- Related Research Areas
- Climate Variability & Change
Model evaluation, one of the key stages in the modeling of natural physical processes, leads to improvements in our knowledge of natural systems. The ultimate goal of model evaluation is to improve the model’s usefulness by ensuring that it addresses the right problem and provides accurate information. Model evaluation, consisting of model validation and verification processes, is important for all numerical models, and more so for reanalysis (numerical systems of the assimilated observations) because they are usually being taken as a “truth” in a process of models evaluation. Global atmospheric reanalysis provides consistent estimates of the short-term or synoptic-scale variations, but variability on longer time scales is usually more difficult to capture by current reanalysis. The NASA/Global Modeling and Assimilation Office is running an atmospheric global reanalysis project named the Modern Era Retrospective-Analysis for Research and Applications (MERRA), covering the period 1979-present and synthesizing the current suite of research satellite observations in a climate data context. When compared with other existing reanalysis, such as NCEP, ECMWF and JRA, the strengths of MERRA include increased horizontal resolution, well-resolved stratosphere, and interactive ozone. In this proposal we test MERRA’s climate skills in reproducing the historical evolution of the polar vortices, tropical dynamics, and local climate with applications in reducing uncertainties in climate sensitivity estimations. We propose to use a simple method of rigorous examination of the numerical models and reanalysis systems. The latter consists of constructing normalized eigenvalues and dominant variability mode maps to document temporal and spatial behavior assimilated variables for comparison with other assimilated systems and numerical models as well as to reveal structural connections between assimilated variables within MERRA itself. The use of MERRA is one of the direct calls of the MAP.
Project PI: Natalia Andronova/University of Michigan
2455 Hayward St Ann Arbor, MI 48109-2143
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