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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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Mr. Kuhl will present results on the assimilation of ozone concentration observations from the Solar Backscatter Ultraviolet (SBUV/2) instrument using the Local Ensemble Transform Kalman Filter (LETKF) data assimilation technique and the 2004 operational version of the NCEP GFS at reduced horizontal resolution of T62 and reduced vertical resolution of 28 levels. The SBUV/2 instrument, which is flown on the NOAA 16 and 17 satellites, provides observations of the global ozone concentration distributions in vertical profiles derived from the ratio of the observed backscattered Earth spectral radiance to the incoming solar spectral irradiance. We assimilate the ozone observations multivarietly with the other meteorological observations; thus, the assimilation of ozone observations affects the analysis of meteorological parameters through the flow-dependent background error covariance matrix of the LETKF. We have modified the NCEP GFS model to incorporate three-dimensional ozone production-loss coefficients from the Real-time Air Quality Modeling System (RAQMS) unified Stratosphere-Troposphere chemical modeling system. He will show results with observations of the real atmosphere using two different ozone observation operator techniques one which accounts for the a priori information in the retrieval and another which does not.
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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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Abstract:

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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Abstract:

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