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The Global Modeling and Assimilation Office (GMAO) has been established as a core resource in the development and use of satellite observations through the integrating tools of models and assimilation systems. Global ocean, atmosphere and land surface models are developed as components of assimilation and forecast systems as well as for addressing the weather and climate research questions identified in NASA's science mission. Assimilation tools are developed to optimize the use of the high resolution information from satellite observations and diagnose their impact on predictions of both weather and climate. Research-quality assimilated datasets, including trace gas, aerosol and climate products, ocean and land surface products, are generated for use by NASA instrument teams and for research analyses. The Office participates in major national collaborations such as the Joint Center for Satellite Data Assimilation (JCSDA), the Earth System Modeling Framework (ESMF), and the Climate Process Modeling Teams (CPTs) of the Climate Variability (CLIVAR) Program. It is also involved with major international programs such as the Climate Variability (CLIVAR) Program, the Global Energy and Water Cycle Experiment (GEWEX), and the Global Ocean Data Assimilation Experiment (GODAE).
To make best use of the information afforded by satellite observations and to address climate research issues identified in NASA's science mission requires weather-capable climate models and climate-reliable weather models. The GMAO is developing a Unified Atmospheric Model, using the finite-volume dynamical core developed in the DAO and physics packages developed by NSIPP, other scientists in the Directorate, and at other modeling centers. The GMAO has a strong land surface model development effort in the Catchment Land Surface Model. The ocean model, developed through NSIPP, has been coupled to a model of marine biogeochemistry.
The GMAO's efforts in assimilation is focused on (i) improvements in NASA satellite data usage; (ii) guidance on observing system development through Observing System Simulation Experiments (OSSEs) and Observing System Experiments (OSEs); and (iii) production of research-quality assimilated datasets. The DAO pioneered efforts in the assimilation of scatterometer data, satellite precipitation observations, ozone measurements, and polar winds. The GMAO includes developments in stratospheric assimilation, aerosol and constituent assimilation, ocean, ocean biology, and land surface assimilation. In the exploration of new data types, the focus will be on new NASA missions and the upcoming NPP and NPOESS, but also on difficult, currently available data types such as high resolution imagery, clouds, and precipitation.
The ocean and land surface data assimilation capabilities will build upon the advanced data assimilation developments by NSIPP, with a focus on using satellite data to enhance seasonal prediction. NSIPP has pioneered advanced techniques such as the Ensemble Kalman Filter (EnKF) for the multivariate assimilation needed to take advantage of surface altimetry. The EnKF has also been developed for the highly inhomogeneous nature of the errors in land surface models, as well as the multivariate assimilation needed to take advantage of surface soil moisture and snow observations. The combination of these activities within the GMAO places NASA in the unique position for contributing to coupled assimilation for Earth system science.
The GMAO generates assimilated data products in near real-time to support the EOS-Terra, Aqua and Aura instrument teams and field experiments. The GMAO also undertakes periodic re-processing of satellite-era observations for research analyses, especially in support of instrument teams and other NASA investigations. A Modern Era Re-analysis for Research and Applications (MERRA) will be conducted at 1 ° resolution with observations from 1979 to the present and an updated atmospheric assimilation system.
Subseasonal-to-Decadal Variability and Prediction
One of the key questions to be addressed in NASA's science mission is how well transient climate variations can be understood and predicted. NSIPP has developed advanced assimilation techniques to improve seasonal prediction skill by using the information in satellite altimetry. They have also shown that soil moisture data can significantly influence predictions of summertime precipitation over some continental regions. Seasonal predictions are contributed to the consensus forecasts at NCEP and at the International Research Institute for Climate Prediction (IRI). The GMAO continues to explore the impact of coupled prediction strategies and observations on prediction skill at seasonal to interannual timescales, with a focus on identifying the role that specific observations play in extending prediction skill. Research extends to the shorter (subseasonal) timescales beyond deterministic weather, and decadal timescales focusing on understanding the sources and predictability limits of long-term droughts.
Stratospheric chemistry and climate
The GMAO currently assimilates ozone data from two instruments, the Total Ozone Mapping Spectrophotometer (TOMS) and the Solar Backscatter Ultraviolet (SBUV) and includes them in ozone forecasts. Measurements from EOS-Aura will be included in the assimilation system (ultraviolet measurements from OMI, limb sounder measurements from MLS and HIRDLS). The GMAO and the Atmospheric Chemistry Branch are developing a coupled chemistry climate model, initially using a parameterized representation of ozone chemistry. Future development will focus on more comprehensive chemistry modules, treating a large number of gases and their interactions. Such chemistry-climate models will be used to address questions related to past variations of ozone and their impact on the climate.
GMAO research on the tropopause will use new space-based observations, as they become available (beginning with EOS-Aura), close collaboration with field experiments (as has begun with CRYSTAL-FACE ), and careful development and testing of model processes and their parameterization.
Tropospheric chemistry, aerosols and air quality
Tropospheric constituent modeling, including gases and aerosols, is motivated by the radiative impacts on climate and by the importance of the air quality to human life and ecosystems. The GMAO will develop an assimilation capability related to air quality monitoring and prediction. It will incorporate aerosol data from instruments such as TOMS, as well as from more advanced sensors such as MODIS and proposed spectral imagers and active sensors. It will also include data from the Troposphere Emission Spectrometer (TES) on Aura, the first instrument designed to resolve tropospheric ozone profiles. Other tropospheric gases such as CO and other pollutants will be included in a comprehensive capability of air-quality research and application.
Development of a carbon-cycle modeling and assimilation system is a priority of GMAO. A major application will be modeling and assimilation studies of the global carbon budget and estimates of North-American contributions to carbon sources and sinks, through involvement with national activities such as the North American Carbon Program and international collaborations.