western Atlantic, western Pacific, and eastern Pacific Oceans. Satellite precipitation products over tropical oceans are compared to a new global acoustic in-situ rain rate database provided by PALs (Passive Aquatic Listeners). We investigate downscaled satellite precipitation products, and compare them to PAL in-situ data. Satellite salinity and precipitation products are evaluated in terms of their ability to reproduce observations of rainfall from ship-based radar and rain gauges, as well as rain’s impacts on upper ocean salinity, stability, and SST. From these analyses, we set expectations for how best to use satellite precipitation products for studying rain’s impact on the ocean. We describe SST and air-sea flux anomalies in the presence of near-surface ocean stable layers due to diurnal warming and fresh layers deposited by rain; these data are compared to a high-resolution research version of the NOAA UFS model. A simple method is also validated that uses surface data to predict when near-surface ocean stable layers exist, i.e. when they modify SST, surface salinity, and air-sea fluxes. The impacts of ocean surface waves on air-sea fluxes is discussed. Methods and results are shown to describe boundary layer vertical air motion and cloud microphysics derived from airborne and shipborne W-band radar. We quantify relationships between different fluxes in the surface energy budget across different regions. Relationships are also documented between oceanic and atmospheric boundary layer depth, the surface atmospheric buoyancy flux, and boundary layer cloudiness. We hope to invite collaborations and discussions about opportunities to use these observational analyses for process-oriented evaluation and improvement of models and satellite products.
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The ocean’s surface velocities are often inferred from sea surface height observations using geostrophy. SWOT will measure sea-surface height at scales never before observed. At these small scales, the internal tide becomes important, so geostrophy is no longer accurate. In order to infer transport-relevant surface velocity from sea-surface height, we need to understand the details of the surface velocity field, and in particular to characterize wave and non-wave components of the velocity field. In this work, we use Lagrangian filtering to partition the surface velocities in ECCO LLC4320, a high resolution ocean model, into wave and non-wave motions. To estimate the non-wave (balanced) velocities, we advect a large number of particles in the surface velocity field and low-pass filter the velocities measured along each particle trajectory. We examine the properties of the non-wave (balanced) flow using frequency-wavenumber spectra and probability density functions of vorticity and strain. In the Lagrangian frame, all of the non-wave flow is subinertial. However, some of the non-wave flow is Doppler shifted by larger-scale and longer-timescale velocities. Hence, in the Eulerian frame, the non-wave flow field has significant energy at frequencies higher than the inertial frequency. In regions with low velocities, Lagrangian filtering is not much better than other methods, but in regions with high velocities, Lagrangian filtering is much more effective at preserving the balanced part of the flow.
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One of the grand challenges of carbon cycle science is to understand how carbon exchange between land, oceans, and the atmosphere will evolve under future climate change and how this will impact atmospheric greenhouse gas concentrations. Large pools of carbon stored in soils and in biomass have potential to be released to the atmosphere and accelerate climate feedbacks. Reducing uncertainties in carbon cycle projections requires methods to constrain overall rates of net carbon exchange and component fluxes (photosynthesis, respiration, fires) at planetary scale and detect feedback processes (e.g., fertilization, drought, heat waves, permafrost degradation), especially in remote regions such as the Arctic where feedbacks are likely to be largest but where observations are the fewest. Here, I will summarize recent efforts and challenges in using Earth Observations and models to detect long term change and feedbacks to biospheric metabolism and carbon storage within the Arctic Boreal Region.
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Identifying the relative importance of El Niño- Southern Oscillation (ENSO), wind-driven ocean dynamics, and air-sea heat fluxes in driving climate variability is crucial to understanding climate impacts and predictability. First, this presentation will highlight the ocean’s role in modifying the sea surface temperature (SST) response and North American precipitation pattern associated with Aleutian low variability. We will show results from coupled model experiments that separate the effects from ENSO, air-sea heat fluxes, and wind-driven ocean dynamics in setting these patterns. We find that the tropical ocean, through tropical forcing associated with ENSO, can destructively interfere with the SST and precipitation patterns associated with Aleutian low variability. Second, this presentation will show how the relationship between air-sea heat fluxes and wind-driven ocean dynamics in driving SST variability may change in a future climate. Preliminary analyses of CMIP6 future climate simulations suggest that as the Hadley cell expands poleward, the role of wind-driven ocean dynamics in the subtropical oceans may significantly change.
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Extreme events, i.e., the normally rare occurrences when a system is far outside its norm, severely impact organisms and ecosystem on land. Yet, in comparison, our understanding of such extreme events in the ocean is generally poor. This is especially the case for ocean biogeochemical extremes, while the knowledge on marine heatwaves has grown rapidly in recent years. With trends in ocean warming, acidification, deoxygenation, and surface nutrient concentrations projected to continue for decades, marine heatwaves and ocean biogeochemical extreme events are likely to intensify, occur more often, persist for longer, and extend over larger regions. Of particular concern are compound events, i.e., when conditions are extreme concurrently for multiple properties. Compound extremes can lead to especially severe impacts, since the individual properties may interact synergistically. Here we combine observations with large ensemble simulations of Earth system models to assess the spatial characteristics, underlying drivers and trends in ocean extremes and compound events and their impacts on marine ecosystems, with a focus on compound marine heatwaves and ocean acidity extremes. Since the conditions exhibited by today’s extreme events are a harbinger of what may become ‘normal’ in the future, our results may help to better understand the response of marine organisms and ecosystems to future climate change.
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Modeling atmospheric chemistry and physics is computationally expensive and limits the widespread use of air quality models. This computational cost arises mainly from solving high-dimensional systems of stiff differential equations. Machine learned (ML) surrogate modeling is a promising area of inquiry which may lead to greatly accelerated air quality model simulations. This talk will cover a series of lessons learned, promising approaches and new advances in the use of machine learning to accelerate atmospheric chemistry modeling while maintaining accuracy and numerical stability.
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Seasonal water supply predictions across the western U.S. rely on knowledge of spring snow information, since snow represents a greater storage of water than man-made reservoirs for many of these systems. A warmer future portends for less precipitation falling in the form of snow, which represents an acute challenge for predicting seasonal runoff in order to allocate resources—particularly critical during drought. The presentation begins with an investigation into recent ‘snow drought’ for parts of the west, followed by an evaluation into how the relationship between snowpack and streamflow is expected to evolve as temperatures rise through the end of this century. Preliminary results indicate that contribution of snowpack to predictive skill is greatly reduced in key regions, particularly in maritime climates and lower elevation mountains, whereas predictive capabilities in cooler, higher elevation zones tend to show greater resilience to warming. Ancillary predictors like accumulated precipitation and soil moisture can aid in recovering some fraction of the lost skill. Lastly, we present the findings from a set of idealized experiments to quantify the impact of non-stationary snow conditions on seasonal drought forecasts relative to the impacts from warm-season precipitation non-stationarity. Overall, this work seeks to understand strategies for enhancing drought prediction capabilities amidst a changing climate. Another emerging challenge is the role of wildfire on the hydrologic and geomorphic response of watersheds, contributing to the cascading hazard of shallow landslides and debris flows. The second part of this presentation will evaluate post-wildfire landslide trigger characteristics by comparing the precipitation preceding events at both burned and unburned locations. Analysis of normalized seven-day accumulated precipitation for more than 5000 landslide sites across six global regions shows that, overall, mass movements at burned sites are preceded by less precipitation than mass movements without antecedent burn events. This supports the hypothesis that fire increases rainfall-driven mass movement hazards. An analysis of the seasonality of mass movements at burned and unburned locations shows that mass movement-triggering storms in burned locations tend to exhibit different seasonality from other 10 rainfall-triggered mass movements, with a variety of seasonal shifts ranging from approximately six months in the Pacific Northwest of North America to one week in the Himalaya region. Overall, this presentation offers an exhibition into regional differences in the characteristics of rainfall-triggered landslides across a variety of climates, geographies, and burn conditions.
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The Antarctic Ice Sheet, the largest freshwater reservoir on Earth, is rapidly changing in response to ongoing atmosphere and ocean warming. Antarctic field observations are scarce, highlighting the need for remote sensing and numerical modeling to assess Antarctic climate, and to understand interactions between the ice sheet and the atmosphere aloft, the snow layer atop, and the ocean surrounding it. In this seminar, I will describe some of the recent work our Ice Sheets and Climate research group is working on, including (1) the impact of atmospheric rivers on Antarctic snowfall; (2) surface meltwater processes on Antarctic ice shelves; and (3) impact of Antarctic mass loss on future Southern Ocean conditions.
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The 2019 Cloud, Aerosol, and Monsoon Processes Philippines Experiment (CAMP2Ex) sampled one of the most dynamic aerosol environments on the planet. Employing remote sensing and in situ assets from the NASA P-3, Stratton Park Engineering Company (SPEC) Lear Jet 35, and a host of satellites and surface-based sensors, CAMP2Ex characterize the coupling of aerosol science, cloud physics and atmospheric radiation within the Maritime Continent’s southwest monsoon. The study region was situated in the waters surrounding the Philippines, between Asia’s biomass burning and industrial aerosol sources and their ultimate sink in the North Western Tropical Pacific monsoonal trough. Aerosol loading ranged from massively polluted, including peat burning from Borneo and industrial emissions from China and the Philippines, to the exceptionally pristine background air of the western Pacific. Accompanying this diversity in aerosol properties was similar variability in tropical weather from tropical cyclones and monsoon enhancements to clear skies and glassy seas. This talk is designed to give an overview of CAMP2Ex for an earth system modeling perspective. We break down mission science objectives and observations into corresponding science and technology components. Data from rapid scan geostationary and high resolution remote sensing, along with a host of modeling and informatics tools, provide detailed contextual information and enable the evaluation of remote sensing and model products. CAMP2Ex provides a benchmark dataset of an environment of extremes, and demonstrates that maturing remote sensing, modeling, and informatics technology can quantify important aerosol-cloud processes within a complex diurnal cloud and radiation cycle of varying convective and dynamical regimes.
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While the monthly or seasonal average might be the best documented component of precipitation, the variability about the average is crucial for extreme precipitation impacts like floods and droughts. But quantifying precipitation variability and interpreting what it means can present challenges that mean precipitation does not. This presentation will explore some of these challenges using satellite-based precipitation products, gauge-based observations, and climate model simulations. A first challenge is deciding what we mean by variability; a selection of measures focusing on different aspects of variability will be presented. A second challenge is that precipitation variability is inherently more uncertain than many other characteristics of climate, including mean precipitation, temperature, or temperature variability. A third challenge is that some mechanisms that drive precipitation variability are specific to particular frequencies or frequency bands (such as the diurnal cycle and ENSO), while others project across a broad range of timescales (such as moisture and turbulence). Finally, the shape of the distribution of precipitation is expected to change in response to greenhouse-gas driven warming, due the physics of energy conservation in the climate system and atmospheric dynamics, with implications for precipitation variability. Connecting local-scale extreme precipitation events to the global-scale changes will require continued increases in our understanding of precipitation variability.
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Processes that occur at scales smaller than the grid scale of climate and weather models are parameterized — their statistical behavior across the grid is approximated — and this parameterization is a source of key uncertainties and errors in these models. There is widespread belief that parameterization schemes can be improved substantially, in particular because many of the processes they represent are uncertain at all scales. For example, cloud and precipitation microphysical processes such as vapor depositional growth, or raindrop collisional breakup, still remain uncertain at the scale of individual particle interactions. Many recent approaches to improve warm rain microphysics, to give one example, have used machine learning to emulate the behavior of bin schemes. Neural networks are attractive in that they have little pre-set structural limitations; on the other hand they do not intrinsically maintain known physical relationships and conservation laws. In recent work, we also address this problem with systematic inference, but supply a physically based and structurally flexible microphysics scheme rather than generic neural networks. This allows for systematic evaluation of the sources of uncertainty and error in warm microphysical processes. By employing Bayesian inference, uncertainty on the process scale can be propagated to weather and climate models, therby allowing process level "bottom-up" constraints to be unified with global scale "top-down" insights gleamed from global satellites. I will discuss work at both scales, using high-resolution radar observations to refine knowledge of individual microphysical processes, as well as using satellite observations to constrain the GISS ModelE global climate model.
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First, I will introduce a new offline one-way coupling of the NASA GISS ModelE2.1 GCM with the GEOS-Chem 3-D chemical transport model. Synthetic MERRA-2 fields are archived from ModelE2.1 to drive GEOS-Chem. I will highlight the influence of the different meteorology products on the resulting atmospheric composition using an identical chemical mechanism. Second, I will explore the mechanistic reasons for which short-lived species like OH and NOx differ so greatly between coupled chemistry-climate models, despite those models’ abilities to consistently reproduce distributions of ozone and CO. Lastly, I will introduce a new 4DVAR assimilation of methyl chloroform using the GEOS-Chem adjoint that constrains interannual variability in monthly mean OH for the past two decades and explore the factors driving those changes.
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Wind energy has been growing steadily in the U.S. and worldwide in the past decades. As wind farms are increasing in size and number, however, concerns are rising about possible undesirable effects of wind turbines near the Earth
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The Southern Ocean is the windiest place in the world, with frequent intense storms. The winds in these storms deepen the mixed layer and drive large fluxes of carbon and heat between the ocean and the atmosphere. Unfortunately, these fluxes cannot be observed directly from space; we rely on vector
 wind measurements and in situ ship and float-based observations to determine them. Our space-based observing network, however, only captures the vector winds over the Southern Ocean twice per day at best. We need more frequent vector winds to ensure that the wind fields in the climate reanalyses we use are accurate. The estimates are that more than 50% of the global air-to-sea uptake and about 50% of the global uncertainty in air-sea carbon exchange is associated with the Southern Ocean. We show that higher winds are consistent with increased outgassing and reduced net uptake of atmospheric carbon by the Southern Ocean. We make the case to add an additional scatterometer to the wind-observing constellation to capture more of the high winds and reduce the uncertainty in the Southern Ocean carbon budget
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We generally think of Earth as a blue planet. The surface of Earth, after all, is approximately 70% covered by oceans that occupy approximately 97% of the Earth’s habitable space, contains approximately 97% of the water on the planet and stores enormous amounts of heat that modulates our climate. A tiny fraction of the water on Earth is found in the atmosphere and about 0.5% of the total atmospheric water content exists as water condensed in the form of cloud droplets, ice crystals, snow flakes and rain. This minute amount of water is how fresh water is delivered to our lakes and reservoirs, replenishes aquifers, fills our rivers and supports human life. This tiny amount of water is also spread across Earth as a thin, white reflecting veil covering approximately 74% of Earth’s surface. Thus Earth is largely a white cloud covered planet reflecting large portions of sunlight to space with the further addition of reflection from areas of white snow and ice cover at the Earth’s surface. Hence Earth might appropriately be considered to be just as much a white planet as a blue planet. A measure of the whiteness of Earth is its albedo. This talk will describe how Earth’s albedo has changed over the period of the modern era of observations made by sensors flown on Earth orbiting satellites. The factors that contribute to the change observed over time are examined. A basic question that arises is to what extent are the changes observed buffered from systematic and ongoing changes and what are the mechanisms by which Earth albedo remains resistant to significant change?
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Cloud microphysics, including their interaction with aerosol particles, is one of the components in numerical weather prediction and climate models that is associated with large uncertainties in its formulation. As cloud processes interact with atmospheric dynamics via latent heating and vertical velocities, with radiative processes and other components of the Earth’s atmosphere, this uncertainty affects both climate predictions and short-term weather forecasts. However, it is not clear (i) in which scenarios the uncertainty in cloud microphysics has a large impact, i.e. dominates over other sources of uncertainty, (ii) which process parameterisation are particular relevant, and (iii) what the role of the limited predictability of atmospheric flows is for the difficulties encountered in finding constraints on cloud representation in models. Some approaches we have explored recently to address these questions and the insight gained will be present and the implications for modelling will be discussed.
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The Cross-track Infrared Sounder (CrIS) hyperspectral infrared (IR) sensor has three bands that are used for remote sensing: a longwave (LW) band from 650 - 1095 cm-1, a midwave (MW) band from 1210 - 1750 cm-1, and a shortwave (SW) band with wavenumbers covering 2155 - 2550 cm-1. In the NOAA operational Global Data Assimilation System (GDAS), only CrIS LW channels and a small number of CrIS MW channels from a subset of 431 CrIS full spectral resolution (FSR) channels are actively assimilated. Work at NOAA/NESDIS/STAR and University of Maryland CISESS has been aimed at evaluating the use of CrIS SW observations in the GDAS and Finite-Volume Cubed-Sphere Global Forecast System (FV3GFS) from the 431 channel set in SW-only and LW plus SW configurations. Initial findings suggest that the assimilation of CrIS SW observations, with the implementation of new CrIS SW-specific quality controls and scene-dependent observation errors, can produce a beneficial impact on FV3GFS forecasts when compared to the assimilation of only the CrIS LW and few MW channels normally used in operational forecasting. The work at NOAA/NESDIS/STAR to assimilate CrIS SW observations is ongoing, and has not been without challenges. To be discussed are what has been done so far, what we have learned from initial results from experiments assimilating CrIS SW channels, what we are still working on, and the direction(s) we would like to take in future efforts.
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Recent years have seen the rise of global operational atmospheric composition models for several applications including climate monitoring, air quality forecasting, provision of boundary conditions for regional air quality models, and energy sector applications, to mention a few. Typically global forecasts are provided in the medium-range, up to five days ahead. Centres with aerosol analysis and forecasting capabilities have invested a considerable amount of effort in exploiting aerosol related observations from spaceborne sensors as well as ground-based networks both for assimilation and verification. For example, ECMWF has developed the capability to run its Integrated Forecast System (IFS) with atmospheric composition variables, thanks to a series of EU-funded projects (GEMS, MACC, MACC-II, MACC-III), and now Copernicus. The composition configuration is at the core of the Copernicus Atmospheric Service (CAMS) which provides operational 4D-Var analyses and forecasts of aerosols and reactive gases. Other operational and research centres such as NRL, NASA and JMA, UK MetOffice, to mention a few, run aerosol forecasts and analysis. The collaborative efforts of these centres have culminated in the International Cooperative for Aerosol Prediction (ICAP) which maintains a multi-model ensemble system for aerosol forecasts, based on the systems run at the individual participating centres. At the heart of this effort there has been constant dialogue and collaboration with data providers such as space agencies (NASA, ESA, EUMETSAT, JAXA, etc) with the intention of using the datasets in the most meaningful way. In this seminar, we will review the state-of-the-art in aerosol analysis by providing an overview of the current status of the aerosol analyses at the various centres with an emphasis on the data used in the assimilation, rather than the specific assimilation approach. Additionally we will provide an outlook on future use of aerosol data for analysis applications based on current and planned missions
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Cold pools play a critical role in the initiation, intensity, longevity and organization of convective clouds. Cold pools also impact latent and dust heat fluxes, enhance the transport of dust, pollen and other aerosols, and pose a threat to human health and safety by virtue of their strong surface winds, low-level turbulence and transport of particulate. In spite of their importance, accurately predicting cold pools remains extremely challenging in LES through global models. This is due, in part, to the fact that they are determined by numerous cloud, land surface and radiative processes which have their own modeling challenges, and, in part, to the fact that they are typically shallow, rapidly evolving features which require high spatial and temporal resolution. In this talk, the role of soil moisture, surface heat fluxes, aerosols and radiation in determining cold pool characteristics will be discussed. The challenges in accurately representing these cold pool processes, and ways in which we can address these, will also be examined.
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While the long term mean CO2 uptake by the global ocean is well established, we know relatively little about how the uptake varies on inter-annual to decadal timescales. In recent years a community effort led to the collection of >25 million surface ocean partial pressure of CO2 (pCO2) data within the Surface Ocean CO2 Atlas (SOCAT). While these data provide valuable information regarding the range of the pCO2, the spatial and temporal heterogeneity as well as short autocorrelation length scales of these measurements limit a quantitative assesment of the CO2 uptake variability. Here, I will present, how I tried to overcome this limitation by employing a neural network methods to explicitly model the relationship between environmental predictor data and target pCO2 observations. This relationship is then used to fill measurement gaps in space and time to reconstruct monthly pCO2 maps from 1982 through present. The results show significantly stronger variations over time than suggested by ocean biogeochemistry models run in hindcast mode, but are in line with independent evidence provided by theory and the accumulation and redistribution of dissolved carbon in the ocean interior from an ocean circulation inverse model. In particular, the results reveal a long-term strengthening in the oceanic uptake of atmospheric CO2, as expected in response to the increasing concentration of atmospheric CO2, but with strong decadal variations in the annual mean uptake and a significant increase in seasonal vacillations. Especially the Southern Ocean, i.e. the most important marine sink for anthropogenic CO2, stalled until about 2000. Thereafter it was reinvigorated, yet the driving mechanisms of this change are still debated. Data sparsity has historically hindered a deeper understanding of the processes driving the Southern Ocean carbon sink variations, but new autonomous sensor technology is currently filling this gap. Therefore, I will also show how sailboats can complement the autonomous measurement efforts in the Southern Ocean and pave the way to overcome the next frontier in marine carbon cycle science.
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The construction of a Biogeochemical Southern Ocean State Estimate (B-SOSE) is introduced that includes carbon and oxygen fields as well as nutrient cycles. The state estimate is constrained with observations while maintaining closed budgets and obeying dynamical and thermodynamic balances. Observations from profiling floats, shipboard data, underway measurements, and satellites are used for assimilation. The skill of the state estimate at fitting the data is assessed. The validity of adjoint method optimization for coupled physical-biogeochemical state estimation is demonstrated with a series of gradient check experiments. The method is shown to be mature and ready to synthesize in situ biogeochemical observations as they become more available. Documenting the B-SOSE configuration and diagnosing the strengths and weaknesses of the solution informs usage of this product as both a climate baseline and as a way to test hypotheses. It is shown that mean biases to data remain in the published product, and an additional resource for quantifying a climate baseline is constructed using an objective mapping methodology to bias correct the BSOSE climatology.
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The North Atlantic Stochastic Hurricane Model (NASHM) is driven by CMIP6 tropical sea-surface temperatures (SSTs) to estimate changes in tropical cyclone (TC) activity on the US and Caribbean in response to climate projections out to 2040. The signal on the US is mixed: All regions are projected to experience decreased rates of low-intensity hurricane landfall. However, the Gulf states and Florida experience increased rates of intense landfalls. In the Caribbean, most island regions increased rates are projected at all intensity thresholds, with the largest fractional increases at the highest intensities. The complexity in the changes in landfall and island-region rates is due to four factors varying simultaneously in NASHM, each with distinct regional signatures and distinct rates of change: 1., the basin-wide intensity frequency distribution, 2., the basin-wide annual TC formation rate and its geographic distribution, 3., TC track propagation patterns, and 4., TC over-ocean termination probability and its spatial pattern, possibly due to changes in wind shear.
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The NASA GISS ModelE3 general circulation model (GCM) exhibits a number of substantial improvements in the representation of moist turbulence, convective cloud systems, and stratiform cloud micro- and macro-physics. The development of new and more advanced parameterizations is associated with the introduction of numerous unconstrained GCM parameters. Here, I discuss a new machine-learning (ML) tuning framework developed to mine this large multidimensional parameter state space. The ML tuning method developed includes quantitative accounting of observational discrepancies or biases in satellite products during the tuning process (an effort expected to become more important as climate models advance and their errors approach the quantitative differences between two satellite products aiming to estimate the same parameter). Additionally, the new tuning approach aims to produce multiple diverse GCM parameter configurations that all lead to comparable agreement in mean-state climatological fields. This latter effort facilitates an investigation of the extent to which GCM emergent properties differ, despite the fact that GCM mean climatological states are similar across diverse parameter configurations.
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The satellite era has brought a wealth of knowledge and complexity to the field of ice-sheet modeling. Yet, these data have also brought acceptance of looming uncertainties in model-based projections of future sea-level change. In order to improve current ice-sheet model projections, it is crucial to take advantage of the symbiotic relationship between data and models to identify which aspects of the climate contribute the most to model uncertainty. The Jet Propulsion Laboratory-University of California at Irvine Ice-sheet and Sea-level System Model (ISSM) is a thermo-mechanical 2D/3D parallelized finite element software used to physically model the continental-scale flow of ice at high resolutions. Here, we discuss the roles that satellite data play in informing ice-sheet model projections, and described the various uncertainty quantification methods available through the ISSM framework for the investigation of how errors in model input impact uncertainty in simulation results. For example, we regionally sample model input and key variables, based on specified bounds of uncertainty, to run a suite of continental-scale ISSM forward simulations of the Antarctic Ice Sheet. Resulting diagnostics (e.g., regional mass balance) inform our conclusion about which boundary conditions and/or forcing have the greatest impact on century-scale model simulations of ice sheet evolution. The results allow us to prioritize the key datasets, measurements, and target regions in Antarctica that are critical for the minimization of ice-sheet model uncertainty.
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In the past decade, NASA has invested in airborne studies aimed towards preparing for the launch of a constellation of geostationary air quality satellites. DISCOVER-AQ and KORUS-AQ helped to develop methods for the integration of multi-perspective observations that included repetitive sampling by in situ and remote sensing aircraft over ground networks. Each observational perspective has unique value for supporting scientific validation and interpretation of satellite observations and assessment of air quality models. GEMS has been on orbit for a year, TEMPO will be launched in 2022, and Sentinel-4 will hopefully follow in 2023. Additionally, international efforts are focusing on developing an improved capacity for air quality forecasting support through the WMO Global Air Quality Forecasting and Information System (GAFIS) and the IGAC-sponsored Modeling, Analysis, and Prediction of Air Quality (MAP-AQ) activity. It is expected that over the next decade, NASA will invest further in airborne studies that contribute to the integration of observations and models to better understand and address air quality challenges. White paper development has recently been initiated for a study called the Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ). Another white paper has been recently drafted for PadUS-AQ, a concept focused on the Po Valley of Northern Italy. Earth Venture Suborbital-4 will offer additional opportunities for funding to enable campaigns focused on air quality. Early details on these campaign concepts will be shared with the hope of soliciting feedback on the needs of the modeling community and how sampling strategies might be improved.
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Land-atmosphere interaction is one of the key elements in Earth system science. Atmospheric effect on land processes is relatively easy to appreciate and understand, as precipitation and downward radiation provide the water and energy fluxes to drive land processes. In contrast, the land effect on atmospheric processes at different spatio-temporal scales is much more complicated. In this talk, I will overview our recent work in land-atmosphere interactions to address three questions: a) From observations, how does land state (e.g., soil moisture, temperature, land use change) affect clouds and precipitation at diurnal to decadal time scales? b) From regional and global modeling, how does land state (e.g., surface temperature, soil moisture, snowpack) affect data assimilation, hurricane forecasting, and drought monitoring? c) Where do we go from here?
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The terrestrial carbon cycle is characterized by a diverse set of ecosystem components with a wide range of turnover times. Earth system models have historically struggled to incorporate and resolve some of the slower components of the carbon cycle, and this lack of representation may bias estimates of carbon cycle feedbacks to climate change. Here I will talk about efforts to represent and understand the dynamics of the slowest components of Earth's soil and vegetation carbon pools—permafrost and big trees, respectively—and how incorporating these dynamics may shift estimates of carbon cycle feedbacks.
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Seasonal to interannual forecasts made by coupled general circulation models (CGCMs) undergo strong climate drift and initialization shock, driving the model state away from its long-term attractor. Here we explore initializing directly on a model’s own attractor, using an analog approach in which model states close to the observed initial state are drawn from a “library” obtained from prior uninitialized CGCM simulations. The subsequent evolution of those “model-analogs” yields an ensemble forecast, without additional model integration. This technique is applied to four CGCMs comprising the multi-model ensemble used operationally by NCEP. By selecting from these long control and/or historical (externally forced) runs those model states whose monthly SST and SSH anomalies best resemble the observations at initialization time, hindcasts are then made for leads of 1-36 months during 1958-2019. Deterministic and probabilistic skill measures of these model-analog hindcasts are comparable to, and in some regions better than, traditionally assimilation-initialized CGCM hindcasts, for both the individual models and the multi-model ensemble; where the model-analog skill is higher, it suggests that CGCM skill is degraded by initialization shock, which may allow for future diagnosis of impactful model errors. On average, ENSO skill of AC>0.5 exists for forecast leads of 18 months for forecasts initialized in summer; similar skill is available for other ocean indices in the Pacific and Indian oceans. More important, we find that some notable ENSO events were predictable two years (or more) ahead of time. In general, we can identify a priori forecast “hits” -- as well as avoid “false alarms” -- by using a simple forecast signal-to-noise metric (SNR; root-mean-squared ensemble mean divided by ensemble spread), determined from the model-analog 60-member ensemble at time of forecast. This study suggests that with little additional effort, sufficiently realistic and long CGCM simulations may offer skillful seasonal to interannual forecasts of global SST anomalies, even without sophisticated data assimilation or additional ensemble forecast integrations. The model-analog method could provide a baseline for forecast skill when developing future models and forecast systems and can provide a practical metric of global climate models and their ability to reproduce nature’s attractor.
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Satellite passive microwave observations are extensively used to characterize the Earth atmosphere (e.g., temperature and humidity profiles, precipitation) or the Earth surface (e.g., ocean surface temperature or wind speed, soil moisture, sea ice concentration). The emission from the Earth surface is considered as a noise source in the first case and as the information source in the second case, but for both applications an accurate estimation of the emissivity is required to exploit the signal. With an increasing use of the passive microwaves within the next years for meteorological and climatological applications from ~1 GHz up to ~700 GHz, a robust understanding and quantification of the surface emission and scattering have to be established, including all types of environments (ocean, ice and snow, vegetated and arid continental surfaces). During this seminar, several ways of estimating the surface emissivities will be discussed, from fully physical models to satellite-derived methods depending on the surface types, along with their associated uncertainties. Different issues will be mentioned, including the large foam impact over ocean at high wind speeds, the sub-surface contributions in sand deserts, or emissivity diurnal variations in the vegetation.
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In order to improve Arctic predictions and reanalyses, which constitute a great tool for Arctic climate monitoring, work is needed in three areas: (i) enhanced coupled modelling, (ii) data assimilation methods and (iii) the effective use of observations in the numerical weather prediction systems. Arctic regions pose specific challenges for each of these three areas because model errors are large, model uncertainty is not necessarily well represented, and in‐situ observations are sparse. Moreover, while the Arctic is very rich in terms of remote sensing observations from polar orbiting satellites, some of these observations are difficult to use in data assimilation because of ambiguous signal properties, particularly over snow and sea‐ice and in cloudy situations. This presentation highlights some of these challenges and priorities in each of the three key areas and describes recent and ongoing efforts to evaluate and improve predictions in the Arctic, and beyond, made in the framework of the YOPP modelling activities and the H2020 APPLICATE project. It also emphasizes the fact that, compared to previous similar initiatives, YOPP puts an additional emphasis on numerical experimentation, in a concerted effort to exploit observations for model improvement and drive developments in data assimilation and the design of observing systems.
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Understanding the controlling factors behind the chemical composition of the Earth’s atmosphere is a critical step toward addressing the modern environmental challenges of air pollution and climate change. Traditional methods interrogating theoretical predictions with observations have been highly successful in addressing these challenges, particularly in light of the recent immense growth of data availability in the Earth System Sciences. However, there are still gaps in our scientific knowledge due to limitations in modern scientific techniques (e.g. theoretical frameworks, observational systems, and computational power). New methods from the machine learning literature, when informed and guided by scientific understanding, present a valuable tool in addressing these knowledge gaps. In this seminar, I will present results from recent work using a variety of machine learning methods to better constrain modern understanding of the Earth System. Specifically, I will discuss recent results on the development of emulators to improve model representations of the interactions between atmospheric chemistry and the plant biosphere, and current work developing improved representations of aerosol-cloud interactions in climate models.
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The German Weather Service (DWD) aims to produce probabilistic information and products for weather forecasts and climate services at all relevant time and space scales. The essence of this "Seamless Prediction" approach is that transitions between different sources of information (observations, models, algorithms, applications) should not be readily apparent to stakeholders and users. A key element is the development of a Seamless INtegrated FOrecastiNg sYstem (SINFONY) to achieve a seamless transition from nowcasting to very short-range numerical weather prediction. Results from a pilot project focusing on severe convection and associated impacts will be transitioned successively to operations from 2021. In this presentation I will present the key elements of the DWD Seamless Prediction strategy and then give an overview of the current developments and future planning for SINFONY.
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The Community Long-term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS) retrieves atmospheric soundings from AIRS/AMSU (CLIMCAPS-Aqua) and CrIS/ATMS (CLIMCAPS-SNPP and -JPSS1) to establish a multi-decadal, multi-instrument satellite record of the atmospheric state. CLIMCAPS uses MERRA-2 as a-priori for temperature, water vapor and ozone in its optimal-estimation retrieval scheme to help stabilize null-space uncertainty caused by differences in radiance information content between AIRS and CrIS so that its long-term sounding record can have scientific value. In this presentation, we will give a brief overview of CLIMCAPS and demonstrate how its retrievals can be used to diagnose retrieval departure from MERRA-2 and thus improve our understanding of atmospheric processes.
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Predicting the fate of terrestrial carbon budgets and its sensitivity to climate change and land use/management strongly relies on our ability to accurately model vegetation dynamics, and carbon and water fluxes exchanged with the atmosphere. However, simulated carbon fluxes remain subject to large uncertainties, partly because of unknown or poorly calibrated parameters. In this talk, I present 10+ years of development of the land model – data assimilation system to better quantify and reduce uncertainties in carbon cycle predictions by assimilating multiple vegetation, carbon and water-related data streams into the ORCHIDEE LSM, the terrestrial component of the Institut Pierre Simon Laplace Earth System Model. We review our past studies in terms of the impact of the optimization on key characteristics of the global carbon cycle – such as the partition of the northern extratropical versus tropical land carbon sink (compared to atmospheric inversion estimates). We also discuss our work in the context of technical challenges we’ve faced, and propose solutions for the community going forward.
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The European Centre for Medium Range Weather Forecasts (ECMWF) is moving toward an Earth system approach. In this context coupled assimilation is used to provide consistent initial conditions to our coupled forecast model, for atmosphere, land, ocean, wave and sea ice components. In this talk, I will introduce activities conducted to develop coupled assimilation across the ECMWF operational systems. I will then focus on coupled land-atmosphere data assimilation developments. We currently use a weakly land-atmosphere coupling approach, with a coupled land-atmosphere background forecast and separate analyses for the atmosphere and for the surface (screen level variables, soil moisture and snow). Different approaches are used for snow and soil moisture initialisation, based on Optimal Interpolation and simplified Extended Kalman Filter. Both conventional in situ observations from the SYNOP network and satellite observations are assimilated. They are located at the land-atmosphere interface and include two-meter temperature and relative humidity, snow depth, and soil moisture. I will describe the data assimilation system and I will show the impact of snow and soil moisture assimilation on the NWP performances. I will present recent developments to assimilate the SMOS (Soil Moisture and Ocean Salinity) neural network observations and to use an ensemble land assimilation approach, and I will discuss perspectives of land surface assimilation developments in the context Earth system approach developments at ECMWF.
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Human emissions of greenhouse gases, and in particular CO2, which are mostly associated with the burning of fossil fuels such as coal, petrol and gas (http://globalcarbonatlas.org) are leading to an increasing level of global warming. CO2 stays centuries to thousands of years in the Earth System, hence only a net-zero CO2 emissions budget can allow to stabilize global warming. Global warming had reached +1°C (+2°F) in 2018. In this presentation, I will give an overview on the main conclusions of the Intergovernmental Panel on Climate Change’s (IPCC, www.ipcc.ch) Special Report on +1.5°C (https://www.ipcc.ch/sr15/; IPCC, 2018). I will show what are the implications of small changes in global warming (+0.5°C, i.e. +1°F) for changes in climate extremes (Hoegh-Guldberg et al., in press). These are large because many extremes strongly increase as function of global warming (Seneviratne et al. 2016). Thereby, regional climate sensitivity is an important quantity determining the regional responses of climate extremes displaying substantial intermodel spread (Seneviratne et al. 2018, Seneviratne and Hauser 2020), in particular due to soil moisture-climate feedbacks (e.g. Seneviratne et al. 2016, Vogel et al. 2017). Limiting global warming to +1.5°C would avoid widespread increases in extremes: hot extremes, but also heavy precipitation in several regions and drought in some regions. Every year of additional emissions leads to additional warming. References: Hoegh-Guldberg, O., D. Jacob, M. Taylor, M. Bindi, S. Brown, I. Camilloni, A. Diedhiou, R. Djalante, K. Ebi, F. Engelbrecht, J. Guiot, Y. Hijioka, S. Mehrotra, A. Payne, S.I. Seneviratne, A. Thomas, R. Warren, G. Zhou, 2018, Impacts of 1.5°C Global Warming on Natural and Human Systems. In: Global Warming of 1.5°C. An IPCC Special Report on the Impacts of Global Warming of 1.5°C above Pre-Industrial Levels and Related Global Greenhouse Gas Emission Pathways, in the Context of Strengthening the Global Response to the Threat of Climate Change, Sustainable Development, and Efforts to Eradicate Poverty [V. Masson- Delmotte, P. Zhai, H. O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. P.an, R. Pidcock, S. Connors, J. B. R. Matthews, Y. Chen, X. Zhou, M. I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, T. Waterfield (eds.)]. In Press. IPCC, 2018: Summary for Policymakers. In: Global warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty [V. Masson-Delmotte, P. Zhai, H.O. Pörtner, D. Roberts, J. Skea, P. R. Shukla, A. Pirani, W. Moufouma-Okia, C. P.an, R. Pidcock, S. Connors, J. B. R. Matthews, Y. Chen, X. Zhou, M. I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, T. Waterfield (eds.)]. World Meteorological Organization, Geneva, Switzerland, 32 pp. Seneviratne, S.I., M. Donat, A.J. Pitman, R. Knutti, and R.L. Wilby, 2016: Allowable CO2 emissions based on regional and impact-related climate targets. Nature, 529, 477-483, doi:10.1038/nature16542. Seneviratne, S.I., J. Rogelj, R. Séférian, R. Wartenburger, M.R. Allen, M. Cain, R.J. Millar, K.L. Ebi, N. Ellis, O. Hoegh-Guldberg, A.J. Payne, C.-F. Schleussner, P. Tschakert, R.F. Warren, 2018a: The many possible climates from the Paris Agreement
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Atmospheric and oceanic variability occurring across scales ranging from millimeters to tens of kilometers presents a difficult observational and modeling challenge. Ocean surface fluxes observed from continuous measurements during field experiments show strong variability on temporal scales that range from the diurnal cycle to the life cycle of storms, and on spatial scales as small as that of an individual convective cloud, up through mesoscale ocean variability. In this presentation I will discuss what has been learned about the effects of diurnal variability on the upper ocean and lower atmosphere across a variety of time and space scales. In addition to diurnal variability, upper ocean stratification can be strongly affected by short duration convective rain events. I will show results from high-resolution one-dimensional model simulations of the impacts of convective rain events on the upper ocean, and the further impact of these fresh pools will be shown through cloud resolving simulations coupled to an ocean mixed layer model. Horizontal sea surface temperature variability also plays a role in air-sea coupling, and I will focus on western boundary current regions and the varying fluxes across the gradient as shown by in situ observations. The importance of direct observations of the fluxes within this context will be discussed, impacts on the bulk flux parameterization will be highlighted, and new directions in in situ measurements of these key variables will be shown.
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The weather and climate community has set ambitious goals to reach global storm-resolving modeling capability on future exascale supercomputers. But currently, state-of-the-art models are executed using much coarser grid spacing and almost none of the productive weather and climate models are capable of effectively exploiting current and emerging supercomputing architectures. While in the past the computing environment has remained relatively stable, the end of Moore’s law is driving a rapid increase in the diversity of supercomputing architectures and corresponding programming models. Adapting our long-lived and complex model codes poses an immense challenge. In fact, there is a real danger that our inability to develop and rapidly adapt model codes to the available hardware will be an impediment to our scientific aspirations. As a consequence, different research groups are exploring ways to balance productivity, performance and portability for the weather and climate models they are developing. In this talk, we will briefly review the hardware and software trends which are the root cause of this gap and argue that the existing approach of incremental model improvements to adjust to changing hardware is likely to fail. Inspired by the machine learning community, we describe how high-level abstractions such as a domain-specific language embedded in a broadly accepted language such as Python is a possible solutions. Results from an investment into two weather and climate models (FV3GFS and COSMO) are presented and the advantages and disadvantages of different software strategies are discussed.
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CIMR is one of six High Priority Copernicus-expansion Missions (HPCMs) being developed by the European Space Agency for the European Union. The CIMR design involves a multifrequency microwave radiometer that will include five channel frequencies (L-, C-, X-, Ku-, Ka-bands) acquired simultaneously from a shared rotating mesh antenna with a spatial resolution requirement of 60 km (L), 15 km (C/X), 5 km (Ku/Ka) and a swath width of 1900 km. CIMR has a planned launch in the mid-2020s and will operate from a series of sun-synchronous polar orbiting satellites spanning up to 15-years of record. While the CIMR instrument and mission design emphasizes the global cryosphere and oceans, other science and applications are being developed in hydrology and ecosystem focus areas. An Earth science community workshop was recently assembled to discuss CIMR utility for NASA Earth science programs, and possible NASA contributions to a collaborative ESA-NASA CIMR mission. Here, I give an overview of the CIMR mission and its relevance to NASA Decadal Survey and Ecosystem Science objectives. I report on ecosystem outcomes from the initial scoping workshop in August 2019, and progress toward a potential NASA contribution to the mission. Potential mission applications for monitoring water storage changes and movement within the soil-plant continuum are discussed in relation to the baseline sensor design, as well as potential enhancements for improving performance and science value-added.
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The Community Radiative Transfer Model (CRTM) is an instrument-resolution scalar radiative transfer model developed and maintained by the Joint Center for Satellite Data Assimilation since 2004. Its primary application is the assimilation of microwave, infrared, and visible satellite observations in weather models at the various partner agencies of the JCSDA, including NASA, NOAA, US Navy and US Air Force. In a secondary role it is also applied to remote sensing, such as for the MiRS instrument 1DVAR retrievals. This very specific context leads to the dual requirement for the CRTM to compute radiances at instrument resolution both accurately and quickly. On the other hand, the CRTM is not designed for radiative flux calculations. As an all-sky model, the CRTM provides a range of solvers for both clear-sky and cloudy atmospheres. Two of the key issues that will be addressed in the upcoming Release 2.4 of the CRTM are atmospheric trace gases and aerosols, both of which are often associated with industrial pollution. Due to the requirement for both speed and accuracy, the CRTM utilizes a complex regression approach to account for absorbing gases. Earlier this year, a code sprint with participants from JCSDA, NASA GMAO, and NOAA aimed at simplifying this regression workflow for the CRTM end user. This seminar discusses the details of the gaseous transmittance regression and further efforts in this area. Lastly, the upcoming release will also contain a more flexible aerosol cloud interface based on the NetCDF file format and a newly updated set of aerosol scattering properties based on the newest iteration of the GOCART aerosol optical data. Thus, this seminar also discusses the details of the scattering data that are used, and how they are implemented in the CRTM REL-2.4. In addition, possible approaches for future improvements regarding both gaseous transmittance and aerosols in future releases of the CRTM, including machine learning and polarized scattering, are also discussed.
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The focus will be on the high resolution Copernicus Arctic reanalysis (CARRA) which uses DMI’s HARMONIE NWP model. Additionally, the HARMONIE-Climate system will be introduced, along with a few other Greenland ice sheet related themes.
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What distinguishes the new Navy Earth System Prediction Capability (ESPC) from other operational global coupled forecast systems is the very high resolution of the ocean and ice components. In this presentation we will motivate the need for high resolution in the ocean, describe system performance, and outline plans for future upgrades.
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MOM6 is a new ocean circulation code that departs from its predecessors in both formulation, algorithms, and numerical methods. Innovations in formulation and numerics were partly motivated to tackle the problem of spurious numerical mixing. It has been demonstrated before that spurious mixing can dominate the physical mixing which closes the ocean circulation and partly controls heat and carbon uptake by the ocean. In the latest GFDL configurations of ice-ocean and fully coupled models we find that MOM6 yields solutions with much less numerical mixing and reduced artificial heat uptake than in the past and as a result the solutions exhibit much less climate drift than is seen in many models. As a consequence, the solutions are closer to observations as measured by many metrics. Reduced drift and smaller discrepancies from observations have advantages not just for climate studies but also for data assimilation applications. In this presentation, we review the history of spurious heat uptake that led to the development MOM6, overview the innovations in formulation and algorithms that make the model attractive for Earth System Modelling, present other aspects about MOM6, including the open development paradigm, that are driving adoption by the larger community, and present the improvements we see in the latest GFDL models known as OM4 and CM4.
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Measurements Of Pollution In The Troposphere (MOPITT) on the NASA Terra spacecraft has been measuring the global atmospheric abundance of carbon monoxide (CO) since March 2000. Direct emissions of CO are mainly produced by incomplete combustion from both natural fires and anthropogenic activities, and CO is also produced chemically from methane and volatile organic carbon (VOC) species. CO plays an important role in atmospheric chemistry and climate because it is a dominant sink for the hydroxyl radical (OH) and thus affects the abundance of methane (CH4) and ozone (O3). Anthropogenic emissions of CO have a significant indirect radiative forcing of 0.22 W/m2. Satellite measurements of carbon monoxide are used to understand how pollution is emitted and transported globally, from large scale fires to urban sources. I will present an overview of the MOPITT mission and show recent science results using MOPITT CO data, including highlights on how MOPITT data are assimilated for understanding model chemistry and emissions. I will also discuss the future of satellite CO observations.
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Predicting the fate of terrestrial carbon budgets and its sensitivity to climate change and land use/management strongly relies on our ability to accurately model vegetation dynamics, and carbon and water fluxes exchanged with the atmosphere. However, simulated carbon fluxes remain subject to large uncertainties, partly because of unknown or poorly calibrated parameters. In this talk, I present 10+ years of development of the land model – data assimilation system to better quantify and reduce uncertainties in carbon cycle predictions by assimilating multiple vegetation, carbon and water-related data streams into the ORCHIDEE LSM, the terrestrial component of the Institut Pierre Simon Laplace Earth System Model. We review our past studies in terms of the impact of the optimization on key characteristics of the global carbon cycle – such as the partition of the northern extratropical versus tropical land carbon sink (compared to atmospheric inversion estimates). We also discuss our work in the context of technical challenges we’ve faced, and propose solutions for the community going forward.
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Smoke from biomass burning in Indonesia causes some of the world's worst air quality episodes. Most of the burning is related to land clearing and preparation, worsened by drier-than-normal conditions when surface fires spread underground into degraded peatlands. The most recent severe event was during in 2015, when hundreds of thousands of people in Indonesia and neighboring countries were exposed to hazardously poor air quality for two months and remnants of the plume stretched halfway around the world at the equator in the upper troposphere for three weeks. 2015 was a repeat of events that have occurred in Kalimantan (Indonesian Borneo) since the 1980s and in Sumatra since the 1960s. Research since the 1997/98 haze disaster has given us a reliable understanding of how much fire and haze will occur for a given drought strength. Because these relationships are so strong over Indonesia, it also allows us to test the effectiveness of major interventions put in place after the 2015 episode, ranging from investments in fire prevention and firefighting to the creation of a new peatland restoration agency and a moratorium on conversion of primary forest to agricultural use. In this talk, I will review current abilities to monitor dryness, fire and smoke over Indonesia using NASA EOS data, focusing on what they tell us about post-2015 interventions. In particular, the 2019 fire season provided the first test of whether these have had any effect. In some provinces, the amount of fire and smoke was commensurate with moderately dry conditions, whereas in others, there was significantly less fire than expected.
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Assessing predictive skill of sub-seasonal forecasts (the timescale ranging from ~14-90 days) is a hot topic of research, and an important prerequisite for the development of successful climate services. In this talk, we first present the predictive capacity of uncalibrated sub-seasonal forecasts for the entire globe, analyzing the seasonality of sub-seasonal skill in different regions via the seasonality of key sources of predictability. Then we describe different Model Output Statistics (statistical calibration) methods, including gridbox-by-gridbox and pattern-based ones, and discuss under which conditions calibrated forecasts exhibit higher skill than uncalibrated ones. We conclude with some recommendations for the model development community, regarding physically-based biases related to misrepresented spatio-temporal patterns in climate models.
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ECCO was established in 1999 as part of the World Ocean Circulation Experiment (WOCE) with the goal of combining a general circulation model (GCM) with diverse observations in order to produce a quantitative depiction of the time-evolving global ocean state. A distinguishing characteristic of ECCO ocean state estimates is that they satisfy GCM primitive equations exactly and are therefore well-suited to applications that require property conservation and closed budgets. In this presentation, I will give a brief overview of the ECCO project and methods and then discuss one particular application, ocean biogeochemsistry, where property conservation is a key requirement. The ECCO project strengthens the connection between NASA’s ocean modeling and remote-sensing strategies and enhances the value of satellite retrievals for studies and discussions about climate and climate variability.
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GEOS-Chem is a global atmospheric chemistry model used by hundreds of research groups around the world. It includes detailed representations of tropospheric and stratospheric chemistry as well as aerosol microphysics. The standard implementation of GEOS-Chem is as an off-line chemical transport model driven by GEOS-5 assimilated meteorological data. More recently, GEOS-Chem has been implemented as a stand-alone on-line chemical module (solving for the local operations of emissions, chemistry, deposition) in GEOS-5 and WRF. It has also been implemented on the AWS cloud including service of GEOS-5 data. Here I will review the current capabilities and implementations of GEOS-Chem and plans for the future, including in particular continued collaboration with GMAO.
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NEMOVAR is a variational data assimilation system developed jointly by CERFACS, ECMWF, INRIA and Met Office for research and operational applications with the NEMO ocean model. This presentation will describe developments to NEMOVAR in ensemble-variational data assimilation in support of reanalysis and seasonal forecasting activities at ECMWF and the Copernicus Climate Change Service (C3S). For several years, ECMWF has been running a reanalysis and real-time analysis system based on NEMOVAR. An integral part of that system is the production of an ensemble of ocean analyses. The ensembles are used to quantify uncertainty in reanalyses and to initialize coupled model ensemble forecasts. The ensemble system also provides valuable information on time-evolving background error, which is poorly exploited in the current ocean analysis system. In this talk, I will present a new ensemble-based, hybrid formulation of the background error covariance matrix, which has been developed for NEMOVAR. I will focus on describing its algorithmic components and illustrate its features using climatological and flow-dependent ensembles from the ECMWF ocean reanalysis.
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The quasi-biennial oscillation (QBO) is the primary mode of variability of the tropical lower stratosphere. As the QBO is forced by a spectrum of waves from tropical convection as well as vertical advection, it is likely to change in a warming climate. Here we present changes to the QBO from experiments with doubled and quadrupled CO2 concentrations and sea surface temperatures increased by 2 K and 4 K, respectively (Experiments 3 and 4 from the Quasi-biennial Oscillation initiative (QBOi)). We will report a considerable spread in the projected QBO metrics among general circulation models in a warming climate, with some models reporting a QBO period of 14 months and some models losing the oscillation completely. Much of the spread in the QBO projections comes from the uncertainty in the representation of parameterized gravity waves in general circulation models. We will discuss differences in projections of the QBO in warming climate for models with fixed vs variable gravity wave source parameterizations.
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Ensemble-based data assimilation systems typically use background error covariance localization to dampen spurious correlations associated with sampling error while increasing the rank of the covariance estimate. Variational methods use model-space localization, in which localization is applied to ensemble estimates of covariances between model variables and is based on distances between those variables, while ensemble filters apply observation-space localization to estimates of model-observation covariances, based on distances between model variables and observations. It has been shown that for non-local observations, such as satellite radiances, model-space localization can be superior. In this talk we will present a method for performing model-space localization in serial ensemble filters using the linearized observation operators. Results of radiance-only assimilation in a global forecast system show the benefit of using model-space localization relative to observation-space localization. The serial ensemble square root filter with vertical model-space localization gives results similar to those of the EnVar system (without outer loops or extra balance constraints), and to ensemble Kalman filter using modulated ensembles to emulate model-space covariance localization.
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Over the past 50 years, hydrology has evolved from a utilitarian endeavor focused mainly on the prediction of streamflow, to an Earth system science focused on a broader range of mechanisms and variables, including the role of water in climate and ecosystems. This evolution has largely been affected by two major drivers: the formulation of increasingly complex Earth system models and the growing availability of satellite observations. I�ll present some background on this evolution and recent work at JPL using NASA satellite data to approach larger questions in the field of terrestrial hydrology, including the importance of subsurface water storage in several science applications. Mostly this talk will focus on science results from NASA�s Gravity Recovery and Climate Experiment (GRACE) mission, and some coverage of ongoing work with SMAP and SWOT missions. I�ll discuss new concepts in drought monitoring, flood identification and long-lead-time flood prediction, and the contributions of GRACE to understanding the global mass budget. Finally, I�ll present some ongoing research and future directions including decadal survey priorities.
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As Earth�s climate warms, rising sea levels are becoming a great concern. Providing substantiated and well-informed guidance on the amount and rate of future sea level rise is important, but remains challenging. Observations of the Greenland and Antarctic Ice Sheets�Earth�s largest freshwater reservoirs�reveal their ongoing, rapid, and complex changes in response to an evolving climate. The improved understanding of ice sheet behavior resulting from observations is driving the development of dynamical ice sheet models, and is allowing better simulation of past, present, and future ice sheet evolution. However, insight into future changes requires a better understanding of how ice sheets interact with other components of the Earth system and associated feedbacks. Ice sheets in the contemporary climate system are not in steady state: they exchange energy and mass between the atmosphere, ocean, and underlying bedrock. An adequate assessment of these processes is important for understanding sea level change. As climate models start to include dynamical ice sheet components, our understanding of such interactions and the feedback mechanisms is advancing. Here, we review the current methods, recent development and limitations for coupling dynamical ice sheets in the NASA GEOS5 and ModelE models, and explore some of the remaining challenges.
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The next century will see unprecedented changes to the climate system with direct consequences for society. As stated in the National Climate Assessment, "changes in extreme weather events are the primary way that most people experience climate change." In this sense, the characteristics of extreme weather are key indicators of climate change impacts, at both local and regional scales. Understanding potential changes in the location, intensity and structure of such extremes (e.g., tropical cyclones, severe thunderstorms and flooding) is crucial in planning for future adaptation as these events have large economic and social costs. The goal of this work is to better understand climate impacts on extreme weather events in various high-resolution configurations of the Community Atmosphere Model (CAM) run at horizontal grid spacings of approximately 28 km and forced with prescribed sea-surface temperatures and greenhouse gas concentrations for past, present, and future climates. This analysis will include the evaluation of conventional (AMIP-style) decadal simulations typical of climate models, short 7-day ensemble hindcasts of recent devastating events (e.g., Hurricane Florence in 2018), and reduced complexity simulations of idealized states of the climate system. Through this hierarchical modeling approach the impact of climate change on the characteristics (frequency, intensity, rainfall, etc.) of extreme weather, including tropical cyclones, can be quantified.
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Ice streams flowing from the Antarctic continent meet the ocean where they cross the grounding line and begin to float as ice shelves. Changes in ice shelf structure due to basal melting have received much attention since ice shelves buttress the continental ice sheets and reduction of the ice shelves will result in a greater flow of continental ice into the ocean. However, there are other reasons besides sea level change why the melting of ice shelves is important. Transport of basal meltwater into the open ocean has been proposed as a significant source of dissolved iron (dFe) and thus a major control on phytoplankton growth over the productive Antarctic continental shelf. Basal melting of ice shelves can drive a vigorous overturning circulation within ice shelf cavities ("meltwater pump") that can also bring "deep" sources of iron to the surface. The flux of basal meltwater into the coastal ocean has been proposed to have an important effect on observed changes in Antarctic Bottom Water (AABW) formation. We use a 5 km resolution ocean/sea ice/ice shelf model of the Southern Ocean to examine these mechanisms over the Antarctic continental shelf. Four possible sources of dFe are simulated with independent tracers, assumptions about end member concentrations, and an idealized summer biological uptake. Direct injection of iron from melting ice is shown to be an important contributor to the total dFe supply to the surface waters. However, there is generally an even larger contribution from deep sources of iron on the shelf. The importance of the "meltwater pump" is quite heterogeneous around Antarctica, but in several locations, it is the primary mechanism for mixing deep dFe up to the surface. Several independent tracers are also used to track the spread of meltwater from specific ice shelves. Results confirm the previously reported idea that transport of meltwater from the Amundsen ice shelves to the Ross Sea is significant, especially with respect to possible changes in AABW formation.
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Earth observing satellite missions are designed around a set of pre-defined measurement needs, and aimed at addressing a specific set of science questions. As such, mission design necessarily incorporates considerations of: measurement accuracy and uncertainty, spatial and temporal sampling, mission science impact, and impact of observations on numerical weather prediction. The ability of a mission to meet its requirements is assessed, and, if possible, quantified, at each stage of design and development. Observing system simulation experiments (OSSEs) have been traditionally used to measure the anticipated impact of a new set of measurements on weather prediction. Forecast OSSEs are useful, in that they measure the effectiveness of a set of measurements in the context of the current global observing system. However, forecast OSSEs also have a number of key limitations. First, they require simulation of all current measurements, along with calibration of their errors. Second, they rely on the availability of a forecast and data assimilation system that is capable of ingesting the new measurements. If measurement uncertainties are not properly calibrated, the measure of impact of a new observing system will be incorrect. If a data assimilation system is not capable of assimilating a new type of measurement, a forecast OSSE is not possible. In this presentation, I will discuss the specific application of OSSEs to the design of missions that target convective-scale processes. Most forecast systems do not represent convection realistically, making it necessary to consider new ways of quantifying the effectiveness of a proposed observing system. I will describe a spectrum of OSSEs that range from simple to complex, and include: 1. Experiments that explore satellite spatial and temporal sampling 2. Quantification of retrieval (and forward model) uncertainty 3. Assessment of mission science goals 4. Observation impact on numerical weather prediction I will focus on the types of experiments that may be required in the development of the 2017 Decadal Survey Clouds, Convection, and Precipitation mission.
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Carbon climate feedbacks constitute a first order uncertainty on predictions of future climate scenarios. To better quantify the feedback mechanisms, we need to quantify the climate response of various surface processes that determine the fluxes of carbon cycle gases such as CO₂ and CH₄. One way of estimating surface fluxes of trace gases is through atmospheric inversions, which infer surface fluxes from observed atmospheric gradients. However, inversions cannot separate different processes contributing to the total atmospheric gradient, unless those processes are spatiotemporally separated. Relative ratios of the different isotopologues of a species, such as ¹³C:¹²C in CH₄, provide another way of separating different surface processes in an atmospheric inversion.

I will discuss the general background of why we can use isotopes to separate surface processes, followed by a more in-depth presentation of how measurements of ¹⁴C of CO₂ in the atmosphere can be used to estimate the fossil fuel CO₂ contribution to the total CO₂ flux in a multi-tracer inversion. NOAA's ¹⁴CO₂ measurements started in 2003, reaching peak coverage in 2010. I will show that given 2010 coverage levels we can estimate monthly and annual national total fossil fuel emissions to within a few percent, and can reach the same accuracy for state and regional emissions in a realistic future. I will also discuss how our atmosphere-based estimates for 2010 compare to more traditional bottom-up fossil fuel inventories, and factors that complicate attempts to reconcile the two estimates. Finally, time permitting, I will present the use of ¹³C of CH₄ to separate the different processes contributing to the atmospheric budget of CH₄, and discuss whether atmospheric ¹³CH₄ measurements can shed light on recent trends in atmospheric CH₄.
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The success of ensemble Kalman filters in oceanography, meteorology, and other fields of geoscience is remarkable, considering the relatively small ensemble sizes permitted by high-performance computing resources available at research and operational centers. Dimension-reduction procedures and methods for treating sampling errors in Gaussian approximated probability densities, via covariance localization, variance inflation, etc., make this achievement possible. As moderate to large ensemble sizes become regularly available for geophysical data assimilation, a natural progression from current Kalman filter-based strategies is to attempt more general (Bayesian) probability density estimation. For the case of numerical weather prediction, advancements of this sort may be necessary to extract information from underused observing systems such as cloud- and precipitation-affected satellite measurements, or better constrain dynamical processes responsible for severe convective storms and tropical cyclones. Each of these applications challenges the underlying assumptions of data assimilation systems currently used operationally. This presentation will summarize recent efforts by one research group to develop Monte Carlo filters that bridge between Gaussian and Bayesian data assimilation methods for numerical weather prediction. Strategies adopted for this purpose rely on sequential importance resampling techniques used for particle filters, but their practical application for high-dimensional systems follow from ideas emerging from decades of ensemble Kalman filtering research. Using a recently developed particle filter method that operates effectively for high-dimensional applications, this presentation will also describe examples that motivate future development of non-Gaussian filters.
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The land surface can have a profound impact on the diurnal evolution of the boundary layer. Additionally, land surface states, and particularly soil moisture, have a longer memory than the atmosphere, so that the land surface provides an important source of atmospheric predictability, up to at least seasonal time scales. Atmospheric forecasts, across a range of time scales, can then be improved by using land data assimilation (DA) to improve a model’s initial land surface states. The characteristics of the land surface differ from the atmosphere in several important ways, and this presentation will review how these differences have affected the design of land DA systems. Land DA is used by both the NWP community (working with AGCMs, to improve the atmosphere), and the hydrology community (working with offline land surface models, to improve water storage and fluxes). Since these communities are ultimately interested in different processes, the approaches to land DA that have evolved within each are different. These differing approaches will be presented and compared. In both cases, the land DA has been tailored to improve the model components of greatest interest to that community (e.g., weather forecasts), however this does not then necessarily improve other model components (e.g., the land states). Nonetheless, progress is being made towards better integrated land DA approaches, capable of improving both the land and the atmosphere. As one example, results will be presented from recent coupled land/atmosphere DA experiments, in which satellite soil moisture is assimilated into a re-run of NASA’s MERRA-2 reanalysis, resulting in improved soil moisture, land surface fluxes, and screen-level temperatures.
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NASA is investigating the utility of a strategically-constructed constellation of infrared sounders onboard small satellites to provide spaceborne measurements of wind. The method proposed by instrument teams is to fly multiple instruments in complementary orbits so that atmospheric motion vector measurements can be made. As part of the investigation of this measurement approach, the Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center performed a set of Observing System Simulation Experiments (OSSEs) to demonstrate the value of the wind measurements as well as the corresponding infrared radiance observations that will come from the constellation. This work was an extension of the GMAO OSSE infrastructure and is in the context of the MISTIC(tm) Winds concept. It is noted, though, that this provided insight to the overall measurement strategy. This talk addresses the simulation of the atmospheric motion vectors retrieved via the constellation, the simulation and validation of the radiance observations measured via the constellation, the specification of observation errors for both winds and radiances, and the extension of the data assimilation system to utilize these additional observations on top of a full global observing system. Finally, the results from a set of OSSE experiments are presented.
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The Indian Space Research Organisation (ISRO) has initiated National Carbon Project (NCP) in early 2007 with an aim of strengthening national data bases for the study of regional carbon cycle over the Indian region. Good efforts were made during past 10 years, and reasonable amount of data were collected to study the regional carbon cycle. An effort is also underway to integrate all these data in the global earth system models. In this regard, we have implemented the Carnegie--Ames--Stanford Approach (CASA) for terrestrial ecosystem, GEOS-Chem offline transport, Regional Ocean circulation Models at a regional scale. The model solutions have been derived and are used to study seasonal dynamics, inter-annual variability and long-term change across different eco-regions over India and surrounding oceans. The key finding of the study are: Indian terrestrial ecosystem without the disturbances can be characterized as the net sink of atmosphere CO2; climate has significant role on driving positive trend of Net Primary Productivity over India; Net Ecosystem Productivity shows significant inter-annual change in response to the climate variability; strong seasonality in the source and sink of CO2 over the coastal and regional oceans; strong vertical Vs relatively weaker horizontal transport for shaping the CO2 tendencies in the lower troposphere.
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Satellite-based monitoring of greenhouse gases has expanded dramatically in the past decade, enabling new insight into greenhouse gas fluxes from regions of the world that were previously difficult to monitor. This talk will focus on insights from two satellites: carbon dioxide observations from NASA
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Atmospheric rivers (ARs) � long, narrow filaments of large vertically-integrated horizontal water vapor transport � are associated with weather, water and climate extremes, including precipitation and wind extremes, flooding, and droughts when they are absent. Most work in recent decades has been focused on regional characterization and impacts of ARs, namely western N. America and Europe. Here we highlight the application of a global detection algorithm for atmospheric rivers to illustrate the widespread global impacts of atmospheric rivers in shaping our weather and climate, including highlights from our studies on global evaluations of model simulation of ARs, weather and subseasonal prediction skill of ARs, climate projections of ARs, and the manner they shape terrestrial hydrology extremes across the globe.
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The European Centre for Medium-Range Weather Forecasts (ECMWF) runs a 24/7 operational service, producing and disseminating numerical weather predictions to its Member States. More recently, ECMWF started to monitor the composition of the atmosphere and the climate through the European Union flagship program Copernicus. All these activities rely on processing all available observations (satellites, surface-based and aircraft reports, etc.) using a four-dimensional data assimilation method (4D-Var). For instance ECMWF routinely processes data from around 90 satellite data products and a total of 40 million observations are processed and used daily. One key component of ECMWF�s 4D-Var assimilation method is the background error covariance matrix. This matrix first weights the information from the previous forecast (background) with respect to the observation error in the assimilation process. It also propagates spatially the information brought by the observations and spreads it among the model variables. The talk will focus on this matrix with two main focuses: 1. A new formulation of the matrix for the operational weather prediction. As for most of Numerical Weather Prediction systems, ECMWF�s background error covariance matrix combines a static part and an ensemble-based part. The ensemble-based part aims at bringing flow-dependent information while the static part acts as a regularization of the possibly noisy information from the ensemble-based part. We are investigating a different approach to the current one by combining static and flow- dependent information in the hybrid background error covariance matrix in the model space instead of the wavelet space. The results of this new approach will be presented. 2. A trial matrix for atmospheric composition that allows to infer information on the wind field from observations of atmospheric tracers. Assimilating observations related to atmospheric constituents could in theory provide information on the model dynamics when they are good tracers of the atmospheric flow. This has been investigated years ago with ozone total columns. At that time, the modelled ozone and the ozone observations biases were found to be too large and the data coverage too sparse to have a significant impact on the inferred wind. Since then, both the models and the quality of the atmospheric constituents data improved, especially with the Copernicus framework. We will present our preliminary result while revisiting the possible impact of atmospheric constituents assimilation on weather forecast in the current context of atmospheric constituent model and observations.
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Ensemble prediction experiments reveal significant skill in predicting the winter North Atlantic Oscillation at seasonal lead times or even longer, and this can be used to provide skilful forecasts of year to year variations in European and North American winter climate. We show evidence of high skill from current prediction systems and investigate the mechanisms that provide predictability. Despite the commonly stated errors in numerical modelling of convection, highly skilful long range predictions of tropical rainfall are possible, and this is a key factor which drives prediction of the winter NAO via a mechanism involving poleward propagating Rossby waves. Despite this success, there is an unexplained error in the amplitude of predicted signals: they are demonstrably too weak by a factor of two or more. We show how this 'signal to noise paradox' may point to an important and widespread error that results in ensemble predictions that are better at predicting the real world than they are at predicting themselves! Despite this outstanding problem, we also illustrate a broad range of new climate services that can be provided from current seasonal predictions.
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I will give an overview of the Department of Energy's "Energy Exascale Earth System Model" (E3SM), including Sandia�s role in numerical algorithms, parallel scalability and computational performance. E3SM, formerly known as ACME, is designed to run on upcoming next generation DOE supercomputers. Adapting simulation codes to these new architectures is expected to be more disruptive than the previous transition from vector to massively parallel supercomputers. E3SM development is driven by several grand challenge science questions focused Earth's cryosphere, biogeochemical and water cycle systems. E3SM has a new land and atmosphere component models branched from the CESM v1.2, coupled to new MPAS ocean, sea ice and land ice models. I will discuss the performance and throughput challenges of the E3SM high-resolution coupled configuration on several DOE computers. Our current focus on the NERSC Cori system with Intel Xeon Phi architecture, and longer term we hope to make effective use of the upcoming NVIDIA GPU based system at Oak Ridge. I will also give a analysis of the E3SM spectral element atmosphere dycore following the NGGPS dycore computational evaluation protocol, with an emphasis on the throughput rates needed for climate simulations. For even higher resolution simulations, we will rely on E3SM's ability to use unstructured grids in all component models. This will allow us to achieve cloud resolving resolution in select regions of interest seamlessly within the global modeling system.
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Data assimilation is the process by which observations and models are used to jointly estimate the state of the atmosphere. Driven by advances in numerical weather prediction, such approaches are increasingly being applied to constrain atmospheric chemistry models. Here we present recent work describing theoretical studies to advance such applications four-dimensional variational (4D-Var) techniques. Hybrid adjoint and stochastic simulation techniques are developed for evaluating the posterior analysis error of an assimilation � a critical property that quantifies the impact of observations on reducing uncertainty in model estimates. This approach is applied to quantify the information content of current and proposed satellite remote sensing instruments for detecting CH4 sources in North America. We then present recent theoretical developments in defining optimal basis sets for large-scale Bayesian problems. We then demonstrate how Monte Carlo simulations can be used to efficiently compute these basis sets using a new Randomized Incremental Optimal Technique (RIOT), reducing the wall-time of a regional inversion of BC emission by a factor of 5 � 10, and how these findings are relevant to improving operational Bayesian forecasting systems in general.
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There is a robustly observed diurnal signature in the timing of both land and ocean precipitation. In organized deep convection, this diurnal signature is modified due to upper cirrus cloud shielding of the boundary layer. Rotation adds an additional level of complexity to the cloud-radiative response to solar insolation, and I will discuss the hurricane response in particular. Satellite observations of cloudy hurricane canopies have shown a universal, daily, wave-like feature that propagates radially outward, as far as 600 km (Dunion et al. 2014). Daytime solar heating of a hurricane's upper eyewall is surely responsible, but the mechanism for the wave was previously unknown. I will discuss numerical experiments that suggest these waves are internal inertia-gravity waves, and in fact propagate through (almost!) the entire depth of the hurricane. Their structure is similar to the classical "St. Andrews cross" pattern response to a bobbing cylinder in a quiescent fluid. Due to the hurricane's flow field, diurnal waves can only begin to propagate well beyond the storm core, though the anticyclonic outflow region is more receptive to near-core diurnal propagation. The prohibited region is highly sensitive to disruptions to the wind field that resemble an eyewall replacement cycle.
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In the last decade, extremely low Arctic summer sea ice cover in some years has prompted new research on predicting sea ice conditions a season or more in advance. There are several promising sources of predictability associated with aspects of sea ice that vary relatively slowly and the integral of coupled interactions with the atmosphere and ocean over many months. The sea ice circulates in large-scale gyres, which transport anomalies with some regularity. However, like most short-term prediction problems, good initialization and post processing are essential, yet remain challenging. I will review the rapid advances on this important topic that have evolved since The Sea Ice Outlook began collecting and reporting predictions in 2008.
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Observations of terrestrial water storage (TWS) from the Gravity Recovery and Climate Experiment (GRACE) satellite mission have a coarse resolution in time (monthly) and space (roughly 150,000 sqkm at midlatitudes) and vertically integrate all water storage components over land, including soil moisture and groundwater. Nonetheless, data assimilation can be used to horizontally downscale and vertically partition GRACE-TWS observations. This presentation illustrates some of the benefits and drawbacks of assimilating TWS observations from GRACE into the land surface model of The Goddard Earth Observing System Model, Version 5 (GEOS-5) over the continental US and India. In particular, the assimilation scheme yields improved skill metrics for groundwater compared to the no-assimilation simulations. A smaller impact is seen for surface and root-zone soil moisture, which have a shorter memory and receive smaller increments from TWS assimilation than groundwater. These results motivate future efforts to combine GRACE-TWS observations with observations that are more sensitive to surface soil moisture, such as L-band brightness temperature observations from Soil Moisture Ocean Salinity (SMOS) or Soil Moisture Active Passive (SMAP). Further, GRACE observes TWS depletion associated with anthropogenic groundwater extraction. The model, however, does not represent anthropogenic groundwater withdrawals and is not skillful in reproducing the interannual variability of groundwater. Assimilation of GRACE TWS introduces long-term trends and improves the interannual variability in groundwater. But the assimilation also introduces a negative trend in simulated evapotranspiration, whereas in reality evapotranspiration is likely enhanced by irrigation, which is also unmodeled. These results emphasize the importance of representing anthropogenic processes in land surface modeling and data assimilation systems.
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We present a multi-scale study examining the impact of a regional exchange of nuclear weapons on global climate. Our models investigate multiple phases of the effects of nuclear weapons usage, including growth and rise of the nuclear fireball, ignition and spread of the induced firestorm, and comprehensive Earth system modeling of the oceans, lands, and atmosphere. This study follows from the scenario originally envisioned by Robock et al. (2007a), based on the analysis of Toon et al. (2007), which assumes a regional exchange between India and Pakistan of fifty 15-kiloton weapons detonated by each side. When the Earth system model is initiated with 5x10^9 kg of black carbon in the upper troposphere (approximately 9 to 13 km), our simulations produce similar results to the previously published work. However, while our thorough simulations of the firestorm produce about 3.7x10^9 kg of black carbon, we find that the vast majority of the black carbon never reaches an altitude above weather systems (12 km). Earth system model simulations conducted with model-informed atmospheric distributions of black carbon produce significantly lower global impacts than assessed in prior studies, as the carbon at lower altitudes is more quickly removed from the atmosphere. Our models indicate that statistically significant effects on global surface temperatures are limited to the first 5 years and are much smaller in magnitude than those shown in earlier works. In addition, we find that the effects on global surface temperatures are not uniform and are concentrated primarily around the highest arctic latitudes, significantly lowering the global impact on human health and agriculture compared with that reported by earlier studies.
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The drought response of transpiration and photosynthetic carbon uptake is often modeled as dependent on the soil moisture response of stomata closure. However, stomata closure depends on leaf water potential, which depends not just on soil moisture but also on the flow of water within the plant, as dependent on its full range of hydraulic traits (xylem conductance loss, capacitance, etc). However, hydraulic traits (especially xylem conductance parameters) have only been measured for a small number of species, mostly in temperate locations. Explicit upscaling of such data to the ecosystem scale is a nearly-insurmountable challenge. Alternatively, microwave remote sensing measurements, which naturally average over large areas and are sensitive to canopy water content (and leaf water potential) may be useful for studying plant hydraulic behavior directly at the ecosystem scale. In this talk, I will introduce a measure of ecosystem-scale isohydricity. I will then use this dataset to show that across North American grasslands, the productivity of isohydric ecosystems (for which stomatal closure and xylem conductance loss act to keep leaf water potential relatively constant during soil drydowns) is far less sensitive to interannual variability in vapor pressure deficit than that of anisohydric ones.
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Extratropical cyclones are responsible for the majority of midlatitude precipitation, and they also create an important amount of cloud cover. This study investigates extratropical cyclones cloud and precipitation biases in GCMs. The model data used is from the CMIP5 archive and CMIP6- prototype model output generated for the NOAA Model Diagnostic Task Force. First, the cyclones are tracked using a Lagrangian tracking algorithm. Then, cyclone-centered composites are generated for the models, and they are compared with reanalysis and satellite observations. The work presented will focus on two aspects of the storms: post cold-frontal clouds, which are known to contribute to a serious bias in the southern hemisphere radiation budget, and warm sector precipitation. For the precipitation, cyclone-centered composites are generated using GPCP, MERRA2 reanalysis, and, separately, GPM data. The first result is focused on the skill and potential biases in each of these datasets. Next, the cyclone-centered composites of precipitation in CMIP5 models are analyzed. The models all generate a similar range of rain rates in the cyclones, although resolution impacts one tail of the distribution: finer resolution models have more zero rain regions. Sensitivity of cyclone precipitation to storm strength and available moisture is examined using a warm conveyor belt estimated rain model. The analysis shows: (1) cyclone precipitation can be strong even when the storm strength is weak, and (2) the models and reanalysis all capture this behavior. Analysis of the influence of parameterized convection on precipitation sensitivity to column moisture and wind speed reveals that larger fractional contributions of parameterized convection cause the warm conveyor belt model to overestimate cyclone rainfall. For the post cold-frontal cloud analysis, we utilize a feature-tracking algorithm to identify the fronts. Then, an analysis of the post cold-frontal cloud properties finds that models and reanalysis have biases in the cloud content.
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Fronts in the oceans have lateral property gradients at least a factor of fifty higher than the regions around them, when averaged over many eddy time and space scales. More than a decade of work has revealed that transport across such fronts provide a critical nutrient supply pathway that fuels subtropical gyre productivity. However, these biogeochemical fronts most often coincide with dynamical fronts or jets, which are often considered barriers to exchange. Therefore, our view of ocean fronts as nutrient gateways must be reconciled with the possibility that they act as barriers to exchange. Ekman transport is one mechanism that allows for nutrient transport across the surface of the fronts and is shown to be a leading term in the subtropical nutrient budgets. Ring formation and mixing beneath the core of jets are other mechanisms that can mediate cross-frontal exchange and have intriguing implications for macro- and micro-nutrient budgets and the phytoplankton that consume these nutrients. Even as our conceptual model of subtropical nutrient cycling and its influence on primary productivity is becoming more complete, a new frontier is now emerging: the influence cross-frontal exchange and water mass formation on the distribution and behavior of mesopelagic animals.
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Atmospheric rivers can cause considerable mayhem along the west coast of North America delivering flooding rains during periods of heightened activity and desiccating droughts during periods of reduced activity. The intrinsic chaos of the atmosphere makes the prediction of atmospheric rivers at subseasonal-to-seasonal (S2S) timescales (~2 to 5 weeks) an inherently difficult task. We demonstrate here that the potential exists to advance forecast lead times of atmospheric rivers into S2S timescales through knowledge of two of the atmosphere's most prominent oscillations; the Madden-Julian oscillation (MJO) and the Quasi-biennial oscillation (QBO). We present evidence of a dynamical relationship between atmospheric rivers, the MJO and QBO through modulation of North Pacific blocking using reanalyses and retrospective forecasts of the ECMWF forecast system. We then present an empirical prediction scheme for anomalous atmospheric river activity based solely on the MJO and QBO and demonstrate skillful subseasonal forecasts of opportunity 4+ weeks ahead. With the wide-ranging impacts associated with landfalling atmospheric rivers, even modest gains in the subseasonal prediction of anomalous AR activity may support early action decision making and benefit numerous sectors of society.
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As anthropogenic emissions of carbon dioxide (CO2) have risen, so too has the total natural sink over land and ocean. Yet the distribution in space and time of the increasing sink, and thus the processes controlling it, remain unknown. This presentation describes the development and evaluation of the GEOS-Carb reanalysis of CO2 and the constraints on monthly budgets of the natural carbon sink that follow from its estimates. The GEOS-Carb modeling and assimilation system is an extension of the GEOS general circulation model and data assimilation system developed at NASA's Global Modeling and Assimilation Office (GMAO). It produces analyses and predictions of mixing ratios of CO2 throughout the atmosphere based on retrievals of column-average carbon dioxide (XCO2) derived from near-infrared radiance measurements from the Orbiting Carbon Observatory 2 (OCO-2) and the Greenhouse Gases Observing Satellite (GOSAT). The resulting fields have gap-free global coverage, are available every 6 hours, and are currently the most complete and highest resolution picture of CO2 consistent with satellite observations. Estimates of monthly global growth rates of CO2, and hence totals of the natural sink, follow from the analysis through a simple derivation. While there is an existing, well-established estimate of the annual growth rate derived from NOAA/ESRL surface network data, these observations cannot constrain the growth rate on sub-annual timescales because they are restricted to the boundary layer. Satellite observations, on the other hand, are sensitive to the entire atmosphere and thus have the potential to constrain growth rates over monthly or shorter timescales. This constraint is vital to understanding the timing and strength of the uptake of CO2 over land in the spring and summer and its subsequent release in the fall and winter.
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The impact of ozone-depleting substances on global lower-stratospheric temperature trends is widely recognized. In the tropics, however, understanding lower-stratospheric temperature trends has proven more challenging. While the tropical lower-stratospheric cooling observed from 1979 to 1997 has been linked to tropical ozone decreases, those ozone trends cannot be of chemical origin, as active chlorine is not abundant in the tropical lower stratosphere. The 1979-97 tropical ozone trends are believed to originate from enhanced upwelling, which, it is often stated, would be driven by increasing concentrations of well-mixed greenhouse gases. This study, using simple arguments based on observational evidence after 1997, combined with model integrations with incrementally added single forcings, argues that trends in ozone-depleting substances, not well-mixed greenhouse gases, have been the primary driver of temperature and ozone trends in the tropical lower stratosphere until 1997, and this has occurred because ozone-depleting substances are key drivers of tropical upwelling and, more generally, of the entire Brewer-Dobson circulation
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Soil moisture regulates net ecosystem land-atmosphere CO2 exchange through its differential impact on vegetation growth and ecosystem respiration processes. Recent studies suggest that interactions between soil water availability in semi-arid ecosystems and global temperatures drives broadscale inter-annual net ecosystem CO2 exchange variability. The lack of comprehensive, accurate global soil moisture information has hindered global model and remote-sensing data synthesis which have previously relied on proxies for soil moisture including precipitation, humidity, and various aridity indices. This has changed with the launch of dedicated soil moisture missions including NASA
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Observing System Simulation Experiments (OSSEs) can be powerful tools for estimating the potential for proposed observing systems to improve numerical weather prediction. The pitfalls and practice of performing OSSEs will be discussed along with a basic overview of how OSSEs work. A sophisticated OSSE framework has been developed at the GMAO that is available for community use. The GMAO OSSE will be described, with particular attention to the validation and calibration of the system. Some examples of the OSSE in action will be shown, both for evaluation of new observing systems and as a tool for the investigation of data assimilation system behavior.
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Changing climate and land use practices coupled with changing population distributions have the potential to dramatically alter coupled hydrologic-biogeochemical processes and associated movement of water, carbon and nutrients through various terrestrial reservoirs. Such changes will ultimately influence the delivery of dissolved and particulate materials from terrestrial systems into rivers, estuaries, and coastal ocean waters. As part of a previous NASA-funded research, our team developed an integrated suite of terrestrial and coastal ocean ecosystem models that were used to examine processes controlling fluxes in the Mississippi-Atchafalaya River basin (MARB), their coupling to riverine systems, the delivery of materials to the northern Gulf of Mexico (GOM) coastal ocean, and the associated marine ecosystem responses. The unique nature of our approach, coupling models of terrestrial and ocean ecosystem dynamics and associated carbon processes, allows for assessment of how societal and human�related land use, land use change and forestry (LULUCF) and climate�related change affect terrestrial carbon transport of materials through watersheds to coastal margins and associated consequences for coastal carbon dynamics. Our efforts seek to aid governance and decision support related to carbon management, including the ability to evaluate different LULUCF scenarios in the context of changing climate conditions. In addition, this research can also contribute to a larger body of work to describe and predict how human activities and climate effects impact coastal water quality including possible effects of coastal eutrophication, hypoxia, and ocean acidification in this important ecosystem.
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Many features of the tropical circulation result from the interplay between precipitation, latent heating, and atmospheric waves. Variations in latent heat release generate a broad spectrum of waves ranging from quasi-stationary, global-scale equatorial Rossby waves to high-frequency, mesoscale gravity waves. These waves in turn organize clouds in global-scale patterns, and even small-scale waves can trigger precipitation remotely. Some propagating waves travel far-afield and influence circulation at long distances across the globe and through deep layers of the atmosphere. Large uncertainties in tropical reanalysis products occur where constraining wind observations are absent, and these uncertainties are likely tied to problems in the representation of divergent tropical waves within the underlying models. We describe a modeling approach for tropical waves that uses observed precipitation and cloud properties to estimate the spatial and temporal latent heat release that is the source of many tropical waves. This approach permits direct validation of the modeled waves with observations, and validation of the modeled waves in turn gives new constraints on the variability in tropical latent heating. Application of the approach to modeling stratospheric wave drag will be shown that point toward ways to improve predictability in models.
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In response to increasing atmospheric greenhouse gas concentrations, most global climate models project that the mid-latitude jet streams will shift poleward over the 21st century. Consequently, the tracks of mid-latitude low-pressure systems and their associated cloud features are also anticipated to shift poleward over this time. As these cloud features move from a lower to a higher latitude, they will move from a latitude of greater incoming solar radiation to one of less incoming solar radiation. Thus, it seems logical to assume that such poleward movement in the clouds will lead to a warming feedback, as the clouds will be reflecting less solar radiation when they move to higher latitudes. In this talk, I will challenge this notion using satellite observations from the NASA CERES mission. When the mid-latitude jet shifts poleward, upward vertical velocity anomalies and high-topped storm track clouds do indeed shift poleward with the jet. However, downward vertical velocity anomalies increase equatorward of the jet, contributing to an enhancement of the boundary layer inversion strength (EIS) and an increase in low cloud amount there. Because shortwave cloud-radiative effects (CRE) depend on the reflection of solar radiation by clouds in all layers, the shortwave cooling effects of mid-latitude clouds increase with both upward vertical velocity anomalies and positive EIS anomalies. Over mid-latitude oceans where a poleward jet shift contributes to positive EIS anomalies but downward vertical velocity anomalies, the two effects cancel, and net observed changes in shortwave CRE are small. Hence, there is little evidence from observations to suggest that a poleward movement of the jet alone could contribute to a large warming feedback. Most global climate models capture the observed dynamical anomalies associated with mid-latitude jet shifts but are incapable of capturing the associated cloud-radiative effects, particularly in the Southern Hemisphere, where many models indicate robust cloud-radiative warming effects with a poleward jet shift. Reasons for these model-observational differences will be diagnosed, and the implications of these model biases for future climate projections will be explored.
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For nearly two years the Global Modeling and Assimilation Office has been running a 25 km hybrid 3dVar in its quasi-operational, near-real-time, suite. This system relies on an ensemble of analyses generated by an ensemble Kalman filter (EnKF) solver. The benefits of this system over our previous 3dVar have been clearly demonstrated, and corroborate what other centers have found when making similar transition from 3dVar to hybrid 3dVar. Both these systems have relied on an Incremental Analysis Update (IAU) approach to initialize the model forecasts embedded in the assimilation cycle. More importantly to allow making better use of the large amount of observations available within the assimilation time window is introducing some kind of 4d-based assimilation strategy. In a hybrid ensemble-variational framework, it is natural to upgrade from hybrid 3dVar to hybrid 4dEnVar. Furthermore, it is also natural to extend the present (3d) IAU approach to a 4d-IAU strategy. This presentation introduces the GMAO hybrid 4dEnVar, discusses possible configurations of the upgraded system and provides illustrations for what to expected improvements will be. Finally, as the upcoming system upgrade also involves a resolution increase, taking us from 25 km to 12.5 km, this presentation provides a few illustrations comparing the present quasi-operational system with the upcoming high-resolution GMAO 4d data assimilation sytem.
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The energy, water and carbon cycles are tightly coupled through both atmospheric radiation and plant physiological effects. In this presentation we will show that this coupling is exacerbated by land-atmosphere interactions over continents. Future heat waves can be partially mitigated by plant physiological effects, while we will show that most dryness is due to physiological effects. Large uncertainties are present in CMIP5 models but novel solar-induced fluorescence allow new assessment of this coupling and can help disentangle the land-atmosphere contribution especially on subseasonal and seasonal time scales. Ultimately the carbon cycle can be used to better constrain our estimates of the energy and water cycles over continents.
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Over the years ECMWF has produced a number of multi-decadal reanalyses of past observations using its modelling and data assimilation systems. In this seminar we will present results from a new reanalysis of the 20th century, CERA-20C, which was recently completed using a fully coupled atmosphere-ocean model. We will also discuss the new ERA5 reanalysis, which is currently in production and expected to complete by the end of 2017, and present some early indications of improvements over ERA-Interim.
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Large-scale atmospheric circulation, and its interaction with organized moist convection across many scales, sets the patterns of tropical cloud cover and relative humidity and their sensitivity to climate change. Possible changes in the amount of organized convection with warming therefore may modulate climate sensitivity. We explore changes in clouds and circulation and the degree of self-aggregation of convection in response to uniform SST change in a set of radiative-convective equilibrium simulations with the System for Atmospheric Modeling (SAM) cloud resolving model. We use a non-rotating, highly elongated three-dimensional channel domain of length >104 km, with interactive radiation and surface fluxes and fixed sea-surface temperature varying from 280�310 K. Convection self-aggregates into multiple moist and dry bands across this full range of temperatures; we describe the time and length scale of the aggregation and explain the physical mechanisms that cause it. We discuss how large-scale overturning circulations, cloud fraction, and cloud feedbacks change in response to warming, and compare these results to the responses in small-domain RCE (which does not have organized convection or large-scale circulation).
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Reducing the uncertainty of weather and climate prediction requires better understanding of the water and energy cycles and the intimate coupling and complex interplay between atmospheric water, temperature and stability, dynamics and short and long wave radiation. Orbiting radar and lidar measurements have greatly increased our understanding of aerosols, clouds and precipitation with Aeolus promising analogous advances on winds in the near future. As we will discuss, greatly increasing the quantity as well as the information content of radio occultation (RO) profiling promises to provide analogous advances about temperature and water vapor as well as constraints on winds. When taken together, these combined, global, precise and high vertical resolution observations of temperature water vapor, winds, aerosols, clouds, precipitation and energy fluxes would enable us to better tie the entire weather and climate system together. This would yield more observationally constrained global analyses and forecast initialization, significantly better process-related constraints to guide model improvements and reduced uncertainty about GCM realism and predictions. The ability of GPS RO to profile atmospheric temperature and pressure with very high vertical resolution, precision and accuracy, particularly in the UTLS, is well established. With regard to water vapor, we will show histograms of low latitude specific and relative humidity derived from COSMIC GPS RO data that reveal present levels of uncertainty among global analyses and climate models. These results indicate that MERRA water vapor analyses are the best in the middle and upper troposphere while ECWMF is better in the lower free troposphere. While present RO sampling densities are too sparse to impact global moisture analyses much, greatly increasing the GNSS RO sampling densities via cubesats would more tightly constrain and improve the analyses. A significant advance beyond GPS RO is achievable with an RO system that probes the atmosphere with a better choice of wavelengths that combines features of GPS RO and MLS. We have been developing the Active Temperature Ozone and Moisture Microwave Spectrometer (ATOMMS) RO system to probe the atmosphere near the 22 and 183 GHz absorption lines of water and 169 and 184 GHz lines of ozone. By profiling both the speed and absorption of light, ATOMMS will profile water vapor, temperature and pressure simultaneously, which GPS RO cannot do, from near the surface to the mesopause, with random uncertainties of ~1% for water vapor and 0.4K for temperature and still better absolute uncertainty over most of the altitude range, with 100 m vertical resolution. Performance in clouds should be within a factor of two of clear air performance. This orbital profiling performance approaches that of radiosondes, but with far better accuracy via RO's inherent self-calibration. With funding from NSF, we have developed a prototype ATOMMS instrument and demonstrated several key ATOMMS capabilities on the ground including retrieving water vapor to better than 1% in optical depths up to 17 through clear, cloudy and rainy conditions. While conceived as a climate observing system, cubesat technology and miniaturization of the ATOMMS instrument have made NWP sampling densities feasible. At ~$5M per ATOMMS satellite (including launch), a 60 satellite constellation delivering ~26,000 ATOMMS and 170,000 GNSS RO occultations each day, with complete global coverage every 6 hours, would cost ~$300M. Such a constellation could be implemented in stages, beginning perhaps with 4-6 ATOMMS satellites focused on polar science for ~$30M. This would then be expanded over time via additional satellites with the number of ATOMMS occultations increasing in proportion to the square of the number of satellites. An ATOMMS observation simulation study would help quantify the impact of large numbers of ATOMMS profiles on NWP analyses and forecasts and move this concept forward.
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High levels of tropospheric ozone have strong negative impacts on human health. One major source of tropospheric ozone variability is stratospheric intrusions of ozone-rich air that occur primarily during boreal winter to early summer. Recent studies have demonstrated considerable uncertainty regarding the physical mechanisms and modes of atmospheric variability that control the frequency and strength of ozone intrusions over the western United States on interannual to decadal timescales. Our preliminary results suggest a strong connection between the richness of the ozone reservoir located just above the dynamic tropopause in the extratropics and polar region at the end of March and the amount of ozone that is subsequently folded into the troposphere via intrusions during the ensuing spring and summer season. In addition, we also find that years with anomalously weak ozone intrusions have an equatorward shift of the Pacific jet, while years with anomalously strong ozone intrusions have a poleward shift of the Pacific jet. Thus ozone intrusions over the western United States likely arise from a combination of interannual variability of the ozone reservoir combined with variations in the Pacific jet stream. In light of these results, we consider modes of low-frequency variability that modulate (1) the strength of the BDC during Northern winter or (2) the north-south displacement of Pacific-North American waveguide or teleconnection patterns during spring and summer as they may represent a source of skill for predicting tropospheric ozone variability over the western United States.
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Urban emissions of Greenhouse Gases (GHG) represent about 70% of the global emissions and will very likely increase rapidly as large metropolitan areas are projected to grow twice as fast as the world population in the coming 15 years. Monitoring these changes using independent approaches is a critical need for current and future regulation policies. To assemble these monitoring systems, a high resolution atmospheric inversion was developed to quantify GHG emissions from cities and larger metropolitan areas. The system is tested with in-situ measurements of CO2 and CH4 from the Indianapolis Flux Experiment (INFLUX), the largest and densest deployment of GHG in situ measurements within one metropolitan area. Uncertainties from the different components of the system are explored with sensitivity experiments. The assimilation of meteorological data to improve the performance of atmospheric models is presented for various sources of observations such as surface stations, aircraft, rawinsondes, and ground-based Doppler Lidars. Because fine-grained emission inventories (such as Hestia) are likely to be unavailable for every city, GHG emission products based on remotely-sensed data such as ODIAC are tested as an alternative to describe fine spatial emission structures across urban domains. Finally, column XCO2 observed from satellites (here OCO2) are compared to high resolution model results to better understand how to utilize space-based information within urban GHG monitoring frameworks.
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The OCO-2 mission has as a primary goal to shed further light on the location and mechanisms of the terrestrial land carbon sink, in addition to evaluating the potential for measuring source emissions from space. OCO-2 began taking science data in September 2014 and continues to operate well, returning nearly 1 million observations per day. Approximately 10% of these are sufficiently free of cloud and aerosol contamination to allow for an accurate determination of the column mean carbon dioxide dry air mole fraction, XCO2. The measurements have relatively low noise, of order 0.5-1.0 ppm for most nadir soundings over land and sun-glint geometry soundings over water surfaces. In this presentation, we summarize the data quality via comparison to a number of validation metrics, discuss the current health and long-term prospects for the instrument, and give an overview of some early science results from the first 18 months of observations. While XCO2 and other products are still being validated to identify and correct biases, OCO-2's XCO2 observations are starting to reveal the most robust features of the atmospheric carbon cycle. At regional scales, fluxes from the eastern U.S. and China are most clear in the fall, when the north-south XCO2 gradient is small. Enhanced XCO2 coincident with biomass burning in the some parts of the tropics, in particular central Africa, is also obvious in the fall. The annual growth rate of CO2 was anomalously high in 2015 according to OCO-2, consistent with NOAA surface measurements and in accord with the warmer annual average surface temperature that year. Other early science results are also presented. Finally, we try to place OCO-2 in the context of future space-based greenhouse gas missions.
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Gravity waves are important drivers of atmospheric motion on both local and global-scales, from the upper troposphere to the thermosphere and ionosphere. However, most global climate models are too coarse to resolve the full spectrum of gravity waves and use gravity wave drag parameterizations to approximate the effects of gravity waves on the circulation. These parameterizations are a large source of uncertainty in models, in part because we do not yet have a global understanding of gravity wave properties and their sources to inform the parameterizations. As computational power increases, atmospheric general circulation models are able to resolve smaller and smaller-scale waves, reducing, but not (yet) eliminating, the need for parameterizations and enabling the study of modeled small-scale waves, their sources, and how they interact with the large-scale circulation. This talk will explore some of the sources and effects of resolved small-scale waves in the global 7-km horizontal resolution Goddard Earth Observing System model (GEOS-5) Nature Run. In particular, we will focus on the wave driving of the quasi-biennial oscillation in the tropics and sources related to convection, frontogenesis, and instability in the Southern Hemisphere.
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This presentation describes the status of the development of a Sea Surface Temperature (SST) analysis for the Goddard Earth Observing System, Version 5 (GEOS-5) Atmospheric Data Assimilation System (ADAS). Its implementation is part of the steps being taken toward the development of an Integrated Earth System Analysis (IESA) that aims at having a coupled Atmosphere-Ocean model supporting GMAO quasi-operational system. Currently, GEOS-ADAS SST is a bulk ocean temperature (namely, OSTIA SST) boundary condition set to be almost identical to the skin sea surface temperature (Ts). The present work introduces changes to the skin layer of GEOS-5 model to allow better representation of diurnal changes in Ts and possible assimilation of near sea surface satellite infrared (IR) observations. During day-time and calm winds, diurnal warming causes the near surface ocean temperature to be ~1- 2K warmer than the bulk SST; whereas during night-time and/or strong winds, there is negligible stratification and hence diurnal warming is almost absent. Very close to the air-sea interface there is a persistent cool-skin layer of few millimeters thickness, it causes a temperature drop ~0.1- 0.5K. Our modeling of Ts includes a consideration of these dynamical effects. The present work, modifies the GEOS-5 surface layer to allow for better modeling of the diurnal variations in skin temperature. Measurements in the thermal IR part of electromagnetic spectrum are highly representative of Ts, and have formed the basis for SST retrievals. In addition to the current set of assimilated IR observations, the present work adds SST relevant Advanced Very High Resolution Radiometer (AVHRR) observations to the GEOS-ADAS observing system. We provide a description of the modifications to the interface between the GEOS AGCM and atmospheric analysis (using the Gridpoint Statistical Interpolation) and the Community Radiative Transfer Model (CRTM). The computational expense of this addition to the GEOS-ADAS is almost negligible. Data assimilation experiments are evaluated by comparing with the current GEOS-ADAS (without the above Ts-related developments). Results show improvements to the assimilation of radiance observations that extends beyond the thermal IR bands of AVHRR. In particular, many channels of hyperspectral sensors, such as those of the Atmospheric Infrared Sounder (AIRS), and Infrared Atmospheric Sounding Interferometer (IASI) are better assimilated. We obtained a closer fit to withheld, in-situ buoys measuring near-surface SST. Evaluation of forecast skill scores corroborate improvements seen in the observation fits. The presentation closes with a summary and an outlook on upcoming IESA development.
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Large freshwater anomalies clearly exist in the Arctic Ocean. For example, liquid freshwater has accumulated in the Beaufort Gyre in the decade of the 2000s compared to 1980-2000, with an extra ~ 5000 km3 (about 25%) being stored. Both the sources of freshwater and the Arctic surface wind are important controls on freshwater anomalies, and both have changed in the last decade. Coupled climate models project continued freshening of the Arctic during the 21st century, with a total gain of about 50000 km3 for the Arctic, CAA, and Baffin Bay (an increase of about 50%) by 2100. Rapid discharges of excess freshwater through Fram or Davis straits appear possible, triggered by the wind, but are hard to predict. This talk will describe recent changes in the Arctic Ocean freshwater system, review attempts to understand the mechanisms causing these changes, and speculate about future prospects, especially for the oceanic export of Arctic freshwater.
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Recent technological advances have led to large and rapid increases in oil and natural gas extraction in the United States. The increasing abundance of lower cost natural gas has the potential to displace less efficient fossil sources, and has been considered a potentially important 'bridge' fuel to more sustainable energy sources. However, the climate value of natural gas requires low leakage rates prior to combustion, as atmospheric methane acts as a potent greenhouse gas. Recently, the leakage rates associated with the new boom in natural gas harvesting with high-volume hydraulic fracturing has come into question, impacting the climate-value of natural gas. To make informed societal decisions we need quantitative information on methane emissions. In this talk I will discuss different observational techniques in which atmospheric observations are used to locate, quantify, and attribute fugitive methane emissions. I will discuss the first example of identifying and quantifying anomalous methane emissions from a region of energy production using space-based observations. I will also present new airborne observations of ethane and methane over multiple oil and gas fields in Texas and North Dakota, and demonstrate how these observations can be used to both quantify ethane emissions and partition methane emissions to specific source sectors. Finally I will introduce a recent campaign focused in the Four Corners region in the Southwest US in which we deployed multiple aircraft and ground measurements in concert to understand our space-based discovery of high methane emissions in this region.
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The winter stratosphere is dominated by a strong cyclonic vortex which encircles the pole, acting to confine cold air to high latitudes. In about 2/3 of winters this vortex breaks down in an event known as a sudden stratospheric warming (SSW). Research over the past two decades has established a link between SSWs and surface weather, such as an increased likelihood of extreme cold events over North America and Northern Europe. However, significant uncertainties remain in understanding the dynamics of SSWs and their surface influence. In this talk, observational and modeling data are used to investigate two classes of SSWs; vortex splits and displacements. In many studies no distinction is made between these events, but they are shown to exhibit very different dynamical behavior. Different surface anomalies following spits and displacements are used to inform the mechanism by which the stratosphere influences the troposphere, and implications for seasonal weather prediction discussed.
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The strength and timing of the seasonal cycle of sea surface temperature plays a crucial role in regulating many processes such as the spring plankton bloom and seasonal weather patterns. In the Arctic this seasonal cycle is weak as a result of the insulating and light scattering effects of sea ice cover and the moderating influence of seasonal storage and release of heat through ice melting and freezing. In the coming centuries the Arctic is expected to experience dramatic climate change in response to the rise in anthropogenic greenhouse gasses. Already the retreat of sea ice in recent decades is warming surface temperatures in winter, thus weakening the seasonal cycle of surface air temperature, and this effect is expected to grow in impact. However the changes in ocean properties may be quite different and much more dramatic. We suggest that the loss of sea ice will increase the amount of solar radiation absorbed in summer, while enhancing radiative and turbulent heat loss in winter. The loss of sea ice will also eliminate the the moderating ice melt/freeze cycle, while enhancing the seasonal cycle in surface salinity. We explore these feedback mechanisms and their consequences for Arctic Ocean climate in the next two centuries as represented in a suite of CMIP5 coupled model simulations.
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Statistical Neural Network (NN) based soil moisture retrievals are developed as an alternative to traditional retrieval methods - such as Radiative Transfer Model (RTM) inversions - with the potential of avoiding many of the difficulties associated with these more physical techniques. Instead of explicitly formulating physical processes, the NN approach aims to create a simple statistical model of the non-linear link between the satellite observations and the surface soil moisture state. As such, the NN retrieval is less dependent on accurate knowledge of the physical processes and requires less a priori knowledge of the land surface characteristics, which is often uncertain or unavailable at a global scale. An additional advantage of the NN technique is that it facilitates the combination of data from different satellite sensors, being able to exploit the complementarity of their information as well as the individual contributions. Here a NN retrieval of daily surface soil moisture from the combination of active (ASCAT) and passive (AMSR-E) microwave satellite observations at a global scale is presented. The analyses shown serve as a preparation for the recently launched Soil Moisture Active/Passive (SMAP) mission in order to optimize the use of both L-band instruments for a soil moisture retrieval. As part of the retrieval algorithm development, several pre-processing techniques are investigated with the aim of highlighting the soil moisture contribution to the satellite signal and thus reducing the need for ancillary surface data, whose uncertainty and availability can impose strong constraints on a retrieval. It is further investigated to what extent the synergy of active and passive observations improves the soil moisture estimates and which synergy strategy optimally exploits the complementarity of the information provided by both sensors.The AMSR-E/ASCAT retrieval is evaluated on different temporal scales against in situ soil moisture observations, the ESA-CCI retrieval product and GPCP precipitation data.
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Of the twenty million observations routinely assimilated every day in global models, over 80% are satellite radiances. Consisting primarily of microwave and infrared sounders in sun-synchronous, polar orbits, it is the constellation of these observations that provides global data coverage and has led to forecasts being comparatively skillful in both the Northern and Southern Hemispheres. Though these data are fundamental to the global observing system and numerical weather prediction skill, only a small fraction of the total observations available are effectively assimilated. This is particularly true in the thermal infrared, which accounts for ~65% of actively assimilated observations globally via only four instruments. This talk will focus in part on the assumptions that result in low yields, including cloud screening and data thinning, and present an explanation of: how these methods work, why they are (or were) necessary, how they have evolved, and how effective they are. The explanation and quantification of these procedures will help illustrate the roadmap of ongoing efforts to further exploit these observations at the Global Modeling and Assimilation Office (GMAO) at NASA/GSFC. Specifically, this talk will address the evolution of observation characterization over the modern (1979-onward) era, moving towards cloud-affected infrared radiance assimilation, and how future developments relate to both reanalysis and our real-time, forward processing systems.
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The sensitivity of tropical cyclones (TCs) to changes in parameterized convection is investigated to improve the simulation of TCs in the North Atlantic. Specifically, the impact of reducing the influence of the Relaxed Arakawa-Schubert (RAS) scheme-based parameterized convection is explored using the Goddard Earth Observing System version 5 (GEOS-5) model at 0.25 degree horizontal grid spacing. The years 2005 and 2006 characterized by very active and inactive hurricane seasons, respectively, are selected for simulation. A reduction in parameterized deep convection results in an increase in TC activity (e.g., TC number and longer life cycle) to more realistic levels compared to the baseline control configuration. The vertical and horizontal structure of the strongest simulated hurricane shows the maximum wind speed greater than 60 m/s and the minimum sea level pressure reaching ~940mb, which are never achieved by the control configuration. The radius of the maximum wind of ~50km, the location of the warm core exceeding 10 degrees C, and the horizontal compactness of the hurricane center are all quite realistic without any negatively affecting the atmospheric mean state. This study reveals that an increase in the threshold of minimum entrainment suppresses parameterized deep convection by entraining more dry air into the typical plume. This leads to cooling and drying at the mid- to upper-troposphere, along with the positive latent heat flux and moistening in the lower-troposphere. The resulting increase in conditional instability provides an environment that is more conducive to TC vortex development and upward moisture flux convergence by dynamically resolved moist convection, thereby increasing TC activity.
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There is strong evidence from observations and model studies that Antarctic ozone depletion was a main driver of Southern Hemisphere climate change during austral summer including a shift of the Southern Annular mode towards its positive phase. Model simulations suggest that Arctic ozone changes can also affect surface climate in the Northern Hemisphere during spring however detecting its influence in observations is quasi impossible due to smaller ozone loss rates in the Arctic versus Antarctic in connection with a weaker and more disturbed Northern Hemisphere stratospheric polar vortex and due to a too short observational record. Recent model studies suggest that large Arctic ozone loss rates like that observed in 2011 may had a detectable imprint on the record Northern Annular mode anomaly in spring during that year. We use a new large ensemble carried out with GEOS-5 chemistry-climate model (Stratchem) with a one-degree horizontal resolution to determine the effect of ozone depletion on the Northern Hemisphere climate. In this talk, we will first evaluate mean climate features of this model version in the stratosphere both in the Southern and Northern Hemispheres and simulated total ozone values. Then we will discuss how both climate change as well as changes in ozone depleting substances affected the simulated Northern Hemisphere climate and related extremes during the 2004-2013 period relative to the 1960-1969 period. If time allows, we will also discuss whether prescribed lower boundary conditions are a suitable tool to study stratospheric changes in a changing climate using a set of perfect model experiments with simulated and subsequently prescribed lower boundary forcings.
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The main objective of Environment Canada's (EC) land surface research and development group is to improve numerical weather prediction (NWP) at day-to-seasonal ranges and local-to-global scales. To achieve this, many aspects of land surface systems have been examined, including the specification of land surface characteristics and the modeling of physical processes over natural and urban areas, the assimilation of surface and space-based observations to initialize surface temperature, soil moisture, and snow, and the development of new approaches to couple land surface and atmosphere. The purpose of this presentation is to illustrate with examples how each of these aspects contributes to improving NWP, to present the most recent and upcoming land surface operational implementations at EC, and to discuss current research topics which are believed to have potential for further improvements.
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A chemical OSSE is composed of several different elements: A nature run, which is the model that represents the atmospheric true state of the atmosphere with an associated observation simulator. We use a novel computational method (Worden et al., 2013) to sample very high global resolution simulation (GMAO GEOS-5 7 km) and simulate a constellation of three geostationary satellites. The fields of view of these instruments are continental US, Europe and Eastern Asia. Focus of this study is on air quality and simulated measurements simulate a MOPITT like carbon monoxide retrieval. We show that this method provide realistic measurements with a very low computational cost. A detailed analysis of the simulated observation sensitivity is then performed, and limitations are discussed. Impacts of clouds are also discussed; showing that efficacy of an air quality instrument on geostationary platform is dependent of the latitude and also of the dominant weather regime in a given region. A data assimilation system with an associated atmospheric model are required that can produce an assimilated run and a control run, respectively. An Ensemble Adjusted Kalman Filter (DART) system has been developed to assimilate simultaneously meteorological and chemical variables in the global scale chemistry-climate model CAM-Chem. Observations of long-lived chemical tracers such as carbon monoxide and ozone contain information that could potentially constrain meteorological fields (winds). In order to optimally integrate chemical measurements in the modeled atmospheric system, we will evaluate the impact of real chemical data assimilation (Terra/MOPITT and IASI/Metop CO observations) on the meteorological state of the atmosphere, and conversely, the impact of meteorological assimilation on the chemical state of the atmosphere. The next step in this work will be to assimilate the GEO-constellation. This will allow an OSSE for the potential future prediction system of global air quality with the same capabilities for each region of interest: the same models (nature run and control run), the same data assimilation system (assimilation run) and the same instrument design (observation simulator). Estimating transcontinental transport of pollution across the Atlantic and Pacific oceans could make the assessment of synergies between the three defined instruments.
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It is well-known that aerosol-cloud interactions have been causing the highest uncertainty in the assessment of climate change. This highest uncertainty is in turn caused by discrepancies in the simulation of aerosol-cloud interactions among models. Different cloud parameterizations and model setup (e.g., resolutions) among models are known to play a major role in the generation of the discrepancies. Hence, as a first step to the reduction of the uncertainty by aerosol-cloud interactions, we need to identify key differences, which are primarily responsible for the discrepancies (in the simulation of aerosol-cloud interactions) among models, in the parameterizations and model setup. After this identification, we can grab a chance to understand mechanisms through which the key differences lead to the uncertainty or discrepancies and this understanding is valuable information to reduce the uncertainty. Motivated by this, instead of daring to eliminate the uncertainty entirely, this presentation simply aims to provide preliminary information on the key differences and associated mechanisms via comparisons among models and parameterizations. This preliminary information will act as a valuable stepping stone to the reduction of the uncertainty in the prediction of climate change, considering that even this type of preliminary information is not yet available to aerosol and cloud communities.
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In this presentation an overview will be given on the current and forthcoming reanalysis activities at ECMWF. This includes the status of the near-real-time continuation of ERA-Interim and work towards its replacement, the usage of historic surface and upper-air data and CMIP5 forcing in recently conducted century-long pilot reanalyses within the EU-funded ERA-CLIM project, and the latest developments of a coupled ocean-atmosphere reanalysis system within the recently started ERA-CLIM2 project.
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Clouds continue to be one of the largest uncertainties in the climate system. Key issues include, though are not limited to, the representation of convection and cloud microphysical processes as well as their interactions with radiation in coupled atmosphere-ocean processes. Conventional global climate models (GCMs), including all the CMIP3 and most CMIP5 models, often consider radiation interactions only with small-particle/suspended cloud mass, ignoring large-particle/falling and convective core cloud mass. We characterize the radiation and atmospheric circulation impacts of frozen precipitating hydrometeors (i.e., snow), using the National Center for Atmospheric Research (NCAR)-coupled GCM, by conducting sensitivity experiments that turn off the radiation interaction with snow. The changes associated with the exclusion of precipitating hydrometeors exhibit a number differences consistent with biases in CMIP3 and CMIP5, including more outgoing longwave (LW) flux at the top of atmosphere (TOA) and downward shortwave (SW) flux at the surface in the heavily precipitating regions. Neglecting the radiation interaction of snow increases the net radiative cooling near the cloud top with the resulting increased instability triggering more convection in the heavily precipitating regions of the tropics. In addition, the increased differential vertical heating leads to low-level eastward advection from the warm pool resulting in moisture convergence south of the ITCZ and north of the SPCZ. This westerly bias, with warm and moist air transport from the warm pool, might be a contributing factor in the model�s northeastward overextension of the SPCZ and the concomitant changes including warmer sea surface temperatures, upward motion, and excessive precipitation. Broader dynamical impacts include a stronger local meridional overturning circulation over the mid- and east Pacific, and commensurate changes in low- and upper-level winds, large-scale ascending motion, with a notable similarity to the systematic bias in this region in CMIP5 surface wind stress, low-level and upper-level winds.
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Atmospheric wave momentum transport is a crucial component of the global atmospheric momentum budget. Waves with scales ranging from planetary to at least tens of kilometers collectively shape the global circulation, and this process is an essential coupling mechanism between troposphere, stratosphere, and mesosphere. Trace gas transport in the atmosphere is most clearly described through the "residual circulation", an approximation to the Lagrangian-mean latitude-height transport circulation, and in the dry middle atmosphere, this circulation is fundamentally driven by wave momentum transport. Wave-driving of the atmospheric circulation depends both on characteristics of the waves and on the large-scale winds: While wave momentum transport drives the winds, the winds in turn determine the propagation pathways and dissipation of the waves. Therefore, the modeled response to climate change can also be sensitive to wind biases. The talk will summarizes the role of atmospheric wave-driving in some key responses to climate change and also highlight how small changes in model winds conspire to effect chemical transport.
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History matching is a type of inverse problem in which observed reservoir behavior is used to estimate reservoir model variables that caused the behavior. Obtaining even a single history-matched reservoir model requires a substantial amount of effort, but the past decade has seen remarkable progress in the ability to generate reservoir simulation models that match large amounts of production data. Progress can be partially attributed to an increase in computational power, but the widespread adoption of geostatistics and Monte Carlo methods has also contributed indirectly. Although the problem of predicting future reservoir performance is an important aspect of history matching, the term history matching typically implies far more than extrapolation. It almost always implies that there is a mathematical model of the reservoir with parameters that have some physical interpretation. One explicit goal of history matching is to assign values to the parameters such that the mathematical model of the reservoir reproduces the observed behavior during the prediction period. The true usefulness of the model, however, is a result of its ability to predict future behavior with increased confidence, and to perform computer experiments on methods of managing the reservoir. With a reliable model, one could optimize the management of a reservoir. History matching problems are almost always ill-posed in the sense that many possible combinations of reservoir parameters result in equally good matches to the historical observations. As a result, a single history-matched model may be useful, but it is unlikely to be sufficient for planning as it does not allow the estimation of risk. The complete solution to a history matching problem should always include an assessment of uncertainty in reservoir properties and in reservoir predictions. In this presentation, I will summarize key developments in history matching and optimization of the past decade, including developments in re-parametrization of the model variables, methods for computation of the sensitivity coefficients, methods for quantifying uncertainty and prediction forecast optimization. A comparison with the methodologies developed in the meteorology and oceanography applications will be presented, highlighting the challenges and some possible solutions.
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TBD
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ECMWF is currently running analysis and forecast ensembles. The Ensemble of Data Assimilation (EDA) provides background error statistics to the high-resolution 4D-VAR, and is used to generate the initial conditions of the perturbed members of the medium-range/monthly ensemble (ENS). ENS and the seasonal system-4 (S4) provide users with coupled ocean-atmosphere forecasts of the most likely scenario and its confidence, expressed for example in terms of probabilities of weather events. In this talk, the key characteristics of these three ensembles (EDA. ENS and S4) will be illustrated and their average performance briefly reviewed. The value of ensemble-based products to estimate forecast confidence in extreme weather events will also be discussed.
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Observed atmospheric variations of carbon, driven by anthropogenic and natural fluxes and atmospheric transport can be used, in theory, together with atmospheric tracer transport models to infer geographical distributions of the fluxes. In practice, however, the situation is less straightforward and there are a number of challenges to overcome. In this talk, I will discuss new scientific insights into the carbon cycle from surface measurements and from GOSAT spaceborne column measurements of CO2 and CH4, using primarily but not exclusively the GEOS-Chem atmospheric transport model and an ensemble Kalman filter.
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Fossil fuel CO2 emissions (FFCO2) are a critical part of conventional inverse estimations of surface CO2 sources and sinks. Unlike land biospheric fluxes and oceanic exchange, FFCO2 is often assumed to be a perfect quantity and is not optimized in the inverse estimation framework. To work with denser CO2 data brought by satellites such as Japanese Greenhouse gas Observing SATellite (GOSAT) and NASA's Orbiting Carbon Observatory (OCO)-2 on top of the existing surface observation network, FFCO2 needs to be characterized more accurately in atmospheric CO2 transport simulations for better flux estimates. We developed the ODIAC (Open-source Data Inventory for Atmospheric CO2) fossil fuel emission model originally for the Japanese GOSAT project. Spatial distributions of emissions in the model were determined/estimated using power plant profiles (emissions and geographical location) in addition to satellite-observed nightlights collected by the Defense Meteorological Satellites Program (DMSP) satellites. The use of nightlight observation plus power plant profiles for disaggregation of national emissions enables us to map emissions at much higher spatial resolution than conventional studies. The ODIAC gridded dataset has been used by many inversion models, including NOAA's CarbonTracker. The latest version 3.0 ODIAC model is further improved/upgraded from the original version. Emission estimates in ODIAC ver. 3.0 are taken from country emission estimates made by the Carbon Dioxide Information Analysis Center (CDIAC) and its projection based on BP's fuel consumption statistics. The ODIAC gridded dataset can thus be viewed as an improved version of CDIAC gridded dataset. In collaboration with CDIAC, emissions from international bunkers were derived and then mapped using available and suitable proxy data. Seasonal variations of land emissions are introduced from temporal profiles in the CDIAC monthly gridded product and AERO2k for aviation emissions. Scaling parameters have been developed by an international research group, using Vulcan, EDGAR and other available resources, that specify weekly and diurnal patterns in emissions. Now that satellites are coming on-line that tell something about man-made CO2 emissions, we are attempting to create a prototype system for verifying emissions. For instance, we have implemented special megacity and power plant observations with GOSAT to detect emission signatures from the intense emission sources. It is extremely challenging. It is an investment, however, that should be useful with future carbon space missions that are expected to be used for measuring, reporting and verification (MRV) in response to a need for help to guide and monitor efforts on emission reduction. We also have developed a Lagrangian-Eulerian hybrid transport modeling system that accepts emissions data at a 1 km resolution: it improves the accuracy of simulating CO2 air from polluted areas. We will introduce these ongoing projects and show what we have seen so far, together with a future perspective of the ODIAC model.
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Soil moisture exerts key controls on surface hydrological fluxes; in many climates, higher soil moisture levels lead to both increased evaporation for a given amount of incoming radiation (increased "evaporation efficiency") and increased runoff for a given amount of precipitation (increased "runoff efficiency"). Evaporation efficiency and runoff efficiency can thus be said to vary with each other, and this provides the motivation for a unique hydroclimatic analysis. Using a simple water balance model fitted, in different experiments, with a wide variety of functional forms for evaporation and runoff efficiency, we transform net radiation and precipitation fields into fields of evaporation and runoff that can be compared to observations-based estimates, allowing for the determination of the optimal combination of the functional forms, and thus for an estimation of how evaporation and runoff efficiencies vary with each other in nature - something that cannot be determined directly at the large scale with traditional measurements. The inferred optimal combination serves as a valuable guide for the development and evaluation of GCM-based land surface models, which by this measure are often found to be suboptimal. The framework also offers some tantalizing sidelights; for example, using the framework, one can infer surprisingly reasonable estimates of rooting depth from knowledge of precipitation, net radiation, and streamflow alone.
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I will present preliminary results from the assimilation of 2004-2005 Gravity Recovery and Climate Experiment (GRACE) data in a global, eddying, ocean and sea ice model. For GRACE data, we used the Jet Propulsion Laboratory (JPL) mass concentration (mascon) solution. The numerical ocean model is the Massachusetts Institute of Technology general circulation model (MITgcm) as configured for ocean data assimilation by the Estimating the Circulation and Climate of the Ocean, Phase II (ECCO2) project. The adjoint method is used to adjust MITgcm initial and surface boundary conditions in order to reduce a weighted quadratic distance between simulation and observations, while satisfying ocean model equations exactly during the complete 2-year assimilation period. Three ECCO2 solutions will be compared to the mascon solution: (1) iter00, a simulation that has not been constrained by ocean data; (2) iter30, a simulation that has been constrained by altimetry, sea surface temperature (SST), and vertical temperature and salinity (T/S) profiles; and (3) iter33, a simulation that also includes GRACE data constraints. With small regional exceptions, e.g., some eddy-rich regions in Southern Ocean and North Atlantic, iter00 explains 50% or more of GRACE data variance. The non-GRACE-data-constrained solution iter30 explains substantially more GRACE data variance than iter00, especially in Southern Ocean where iter30 explains up to 60% of the GRACE-iter00 residual variance. A notable exception is the South Atlantic, where, for yet-to-be-determined reasons, iter30 explains less GRACE variance than iter00. At writing of abstract, three forward-adjoint iterations have been carried out based on iter30 as baseline simulation and including GRACE data constraints in addition to altimetry, SST, and T/S profiles. The resulting simulation, iter33, reduces the GRACE contribution to the cost function without increasing the cost of the preexisting model-data difference terms. These preliminary model-data comparison and data assimilation results indicate the potential for GRACE mascon solutions to help separate steric from non-steric contributions to sea surface height in time-evolving estimates of the global ocean circulation.
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The seminar will demonstrate a new approach to hybrid data assimilation with more robust stability than traditional hybrid methods when applied to the Lorenz-96 model. This approach comes from the perspective of stabilizing an Ensemble Kalman Filter rather than the perspective of incorporating dynamic ensemble information into a variational method. A hybrid data assimilation scheme was recently adopted at NCEP for the atmosphere, and this method is being developed for the ocean as an advancement of the existing Global Ocean Data Assimilation System.
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The spatial heterogeneity of the mountainous snowpack and a continuously changing climate affects a variety of processes including surface water discharge. Understanding the geophysical controls and interannual variability of the spatial patterns of the snow accumulation and ablation are critical for predicting the effects of climate variability on the snowpack water storage. A continuous space-time characterization of the snowpack water content (snow water equivalent, SWE) that uses spatially and temporally extensive remotely sensed information is necessary to improve our ability to predict and monitor this vital resource over complex mountainous terrain. Toward this end, this research applies a Bayesian reanalysis data assimilation approach, similar to an Ensemble Kalman Smoother, capable of merging remotely sensed Fractional Snow Covered Area (FSCA) data into snow prediction models, and at the same time account for the limitations of each. The assimilation of the three decades record of Landsat-5 thematic mapper FSCA information into the land surface model, coupled together with a snow depletion model, provides and unprecedented dataset of continuous (in space and time) SWE and FSCA at a nominal 90m spatial resolution over the Sierra Nevada Mountains (CA). The resulting dataset from the assimilation framework, and its relation to different physiographic properties, can be studied to explore specifc information related to how snow accumulation and snow melt has evolved and has been affected by climate variability and change. This research provides a demonstration for the assimilation method to be extended to other regions, where in-situ observation may not be present.
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The Ensemble Forecast Application System (EFAS) is a post processing engine that exposes the information rich ensemble data to forecasters and on-scene operators, and to existing Tactical Decision Aids (TDAs). Ensembles of oceanographic and meteorological numerical forecast models are an excellent tool for quantifying the uncertainties in the marine environment that impact tactical operations. One challenge is to distill the information into a look and feel that the forecaster and operator quickly understand and to format it so that it can be put into TDAs. EFAS distills ensembles into a the familiar look and feel of a deterministic forecast for the operator, and it fits into their existing TDAs. Our approach is to use the probabilistic information contained in the ensemble to improve forecast skill and guidance as it applies to the specific operation being supported. EFAS first applies a bias-correction to some of the ensemble parameters to improve their forecast skill. For example; we see that forecasts of temperature, pressure, wave height, and wind speed are improved by applying bias-corrections, while wind direction forecasts are not. Then by using a consensus finding algorithm based on the RMSE history of the forecast parameter, it is possible to select the most skillful forecast value or forecast field or member from the ensemble. From the ensemble members one can also extract a spread around that forecast value. This spread, based on the operator's requirement for accuracy or the operator's tolerance in the forecast error, can also be used to state the confidence (high or low) that the forecast will meet the operator's needs as it pertains to the operation. The ensemble generation is accomplished at any large central computing sites. The EFAS post processing, which aggregates and distills the ensembles for use, can be at any location provided EFAS has access to the raw ensemble data sets. The on-scene operator interfaces the EFAS output which has the look and feel of a deterministic forecast, to derive the specific information needed to support their operation.
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Ocean models suffer from errors in the treatment of turbulent sub-grid scale motions causing mixing and energy dissipation. Unrealistic small-scale features in models can have large-scale consequences, such as biases in the upper ocean temperature, a symptom of poorly simulated upwelling, currents and air-sea interactions. This is of special importance in the tropical Pacific Ocean, which is home to energetic air-sea interactions that affect global climate. It has been shown in a number of studies that the simulated ENSO variability is highly dependent on the state of the ocean, e.g., the background mixing. Moreover, the magnitude of the vertical numerical diffusion (kv) is of primary importance in properly reproducing the Pacific equatorial thermocline. Yet, it is a common practice to use spatially uniform mixing parameters in ocean simulations. In my talk, I will discuss how vertical mixing affects the simulation of the thermocline in the tropical Pacific, using one-dimensional models, forward integrations, adjoints sensitivities, and an ocean state estimation. The usual overly diffuse thermocline in the tropical ocean simulated by ocean models is most efficiently overcome by vertical mixing coefficients with a tripole vertical structure of kv. Zonal ocean currents are affected via the thermal wind by quadrupoles of kv. An ocean state estimation using kv as a control parameter shows adjustments of the thermocline sharpness and Equatorial Undercurrent consistent with these vertical structures. Anyone interested in meeting Dr Avelleneda on April 16 or 17, should contact Sirpa Hakkinen, sirpa.hakkinen@nasa.gov, or call 4-5712.
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Several recent studies have concluded that coupled climate models should utilize a diurnally varying SST to examine the details of the boundary layer response and ensuing air-sea interactions. The global distribution of diurnal warming is clearly linked to wind speed and will therefore respond to the climatic distributions and seasonal or anomalous changes in wind speed, as shown by the response to ENSO wind speed anomalies. The Subtropical High regions in each ocean basin, and the Tropical Indian and Western Pacific Oceans have the largest averages of diurnal warming. The intra-day variability of surface warming has been related to the stability of the boundary layer and atmospheric convection. Since the tropical convection is an important driver of global atmospheric circulation, this example of ocean-atmospheric feedback underscores how diurnal warming of the ocean surface may influence larger scale weather patterns and climate. Results from several satellites show significant diurnal warming present over large regions. Several models (both empirical and physical) of diurnal variability have been developed, but show little agreement with each other. Comparisons of data and models will be used to discuss the global spatial/temporal distribution of diurnal warming and how accurately we actually understand it.
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El Nino events, the warm phase of the El Nino/Southern Oscillation (ENSO), are known to affect other tropical ocean basins through teleconnections. Conversely, mounting evidence suggests that temperature variability in the Atlantic Ocean may also influence ENSO variability. Here we use reanalysis data and general circulation models to show that sea surface temperature anomalies in the north tropical Atlantic during the boreal spring can serve as a trigger for ENSO events. We identify a subtropical teleconnection in which spring warming in the north tropical Atlantic can induce a low-level cyclonic atmospheric flow over the eastern Pacific Ocean that in turn produces a low-level anticyclonic flow over the western Pacific during the following months. This flow generates easterly winds over the western equatorial Pacific that cool the equatorial Pacific and may trigger a La Nina event the following winter. In addition, El Nino events led by cold anomalies in the north tropical Atlantic tend to be warm-pool El Nino events, with a centre of action located in the central Pacific, rather than canonical El Nino events. We suggest that the identification of temperature anomalies in the north tropical Atlantic could help to forecast the development of different types of El Nino event.
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The onset of cumulus convection in a grid column is a catastrophe, a.k.a. a subcritical instability. Accordingly, in designing a cumulus parameterization scheme the onset of cumulus convection requires that a parameter crosses a critical value and the termination requires that the same or a different parameter crosses a different critical value. Once started, cumulus convection stays on, regardless if the onset criterion is still met, until the termination criterion is met. Also, the intensity of cumulus precipitation is related to how far the state is from the termination, not the onset, criterion. In contrast, the cumulus parameterization schemes currently in use treat the onset of cumulus convection as a supercritical instability. Namely, convection is on when a parameter exceeds a critical value and is off when the same parameter falls below the same critical value. Also, the intensity of cumulus precipitation is related to how much this critical value has been exceeded. Among the adverse consequences of the supercritical-instability-concept-based cumulus parameterization schemes are that over relatively flat land the precipitation peak occurs around noon-4~6 hours too soon-and that the amplitude of the precipitation diurnal cycle is too weak. Based on the above concept, a new cumulus parameterization scheme has been designed by taking advantage of the existing infrastructure in the relaxed Arakawa-Schubert scheme (RAS), but replacing RAS's guiding principle with the catastrophe concept. Test results using NASA's GEOS-5 GCM show dramatic improvement in the phase and amplitude of the precipitation diurnal cycle over relatively-flat land.
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The recent acceleration in Arctic sea ice decline is larger than predicted in most modeling studies. This suggests a heat source for the ice melt that is absent or underrepresented in the models. A relatively warm layer of Atlantic origin water provides a large heat reservoir; however, this layer is separated from the surface by a cold halocline layer. For this heat to play a significant role in ice melt requires a vertical advective heat flux extending from the top of the Atlantic layer (~250 m depth) to the surface mixed layer. Across shear lines in the ice cover, there is a discontinuity in the ice-ocean momentum transfer. Associated with this are strong Ekman pumping velocities below the shear lines. Here, we consider whether this can lead to significant advective fluxes allowing heat from the warm Atlantic layer to influence ice melt. This is addressed using a high-resolution (4.5 km) coupled ocean-ice general circulation model (the MITgcm) and a lower resolution global climate model (100 km resolution).
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Environmental system models are frequently used to study water, carbon and nutrient dynamics in agricultural and natural ecosystems. The predictions of these models are prone to multiple sources of uncertainty, involving input, output, model structural and parameter error. Standard model evaluation methods typically attribute all sources of error to parameter uncertainty, without recourse to considering other sources of error. This talk will highlight some Bayesian procedures for confronting environmental models with data, with specific attention to distributed computing and improved model evaluation. Applications are at the interface of several disciplines including hydrogeology, ecohydrology, geophysics, soil hydrology, and agro-meteorology.
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Ensemble Kalman filters perform data assimilation by forming a background covariance matrix from an ensemble forecast. Most of the literature on ensemble Kalman filters assumes that all ensemble members come from the same model. This presentation describes a modified Local Ensemble Transform Kalman Filter (LETKF) that takes its background covariance from a combination of a high resolution ensemble and a low resolution ensemble. The computational time and the accuracy of this mixed-resolution LETKF are explored and compared to the standard LETKF on a high resolution ensemble, using simulated observation experiments with the Lorenz Models II and III (more complex versions of the Lorenz 96 model). The results show that, for the same computation time, mixed resolution ensemble analysis achieves higher accuracy than standard ensemble analysis.
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A first-order (tau-omega) microwave radiative transfer model (RTM) is coupled to the Goddard Earth Observing System, Version-5 (GEOS-5) Catchment Land Surface Model in preparation for the assimilation of global brightness temperatures (Tb) from the L-band (1.4 GHz) Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions. Simulations using literature values for the RTM parameters result in Tb biases of 10-50 K against SMOS observations. Multi-angular SMOS observations from the year 2010 are used to calibrate parameters related to the microwave roughness, vegetation opacity and/or scattering albedo separately for each observed 36 km land grid cell. A particle swarm optimization is used to minimize differences in the long-term (climatological) mean and temporal standard deviations between SMOS observations and simulations, without attempting to reduce the shorter-term (seasonal to daily) errors. After calibration, global Tb simulations for the validation year 2011 are largely unbiased for multiple incidence angles and both H- and V-polarization (e.g. global average absolute difference of 3.1 K for Tb_H(42.5 degrees)). The resulting parameters show a realistic spatial distribution that is largely but not uniquely dominated by the vegetation. Applying the RTM with aggregate optimal parameter estimates for each vegetation class maintains low global biases but increases local biases (e.g. global average absolute difference 7.6 K for Tb_H(42.5 degrees)).
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Space monitoring of aerosol optical depth (AOD) trends over megacities can serve as a potential space indicator of global anthropogenic air-pollution. Three space aerosol sensors, MODIS Terra, MODIS Aqua, and MISR, were used in order to study recent decadal trends of AOD over megacities around the world. Space monitoring of AOD trends has the advantage of global coverage and applies the same approach to detecting AOD trends over different sites. In spite of instrumental and time differences among the three sensors investigated, their global pictures of AOD trends over the 189 largest cities in the world are quite similar. The increasing AOD trends over the largest cities in the Indian subcontinent, the Middle East, and in Northern China can be clearly seen. By contrast, megacities in Europe, the North-East region of the United States, and South-East Asia show mainly declining AOD trends. In the cases where all three sensors show similar AOD trends, the results can be considered as reliable. This is supported by the observed trends in surface solar radiation, obtained by using network pyranometer measurements in North and South China, India, and Europe. In the cases where the three sensors show differing AOD trends (e.g. South America), the results cannot be considered as reliable.
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The formation of Subantarctic Mode Water (SAMW) and Antarctic Intermediate Water (AAIW) significantly contributes to the total uptake and storage of anthropogenic gases, i.e. CO2 and CFCs within the Southern Hemisphere. These water masses play an important role in the earth's heat, freshwater, carbon budgets and resupply of oxygen and nutrients to the subtropical oceans sustaining the marine ecosystem. The South Pacific is a principal formation site of SAMW and AAIW in the Southern Hemisphere. Formation rates of SAMW and AAIW within the South Pacific are calculated based on CFC-12 inventories from World Ocean Circulation Experiment (WOCE), Climate Variability and Prediction (CLIVAR), and hydrographic data collected in the southeast Pacific in the winter of 2005. These programs allow for the direct comparison of model CFC fields with hydrographic observations. CFC uptake within the National Center for Atmospheric Research (NCAR) Community Climate System Model version 4 (CCSM4) in the South Pacific is underestimated compared to observations particularly in the density surfaces that define SAMW and AAIW. To quantify this bias, observed and model formation rates of SAMW and AAIW based on CFC-12 inventories across the South Pacific are compared. Model formation rates in the South Pacific for SAMW are about one-third of the observational rate. Shallow mixed layer depths and insufficient meridional transport of high CFC waters in CCM4 are likely reasons for lower SAMW formation rates. However, for AAIW in CCSM4, formation rates are slightly higher than the observational rates. Higher CFC-12 inventories in CCSM4, particularly in the southwest and central Pacific, and higher surface inventories are likely the main reasons for greater formation rates of AAIW. This research demonstrates the importance of model-observational comparisons to better to identify the important biases in model simulations, as well as increasing our understanding of SAMW and AAIW in the South Pacific.
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Edward Teets Jr. of NASA's Dryden Flight Research Center, Propulsion and Aerodynamics Branch, will discuss several uses of Goddard's Earth Observing System Model, Version 5 (GEOS-5) data. Originally used for post-flight atmospheric reconstruction (or a Best Estimate Atmosphere (BEA)) for the Air Force and DARPA HTV-2 hypersonic glide body program, it became apparent after several years that other flight programs at Dryden could benefit from these data sets. To support these programs, unique sets of tools were developed to extract, analyze and display the high-resolution atmospheric forecast and observation data. GEOS-5 data is now commonly used for all the NASA supported hypersonic programs operating at very-high altitudes and traversing great distances. These include the HTV-2, X-51 and the AHW vehicles, as well as NASA's Stratospheric Observatory for Infrared Astronomy (SOFIA) as a flight planning tool to estimate water vapor loading, on-going sonic boom studies and as a simplified air-data calibration tool for the NASA Dryden Global Hawks. Edward will also discuss some future projects that are considering using GEOS-5 as a source for their high-resolution atmospheric data.
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The NCEP/EMC Land Data Assimilation System (LDAS) team has collaborated with its partners in developing two systems: the North American LDAS (NLDAS, www.emc.ncep.noaa.gov/mmb/nldas) and the Global LDAS (GLDAS). NLDAS provides initial land states that may be used for regional weather and climate forecasts, and drought monitoring and seasonal hydrological prediction via an uncoupled land modeling system over the continental U.S. (CONUS). NLDAS uses the NCEP North American Regional Reanalysis and observed gauge precipitation as surface forcing to drive four land surface models, including the NCEP Noah land surface model (LSM), at 0.125-deg resolution to produce a 29-year (1979-2007) retrospective and more than three-year (2008-present) near real-time hydrometeorological/climatological set of products. GLDAS provides initial land states to the NCEP Climate Forecast System version 2 (CFSv2) for global seasonal climate prediction, and generates hydrometeorological/climatological reanalysis products. GLDAS is a semi-coupled land modeling system which uses hybrid precipitation (combination of gauge, satellite and model) and observed snow, along with CFSv2 model analysis for the other surface forcing to drive the NCEP Noah LSM, with land states provided daily to the operational CFSv2; GLDAS was part of the CFSv2 reanalysis (1979-present, cfs.ncep.noaa.gov/cfsr). GLDAS operates under the NASA Land Information System (LIS) in executing the Noah LSM within CFSv2, and will be upgraded to use of a new version of LIS that has an Ensemble Kalman Filter (EnKF) data assimilation capability that will allow assimilation of satellite sources of land surface states, such as land surface skin temperature (e.g. NESDIS GOES products) and soil moisture (e.g., NESDIS SMOPS and NASA SMAP products). As a first step in assessing the impact of soil moisture data assimilation on weather forecasts, an EnKF soil moisture data assimilation algorithm has been implemented in the NCEP Global Forecast System (GFS) in an EnKF-GFS coupled approach. In another LIS effort, Noah and SAC-HT/SNOW17 LSMs are run at 4-km over CONUS as part of a high-resolution NLDAS; in this setting, to address early snowmelt biases due to immature snow evolution physics, high-resolution MODIS snow cover is assimilated to improve snow water equivalent values, and ultimately other land-states and hydrological outputs such as streamflow. Finally, model physics improvements and assimilation of surface data sets are closely related, e.g. to address GFS daytime low-level temperature biases due to model physics limitations, modifications are made to surface-layer formulations and surface characterization in microwave emissivity calculations, resulting in improved brightness temperatures at surface-sensitive channels and increased satellite data utilization.
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Over the last decade data assimilation schemes have evolved towards very sophisticated systems. The scheme handles a large variety of both space and surface-based meteorological observations. It combines the observations with prior (or background) information on the atmospheric state and uses a comprehensive (linearized) forecast model to ensure that the observations are given a dynamically and statistically realistic response in the analysis. Effective performance monitoring of such a complex system, with an order of 109 degrees of freedom and more than 107 observations per 12-hour assimilation cycle has become an absolute necess ity. The assessment of each observation contribution to the analysis and forecast is among the most challenging diagnostics in data assimilation and numerical weather prediction. Recently, adjoint-based observation sensitivity techniques have been used to measure the observation contribution to the forecast, where the observation impact is evaluated with respect to a scalar function representing the short-range forecast error. Moreover, the theoretical framework to evaluate the other analysis input parameters impact, not only the observation impact, has been derived and application will be shown. The performance of the current ECMWF operational version of the data assimilation and forecast system for June 2010 shows a consistent overall positive impact of the observations. In particular, a comprehensive assessment of the impact of GPS radio occultation observations and all-sky microwave imager radiances in the assimilation and forecast system is in this talk provided, throughout the diagnostic tools introduced above.
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The Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalysis provides space-time information about the atmospheric structure between the surface and the stratopause. This work examines the integrity of the upper troposphere and the stratosphere in MERRA. Evaluations of the quality of MERRA analyses are provided by comparisons with other reanalyses (such as ERA-Interim), independent observations (such as the EOS-MLS), and from the internal statistics of the system (the "observation minus analysis" residuals). These comparisons show a robustness of analyses in the lower stratosphere, with increasing uncertainty in the upper stratosphere. Difficulties in producing accurate meteorological analysis when only deep-layer thermal information from nadir sounders (AMSU and SSU) will be discussed, along with the additional complications of model biases in the upper stratosphere. The balance between physical forcing mechanisms and the "incremental analysis update" forcing added to the model in order to drive it towards the observations will be discussed: in particular, the impact of this balance on long-term changes in the stratosphere will be emphasized. Finally, impacts of long-term changes in tropospheric circulation on the structure of the tropopause region will be discussed.
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Soil moisture is known for its integrative behavior and resulting memory characteristics. Soil moisture anomalies can persist for weeks or even months into the future, making initial soil moisture an important potential contributor to skill in weather forecasting. A major difficulty when investigating soil moisture and its memory is the sparse availability of long-term measurements and their limited spatial representivity. In contrast to this there are plenty of long-term runoff measurements for catchments of various sizes available across the world. These runoff measurements can perhaps be used to characterize local soil moisture memory. This presentation will describe a simple water balance model in which evapotranspiration and runoff ratios are simple functions of soil moisture. Optimized functions for the simple model are determined using runoff observations, and the optimized model is then used to extract information on local soil moisture memory. The validity of the approach is demonstrated with data from three heavily monitored catchments. The approach is then applied to runoff data in several small catchments across Switzerland to obtain a spatially distributed description of soil moisture memory.
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Air-sea interaction is unquestionably a critical aspect of hurricane structure and evolution. Recent experiments with high resolution (km-scale) nested models have shown strong sensitivity to the representation of air sea fluxes � both the direct (interfacial) transfers and contributions of sea spray. With the advent of fully coupled (air-wave-ocean) models, it becomes possible to represent the surface fluxes in more physical detail (as opposed to simple wind-speed driven bulk flux algorithms). In this paper we describe a representation of sea spray fluxes that is driven by the fundamental processes associated with blowing large droplets off the tops of breaking waves. The droplets are produced at the interface via a cascade of energy released by breaking waves and enter the atmosphere if they receive sufficient forward speed from wind gusts and the speed of the breaker to escape the surface. The actual contributions of this droplet spectrum to the sensible and latent heat fluxes are computed interactively as a balance of heat sources available to evaporate the droplets before they reenter the ocean. The parameterization uses input variables from the numerical model: energy lost to wave breaking, phase speed and slope of the breaking waves, and the surface stress. Time permitting, results from several high-resolution model studies will be shown.
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A four-dimensional ensemble-variational data assimilation capability is developed for the current 3DVAR-based GSI-EnKF hybrid data assimilation system. Like the classic TL/ADJ 4DVAR, four dimensional analyses are obtained with the Ensemble-4DVAR by fitting observations spanning the assimilation window. Temporal evolution of the error covariance within the assimilation window is realized through the use of ensemble perturbations. Forecasts initialized by the analyses generated by the Ensemble-4DVAR were compared with the 3DVAR-based GSI-EnKF hybrid for both summer and winter periods in 2010, assimilating all operational conventional and satellite observations. The presentation will focus on the summer period test. Various verification metrics, including global forecasts and hurricane track forecasts show that the Ensemble-4DVAR improves upon the 3DVAR-based hybrid. A strong balance constraint applied to the ensemble covariance was found to degrade the hurricane track forecasts, but benefit the general global forecasts. Various extensions to Ensemble-4DVAR are being tested for preparation of the coming hurricane season. The impacts of observations in the GSI and hybrid GSI-EnKF have been assessed using observing system experiments (OSEs) and an ensemble-based observation impact metric in the case of the GSI-EnKF. Initial results show that ensemble based observation impact metric can provide a good estimate of the impact, although the quality of the estimate can vary depending on the observation type and forecast case. Progress and recent results on unifying and testing the GSI-EnKF hybrid for regional modeling systems will also be presented as time permits.
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W. G. Read, T. Flury, H. Su, M. L. Santee, and N. J. Livesey: Since May 2008, the measurement tracks of the CALIPSO cloud profiling LIDAR and MLS constituent measurements of atmospheric trace grases (e.g. H2O) and temperature are nearly aligned. The cloudiness of all the MLS H2O measurements is known which provides insights into the processes by which dehydration is occurring. CALIPSO also determines whether the clouds are of convective origin or isolated thin cirrus. We find, particulary during the Boreal winter, that a high percentage of the driest and coldest air occurs in convective and thick layered cirrus clouds situated above the nominal clear sky level of zero radiative heating (~120~hPa, LZH). The Boreal summer shows fewer such events and hence the height-of-convection shows a strong annual cycle. Another interesting discovery is that over the tropical western Pacific, total H2O (IWC and humidity) at the tropopause has nearly no annual cycle. The mean zonal IWC at altitudes below the LZH shows a weak annual cycle. We will also show some initial results from CH3Cl, a new MLS v3 product. CH3Cl, like CO, is enhanced in biomass burning activities, but has a much longer chemical lifetime in the TTL than CO. Because of this, CH3Cl will be a better tracer of convective transport and supply into the TTL than CO. Preliminary results from measurements and a comparison with a simple 2 D model will be shown.
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Recent land surface developments at ECMWF have led to an improved representation of some of the physical processes occurring at the land-atmosphere interface, verified against a variety of independent observational sources. In particular, a satellite-based leaf-area-index climatology, which describes the seasonal evolution of vegetation, has replaced a fixed-in-time vegetation, and a revised bare-soil evaporation has introduced a larger extraction of superficial water in non-vegetated areas. These two schemes revisions are shown to improve near-surface temperatures and soil moisture simulations. In an attempt of moving towards interactive ecosystems, a photosynthesis-based module has also been introduced in order to simulate natural CO2 emissions over land. However, the land-carbon parameterization does not yet interact with the evapotranspiration formulation, and similarly, the vegetation seasonality representation does not interact with the momentum budget. These "ad-hoc" separations of processes have a practical advantage of modularizing the model development (particularly useful in community models) but may present some caveats of realism when representing naturally inter-dependent processes occurring in the Earth system. Examples from recent simulations will be used to illustrate this paradigm and the problems associated to full coupling between processes.
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This study aims to explore the dynamical seasonal predictability of tropical storms using high-resolution global climate models. Using a series of 5-member ensemble predictions using the 50-km resolution Goddard Earth Observing System version 5 (GEOS-5) atmospheric general circulation model, which was driven by the observed sea surface temperature (SST) for the period of 1997-2007, we compared the long-term climatology and the interannual variation of the tropical storm activity with the observation database from the International Best Track Archive for Climate Stewardship (IBTrACS). The analysis was specifically focused on the domains in the Northwest Pacific and the Atlantic Oceans. The model in general tends to reproduce realistic statistics of the annnual and interannual variation of the observed tropical storm activity, although it is not able to reproduce the tropical cyclones of category 4 and higher, presumably due to the limitations from the model resolution. Climatological-mean distribution of the tropical storm genesis location and tracks are also realistic, although the model tends to simulate less recurving storms evolving toward the extratropics. We attribute the model bias to the abnormally strong subtropical highs simulated by the model, which results emphasize the important role of the model's simulated basic climate state. The model shows a marginal prediction skill for the yearly tropical storm counts, with a correlation below 0.5 in the North Atlantic basin with the observation, implying that there should be much room to improve the model simulation by improving moist physics parameterization and the initialization of land surface.
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An idealized experiment examined the impacts of entrainment in a coupled ecosystem-atmosphere model by implementing an enhanced entrainment parameterization based on the assumption that the heat flux at the top of the PBL is negatively proportional to the heat flux at the surface. This experiment found that entrainment produced a warmer, drier, and deeper PBL and that the surface fluxes of heat and moisture were modified by the vegetative response to the altered atmospheric conditions. A realistic simulation for the summer of 1999 found that enhanced entrainment produced stronger early morning growth of the PBL and a deeper midday depth. This better captured the monthly mean diurnal cycle of PBL depth from observations by a radar-sounding system in northern Wisconsin. Additionally, the complex land-atmosphere interactions produced a time-mean spatial CO2 gradient of 7 ppm over 1000 km. In order to evaluate and improve model simulations, PBL depth has been estimated using the backscatter from the LIDAR onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite. Using an automated method, millions of estimates have been derived to which model results can be compared. This method evaluates the maximum vertical variance of the backscatter in order to identify backscatter features associated with the top of the PBL and helps to identify the vertical extent of turbulent mixing. This analysis sheds some light on the spatial heterogeneity of boundary layer processes. The derived depths are shallower over water than over land and show a local minimum along the Mississippi River valley. Deeper features are found over the desert Southwest and deeper than expected values are retrieved over the Boreal forests.
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Previous studies have shown that there is stronger isentropic mixing between stratosphere and troposphere in summer than in winter, leading to a strong seasonal variation of isentropic stratosphere-troposphere exchange (STE). However, at the extratropical tropopause there are large dynamical and chemical discontinuities, demonstrated by strong quasi-horizontal gradients in potential vorticity (PV) and chemical species on isentropic surfaces. In this study, we introduce a new meridional coordinate �tropopause latitude� (TpLat), defined as the latitudinal distance from the nearest tropopause on an isentropic surface, to investigate the discontinuity feature of the extratropical tropopause. Our analysis shows that the TpLat coordinate is much better than traditional geographic or equivalent latitude coordinates for emphasizing the sharp distinction in tracer abundances between the upper troposphere and lower stratosphere in large global datasets, such as space-borne measurements. Our study also shows that meridional gradients in GEOS-5 PV and Aura/MLS tracer abundances in summer are as large as those in winter. A definition of the tropopause layer boundaries based on PV distributions in TpLat coordinates is proposed and the statistical distributions of features inside the extratropical tropopause layer will also be shown. To summarize, although there is stronger STE inside the tropopause layer in summer than in winter, the distinction between the upper troposphere and lower stratosphere is nearly equally pronounced in winter and summer.
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This presentation introduces an approach to estimate system (model) error using observation residuals. Based on the sequential fixed-lag smoother, a diagnostic procedure is developed to allow estimating system error over a dense observing system. Optimality considerations are examined in light of the sequential results. The procedure is re-interpreted in the language of variational data assimilation, such as 4d-Var. Illustrations of the approach are given by studying both identical-twin and fraternal-twin experimental settings for simple linear and nonlinear dynamics. Preliminary results are shown by examining observation residual statistics.
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In this presentation a new approach to the estimation of key unknown parameters of water and energy balance equation (moisture and heat diffusion equation) and the closure function which links these two equations is introduced. Parameters of the system are estimated by developing objective functions that link atmospheric forcing (precipitation and incident radiation), surface state and unknown parameters. This approach is based on conditional averaging of heat and moisture diffusion equations on land surface temperature and moisture states respectively. Based on conditional averaging, a single objective function is posed that measures the moisture and temperature dependent errors solely in terms of observed forcings (e.g. precipitation, radiation) and surface states (moisture and temperature). This objective function can be minimized with respect to parameters to identify evaporation and drainage models and estimate water and energy balance flux components. The uncertainty of the estimated parameters (and associated statistical confidence limits) is obtained through the inverse of Hessian of the objective function which is an approximation of the covariance matrix. The accuracy of this method at point scale is examined through the use of synthetic data and various field data obtained from Ameriflux network. The proposed methodology is applied to the arid sahara-sahelian climate of the mesoscale site of Gourma in West Africa. Multi-platform and frequency remote sensing data is used to obtain Evaporative Fraction as a function of soil moisture (EF(s)), neutral turbulent heat Coefficient (CHN) as a function of vegetation phenology and Drainage as a function of soil moisture using the proposed methodology. The estimation results are verified against Agoufa field site data set located in this region and the hydrological characteristics of the sahara-sahelian climate of Gourma region in West Africa. The results of this research project, demonstrate the feasibility of this new scale free technique in providing a good estimate for water and energy balance flux components and their closure relationship, at point scale and at regional scale using remote sensing data.
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Speaker: Masahiro Watanabe (AORI, Univ of Tokyo) *** Title: Development, verification, and natural variability in MIROC5 *** Abstract: One of the Japanese community climate model, called Model for Interdisciplinary Research on Climate (MIROC), has been developed for use of various aspects of climate research. We are currently under operation of MIROC with three types of configuration: a high-resolution climate model for the decadal climate prediction, a low-resolution Earth system model, and a low-resolution new version of the climate model (MIROC5). In this talk, I would like to introduce outcomes from MIROC5, in which many of the atmospheric parameterization schemes have been updated. Improvements in the climate simulation with MIROC5 and a high sensitivity of the ENSO simulation to a perturbation in the cumulus convection scheme are remarked. ***************** Speaker: Yoshimitsu Chikamoto (AORI, Univ of Tokyo) *** Title: Overview of decadal climate prediction using a coupled climate model MIROC *** Abstract: Toward coming IPCC AR5, we have performed decadal climate prediction using three versions of the coupled atmosphere-ocean model MIROC. In these hindcast experiments, initial conditions were obtained with an anomaly assimilation procedure using the observed oceanic temperature and salinity while prescribing natural and anthropogenic forcing based on the IPCC emission scenarios. Our hindcast experiments show that initialization contributes to enhance the predictive skills of AMO and PDO indices for several years in advance. In addition to these major climate phenomena, the hindcast experiments tend to simulate a pattern of stepwise sea surface temperature increase in the Pacific during the late 1990s. Although further studies are need to enhance predictive skills of decadal climate variability, our results suggest that the decadal climate prediction has a potential to provide useful information in order to solve socioeconomic problems arising from climate change.
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The interannual and the intraseasonal modes of the South Asian summer monsoon are identified in a long free coupled simulation of the National Centers for the Environmental Prediction�s Climate Forecast System version-1 (CFS) by applying multi-channel singular spectrum analysis on the daily anomalies of rainfall. The CFS has two intraseasonal oscillations with periods around 106 and 30 days. The 106-day mode has spatial structure and propagation features similar to the northeastward propagating 45-day mode in the observations. The 30-day mode is northwestward propagating and is similar to its observational counterpart. The dominant interannual signal in the rainfall is related to the El Niño�Southern Oscillation (ENSO), which has maximum variance in the eastern equatorial Indian Ocean. Although an Indian Ocean Dipole mode is not obtained as a separate mode, a dipole structure in the Indian Ocean is almost always accompanied with an ENSO. Regionally coupled experiments are performed to examine the sensitivity of these modes to the air-sea coupling in the Indian and Pacific basins. Regional coupling is employed by prescribing daily mean or climatological sea surface temperature either in the Indian or the Pacific basin while allowing full coupling elsewhere. Results of these sensitivity experiments will be discussed in detail.
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Climate feedbacks constitute the largest part of the Earth System response to climate forcings, including the forcing from anthropogenic greenhouse gases. At the same time, feedback mechanisms are the hardest ones to evaluate in climate simulations, as they involve complex interactions between numerous components of the climate system. As a result, feedbacks like those from clouds and water vapor constitute the primary source of uncertainty in climate model projections. Over the past three decades, a wealth of global observations has been collected that, even if they do not contain a sufficiently long climate warming signal, they allow us to resolve the operation of feedback mechanisms for a large subset of time and space scales. In this talk, different data analysis methods are presented that aim to document and study feedback processes in observational data, and to use the results of the analysis in order to evaluate the ability of models to simulate those processes. The examples presented here focus on cloud, radiation, and precipitation processes, but the analysis methods define dynamic regimes that can be used to examine processes in a number of other climate system components.
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A new component of the next IPCC assessment report will be decadal time-scale predictions in which models are initialized with states based on the observed state of the climate system. Since the climate system is chaotic, there is an inherent limit on the range at which the information in these initial states can have an impact on the skill of the forecasts. In this presentation we measure this limit for six CGCMs from various research centers. We concentrate on the predictability of upper ocean heat content in the two northern basins, and we use two methods that only require long control runs for estimating predictability limits. One method makes use of analogs and the other uses multivariate linear regression. In contrast to the conventional ensemble technique both methods are able to estimate the average predictability characteristics of very many initial states. When we use relative entropy as a measure of predictability and consider entire basins, we find that on average the effect of initializing a forecast from a specific initial condition can be detected for about a decade, but this limit can vary by as much as a factor of three from one model to another. Furthermore, for a given model, there are variations in predictability of a factor of four at different locations within a basin. The model-to-model variations can be traced to variations in the properties of horizontally propagating disturbances in each model, including prominent modes. Given the large variations in predictability that exist from one model to another, we conclude that a) the predictability characteristics of each model used for decadal predictions must be carefully determined for proper design and interpretation of forecasts, and b) the scientific community currently does not have a reliable estimate of the decadal predictability characteristics of nature.
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The Modern-Era Retrospective Analysis for Research and Applications (MERRA) is used to examine the physical mechanisms associated with the development of summer-time climate extremes including heat waves and dry spells that typically occur on weekly to monthly time scales. The results indicate an important role for stationary Rossby waves that can at times span much of the summer hemisphere. Examples are presented that show such waves have played an important role in past major climate extremes such as the 2003 European and 2010 Russian heat waves, and the 1988 US drought. A stationary wave model is used to show that sub-monthly atmospheric transients play a key role in forcing the waves. Some early results from GEOS-5 AGCM experiments will be presented that suggest important roles for SST and land forcing in the development/maintenance of the Russian heat wave as well as the recent (2011) heat wave over the United States.
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This presentation will give an overview of the global and mesoscale data assimilation systems developed at the Naval Research Laboratory and run operationally for the U.S. Navy. These systems include the mesoscale three-dimensional variational system NAVDAS (NRL Atmospheric Variational Data Assimilation System) and its global 4D-Var counterpart NAVDAS-AR (for Accelerated Representer). A summary of recent improvements for each system (e.g., new capabilities and newly assimilated observing platforms) will be presented first, followed by a review of ongoing data assimilation research and development activities. Topics to be discussed include preparations for NPP, improving the assimilation of existing sensor platforms, using adjoint methods to estimate mesoscale observation impact, development of mesoscale 4D-Var, and incorporating ensembles DA components into NAVDAS-AR. The talk will conclude the talk with a few words about the Navy
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The NASA Moderate Resolution Imaging Spectroradiometer (MODIS), launched onboard the Terra and Aqua spacecrafts, began Earth observations on February 24, 2000 and June 24, 2002, respectively. Among the algorithms developed and applied to this sensor, a suite of cloud products includes cloud masking/detection, cloud-top properties (temperature, pressure), and optical properties (optical thickness, effective particle radius, water path, and thermodynamic phase). All cloud algorithms underwent numerous changes and enhancements for the latest Collection 5 production version; this process continues with the current Collection 6 development. We will show example MODIS Collection 5 cloud climatologies derived from global spatial and temporal aggregations provided in the archived gridded Level-3 MODIS atmosphere team product (product names MOD08 and MYD08 for MODIS Terra and Aqua, respectively). Data sets in this Level-3 product include scalar statistics as well as 1- and 2-D histograms of many cloud properties, allowing for higher order information and correlation studies. To assist in climate model evaluation, we have developed a MODIS cloud simulator with an accompanying netCDF file containing subsetted monthly Level-3 statistical data sets that correspond to the simulator output. We will also show analyses of trends and statistical significance in monthly mean anomalies for a variety of the MODIS cloud and other atmospheric properties, as well as the time required for detection given assumed trends. Correlations of cloud properties with ENSO offer the potential to evaluate model cloud sensitivities as well as provide guidance in interpreting trending results.
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The Earth's most powerful year-to-year climate fluctuation -- the El Niño / Southern Oscillation (ENSO) -- affects weather, ecosystems, and economies worldwide. Yet despite great progress towards understanding and predicting ENSO, its future remains uncertain. Will coming decades bring a barrage of strong El Niño and La Niña events, or could there be no events at all? Historical reconstructions, paleo records, and model simulations all display prolonged epochs of active or quiet ENSO, which challenge ENSO theories, hamper detection of anthropogenic impacts, and complicate model evaluation and intercomparison. I shall discuss these challenges, and describe recent efforts to probe the limits of ENSO predictability and its sensitivity to climate change.
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Small changes in the surface layer parameterization over oceans leads to a widespread decrease in model biases in the GEOS-5 AGCM. Surface winds, stationary waves, momentum fluxes, heat fluxes, and cloud distribution, are all improved relative to a control run. Fidelity to observations is improved in a 2x2.5 degree run without stratospheric chemistry, in a 2x2.5 degree run with stratospheric chemistry, in a 1x1.25 degree run, and in a series of 0.25 degree forecasts. It appears that other GCMs (both for operational weather forecasting and climate) use a similar class of parameterization; we therefore expect that results from GEOS-5 are relevant to other models as well.
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The difficulty of evaluating large scale soil moisture fields presents a major obstacle to the development and application of remotely sensed near-surface soil moisture data sets. Traditionally, remotely sensed soil moisture observations have been assessed against in situ soil moisture observations, however these in situ observations are available at only a few locations, and their utility is greatly limited by the substantial representivity errors between the soil moisture quantities observed by remote and in situ sensors. Consequently, this study pursues an alternative approach to evaluating near-surface soil moisture observations derived from ASCAT, by examining the impact of assimilating those data into the SAFRAN-ISBA-MODCOU (SIM) hydrological modelling suite over France. Specifically, the root-zone soil moisture in the ISBA land surface model has been analysed over a three and a half year period, by assimilating the 12.5 km ASCAT near-surface soil moisture observations disseminated by EUMETSAT (the SM OBS1 product), using an Extended Kalman Filter. For these assimilation experiments ISBA has been forced with the near-real time SAFRAN analysis, which analyses the variables required to force ISBA from all relevant observations available within the real time data cut-off period. The soil moisture profile and surface water budget terms generated from the ASCAT assimilation have then been compared to the corresponding forecasts from the ISBA model forced with the climatological SAFRAN analysis, which ingests additional observations that become available after the near real-time data cut-off (including data from 3000 climatological observing stations). This comparison tests whether assimilating the ASCAT soil moisture observations can correct the ISBA soil moisture profile in response to errors in the near-real time SAFRAN forcing, since this provides strong evidence that the ASCAT data set can accurately detect temporal variations in near-surface soil moisture. Likewise, the impact of assimilating the ASCAT near-surface soil moisture observations on the river discharge simulated by the MODCOU river routing scheme has been evaluated, by comparison to discharge observations from an extensive network of gauging stations throughout France.
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As part of the Interim Synthesis effort for the North American Carbon Program, an observational dataset of surface carbon, water, and energy fluxes and associated ecological, biological, and physical data has been developed, drawing on measurements made at over thirty eddy covariance flux tower sites in the U.S and Canada. Extensive data processing and quality control measures have been taken to define observational uncertainty and to provide gap-filled surface weather forcing at sub-daily time steps, suitable for input to ecosystem and land surface models. Based on these observational resources, a simulation protocol has been developed that defines a series of progressively sophisticated modeling experiments, designed for the purpose of drawing quantitative comparisons between observed and predicted fluxes and states across many sites. Following this protocol, we used the Community Land Model, version 4 (CLM4), which includes prognostic carbon and nitrogen cycles, to predict fluxes and state variables at 15 forested sites in diverse climates. We focus here on a comparison of predicted vs. observed diurnal cycle of carbon, water, and energy fluxes at different sites and in different seasons. We found a serious and pervasoive underprediction bias in the CLM4 mid-day carbon fluxes, while water and energy fluxes were more reasonable. We identified the treatment of time scale for growth limitations from nutrient availability as a likely source of this bias, and developed a simple theory that helps to explain and correct the discrepancy with observations. This theory was developed and implemented at one site, then tested across all 15 forested sites. We found a quantitative improvement in model performance across sites, expressed as a reduction in prediction bias and RMSE. This work demonstrates the utility of the site synthesis approach, and also illustrates a major principle of model design in action: the mechanistic representation of the model should be as simple as possible, with additional complexity added as dictated by observational constraints and consistency with understanding of the natural system.
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Global reanalysis is an invaluable asset to study weather and climate, and it has been extensively used to understand the pronounced atmospheric variability and improve its prediction using global climate models. As a part of coordinated efforts in examining the realism and limitations of the recently-released, Modern Era Retrospective-analysis for Research and Applications (MERRA) atmospheric reanalysis by NASA Global Modeling and Assimilation Office, this study validates two phenomena: the tropical Madden-Julian oscillation (MJO) and the tropical storm. Representation characteristics of these two phenomena seem to be closely related with the parameterization of moist convection, which tends to produce a large spread in the simulation across various reanalysis products. Results presented are based on the multi-year data comparison between MERRA and observations, as well as with various sources of reanalysis products. It is found that MERRA represents the MJO in a realistic manner in terms of its amplitude and the dominant spatial and temporal scale. Simulations of convection and accompanied precipitation are compared reasonably well with those of CFSR and ERAinterim, and the simulation is relatively better than the old reanalyses from NCEP/NCAR and NCEP R2. We discuss the possible causes for the improvement based on a better representation of the observed relationship between moisture variability and deep convection associated with the MJO. We also present the validation results from the tropical storm simulations over the North Atlantic and the Northwestern Pacific for recent 12 years (1998-2009). Climatological-mean features of the genesis location, tracks and their maximum intensity will be presented with the observed tropical storm characteristics obtained from the International Best Track Archive for Climate Stewardship (IBTrACS).
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The speaker will present his recent work on the diagnosis and correction of the excessive summertime precipitation over steep mountains such as the Andes and the Himalayas in the GEOS-5 GCM. This systematic error is common to all global and regional atmospheric models and it impairs data assimilation products. Possible causes investigated include a lack of upward heat flux out of the boundary layer due to sub-grid scale vertical circulations in the affected regions; poor design of horizontal moisture flux; the criterion for cumulus convection being too easily met over high terrain; conditional instability of the computational kind; and insufficient friction in the boundary layer on steep mountain slopes. Of these possible causes the first two are the significant ones. Correction methods and some preliminary results will be presented.
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The climatological spatial structure and variability of the African Easterly Jet and Waves (AEJ/AEWs) originating from West Africa are analyzed in the recently completed Modern Era Retrospective-Analysis for Research and Applications (MERRA). The results are compared with other existing reanalyses such as the ERA-40, NCEP-R2, and JRA-25. The basic mechanisms (e.g., barotropic/baroclinic instability) responsible for the generation of the AEWs and how they vary with time scale, as well as the factors controlling the variability of the AEJ and AEWs are examined using a compositing approach that highlights the role of ENSO and the MJO.
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In this talk, I will briefly share my personal journey in interdisciplinary research from atmosphere, to land, to ocean, and back to atmosphere. Examples include: using atmospheric hydrostatic subtraction idea to improve the numerical solution of soil moisture Richards equation (implemented in NCAR CESM); using soil temperature diurnal cycle idea to develop the ocean surface skin temperature prognostic scheme (implemented in ECMWF model); and using the ocean-atmosphere turbulence consistency idea to improve turbulence over land (implemented in NCEP model). Furthermore, I will briefly discuss our most recent work on the evaluation of ocean surface turbulent fluxes from 11 flux products (including MERRA and GSSTF2/2b from GSFC) using cruise observations and on the comparison of land surface turbulent fluxes and mean quantities from various reanalyses (including MERRA and GLDAS from GSFC) using 33 flux tower measurements.
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Ocean circulation off the west coast of Australia is dominated by the anomalous, poleward-flowing eastern boundary current, the Leeuwin Current. This current flows adjacent to Ningaloo Reef, Australia�s longest fringing coral reef. Using chlorophyll a from satellite-derived ocean colour and in situ field observations, we identified the existence of an autumn bloom in this region and evaluated the physical mechanisms that influence the seasonal variability in chlorophyll a concentration. This involved combining historical field data sets, satellite-derived ocean colour observations and output from a data-assimilating numerical ocean model. The results show that the mixed layer depth deepens to ~100 m in winter and is driven by a combination of surface cooling and the strengthening of the Leeuwin Current. This coincides with an increase in nutrient concentrations and the observed autumn bloom. In the second part of this talk, I will give an overview of the project that I will undertake next year with Watson Gregg. This project will use the NASA Ocean Biogeochemical Model to analyse the spatial and temporal variation of cyanobacteria in the Pacific Ocean. Our preliminary analysis has shown a decrease of cyanobacteria concentration between 1998 and 2007 in the North Central Pacific and to a lesser extent in the Equatorial Pacific. The largest decrease in cyanobacteria concentration was observed in the North Central Pacific and coincided with increasing nitrate concentration. I will present the potential implications of these findings and the way forward for this project.
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We present an overview of the Microwave Limb Sounder (MLS) experiments flown on NASA's Upper Atmosphere Research Satellite (UARS) and, more recently, NASA's Aura mission. The MLS instruments measure vertical profiles of atmospheric composition, temperature and cloud ice from the upper troposphere to the mesosphere. MLS observations have been used to characterize and quantify important processes affecting climate, ozone layer stability and global air quality. The fundamentals of the MLS technique will be reviewed, and selected findings from both UARS and Aura MLS will be highlighted. Particular emphasis will be given to Aura MLS observations in the upper troposphere, where water vapor and ozone are strong greenhouse gases. Six years of Aura MLS observations show strong geographical and temporal variability in the behavior of upper tropospheric ozone and carbon monoxide, owing to the complex interplay of horizontal transport, deep convective lofting and chemistry. Finally, concepts for future microwave instruments will be discussed.
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TBA
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Uncertainty in future climate change presents a key challenge for adaptation and mitigation planning. In this talk, I will discuss an overlooked source of uncertainty, internal climate variability, using a new 40-member ensemble of greenhouse-gas forced simulations with the National Center for Atmospheric Research Community Climate System Model Version 3 (CCSM3) over the period 2010-2060. The dominant source of uncertainty in the simulated extra-tropical climate response is associated with the annular modes of intrinsic atmospheric circulation variability. Coupled ocean-atmosphere variability plays a dominant role in the tropics, with secondary effects over higher latitudes. Uncertainties in the forced response are generally larger for atmospheric circulation than precipitation and temperature. Implications for detection and attribution of observed climate change and for multi-model climate assessments will also be discussed.
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Hydrologic uncertainty arises from inherent variability across space and time in the hydrologic cycle. This uncertainty is further exacerbated by our inability to accurately measure all relevant hydrologic states and fluxes at all relevant spatial and temporal scales. The advent of satellite-based remote sensing in conjunction with parallel computing has, however, created new opportunities to better characterize, and ultimately reduce, hydrologic uncertainty via fusion with physically-based models. In this talk, a satellite-derived, ensemble-based framework used to generate downwelling radiation (energy) at the Earth�s surface is presented. A precipitation (mass) model using many of the same satellite inputs is currently under development, but is not presented here. Much of the motivation for this work is the creation of an ensemble of forcing fields (energy + mass) for use in distributed hydrologic model applications. Improved representation of forcing uncertainty within the hydrologic system is expected to improve our understanding of system dynamics thereby improving our ability to better manage freshwater resources. Application of this framework in the Southern Great Plains of the United States shows the resulting prior (unconditioned) ensemble implicitly contains the complex spatiotemporal uncertainty structure overlooked by more commonly used techniques. A data assimilation (DA) scheme is then employed that involves Bayesian conditioning of the prior estimate by relatively coarse-scale, readily-available measurements. Comparison against an independent, ground-based observational network shows the posterior (conditioned) ensemble is more accurate and contains less uncertainty relative to the prior. Furthermore, it is demonstrated that prior uncertainty structure dictates much of the conditioning capability of the DA scheme. Careful consideration of prior uncertainty can lead to an improved posterior whereas commonly used techniques can actually lead to posterior degradation.
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In the near future the NASA Global Precipitation Measurement (GPM) Mission will provide new sources of precipitation observations with unprecedented accuracy and coverage of the globe. For hydrological applications, the satellite observations need to be downscaled to the required finer spatial and temporal resolution precipitation fields. A WRF ensemble data assimilation system is developed to explore the potential of using ensemble data assimilation techniques and cloud-resolving models to dynamically downscale satellite observations. A high-resolution regional WRF model with multiple nesting grids is used to provide the first guess and ensemble forecasts. An ensemble assimilation algorithm based on Maximum Likelihood Ensemble Filter (MLEF) is used to perform analysis. Precipitation-affected radiances are assimilated along with the observations from the NCEP regional data stream. Prognostic hydrometeors and dynamical variables are simultaneously updated by the analysis every 3 hours. The experiments using the current available satellite precipitation data (AMSR-E and TRMM-TMI radiances) will be presented to discuss some of the challenging issues, such as model-predicted hydrometeor control variables and associated background error covariance, quality control and bias estimation in radiance space over land.
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Accurate coupling between the resolved scale dynamics and sub-grid scale physics is essential for accurate modelling of the atmosphere. Previous emphasis has been towards the temporal aspects of this so called physics-dynamics coupling problem, with little attention towards the spatial aspects. When designing a model for numerical weather prediction there is a choice for how to vertically arrange the required variables, namely the Lorenz and Charney-Phillips grids, and there is ongoing debate as to which is the optimal. The Charney-Phillips grid is considered good for capturing the large scale dynamics and wave propagation whereas the Lorenz grid is more suitable for conservation. However the Lorenz grid supports a computational mode. Further it is argued that the Lorenz grid is preferred for modelling the stably stratified boundary layer parametrisation. This presents the question: which grid will produce most accurate results when coupling the large scale dynamics to the stably stratified planetary boundary layer? The normal mode analysis approach, as used in previous work of a similar nature, is employed to address the question. This is an attractive methodology since it allows one to pin down exactly why a particular configuration performs well. Applying this method encounters issues when the problem is non normal, as it will be when including boundary layer terms. It is shown that when addressing the coupled problem the lack of orthogonality between eigenvectors causes interpretation of mode analysis to become very difficult. It is shown that the Lorenz grid is favoured when the boundary layer is considered on its own; it captures the structures of the steady states and examinable transient modes more accurately than the Charney-Phillips grid. For the coupled boundary layer and dynamics the Charney-Phillips grid is found to be most accurate in terms of capturing the steady state. It is also shown that certain Charney-Phillips configurations for the boundary layer parametrisation should be avoided. Results from examining transient modes are limited due to the problems with the methodology, however important understanding is gained in the application of the overall technique. The Lorenz grid computational mode is examined and found to be suppressed by the boundary layer, but only in the boundary layer region.
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We investigate downward wave coupling between the stratosphere and troposphere in the southern hemisphere in the Goddard Earth Observing System chemistry-climate model (GEOS CCM) Version I. Downward wave coupling occurs when planetary waves reflected in the stratosphere impact the troposphere and is distinct from zonal-mean coupling, which results from wave dissipation and its subsequent impact on the zonal-mean flow. A suite of different GEOS CCM simulations of the past and future are used to evaluate the modeled downward wave coupling and understand the impact of ozone changes. The GEOS CCM captures the main features of the observed seasonal cycle of downward wave coupling in the southern hemisphere. Temporal changes in ozone related to past depletion and future recovery significantly impact downward wave coupling in the model during November and December. The results reveal a new mechanism wherein stratospheric ozone changes can affect the tropospheric circulation.
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The Navy Operational Global Atmospheric Prediction System (NOGAPS) is the Department of Defense's global numerical weather prediction (NWP) system. In 2000, the Naval Research Laboratory (NRL) inaugurated a collaborative project among scientists in its Space Science, Marine Meteorology, and Remote Sensing Divisions to extend NOGAPS from its current upper boundary (then at ~35 km altitude) to altitudes nearer 100 km. This has led to an Advanced-Level Physics High-Altitude (ALPHA) NOGAPS prototype, known as NOGAPS-ALPHA, that runs at NRL as a fully functional research NWP system extending to ~100 km altitude. In this talk, I'll summarize this NOGAPS-ALPHA development history, focusing on advances in both the forecast model and data assimilation components that were required to operate it at altitudes that extend to the edge of space. Some of the research highlights made possible through forecasting and data assimilation experiments with this new system will then be described. I'll conclude with some future research and development avenues that are planned for this system and its successors, including recent and imminent transitions of NOGAPS-ALPHA technology to the NOGAPS run operationally at the Fleet Numerical Meteorology and Oceanography Center.
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Anthropogenic atmospheric aerosols can modify the radiative balance (and climate) of the Earth by altering the properties and global distribution of clouds. Current climate models however cannot adequately account for many important aspects of these aerosol-cloud interactions, ultimately leading to a large uncertainty in the estimation of the magnitude of the effect of aerosols on climate. The seminar will focus on the development of physically-based parameterizations of aerosol-cloud processes for climate models that help to address some of such predictive uncertainty. The parameterizations are analytical solutions to the cloud ice and water particle nucleation problem, developed within a framework that considers the mass and energy balances associated with the freezing and droplet activation of aerosol particles. They explicitly account for the impact of cloud formation dynamics, the aerosol size and composition, mixing, and the dominant freezing mechanism (homogeneous vs. heterogeneous) on the ice crystal and droplet concentration and size distribution. Application of the new parameterizations is demonstrated in the NASA Global Modeling Initiative atmospheric and chemical and transport model to study the effect of aerosol emissions on the global distribution of ice crystal concentration, and, the effect of entrainment during cloud droplet activation on the global cloud radiative properties. The ice cloud formation framework is also used within a parcel ensemble model to understand the microphysical structure of cirrus clouds at very low temperature. The new analytical parameterizations provide an efficient, yet rigorous, microphysical link between aerosol emissions and cloud formation, contributing to the improvement of the predictive ability of atmospheric models and the understanding of the impact of human activities on climate.
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We evaluate North American carbon fluxes using a monthly global Bayesian synthesis inversion that includes well-calibrated carbon dioxide concentrations measured at continental flux towers. We employ the NASA Parameterized Chemistry Tracer Model (PCTM) for atmospheric transport and a TransCom-style inversion with sub-continental resolution. We sub-sample carbon dioxide time series at four North American flux tower sites for mid-day hours to ensure sampling of a deep, well-mixed atmospheric boundary layer. The addition of these flux tower sites to a global network reduces North American mean annual flux uncertainty for 2001-2003 by 20% to 0.4 PgC/yr compared to a network without the tower sites. Uncertainty reduction is found to be local to the regions within North America where the flux towers are located, and including the towers reduces covariances between regions within North America. We will also show sensitivity of this inversion to different background terrestrial fluxes and fossil fuel emissions maps, and the impact of increasing the density of the North American surface network.
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With the launch of the European Space Agency's Atmospheric Dynamics Mission (ADM-Aeolus) in 2011 and the call for the 3D-Winds mission in National Research Council's decadal survey, direct spaceborne measurements of vertical wind profiles are imminent via Doppler wind lidar technology. Part of the preparedness for such missions is the development of the proper data assimilation methodology for handling such observations. As active measurements, the platforms will have largely predictable lifetimes. With ADM, the lifespan of the instrument is expected to be three years. To maximize the utility of the instrument, an Observing System Simulation Experiment (OSSE) framework is being utilized to generate a realistic proxy dataset for development of the Gridpoint Statistical Interpolation (GSI) data assimilation system utilized at a number of centers through the United States including locally at the Global Modeling and Assimilation Office (GMAO). This effort will be presented, including the methodology and status of proxy data generation, validation of necessary fields in the Joint OSSE Nature Run, and the assimilation of such measurements within the GSI.
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The Irminger sea is located between southern Greenland and the Reykjanes ridge, which is south of Iceland. It is an important area of the North Atlantic ocean due to the complex and important consequences of the physical processes that occur in this region. For example, here the Arctic freshwater first meets the North Atlantic saline waters, an important contribution to the meridional overturning circulation (MOC). Detailed study of the various processes and their relationship to another is the main goal of our work. Using an eddy-resolving (~4KM horizontal resolution), regional configuration of the MITgcm ocean general circulation model, we synthesized real, observed data, over a period chosen to coincide with the cruise period of Oceanus-395, during Aug 5-- 13, 2003. Gridded, blended sea-surface-height data was taken from the AVISO mission and the sea-surface-temperature was from GHRSST. In addition, various in-situ temperature and salinity data from the cruise was also assimilated. Our 4d-VAR procedure yielded model initial conditions while minimizing model-data misfit. In this talk, we will discuss these results from our data assimilation experiments and how they could be used for hindcasting for the Irminger Sea.
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The ECMWF forecast system was not able to maintain the amplitude of an MJO event for more than a few days until the implementation of a recent version (referred to as CY32r3) which is able to maintain the amplitude of an MJO for at least 30 days. The various changes in the ECMWF model physics that led to this improvement will be discussed. A series of 46-day ensemble integrations starting on the 15th of each month from 1989 to 2008 has been completed to assess the skill of the ECMWF forecast system to predict the MJO. Results indicate that the dynamical model displays skill for up to about 20 days in predicting the evolution of the MJO. However, the MJO simulated by the model has a too slow eastward propagation and has difficulties crossing the Maritime Continent. The impact of the MJO in the Tropics and Extratropics has been evaluated in this set of hindcasts. In particular, the impact of the MJO on tropical storms is consistent with observations which explain the sig nificant skill of the ECMWF forecast system to predict weekly probabilities of tropical storms up to week 4. The representation of the MJO and tropical storms in the ECMWF seasonal forecasting system will also be discussed. At this time range, the ECMWF model displays significant skill to predict the interannual variability of tropical storms over the Atlantic.
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We assess the impact of satellite-rainfall error structure on soil moisture simulations with the NASA Catchment Land Surface Model. Specifically, the study contrasts a multi-dimensional satellite rainfall error model (SREM2D) to the standard rainfall error model used to generate an ensemble of rainfall fields as part of the Land Data Assimilation System developed at the NASA Global Modeling and Assimilation Office (NASA-LDAS). We used high-resolution (25-km / 3-hourly) satellite rainfall fields over Oklahoma (derived from the NOAA CMORPH global satellite product) and corresponding rain gauge-calibrated radar rainfall fields (considered as the reference or true rainfall). Land model simulations are evaluated in terms of rainfall and soil moisture errors. Comparisons of rainfall ensembles generated by SREM2D and NASA-LDAS against reference radar rainfall show that both rainfall error models preserve the rainfall error characteristics across a range of spatial scales. The multi-dimensional error-structure in SREM2D, however, generates rainfall replicates with higher variability that better envelope the reference rainfall than those generated by the NASA-LDAS error model. Because rain-to-soil moisture error propagation is a non-linear process, soil moisture simulations are less sensitive to the complexity of the precipitation error modeling approach. Nonetheless, perturbing satellite rainfall fields with a complex error model leads to improved spatial variability in the simulated soil moisture ensemble, which is expected to benefit soil moisture data assimilation.
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Some validation results from what we are calling version 1 of the GMAO OSSE will be presented. This uses a 13-month ECMWF model simulation as a nature run and the NCEP/GMAO GSI as the data assimilation system. The simulated observations include most types used operational during the winter of 2005-2006. Metrics examined are O-F and A-B statistics, including horizontal correlations. We obtain close agreements between corresponding results for OSSE and real observation assimilations for most observation types. Although some deficiencies remain apparent, the version-1 simulated observations can serve as a baseline for some less-demanding OSSE studies as well as a step for continuing development.
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Construction of the multi-model high-resolution seasonal prediction system is introduced in this study. A set of dynamical and statistical regional models is considered for the construction of this system. In this study, coarsely resolved atmospheric fields from the global climate model (GCM) (or reanalysis with 2.5 degree resolution) are downscaled to a fine spatial scale of ~20 km for an assessment of model fidelity for this fine-scale seasonal prediction. The model test is conducted for summer season for the southeast United States (Florida, Georgia, Alabama, South and North Carolina), where accurate prediction is challenging due to a heavy influence of small-scale subtropical (or tropical) convection. Dynamical climate models considered are the Florida State University/Center for Ocean-Atmospheric Prediction Studies Nested Regional Spectral Model (FSU/COAPS NRSM), Experimental Climate Prediction Center Regional Spectral Model (ECPC RSM), Regional Climate Model 3 (RegCM3), and Weather Research and Forecasting Model (WRF). The conceptual bases for the statistical models in this study are advanced eigentechnique with multiple regression, nonlinear canonical correlation analysis, and geo-spatial weather generator. The present talk discusses the improvement of predictability on seasonal anomaly and frequency of subseasonal extremes by this forecasting system. Specifically, capability of the ECPC RSM for adding smaller scale information that the large-scale forcing fields do not resolve is investigated to suggest the promise for the combination of GCM-RCM for seasonal prediction of summer precipitation at 20km resolution.
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The Atlantic Ocean contains a strong signal of decadal to multidecadal variability. Recent work has shown that the associated Atlantic sea surface temperature fluctuations have a significant impact on climate throughout the hemisphere, including droughts from Africa to North America, hemispheric mean temperature, and Atlantic hurricane activity. It has been hypothesized that both radiative forcing changes and internal variability contribute significantly to the observed SST fluctuations. Here we assess the degree to which we understand the causes of Atlantic decadal variability. In addition, we assess the prospects of predicting such decadal scale fluctuations and their impacts.
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Environmental monitoring using satellite and in-situ data has become a central focus of several international efforts. In particular, the EU-funded Global Monitoring for Environment and Security (GMES) initiative has funded the GEMS (Global and regional Earth-system Monitoring using Satellite and in-situ data, concluded in May 2009) and MACC (Monitoring Atmospheric Composition and Climate) projects to develop and implement its "Atmosphere Component Service". Both are coordinated by ECMWF. The main goal of these projects is to develop state-of-the-art systems for the retrospective monitoring and real-time forecasting of atmospheric composition (greenhouse gases, reactive gases and aerosol). This talk will present results from the multi-year reanalysis (2003-2009) which was completed under GEMS and the near-real time forecasts that are currently produced daily using these systems. Particular attention will be given to the aerosol component which is based on an aerosol model which is fully integrated in the ECMWF NWP model and relies on assimilation of aerosol optical depth data from MODIS. Examples such as Saharan dust transport over the Atlantic, California fires of July 2008 and the recent Sydney dust storm of September 2009 will be presented. A technical description of the aerosol assimilation system will also be provided along with recent developments and future plans.
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Reanalysis has long been considered unsuitable for estimating climate trends, because of the effects of changes in past observing systems on systematic errors in the reanalysis data. This prevailing view is based mostly on experiences with first- and second-generation reanalysis products, but a great deal of progress has been made recently in improving the 'climate quality' of reanalysis data. Ultimately the problems with estimating trends from observations are fundamental and not specific to reanalysis. Inherent uncertainties in the existing observational record can only be exposed by combining multiple sources of information, and this is exactly what reanalysis is designed to do.
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In 4D-Var, the forecast model is used to propagate the analysis increment within the assimilation window to the time of observations. Usually, this model is assumed perfect, or, at least, it is assumed that the errors due to the model can be neglected compared to other errors in the system. As most aspects of the data assimilation system have improved over the years, and as the assimilation window might become longer in the future, this assumption becomes less realistic. Weak constraints 4D-Var provides the general framework for accounting for model error. We will present two of formulations of weak constraints 4D-Var. The first formulation, which became operational at ECMWF in 2009, aims at capturing the systematic model error in the stratosphere. This is achieved through the use of a model error forcing term which is kept constant over the length of the assimilation window. Experimentation showed that the error captured by the model error term was not always model error. For example, a feedback effect between the model error term and the Jb balance term of the 4D-Var cost function in the stratosphere was noticed. Results obtained with a model error forcing term in the ECMWF operational 4D-Var system will be presented. The other formulation is more general and comprises a four dimensional state control variable. In principle, this allows for parallelism in the 4D-Var algorithm in the time dimension of the assimilation window which would be essential for computational efficiency with long assimilation windows. However, the nature of the optimization problem in this implementation of weak constraints 4D-Var is more challenging and new preconditioning techniques will have to be implemented. An overview of the expected advantages, challenges and possible approaches towards a fully weak constraints 4D-Var system will be presented.
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This presentation is concerned with the potential to improve the root-zone soil moisture in Numerical Weather Prediction (NWP) models by assimilating remotely sensed near-surface soil moisture observations. In many NWP models, including those at Meteo-France and the Australian Bureau of Meteorology, soil moisture is initialised based on errors in screen-level temperature and relative humidity forecasts. While this approach effectively improves low-level atmospheric forecasts, it often degrades the model root-zone soil moisture. An Extended Kalman Filter (EKF) capable of assimilating both near-surface soil moisture and screen-level atmospheric observations has been developed, in the hope that including the near-surface soil moisture observations will improve the model soil moisture while maintaining reasonable low-level atmospheric forecasts. The EKF is tested with the French and Australian NWP land surface schemes, using near-surface soil moisture retrieved from Advanced Microwave Scanning Radiometer (AMSR-E) C-band observations by the Free University of Amsterdam and NASA. In experiments over one year with the Australian NWP land surface scheme (MOSES) the near-surface soil moisture generated by the EKF assimilation of AMSR-E generated a better fit to in situ observations from the Murrumbidgee Monitoring Network than either of the AMSR-E data or the model alone. The assimilation also improved the fit between the model soil moisture and the in situ observations in the root-zone. Experiments assimilating the AMSR-E data into the French NWP land surface model (ISBA) over one month confirmed that while the screen-level and AMSR-E soil moisture data generate conflicting signals regarding the root-zone soil moisture, the EKF can effectively reduce the observation departures for both observations types, although these reductions are modest. Despite these generally positive findings several challenges to implementing the assimilation of near-surface soil moisture in an operational NWP setting have been identified.
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The Weather Research and Forecasting (WRF-ARW) model and its associated data assimilation (DA) system WRFDA are developed by the National Center for Atmospheric Research (NCAR) and distributed to the global scientific community. In this talk, we will briefly summarize the recent expertise acquired with advanced variational (4DVar) and variational/ensemble (hybrid) DA systems. The latest developments on the representation of background error statistics will be presented. Specific issues related to the assimilation of satellite radiances will be addressed, with a focus on observation bias correction, cloud detection and steps toward the use of cloudy radiances. We will also discuss the implementation of a diagnostic tool using an adjoint technique to calculate the impact of observations on forecast error. NCAR is leading the developments for the Air Force Weather Agency (AFWA) next generation cloud analysis system, which aims at updating hydrometeors jointly with the dynamical variables. The focus on cloud resolving DA and the decision to transition to the Gridpoint Statistical Interpolation (GSI) system open new avenues for external collaborations that will be explored.
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From a large-scale perspective the West African monsoon (WAM) can be described in terms of the annual march of the ITCZ and its associated regional circulations. On the synoptic and mesoscale, the WAM is comprised of a complex collection of wave patterns, organized weather systems and deep convection. These include synoptic systems such as African easterly waves (AEWs) and mesoscale convective systems (MCSs), the main rain-producers in the region. AEWs also initiate many of the Atlantic tropical cyclones (TCs) downstream and thus are an important part of the interactions that take place between West Africa and the tropical Atlantic. The first part of this talk will provide an analysis of the climatological annual cycle of the West African monsoon, emphasizing the coastal rainfall in Spring in addition to the more commonly emphasized summer rainfall in the summer. Particular focus is given to the potential roles played by the Atlantic cold tongue and Saharan heat low in this regard. The second part will emphasize more the nature of the AEWs. It will include discussion of three phases of the observed AEW life-cycle: (i) genesis, (ii) baroclinic development and (iii) West coast developments including tropical cyclogenesis. Some perspectives for future avenues of research will be provided.
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A hybrid variational and ensemble transform Kalman filter (ETKF) analysis method has been implemented recently for regional NWP using the WRF model. In the hybrid method, the variational framework is used to calculate the analysis increment using ensemble-based flowdependent background-error covariances. To accommodate flow-dependent ensemble-based covariances within existing variational systems, an "extended control variable method" that includes covariance localization to account for sampling error was implemented. The ensemble transform Kalman filter was used to generate ensemble forecasts. Recent studies have suggested that the hybrid systems may yield the "best of both worlds" by combining the best aspects of variational and ensemble Kalman filter (EnKF) systems. The advantages of the hybrid method will be discussed. A scope of applications of the hybrid VARETKF using WRF for various scales and data types will be discussed in the seminar. These include synoptic scale month-long experiments over North America domain, hurricane forecasts and radar assimilation for continental precipitation.
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The seminar introduces the modeling works and the achievement, which have been done at Climate Modeling Group of Seoul National University for last one year. Therefore it updates the progress for the development of new moist physics scheme, which is suitable to the high-resolution climate model of a spatial scale of the order of 10km. The convective parameterization is developed based on the bulk formula and the turbulence ensemble concept is introduced to generate the shallow convection more effectively. In the seminar, also discussed are the problems related to extending the cloud resolving model to the global domain with a grid spatial scale of 10km and the way to include the microphysics in the convectional GCMs.
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Marine boundary layer (MBL) clouds can significantly regulate the sensitivity of climate models, yet they are currently poorly simulated in GCMs. In this presentation, I will first describe the seasonal variations of physical properties of these clouds off the California coast by using measurements from several satellite programs. I will then show how simulations of the seasonal cloud properties in the NCAR Community Atmosphere Model (CAM) compare with the observations. I will finally discuss the relevance of seasonal variation of clouds as a test of cloud-climate feedback in GCMs.
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As well known, satellite observations constitute a majority of atmospheric observations today, providing an invaluable, nearly continuous flow of information about the state and evolution of the atmosphere. In operations, these data are used routinely for nowcasting and in data assimilation systems to produce initial conditions for numerical forecasts. In the former application, the satellite data are used under all weather conditions, often focusing on locations with significant weather that is characterized by presence of clouds and precipitation. In the data assimilation systems, on the other hand, majority of the satellite data are not utilized in areas that are affected by clouds and precipitation. The data assimilation systems exclude the cloud- or precipitation-affected satellite radiance observations primarily to avoid negative influences of the numerical model forecast background which traditionally has low skill in representing the cloud and precipitation properties, including their spatial and temporal distribution. Besides, simulation of cloud and precipitation affected satellite observations in the data assimilation requires application of more complex radiative transfer models and is more computationally demanding than simulation in clear sky conditions. Recent data assimilation research has demonstrated that operational global forecast models are approaching a level of skill that would allow benefits from using cloud- and precipitation- affected radiance observations (Wilkinson et al., 2008; Bendedetti and Janiskova, 2007). Similarly, data assimilation studies with a cloud-resolving research model show that the assimilation of the cloud-affected IR observations improves model simulations, suggesting potential for extending the benefits of the satellite data assimilation into high-resolution forecast models (Vukicevic et al., 2004, 2006). In this talk a summary of recent past and current research results on the assimilation of GOES Imager (Geostationary Operational Environmental Satellites) observations into a cloud resolving model using 4DVAR data assimilation approach is presented. The past studies include research on the properties of radiative transfer modeling in the cloudy atmosphere, information content of the imager observations with respect to bulk cloud properties, nonlinear 4DVAR, dynamic coupling between the clouds and environment by dynamic data assimilation approach, and benefits of high temporal and spatial resolution in the observations. In the current research several approaches for improving conditioning of the data assimilation for mixed cloud scenes are tested including use of dynamical cloud mask, limit on maximum innovation amplitude in brightness temperature , observation-based estimate of background error decorrelation length for water variables, smooting of adjoint solution and correcting of surface temperature bias in the model. The results are verified against a set of independent ground-based data which include data from AERI (Atmospheric Emitted Radiance Interferometer), MWR (Microwave Radiometer), cloud radar, and radiosonde. The numerical experiments are performed on cases of cloudy atmosphere that were associated with synoptic scale systems in south-central Great Planes in U.S.A. Overall, the results indicate that the data assimilation of the cloud affected geostationary observations is feasible. The results show improvement in the model representation of the cloudy atmosphere and consistent change in the dynamical cloud environment. The latter is achieved by 4DVAR in the current study, but similar dynamical consistency between the cloud and other analysis variables could be achieved with alternative dynamic data assimilation approaches such as Ensemble Kalman Filter. Using the dynamic cloud mask and maximizing the number of observation points by allowing large innovations in the observation space produce the most beneficial control on the conditioning of the cloudy radiance assimilation.
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In June 2009 the European Commission awarded funding to the MACC (Monitoring Atmospheric Composition and Climate) project within their seventh Framework programme as a follow-on of the GEMS (Global and regional Earth-system Monitoring using Satellite and in-situ data) project. MACC is a collaboration between the European Centre for Medium-Range Weather Forecasts (ECMWF) and many European research institutes and aims at creating a new European operational system for global monitoring of atmospheric chemistry and dynamics, and an operational system to produce improved medium-range and short-range air-chemistry forecasts through much improved exploitation of satellite data. The main task of ECMWF within the MACC project is to build a monitoring system based on the operational four-dimensional variational (4D-Var) data assimilation system. This allows the monitoring of greenhouse gases, reactive gases, and aerosols in the atmosphere using all available relevant satellite and in-situ data. These four-dimensional fields are then used by the partner institutes to study long-range transport and surface flux inventories. The system is also able to produce accurate boundary conditions for regional air-quality models. I will present an overview of the environmental monitoring system and results representing the various components of the system. Some important issues, such as the background constraint, the observation operators and the interaction with chemistry models, will be discussed as well.
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The water vapor feedback is the strongest positive feedback operating in the climate system, with the power to about double the direct warming from long-lived greenhouse gases. In our analysis, we estimate the strength of the water vapor feedback by analyzing the changes in tropospheric specific humidity during El Nino-Southern Oscillation (ENSO) cycles in climate models and in the MERRA and ERA40 reanalyses. We find a wide range in predicted feedback strengths among the models and between the models and the reanalyses, although they all predict a water vapor feedback that is strong and positive. The models and the reanalyses show a consistent relationship between the variations in the tropical surface temperature over an ENSO cycle and the radiative response to the associated changes in specific humidity. However, the feedback is defined as the ratio of the radiative response to the change in the global average temperature. Differences in extratropical temperature variations will therefore lead to different inferred feedbacks, and this is the root cause of large spread in feedbacks among the models and between the models and the reanalyses.
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Clouds and convection play an important role in climate and weather related issues over various spatial and time scales. Despite the efforts have been made, representing clouds, convections and their radiative and precipitating processes in numerical weather and regional/global climate models remains a challenge. To help resolve these issues, a CloudSat-centric, multi-parameter A-Train (e.g., CloudSat, Calipso, AIRS, AMSR, MODIS, CERES) and high-resolution ECMWF analyses (INTRIM and YOTC) data set is being developed to characterize dynamical, macro-/microphysical, precipitating and radiative processes associated with clouds and convection to evaluate and constraint the key relevant model physical parameters/processes in numerical models. In this presentation, results from the comparisons between cloud, convection, and precipitation statistics derived from using the above data set will be presented. The errors in the estimated radiative fluxes and heating associated with ignoring convective/precipitating ice (something commonly done in GCMs) relative to the standard CloudSat radiation product as well as results from a stand alone Fu-Liou radiative transfer model will also be discussed.
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Characteristics of water vapor transport were analyzed in terms of the flux-gradient (or flux-profile) function for water vapor (Phi_q) and eddy diffusivity (K_q) in the atmospheric surface layer. Roughness length of water vapor (z_0q) over land surfaces is also analyzed. Phi_q and K_q were estimated using the CASES-99 (Cooperative Atmosphere-Surface Exchange Study-1999) filed data and z_0q was analyzed using the FLOSS-II (Fluxes Over Snow Surface-II) data. It is found that, contrary to the previous assumption of scalar similarity, water vapor is transported less efficiently than heat under unstable atmosphere while being transported comparatively or even more efficiently under stable conditions in the atmospheric surface layer. Therefore, it is revealed that the water vapor is dissimilar to the heat. The degree of discrepancy between the fluxes of water vapor and heat is dependent on the atmospheric stability. Optimally determined functions for Phi_q were provided according to atmospheric stability. It is also found that z_0q is significantly smaller than those of the previous studies over bare soil and grass surfaces. Over snow surface, z_0q agrees quite well to the one of reference functions. Modified parameterizations for kB_q^(-1)[=ln(z_0m/z_0q)] for different land surface types are found to estimate the latent heat flux more accurately. Present results suggest dissimilarity between scalars (e.g., water vapor and heat) in the atmospheric surface layer. The cause of the dissimilarity is attributed to the different magnitude of buoyancy effect of the temperature and humidity fluctuation. The differences in the scalar fluxes suggest that different parameterizations for the water vapor and the heat are required for the flux estimations (e.g., Bowen ratio energy balance method, flux-profile method) and numerical models.
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Anthropogenic aerosols affect the radiative balance of the Earth-atmosphere system by scattering or absorbing sunlight, by acting as cloud condensation nuclei (CCN) or ice nuclei (IN) and thus modifying the optical properties and lifetimes of clouds, and by altering the local atmospheric thermodynamic status and thus cloud formation and dissipation. To understand the detailed mechanisms and strengths of the climate impacts of anthropogenic aerosols, an interactive aerosol-climate model has been developed based on the Community Climate System Model (CCSM) of NCAR. Its aerosol module describes the size, chemical composition, as well as the mixing states of various sulfate and carbonaceous particulate matters. Modeled aerosol properties including the strength and distribution of solar particulate absorption have been compared with satellite and surface network data. Several sets of long-term integrations have also been conducted to study the climate responses to the direct radiative forcing of various types of anthropogenic aerosols. One interesting finding of the recent study suggests that the radiative forcing of anthropogenic aerosols can cause a significant change in both quantity and distribution of tropical precipitation, ranging from Pacific and Indian to Atlantic Ocean. Because this change occurs mostly in places away from any major source regions of aerosols, it must have been implemented primarily through forced changes in the large-scale circulation. These results along with some detailed features of the model will be discussed.
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The population of northern Africa, especially in the Sahel, is vulnerable to climate change because regional economies are highly dependent on climate for agriculture and water resources. Precipitation records of the 20th and 21st c. show that the region exhibits strong decadal variations in climate, with a propensity for long-period droughts, and we know that, abrupt climate change has occurred over northern Africa on millennial time scales. These factors combine to suggest that we should pay special attention to climate change projection for this region. Regional climate change projections for northern Africa at the end of the 21st c. are presented, and the possibility for abrupt climate change in the region is discussed.
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Optimal initialization technique to obtain accurate and well-balanced initial condition and generation of fast-growing perturbation for ensemble prediction are developed and examined in coupled model framework. Ensemble Kalman Filter (EnKF) data assimilation system with a SNU coupled GCM has been successfully developed. Parallelism for the data assimilation step is achieved by regionalization of the background-error covariances that are calculated from the phase-space distribution of the ensemble. The hindcast experiments from 1980 to 1999 show that forecast skill of ensemble forecasts with EnKF is superior to that with nudging, especially during longer forecast lead months. Among several benefits of state-dependent error covariance matrix in EnKF, it helps to successfully resolve the small-scale Tropical Instability Waves (TIWs) whose activity is highly dependent on ENSO phase. However, with simple initialization methods (e.g. 3DVAR or OI methods), TIW variability in oceanic initial conditions is excessively suppressed which ruins the interaction between TIWs and climate states. Through 20-yr hindcast experiments with and without TIWs, it is shown that nonlinear relationship between TIWs and ENSO is realistically simulated with state-dependent TIWs, which leads realistic simulation of the El Nino-La Nina asymmetry. It is shown that correlation improvement of simulated NINO3 index is over 0.1 at 4-month lead time. In addition, new initialization method to reduce the forecast errors by assimilating prospective observations are developed to supply the deficiency of conventional EnKF that only observations at analysis time can be used,. The new EnKF scheme is examined with CZ-SPEEDY hybrid coupled model within perfect model context. The analysis and forecast experiments shows that new EnKF scheme successfully cancels out the analysis errors at the target time, therefore, seasonal prediction skill using new EnKF scheme is superior to that using conventional EnKF scheme. To reduce the extra computation and initial shock by adding initial perturbations to initial states, a method for selecting optimal initial perturbations is developed within the initialization with EnKF. Among the initial conditions generated by EnKF, ensemble members with fast growing perturbations are selected to optimize the ENSO seasonal forecast skills. Seasonal forecast experiments show that the forecast skills with selected ensemble members are significantly improved than that with other ensemble members up to 1-year lead forecasts. It is also shown that the forecast skills are significantly improved during ENSO onset and decaying phase, which are most unpredictable ENSO periods.
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Composites based on observations and model outputs from the CLIVAR drought experiments were used to examine the decadal modulation of the impact of El Nino Southern Oscillation (ENSO) on the hydro-climate over the United States. The impact of ENSO on drought is not stationary. It is modulated both by the trends and the Atlantic multi -decadal Oscillations (AMO). For warm (cold) ENSO winter and spring, composites show warming (cooling) over the Northwest and cooling (warming) over the Southeast and the Gulf States. However, the patterns and magnitudes of composites change over time. In the recent periods, cooling over the South has been weakening. For precipitation, the largest ENSO impact occurs in winter. While the ENSO impact strengthened over the Southwest, California and the Gulf states for the recent periods, the impact over the Ohio Valley reached a peak in 1930-1970 and has been weakening in recent years. The direct influence of the AMO on drought is small. The major influence of the AMO is to modulate the impact of ENSO on drought. The influence is large when the SSTAs in the tropical Pacific and in the North Atlantic are opposite in phase. A cold (warm) event in a positive (negative) AMO phase amplifies the impact of the cold (warm) ENSO on drought. The ENSO influence on drought is much weaker when the sea surface temperature anomalies in the tropical Pacific and in the North Atlantic are in phase.
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The currently available theoretical and observational evidence for a latitudinal structure of thermocline vertical diffusivity is synthesized and included in a state of the art coupled climate model. Compared to the standard background value of 0.1 cm2/s, the simulations with the latitudinal structure show only little change in the meridional overturning circulation or northward heat transport. However, two regions are identified which are sensitive to the value of vertical diffusivity: the equatorial band, where only small changes in sea surface temperature lead to precipitation responses with basinwide teleconnections; and the North Atlantic where diffusivity affects the spiciness of Labrador Sea water and subsequently the Gulf Stream path.
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We briefly discuss the Local Ensemble Transform Kalman Filter (LETKF, Hunt et al., 2007), and several extensions recently developed at UMD that extend the usefulness of EnKF, and in particular the 4D-LETKF. Among them: A "no-cost smoother" based on applying the ensemble background weights derived at analysis time--but valid throughout the assimilation window--to the start of the window. "Outer loop" and "Running in place" algorithms that take advantage of the no-cost smoother to handle nonlinearities, non-Gaussianities and spin-up. A coarse resolution LETKF that interpolates weights without degrading the analysis. A simple method to estimate and correct model errors in model space and its application to data assimilation. A method to estimate simultaneously the background error inflation and observation errors. Forecast "adjoint sensitivity" without adjoint in EnKF, and its extension to longer forecasts. Analysis sensitivity to observations and exact cross-validations. The seminar will conclude with a discussion of the potential of hybrid systems.
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Tropospheric ozone is a major atmospheric pollutant and a greenhouse gas. Its distribution represents a complex interplay between transport and chemistry. The wealth of recent satellite observations of trace gases in the troposphere offer a unique opportunity to better understand the chemical and transport processes regulating tropospheric ozone and its precursors. Chemical data assimilation provides a powerful means of exploiting these data. We have integrated data from the Tropospheric Emission Spectrometer (TES), the Ozone Monitoring Instrument (OMI), and the SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) satellite instruments into the GEOS-Chem global chemical transport model to assess the constraints that these data provide on the processes influencing the distribution of tropospheric ozone, with a particular focus on the summertime ozone distribution in the extratropical northern hemisphere.
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Model physics (e.g., cloud microphysics, convection, and radiation) schemes represent an important source of uncertainty in numerical models that range in scale from large eddy simulation to general circulation models. Much of this uncertainty is associated with specification of parameters that control the rates and/or characteristics of physical processes. In contrast to errors in forecast initial conditions, the characteristics of model physics uncertainty are not well understood, hence model physics error is not included in most operational data assimilation systems. As ensembles of simulations are increasingly used in data assimilation and probabilistic forecasting, it is desirable to perturb both initial conditions and model physics parameters. To do so properly requires knowledge of which parameters have the greatest effect on model results, as well as the characteristics of the relationship between model output and changes to parameters. In this paper, we explore the functional relationship between model parameters and observations using a Markov chain Monte Carlo (MCMC) algorithm. We examine bulk cloud microphysics and radiation packages from a cloud resolving model that are similar to schemes used in modern regional and general circulation models, and demonstrate how the joint probability distribution returned from MCMC can be used to 1) map the functional relationship between changes in model physics parameters and changes in model output, 2) identify which parameters have the most significant effect on various model output fields, 3) describe the nature of nonlinearity in the parameter-state relationship, and 4) explore how changes in the characteristics of observations affect the model state. The results of the MCMC-based inversion shed light on the reasons behind the loss in �parameter identifiability� noted in previous parameter estimation studies, and suggest ways in which nonuniqueness can be avoided in the construction of an ensemble based data assimilation scheme that includes model physics parameters as control variables.
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The Atmospheric Infrared Sounder (AIRS) onboard the NASA Aqua satellite and the Infrared Atmospheric Sounding Interferometer (IASI) onboard the METOP-A satellite both are cutting-edge spectrometers for 3-dimensional mapping of air and surface temperature, moisture, greenhouse gases, and cloud properties. With several kilo-channel measurements in the infrared spectral coverage, AIRS and IASI has a spectral resolution more than 100 times greater than previous IR sounders, and provides more accurate information to improve weather forecasting and support climate research. To enjoy the greatest advantage of such high resolution infrared observations, high-performance-computing radiative transfer models for AIRS and IASI are needed to facilitate more effective applications in physical retrievals and data assimilation. Approximately every six months there is a doubling in the speed of Graphics Processing Units (GPUs). Currently, the flagship NVIDIA GPU has 240 computing cores, compared to the best INTEL CPU with just 6 cores. The computing performance of the GPU has significantly outpaced its CPU counterpart, with a theoretical peak performance of 1 TFlops per GPU in single precision and 345 GFlops per GPU in double precision. The combined features of general-purpose supercomputing, high parallelism, high memory bandwidth (102 GB per GPU), low cost, and compact size are what make a GPU-based desktop computer an appealing alternative to a massively parallel system made up of commodity CPUs (e.g. Beowulf clusters). This paper presents our effort to develop the GPU-based high-performance AIRS/IASI radiative transfer model running on NVIDIA GPUs via CUDA (Compute Unified Device Architecture), the compute engine in NVIDIA GPUs for massively multi-threaded parallel computation. Our GPU implementation of the forward model is tested on a low-cost (~$8,000) NVIDIA S1070 personal supercomputer with 4 Tesla GPUs (total 960 cores) delivering 4 TFlops peak performance. The result is compared with the native INTEL multi-core CPU implementation to show the significant speed-ups of computing the AIRS/IASI radiance spectrum using a GPU-based system. This is our first step towards the development of a GPU-based high-performance full-spectrum AIRS/IASI physical retrieval system for product applications and data assimilation.
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The variational data assimilation problem can be solved in either its primal (3D/4D-Var) or dual form (3D/4D-PSAS). As shown in El Akkraoui et al. (2008), both methods are equivalent at convergence but the dual method exhibits a spurious behavior at the beginning of the minimization which leads to less probable states than the background state. This is a serious concern when using the dual method in operational implementations when only a finite number of iterations can be afforded. Two minimization algorithms are examined: the Conjugate Gradient (CG) and the Minimum Residual (Minres) methods. The Minres algorithm insures a monotonic decrease of the gradient norm and when applied to the dual problem, it also leads to a monotonic decrease of the primal cost function. Moreover, it is shown that a new termination criteria, based on the error norm in model space, can be used in the dual case to achieve the same accuracy in the analysis state when only a finite number of iterations are completed. This is of great importance for an implementation of the dual form of a weak-constraint 4D-Var. This will be discussed in the presentation and preliminary results will be presented.
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Climate and weather prediction models are still quite inaccurate in representing clouds, turbulence, convection and the interaction between the ocean and the atmosphere. The problem is that these processes can occur in a variety of scales, from the planetary scale to very small scales that cannot be represented explicitly in any atmospheric model. A major challenge in climate and weather prediction is in how to improve the representation of these sub-grid scale physical processes: the parameterization problem. This talk will focus on two major issues in particular: (i) how to represent the sub-grid scale turbulent motions and (ii) how to represent clouds. Recent model results from an international evaluation effort (the GCSS Pacific Crossection Intercomparison - GPCI) will be presented in order to highlight major model deficiencies. New approaches for cloud parameterization that make use of probability density functions (pdfs) and unified parameterizations for turbulence and convection involving optimal combinations of Eddy-Diffusivity and Mass-Flux methods will be discussed in detail.
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The ocean plays a major role in regulating the atmospheric greenhouse gas concentrations produced by natural and anthropogenic processes. The oceanic drawdown/outgassing of CO2 is determined by the solubility pump, i.e. physical ocean processes (air-sea interaction, convection and the thermohaline circulation) and by the biological pump, i.e. the carbon removal from the atmosphere by the sea organisms through photosynthesis and respiration, its transport to the deep ocean and its sedimentation there. Changes in the atmospheric concentrations of CO2 due to natural or anthropogenic factors result in changes in the behavior of these pumps which in turn further adjust (positively or negatively) the atmospheric concentrations of CO2. Such feedbacks involve changes to the oceanic biodiversity, acidification and the ocean surface albedo. The correct estimation of the sign and magnitude of carbon climate feedbacks is featured as a major unresolved task in the upcoming IPCC AR5 report. The ocean carbon modeling effort at NASA-GISS combines GISS and GSFC modeling components in order to simulate the magnitude of the ocean carbon sink and the associated climate feedbacks, as well as its behavior under different climate change scenarios. In the current phase, the primary objective is to incorporate the NASA Ocean Biogeochemical Model (NBOM) developed at GSFC by Watson Gregg into the two operational NASA-GISS ocean models, the Russell ocean model and HYCOM, which employ different geometry and vertical coordinate systems, and to couple the ocean-carbon modules to the latest modelE atmospheric component. The fully coupled systems are run to examine ocean carbon feedbacks both in the context of AR5 and of the NASA biogeochemistry program. Preliminary results indicate that to first order, the fidelity of the simulated ocean carbon cycle depends on the realism of the background physical model. The model agrees with observations with regards to surface partial CO2 pressure and the air-sea flux, the surface and vertical distributions of DIC as well the nutrient concentrations. HYCOM presents a better depiction of ENSO and southern ocean ice coverage variability whereas the Russell ocean model has more realistic global SST. A problematic region common in both ocean models remains the North-East Pacific region with intense heat losses to the atmosphere which are not supported by observations. Surface albedo changes due to real chlorophyll distributions are small but may prove regionally important. Simulations with interactive alkalinity show that the current OCMIP scenarios may be overestimating the impacts on the global ocean.
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Most data assimilation algorithms are based on classical statistical methods that adopt pixel-oriented descriptions of natural phenomena. While these methods are convenient they can have difficulty capturing the distinctive spatial and temporal features of special interest in geoscience applications. Such features include storm systems, ocean currents, algae blooms, geological formations, and wildfires. The problem is that the spatial structure that is clearly evident in nature may not be adequately conveyed through the means and covariances of large pixel-oriented state vectors. In this talk we consider how methods from computer vision and machine learning might be applied to feature-based data assimilation problems. These methods typically adopt a Bayesian perspective that focuses on the population of possible features rather than the population of possible state vectors. The objective is to derive an ensemble of conditional features that are samples from the posterior Bayesian distribution (i.e. incorporate both observations and prior information). Prior information is conveyed by an ensemble of prior features generated from training images. These images may be postulated, derived from observations, or obtained from physically-based stochastic models. Posterior features can be generated with Monte Carlo sampling techniques that make relatively few assumptions but are currently computationally infeasible for large problems. The success of a feature-based data assimilation will depend on our ability to 1) characterize the distinctive properties of natural features in feature spaces that are significantly smaller than the original pixel-based space (e.g. using image compression techniques), 2) generate candidate samples (proposals) that incorporate measurement information while remaining physically realistic (i.e. consistent with the training image), 3) determine the prior probability and likelihood of candidate features (i.e. define probability measures over feature spaces) , 4) develop computationally efficient reduced-order models that make it possible to generate and test a large number of candidate features. In this talk we illustrate concepts and assess some of the major research challenges and opportunities posed by a feature-based perspective.
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We analyze the surface energy balance and the estimation of fluxes of heat and moisture from land to the atmosphere. We introduce a variational data assimilation scheme that use sequences of radiometric surface temperature measurements to estimate both surface boundary effects as well as moisture-related surface control on the partitioning between turbulent heat fluxes. The objective is to develop a methodology based on remote sensing data that will enable mapping of the energy balance components over extended areas. The surface moisture control on evaporation is captured by the dimensionless evaporative fraction (ratio of latent heat flux to the sum of the turbulent fluxes) which is nearly constant for near peak radiation hours on days without precipitation. The parameter capturing the turbulent transfer characteristics (bulk scalar turbulent transfer coefficient) includes the impacts of surface roughness effects and atmospheric stability conditions. The approach is tested over the FIFE field experiment site (Kansas, USA, 1987 and 1988). It is shown that sequential radiometric surface temperature data contain useful information on the partitioning of available surface energy and may even be used to infer some key characteristics of surface turbulent transfer. The feasibility of extending the land data assimilation to use only sparse samples of radiometric surface temperature measurements (corresponding to overpass times of currently operational satellites) is demonstrated through an observing system simulation experiment. The data assimilation model is applied to the Southern Great Plains 1997 (SGP97) field experiment, using land surface temperature maps obtained from three satellites, namely, Advanced Very High Resolution Radiometer (AVHRR), the Special Sensor Microwave/Imager (SSM/I) and Geostationary Operational Environment Satellite (GOES). The application over the SGP domain gives reasonable estimates of surface fluxes, evaporative fraction and roughness-related parameters, confirming the potential of the land data assimilation scheme as an operational tool for the monitoring of surface energy balance and fluxes.
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In order to successfully validate and account for errors in remotely sensed estimates of near-surface soil water content, corresponding ground-based measurements must be accurate and representative. Complicating design of optimal sampling schemes for this purpose is the scale mismatch of the ground-based (point) measurements and the remotely sensed (areal average) footprint, and the fact that soil moisture patterns vary in both space and time. Our work attempts to identify underlying and time-invariant patterns in repeated soil moisture observations across and within successive seasons, and to characterize the drivers of those patterns. We are using traditional techniques like the Vachaud temporal stability analysis, along with emerging approaches like genetic algorithms and self-organizing maps. The overall goal is a methodology to understand the evolution of near-surface soil moisture patterns in a way that informs design of ground-based field campaigns of soil moisture monitoring. Preliminary results indicate that across-season variability is high, but that common patterns do exist and are to some extent predictable.
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Atmospheric deposition is a major source of mercury to aquatic and terrestrial ecosystems. Source attribution for mercury deposition is complicated by the presence of both natural and anthropogenic sources of mercury, and the cycling of mercury between land, ocean, and atmosphere. This study uses the GEOS-Chem global tropospheric chemistry model to quantify source contributions to mercury deposition and the role of long-range transport. The model is compared to observations to examine the consistency of a priori emission estimates with observed concentrations. This method provides improved constraints on modern mercury emissions from Asia and historic emissions from gold mining, both of which have large uncertainties.
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Ocean models commonly used as components of climate models, or coupled to models of ocean ecology, are implemented with resolution too coarse to resolve western boundary currents or eddies. Thus, signals present in observed data resulting from these phenomena cannot be usefully assimilated. The unmodeled part of the variability contributes to the model-data misfits and must be treated as error. This unmodeled variability is referred to as "representation error," since the physics of the model are inadequate to represent it. A method is proposed for estimation of the statistics of representation error, and results are presented to show that, at least in one case, our statistics are consistent with observation.
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Coupled climate model simulations reveal that a dipole like SST pattern and southward displacement of the ITCZ emerges in response to a slow-down of the Atlantic Meridional Overturning Circulation (AMOC). Using a 2-1/2-layer reduced gravity ocean model, we conducted a systematic investigation into oceanic processes controlling tropical Atlantic sea-surface temperature (SST) response to AMOC changes by varying the strength of northward mass transport at the open boundaries. It is found that a prominent equatorial warming occurs when the AMOC is weakened below a threshold value. A dynamic mechanism is proposed to explain how the AMOC change can affect the SST variability in the tropical Atlantic sector. The relative importance of oceanic versus atmospheric processes on AMOC-induced tropical Atlantic variability/change is further investigated within a framework of regional coupled model.
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Present-day shortcomings in the representation of clouds in general circulation models (GCMs) lead to errors in weather and climate forecasts as well as account for a source of uncertainty in climate change projections. An ongoing challenge in rectifying these shortcomings has been the availability of adequate, high-quality, global observations that discriminate and provide vertical structure of liquid and ice clouds, and related precipitating hydrometeors. In addition, the inadequacy of the modeled physics and the often-disjointed nature between model representation and the characteristics of the retrieved/observed values have hampered GCM development and validation efforts from making effective use of the measurements that have been available. Thus, even though parameterizations in GCMs accounting for cloud ice and liquid processes have, in some cases, become more sophisticated in recent years, this development has largely occurred independently of the global scale measurements. With the relatively recent addition of satellite- derived products (e.g., CloudSat, MLS, CALIPSO), there are now considerably more resources with new and unique capabilities to evaluate GCMs. In this presentation, we illustrate the shortcomings evident in model representations of cloud ice and liquid through a comparison of the simulations assessed in the IPCC Fourth Assessment Report, briefly discuss the range of global observational resources that are available, and describe the essential components of the model parameterizations that characterize their ice and liquid "clouds" and related fields. Using this information as background, we discuss some of the main considerations and cautions that must be taken into account in making model-data comparisons related to clouds from these satellite fields, illustrate present progress and uncertainties in applying these data -- specifically judiciously filtered versions CloudSat - to model diagnosis, and finally discuss a number of remaining questions and suggestions for pathways forward.
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Biophysical responses of the upper ocean to hurricanes in the Gulf of Mexico were examined using satellite observations and model simulations. It was important that both satellite observations and model simulations were used, since satellite sensors were sensitive to cloud interference, heavy rainfall, and only provided measurements near the ocean surface during hurricane periods. This study utilized 1/25 degree nested HYCOM simulations, 1/20 degree biophysical model simulations, and various satellite observations including QuikSCAT scatterometer winds, SeaWiFS and MODIS chlorophyll-a concentrations, and AVHRR and TMI sea surface temperatures. Such data were used to (1) assess the ocean surface response to Hurricanes Katrina, Rita, and Wilma of 2005 in the Gulf of Mexico, (2) examine the evolving three-dimensional (surface and subsurface) ocean response to Hurricane Katrina, and (3) analyze ecosystem dynamics, plankton biomass, and plankton distribution during Hurricane Katrina. Satellite observations of biophysical responses associated with Hurricanes Katrina, Rita, and Wilma of 2005 illustrated sea surface temperature changes of 6-7 degree C, 4-5 degree C, and 5-6 degree C, and chlorophyll-a enhancement of 3 mg m-3, 2 mg m-3, and 4 mg m-3. The degree and orientation of the responses exhibited were greatly affected by the oceanic processes that occurred within the Gulf of Mexico, as well as the translation speed of each hurricane. Satellite-detected surface responses associated with Hurricane Katrina occurred within a region from 23.5-25.5N and 85-83W. Analysis of model surface and subsurface dynamics in this region revealed strong upwelling/downwelling, wind-driven currents dominating the surface circulation, and near-inertial oscillations following Hurricane Katrina. The storm generated sea surface temperature cooling of 3-4 degree C and salinity freshening of 0.1-0.2. Analysis of heat-budget terms in the mixed layer indicated that wind-driven mixing dominated net upper-ocean cooling during hurricane passage, whereas at the mixed layer base temperature changes were largely due to vertical advection. Biophysical model simulations revealed that large phytoplankton were most responsive to hurricane-induced turbulent mixing and nutrient injection, with increases in biomass along the hurricane track. Small phytoplankton, microzooplankton, and mesozooplankton biomass primarily shifted in location and increased in spatial extent as a result of Hurricane Katrina. Hurricane passage disrupted the distribution of plankton biomass associated with mesoscale eddies.
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The nearby planet Mars represents an actual foreign climate system, yet Mars has a climate that is similar to Earth's in many ways. The Martian climate system has proven feasible to observe and model, and thus provides a rare opportunity for validation within the study of alternative climates. Earth and Mars have similar rotation rates and, somewhat coincidentally, similar atmospheric dynamical length scales (i.e., deformation radius, scale height). These factors have fostered a tradition of planetary atmospheric scientists borrowing and adapting models from their terrestrial counterparts. We have attempted to further borrow and adapt a data assimilation system - an ensemble Kalman filter - for use with a Martian GCM. The available observations appropriate for data assimilation are from remote sensing platforms in sun-synchronous orbits. These data sets are very sparse compared to the observational coverage available on Earth. After providing a brief introduction and overview to the Martian atmosphere and its existing observational record, we will report on our progress directly assimilating radiances and the challenges encountered.
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River not only plays a significant role to form a part of hydrological circulation on the Earth linking continents and oceans, but also it is the major part of available renewable freshwater resource. Although the flux of it (discharge) has received much attention, its storage does not. Most of previous studies in terrestrial water storage estimations such as GRACE applications implicitly assumed that soil moisture and snow water are the only major TWS components. In the present study, we investigated the temporal variation of three major TWS components, i.e., soil moisture, snow water, and river storage by land surface modeling approach. The role of river storage in total terrestrial water storage (TWS) variation was evaluated in 29 river basins globally. We quantified the contributions of major water storage components, and the GRACE data were used to validate the simulated total TWS variation. It is found that the simulated TWS variations are more accurate when river is taken into account for each target basin except in dry regions. Rivers take different roles in various climatic regions and are clearly classified through the indices devised here. River storage explains up to 73% of total TWS variation at Amazon, while it is negligible in dry basins. It also acts as a "buffer" between the signals of snow water and soil moisture seasonal variations in snow dominant basins. River is thus an important storage component of TWS for a major basin, and neglecting it will lead to insufficient amplitude and mismatch in TWS seasonal variation.
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The Madden-Julian Oscillation (MJO) remains an embarrassment to global atmospheric models: well resolved yet poorly simulated, presumably due to physical process parameterization shortcomings. MJO dynamics are delicate enough (with multiple processes including background state effects) that realistic modeling is the only hope for a true and robust "understanding". MERRA is a new opportunity: assimilation of observations keeps the model's state realistic, while access to analysis tendencies data sets permits diagnosis of model physics errors. Hypotheses about these errors, testable through re-reanalysis of selected time periods with adjusted physics, may guide model development usefully.
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A successful GCM simulation of the location and intensity of the ITCZ is vital to a successful simulations of tropical climatic events such as El Nino, of monsoons, and of realistic hurricane tracks. Although there has been some recent progress toward achieving such a simulation, this progress has been obtained through empirical means. As such, there is a need to understand why these empirical solutions are highly helpful. In this seminar, the speaker will present his contribution to such an understanding. The speaker will first review the basic mechanisms he has found that determine the latitudinal location of the ITCZ. (A comparison of the speaker's ITCZ theory with the previous CISK-based ITCZ theory will be presented.) The concepts behind these mechanisms will then be used to explain why a successful ITCZ simulation in GCMs is so elusive and how systematic errors, including the well-known double-ITCZ bias, arise. This new explanation provides some insight into the empirical results mentioned above. If time permits, an unrelated topic-how to better present the wavenumber-frequency spectrum of tropical large-scale waves-will be presented.
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Climate change and the threats of extremes to human life and natural ecosystems constitute a fundamental concern. Reliable regional climate change projection sufficient for the application to impact assessment and adaptation studies are increasingly required by the policy community.

In the talk, an attempt at dynamical downscaling of future hydrologic projection under global climate change in Asia is addressed. We conducted present and future Asian regional climate simulations which were nested in the results of Atmospheric General Circulation Model (AGCM) experiments. The regional climate model could capture the general simulated features of the AGCM. Also, some regional phenomena such as orographic precipitation, which did not appear in the outcome of the AGCM simulation, were successfully produced. As a result of the changes in the synoptic flow patterns and precipitation under global warming, the increases of annual mean precipitation and surface runoff were projected in many regions of Asia. However, both the positive and negative changes of seasonal surface runoff were projected in some regions which will increase the flood risk and cause a mismatch between water demand and water availability in the agricultural season.

In Japan, to add spatial resolution to accurately assess regional climate change impacts, the climate research project (S-5, "Getting a feel for climate change") has started (FY2007-2011). Dr. Dairaku would like to introduce the downscaling project in Japan and a part of our preliminary verification of simulations using two regional climate models with 20km horizontal grid spacing which used Japanese 25-year ReAnalysis (JRA-25) as lateral boundary conditions.

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Japanese GOSAT (Greenhouse gases Observing SATellite) was launched on January 23, 2009 and measurements will begin in late March. One instrument on GOSAT, the Thermal And Near infrared Sensor for carbon Observation Fourier-Transform Spectrometer (TANSO-FTS) will be used to derive columns and profiles of CO2 and CH4. It measures reflected solar radiation in three narrow bands (0.76, 1.6, and 2 micron) and thermal emissions from the ground and atmosphere in a wide band (5.5-14.3 micron), with 0.2 cm^-1 spectral resolution.

The a priori states for CO₂ and CH₄ retrievals will be obtained from simulations with the NIES TM (National Institute for Environmental Studies Transport Model) with forecast meteorological parameters and climatological flux data. The error covariance matrices are also derived from the NIES TM. Model-simulated CO₂ distributions often have biases. For example, the simulated amplitude of the seasonal cycle in Northern mid-latitudes tends to be smaller than the observed amplitude (Yang et al., 2007). However, the discrepancy between model and observation is not fully understood. The effort of the present study is to reduce the uncertainty of NIES TM though the comparison with the other simulated data and observational data.

I will first show the covariance matrices for CO2 and CH4 derived from NIES TM for the actual operational retrieval algorithm. Next I will show comparisons of CO2 between GEOS-5 and NIES05. The CO2 time series at each grid point in 2002 and 2003 are simulated in both models using the protocol of TransCom continuous experiment (TransCom04). Some climatological fluxes of CO2 (e.g. Fossil98, Takahashi02 and CASA) are imposed in the simulations. The seasonal and diurnal cycles of both models show similar features and both have smaller variability than the ground-based observations. It is found that vertical transport processes are quite different between GEOS-5 and NIES05, especially in the convective regions, due mainly to the different convective schemes. These differences have a clear signature in the CO2 covariance matrices computed using the two models.

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Stratospheric-tropospheric coupling in the polar region, through the vertical propagation of planetary waves, is well known as a cause of stratospheric sudden warmings. For a long time it is has been known that the high-latitude sinking motion produces warming in the polar region, while cooling occurs in the tropical stratosphere by a compensating upward motion. What is not well known is that whether such a stratospheric meridional circulation or Brewer-Dobson circulation change can further affect equatorial troposphere. Here we will show some results of our work suggesting a measurable impact of the stratospheric circulation change on the equatorial convective activity.

A case study of the unique major warming of the southern hemisphere in September 2002 revealed that convective activity in the equatorial southern hemisphere (SH) increased following the stratospheric warming, while that in the northern hemisphere (NH) was suppressed. A composite analysis of mid-winter sudden warmings in the NH also showed an impact on the tropical convection. The result of a preliminary general circulation model forecast study of the sudden warming in December 2001 also supports the notion of an impact on the tropical convection. For further understanding, it is necessary to investigate how the modification of cloud processes takes place in the lower stratosphere/upper troposphere region.

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Quantitative assessments of large-scale precipitation over the world's oceanic regions are problematic, particularly for significant regions of the data-sparse Southern Hemisphere. Available data sets are based on the assimilation of land-based measurements, satellite radiance values, numerical weather forecast models, or some combination of the three. In this study we examine several products that cover most or all of the satellite era 1979-2007 over the Southern Ocean and surrounding mid-latitudes to 45S. These include CMAP, the NCEP Reanalysis II, ERA-40, GPCP version 2, and the Japanese Re-analysis. Averaged fields from these data show large discrepancies in the mean spatial depiction and the annual cycle. Comparisons with unique in situ snowfall measurements are presented. The available record of oceanographic measurements in the Ross Sea indicates that salinity below 200 m in the Ross Sea has decreased by 0.03 per decade since 1958, with the highest (lowest) values in 1967 (2000). The fields examined here suggest that precipitation is likely not providing a directly influence on oceanic freshening in coastal seas adjacent to Antarctica. The salinity anomaly is consistent with increasing attrition of continental ice. Potential contributions to oceanic freshening from changes in sea ice extent, transport, and thickness are discussed.

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A high-resolution global climate model with a horizontal resolution of about 20 km has been developed and utilized to simulate seasonal mean anomalies, Madden and Julian oscillation (MJO), synoptic transients, Typhoon, and diurnal cycle. The six ensemble simulations for 1997 (El-Nino) and 1999 (La-Nina) summers show that the tropical anomalies with time scales longer than few weeks such as ENSO anomalies and MJO are not much affected by the change of horizontal resolution, indicating that those anomalies are mainly controlled by the physics. However, shorter-term variations appear to be very much affected by the resolution. The transients are well represented by the high-resolution model and the streamfunction tendency due to transient vorticity flux divergence is greatly improved by increasing the resolution, which affects the seasonal mean anomalies in the extratropics. The benefit of high resolution model is mainly in the simulation of extreme events such as Typhoon. The typhoon structure and its statistics within the seasons are reproduced very well by the high-resolution model. In particular, the ENSO differences in the location of Typhoon generation over the western Pacific are remarkably well reproduced. Also, preliminary results of high-resolution ocean-atmosphere coupled model will be presented in the seminar.
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12/22/2008

12/12/2008

12/09/2008

08/05/2008

07/28/2008

07/24/2008

07/16/2008

04/10/2008

04/07/2008

03/31/2008

03/10/2008

03/10/2008

03/03/2008

02/25/2008

12/06/2007

06/4/2007

01/19/2007

11/28/2006

Xiaohong Liu

11/02/2006

Brian Mapes

10/05/2006

Dara Entekhabi

09/07/2006

Rui Xin Huang

07/20/2006

Shaoqing Zhang

05/11/2006

Roger Saunders