GEOS Systems

The GMAO conducts modeling and assimilation activities that support NASA's current suite of Earth Observation missions, use data gathered by past and current missions, and help plan for future observing systems. The modeling and analysis components needed for this work are built around a modular system that allows for application-specific system configurations that are designed for different purposes. This modularity also allows for efficient use of NASA’s High-Performance Computing resources. GEOS developments, including the funding of a broad science team, are supported by NASA's Modeling, Analysis and Prediction program

The GEOS Earth System Model

The Goddard Earth Observing System (GEOS) model consists of a group of model components that can be connected in a flexible manner in order to address questions related to different aspects of Earth Science. GEOS model development adheres to the modular architecture of the Earth System Modeling Framework (ESMF). This modular structure simplifies the management of both the model code and the model configurations, to enable progress with forefront applications of coupled processes in the Earth System. GMAO’s work with GEOS spans a large range of space and time scales and encompasses the representation of dynamical, physical, chemical and biological processes.

There are several widely used configurations of the GEOS system:

The Atmospheric General Circulation Model (AGCM): this configuration uses the predictive model components for the atmosphere and land, and is integrated using specified information about the oceanic state (surface temperature and sea ice concentration) as a time-dependent boundary condition. The GEOS AGCM is constructed in the GMAO, using components developed both locally and by collaborators. Many applications of the AGCM include a representation of atmospheric aerosols.

The Ocean General Circulation Model (OGCM): this configuration uses the predictive model components for the ocean and sea ice, using a specified atmospheric state to force the ocean.

The coupled Atmosphere-Ocean General Circulation Model (AOGCM): this configuration is a fully coupled model, with predictive capabilities for atmosphere, land, ocean, and ice states.

The Chemistry-Climate Model (CCM): this configuration includes an interactive chemistry component, allowing for feedbacks between chemical composition and the circulation. Originally used in context of an atmospheric computation with prescribed oceanic states, some recent applications have begun to use a coupled configuration (the AOCCM).

The Chemistry-Transport Model (CTM): this configuration incorporates the ability to compute chemical reactions in a predictive framework, using specified meteorological conditions (winds, temperatures, moisture, cloud distributions and in-cloud transport) that are pre-computed. This configuration enables the impacts of chemical processes on constituent distributions to be examined without allowing for feedbacks on the circulation.

The GEOS model components have been developed using both local resources and through extensive collaborations with a team of partners. The Catchment Land Model was conceived and developed in the GMAO. It differs from traditional layer-based land-surface models by including an explicit treatment of the spatial variation within each hydrological catchment (or computational element) of the soil water and water table depth, as well as its effect on runoff and evaporation. It also includes a multi-layer global snow model. The Modular Ocean Model, which is the basis of ocean analyses and the coupled modeling system, was developed at NOAA’s Geophysical Fluid Dynamics Laboratory. It represents the oceanic circulation and is also used in conjunction with a sea-ice model. The GEOS AGCM has been developed and tuned in the GMAO, but includes some important components from collaborating groups.

A unique feature of GEOS is the ability to run the AGCM in "replay" model, in which existing meteorological analyses are used to constrain the flow to a specific state. This can be achieved in one of two ways. The first involves overwriting the predicted meteorological fields (surface pressure, temperature winds, and moisture) every few hours with an existing analysis. The second approach is to compute additional tendency terms, known as Incremental Analysis Updates (IAUs) that are used to force the model to the analyzed state.

Data Assimilation

GMAO’s core activities are based around NASA’s Earth Observing systems, including the support of current missions and the use of space-based data in Earth System analysis. GMAO develops and improves the capabilities to use NASA’s data of the atmosphere, land, ocean and cryosphere in combination with other observations. Data assimilation is presently performed separately for each Earth System component, but techniques are in development to build coupled analyses that promote the enhancement of processes at the interfaces in the climate system.

The current analysis and assimilation types used in the GMAO are:

The Atmospheric Data Assimilation System (ADAS): for some years, GMAO has contributed to the development of the Gridpoint Statistical Interpolation (GSI) system, originally devised at NCEP. Current activities are expanding from a three-dimensional variational approach to ensemble-based systems in three and four dimensions. GMAO has pioneered the use of the incremental analysis update (IAU) technique as a means of smoothly introducing the observations into the model, for the data assimilation problem. Alongside the meteorological fields, analysis of aerosols and trace gases are themes of the GMAO’s research and production activities.

The Ocean Data Assimilation System (ODAS): To analyze the oceanic state, GMAO uses information about surface and sub-surface temperature, ocean currents, salinity, sea-surface height, and sea-ice extent from a variety of in-situ sensors and satellite observations. Atmospheric analyses are used as drivers for the ocean assimilation system. GMAO’s ocean analyses are used as initial states for the seasonal forecasting system and are presently based on a Ensemble Optimal Analysis (EnOI) technique, while research activities focus on development of Ensemble Kalman-Filter approaches. Alongside the work in physical oceanography, GMAO also has a mature ocean-color assimilation activity, which is based on NASA’s ocean-color observations.

The Land Data Assimilation System (LDAS): GMAO’s LDAS uses an Ensemble-Kalman-filter-based (EnKF) technique, built around the Catchment Land Model. This provides an efficient implementation for GMAO’s work in combining information about the surface temperature and moisture, as well as deducing information about the root-zone soil moisture.