A simple snow temperature index model exposes discrepancies between reanalysis snow water equivalent products

Abstract

Current global reanalyses show marked discrepancies in snow mass and snow cover extent for the Northern Hemisphere. Here, benchmark snow datasets are produced by driving a simple offline snow model, the Brown Temperature Index Model (B-TIM), with temperature and precipitation from three reanalyses. The B-TIM offline snow performs comparably to or better than online (coupled land–atmosphere) reanalysis snow when evaluated against in situ snow measurements. Sources of discrepancy in snow climatologies, which are difficult to isolate when comparing online reanalysis snow products amongst themselves, are partially elucidated by separately bias-adjusting temperature and precipitation in the B-TIM. Interannual variability in snow mass and snow spatial patterns is far more self-consistent amongst offline B-TIM snow products than amongst online reanalysis snow products, and the self-consistent products are more similar to in situ observations, as evaluated in a validation study. Specific artifacts related to temporal inhomogeneity in snow data assimilation are revealed in the analysis. The B-TIM, released here as an open-source, self-contained Python package, provides a simple benchmarking tool for future updates to more sophisticated online and offline snow datasets.