Assessment of Snow Modeling in the North American Land Data Assimilation System (NLDAS)

Wood, E. F., M. Pan, J. Sheffield, plus NLDAS Coauthors

Princeton University, Dept. Civil and Environmental Eng., Princeton, NJ 08544 United States.

This study assesses the cold season process modeling in the North American Land Data Assimilation System (NLDAS). Simulations from the four land surface models (Noah, MOSAIC, SAC and VIC) in the NLDAS are compared with observational data for a 3-year retrospective period over the USA. Observed snow cover extent data are from the IMS (Interactive Multisensor Snow and Ice Mapping System) and observed snow water equivalent data are from the Natural Resources Conservation Service's SNOTEL network. In general, all models simulate reasonably well the regional scale spatial and seasonal dynamics of snow cover. Systematic biases are seen in the model predictions with certain models consistently under- or over-estimating snow cover extent, although the level of bias is dependent on geographic location and elevation variability. Larger discrepencies are seen over higher elevation regions of the northwest of the United States that may be due, in part, to errors in the meteorological forcings and also at the snow line boundary. Inter-model differences can be explained to some extent by differences in the model representations of sub-grid variability and parametrizations of snow cover extent. Comparisons of simulated snow water equivalent (SWE) are made with SNOTEL data at selected sites in the mountainous regions of the western United States. All models show systematic low bias in the maximum annual simuated SWE that is most notable in the Cascade and Sierra Nevada regions. Comparison of NLDAS precipitation forcing with SNOTEL measurements reveal a large bias in the NLDAS annual precipitation and experiments with the VIC model indicate that most of the bias in SWE can be removed by scaling the precipitation to match the local station SNOTEL record. Furthermore, the NLDAS air temperature is shown to be generally colder in winter months and biased warmer in spring and summer when compared to the SNOTEL record, although the level of bias is regionally dependent. Detailed analysis at a selected station indicate errors in the air temperature forcing may cause the partitioning of precipitation into snowfall and rainfall by the models to be incorrect and thus may explain some of the remaining errors in the simulated SWE.