9A.6 Multi-source and Multi-scale Land Data Assimilation and Its Role in Seasonal Climate Prediction

Wednesday, 25 January 2017: 11:45 AM
604 (Washington State Convention Center )
Zong-Liang Yang, Univ. of Texas, Austin, TX; and L. Zhao, P. Lin, Y. Kwon, and Y. Zhang

Over the past six years, we have developed a global-scale multi-source and multi-scale land data assimilation system based on the National Center for Atmospheric Research (NCAR) Data Assimilation Research Testbed (DART) and Community Land Model version 4 (CLM4). The DART has an unprecedented large ensemble (80-member) atmospheric forcing (temperature, precipitation, winds, humidity, radiation) with a quality of typical reanalysis products, which facilitates ensemble land data assimilation. This paper will evaluate land state variables including the snow water equivalent that results from the CLM/DART assimilation of Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction, Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage, and Advanced Microwave Scanning Radiometer–EOS (AMSR–E) snow bright temperature. Additional results from using the snow data assimilation outputs as initialization fields in seasonal hydroclimate predictions will be presented, with a focus on relative contributions from the snow albedo–temperature feedback and soil moisture–precipitation feedback. Our results provide the first evidence that satellite-constrained snow initialization improves seasonal climate prediction in the Tibetan Plateau region and at the northern high latitudes, with joint GRACE and MODIS data assimilation outperforming MODIS data assimilation only, and that the magnitude of improvements depends on latitude and lead time. Our ongoing work has expanded this data assimilation effort to soil moisture data assimilation.
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