Michele Rienecker (PI)
NASA Goddard Space Flight Center
michele.rienecker@nasa.gov

Satellite Data in Modeling and Prediction of Seasonal-to-Interannual Climate Variability

One of the primary goals of the  Climate Change Science Program is to address scientific questions relating to climate variability: to  improve our understanding of the causes of the dominant modes of natural variability, changes in their frequency and/or intensity that may be associated with long-term climate change, and improved ability to predict El Nino and other seasonal-to-interannual modes.  This proposal will address many of the important problems of any prediction problem, but focus on short-term climate variations:  initialization (here particularly, the use of satellite data to initialize the ocean and land surface states), model forecast bias, characterization of uncertainty in predictions, and identification of the inherent predictability in the system.  The longer time scales in the ocean and land surface are the key sources of memory in the climate system that promises skill in predicting short-term climate variability.  The proposal focuses on improving methods to estimate the state of the ocean as part of the initialization procedure, with the focus on optimizing the use of satellite altimeter data and evaluating the impact of planned satellite observations such as  high resolution altimeter data from the OSTM and surface salinity data from Aquarius.  A key element in the use of these surface observations is the use of multivariate  and flow-dependent statistical relationships derived through the Ensemble Kalman Filter.  Building upon collaborations established through ODASI and the second phase of ECCO, the proposal will both compare the efficacy of different techniques for ocean assimilation (techniques developed at GMAO, JPL, NCEP, and GFDL), and use the different ocean state estimates and coupled model forecasts (from GMAO, NCEP, and GFDL) as contributions to multi-model ensembles to provide improved forecasts and forecast uncertainty.  The GMAO system also focuses on the initialization of the land surface model, with plans for assimilation of soil moisture estimates from AMSR-E.  The GMAO experimental forecast system will be exercised on a near real-time basis as a continual check of the system performance and predictability in the evolving background climate. One of the critical elements to be addressed is the initialization of the coupled system in order to reduce coupling shocks.  An necessary element of climate prediction is the conduct of hindcasts to assess statistical reliability of the forecasts  and to remove climate drift. The same ocean assimilation systems used to initialize the coupled forecasts will be used to estimate the larger scale variations in ocean state and document interannual fluctuations and decadal changes in heat storage.  The use of different models and assimilation systems will provide an indication of uncertainty in the estimates feasible from a sparse in situ observing network.

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