77 Assessment of the Community Land Model version 04 snow model output estimates

Tuesday, 8 January 2013
Exhibit Hall 3 (Austin Convention Center)
Ally M. Toure, NASA, Greenbelt, MD; and M. Rodell, Z. L. Yang, Y. Zhang, Y. Kwon, and H. Beaudoing

Handout (2.5 MB)

The Community Land Model (CLM), the land model for the Community Earth System Model (CESM) is a spatially distributed one-dimensional vertical model that provides the lower boundary condition for the Community Atmosphere Model (CAM). CLM snow outputs were assessed in preparation for the mutlisensor data assimilation into the land model. The primary goal of the assimilation is to develop an optimized approach for merging Terra MODIS snow cover, Aqua AMSR-E snow water equivalent (SWE), and GRACE terrestrial water storage change observations to generate spatially and temporally continuous global snow water equivalent fields, at high resolutions (~1/8 degree).

CLM simulation was conducted in offline mode for the period 2000-2010 at a spatial resolution of 0.9ºx1.25º in latitude and longitude, respectively. The simulation was driven by meteorological forcing obtained from NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA). CLM global estimates of snow cover fraction (SCF), snow depth and snow water equivalent (SWE) were evaluated using observations from: 1) the Moderate Resolution Imaging Spectroradiometer (MODIS) SCF, 2) the Interactive Multisensor Snow and Ice Mapping System (IMS) data, 3) the Canadian Meteorological Centre (CMC) daily snow depth Analysis data, 4) the snowpack telemetry (SNOTEL), 5) the Cooperative Station snow depth and water equivalent Observations (COOP). CLM SCF agrees well with MODIS and SCF observations as well as with IMS snow cover product, especially in January and February when snow cover extent is at its maximum. Generally, false alarms and misses occur in southern Russia, US Rocky Mountains, and in areas near the US-Canadian border, southwestern Russia, and on the Tibetan plateau. The results of the evaluation will have an implication on the choice of algorithm used in the data assimilation.

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