Can Convection‐Permitting Modeling Provide Decent Precipitation for Offline High‐Resolution Snowpack Simulations Over Mountains?

Abstract

Accurate precipitation estimates are critical to simulating seasonal snowpack evolution. We conduct and evaluate high‐resolution (4‐km) snowpack simulations over the western United States (WUS) mountains in Water Year 2013 using the Noah with multi‐parameterization (Noah‐MP) land surface model driven by precipitation forcing from convection‐permitting (4‐km) Weather Research and Forecasting (WRF) modeling and four widely used high‐resolution datasets that are derived from statistical interpolation based on in situ measurements. Substantial differences in the precipitation amount among these five datasets, particularly over the western and northern portions of WUS mountains, significantly affect simulated snow water equivalent (SWE) and snow depth (SD) but have relatively limited effects on snow cover fraction (SCF) and surface albedo. WRF generally captures observed precipitation patterns and results in an overall best‐performed SWE and SD in the western and northern portions of WUS mountains, where the statistically interpolated datasets lead to underpredicted precipitation, SWE, and SD. Over the interior WUS mountains, all the datasets consistently underestimate precipitation, causing significant negative biases in SWE and SD, among which the results driven by the WRF precipitation show an average performance. Further analysis reveals systematic positive biases in SCF and surface albedo across the WUS mountains, with similar bias patterns and magnitudes for simulations driven by different precipitation datasets, suggesting an urgent need to improve the Noah‐MP snowpack physics. This study highlights that convection‐permitting modeling with proper configurations can have added values in providing decent precipitation for high‐resolution snowpack simulations over the WUS mountains in a typical ENSO‐neutral year, particularly over observation‐scarce regions.

Assessment of Uncertainty Sources in Snow Cover Simulation in the Tibetan Plateau

“…On the mountain-range scale (usually from 1 km to less than 4 km in horizontal resolution), Mott et al (2018) pointed out that snow depth distribution mainly attributes to topographic precipitation, which is consistent with studies using high-resolution convection-permitting regional climate models to produce precipitation for snow modeling (Gao et al, 2020;He et al, 2019;Quéno et al, 2016;Vionnet et al, 2016Vionnet et al, , 2019. At finer scales (usually several meters to less than 500 m), impacts of wind speed on snowfall sublimation and snowpack redistribution (drifting snow) are more significant.…”

“…Atmospheric forcing conditions represent another uncertainty source of modeling snow cover (Chen et al, 2014; He et al, 2019; Mizukami et al, 2014). Clark et al (2011) summarized that the watershed‐scale snow cover is mainly shaped by meteorological data, which controls the freezing level and melt energy.…”

“…A series of studies were applied to explore the impacts of precipitation data sets in different spatial resolutions produced by regional climate model on snowpack modeling over the French Mountains and found significant improvements of snow simulation with higher resolution precipitation data (Quéno et al, 2016;Vionnet et al, 2016Vionnet et al, , 2019. He et al (2019) assessed the impacts of precipitation uncertainty on snowpack simulation in the western America using five precipitation data sets and found added values of convection-permitting ) averaged at six elevation zones (0-2.5, 2.5-3.5, 3.5-4.5, 4.5-5, 5-5.5, and >5.5 km) from 1 September 2013 to 31 August 2014 simulated by the CTL, OPT, and OPT with the optimized N07 scheme in 10-km resolution (OPT/ON07) and in 4-km resolution (OPT/ON07-4km), respectively.…”

“…In the French mountains, positive biases of snowpack for the regional model and CROCUS snow model were found (Quéno et al, 2016; Vionnet et al, 2016, 2019). He et al (2019) found systematic positive biases in snow cover fraction (SCF) and surface albedo in the western United States using five precipitation data sets as forcing data and pointed out the urgent need to improve snowpack physics. For the TP area, Orsolini et al (2019) found systematic overestimates of snow depth and snow cover in various reanalysis products, including the European Center for Medium‐Range Weather Forecasts (ECMWF) ERA5 and ERA‐Interim data sets, the Japanese 55‐year Reanalysis (JRA‐55), and the NASA Modern‐Era Retrospective analysis for Research and Applications (MERRA‐2).…”

“…Model physics and parameterizations of snow processes are major uncertainty sources in snow simulation (C. He et al, 2019; Chen et al, 2014). Previous studies have developed and evaluated a number of SCF parameterizations in land surface models.…”