An Extensible Global Land Data Assimilation System Based on NCAR's Community Land Model (CLM) and Data Assimilation Research Testbed
Friday, 18 December 2015: 15:25
3022 (Moscone West)
Land plays an important role in shaping regional and global climate and the water cycle. However, many of these processes are not well understood, which is largely due to the lack of high quality datasets. Over the past 5 years, we have developed a global-scale multi-sensor snow data assimilation system based on NCAR’s Data Assimilation Research Testbed (DART) coupled to the Community Land Model version 4 (CLM4); CLM4 can be replaced by CLM4.5 or the latest versions as they become available. This data assimilation system can be applied to all land areas to take advantage of high-resolution regional-specific observations. The DART data assimilation system has an unprecedented large ensemble (80-member) atmospheric forcing (temperature, precipitation, winds, humidity, radiation) with a quality of typical reanalysis products, which not only facilitates ensemble land data assimilation, but also allows a comprehensive study of many feedback processes (e.g. the snow albedo feedback and soil moisture–precipitation feedback). While initial findings were reported in the past AGU, AMS and GEWEX meetings, this paper will present comprehensive results from the CLM/DART with assimilating MODIS (Moderate Resolution Imaging Spectroradiometer) snow cover fraction and GRACE (Gravity Recovery and Climate Experiment) terrestrial water storage. Besides our prototype snow data assimilation, the coupled CLM4/DART framework is useful for data assimilation involving other variables, such as soil moisture, skin temperature, and leaf area index from various satellite sources and ground observations. Such a truly multi-mission, multi-platform, multi-sensor, and multi-scale data assimilation system with DART will, ultimately, help constrain earth system models using all kinds of observations to improve their prediction skills from intraseasonal to interannual. Some preliminary results from using our snow data assimilation output in seasonal climate prediction will be presented as well.