Why Do Global Reanalyses and Land Data Assimilation Products Underestimate Snow Water Equivalent?

• Main Authors: Broxton, Patrick D., Zeng, Xubin, Dawson, Nicholas
• Other Authors: Univ Arizona, Dept Hydrol & Atmospher Sci
• Language: en
• Published: AMER METEOROLOGICAL SOC 2016
• Online Access: http://hdl.handle.net/10150/622474; http://arizona.openrepository.com/arizona/handle/10150/622474

There is a large uncertainty of snow water equivalent (SWE) in reanalyses and the Global Land Data Assimilation System (GLDAS), but the primary reason for this uncertainty remains unclear. Here several reanalysis products and GLDAS with different land models are evaluated and the primary reason for their deficiencies are identified using two high-resolution SWE datasets, including the Snow Data Assimilation System product and a new dataset for SWE and snowfall for the conterminous United States (CONUS) that is based on PRISM precipitation and temperature data and constrained with thousands of point snow observations of snowfall and snow thickness. The reanalyses and GLDAS products substantially underestimate SWE in the CONUS compared to the high-resolution SWE data. This occurs irrespective of biases in atmospheric forcing information or differences in model resolution. Furthermore, reanalysis and GLDAS products that predict more snow ablation at near-freezing temperatures have larger underestimates of SWE. Since many of the products do not assimilate information about SWE and snow thickness, this indicates a problem with the implementation of land models and pinpoints the need to improve the treatment of snow ablation in these systems, especially at near-freezing temperatures.