Abstract
apturing the spatial and temporal variability of precipitation at fine scale is necessary for high-resolution modeling of snowpack and glacier mass balance in alpine terrain. In this study, we assess the impact of three sub-kilometer precipitation datasets on distributed simulations of snowpack and glacier mass balance with the detailed snowpack model Crocus for winter 2011-2012. The different precipitation datasets at 500-m grid spacing over the northern and central French Alps are coming from (i) the SAFRAN reanalysis specially developed for alpine terrain interpolated at 500-m grid spacing, (ii) the numerical weather prediction (NWP) system AROME at 2.5-km resolution downscaled with a precipitation-elevation adjustment factor and (iii) a version of AROME at 500-m grid spacing. The spatial patterns of seasonal snowfall are first analyzed for the different precipitation datasets. Large differences between SAFRAN and the two versions of AROME are found at high-altitude and in regions of strong orographic precipitation enhancement. Results of Crocus snowpack simulations are then evaluated against (i) point measurements of snow depth, (ii) maps of snow covered areas retrieved from optical satellite data (MODIS) and (iii) field measurements of winter accumulation of six glaciers. The two versions of AROME lead to an overestimation of snow depth and snow-covered area, which are substantially improved by SAFRAN. However, all the precipitation datasets lead to an underestimation of snow depth increase at the daily scale and cumulated over the season, with AROME 500 m providing the best performances at the seasonal scale. The low correlation found between the biases in snow depth and in cumulated snow depth increase illustrates that total snow depth has a limited significance for the evaluation of precipitation datasets. Measurements of glacier winter mass balance showed a systematic underestimation of high-elevation snow accumulation with SAFRAN. The two versions of AROME overestimate the winter mass balance at four glaciers and produce nearly unbiased estimation for two of them. Our study illustrates the need for improvements in the precipitation field from high-resolution NWP systems for snow and glacier modeling in alpine terrain.
Highlights
Accurate estimation of winter precipitation stored as snow in mountainous terrain is critical for many applications
This paper first compared three precipitation datasets at 500-m grid spacing in the French Alps: two of them were derived from the numerical weather prediction (NWP) system AROME (ARO_0p5: dynamical downscaling from AROME 2.5 km; ARO_2p5D: simple downscaling from AROME 2.5 km) and the third one was obtained from the SAFRAN analysis
The differences between AROME and SAFRAN are similar to the differences obtained between the Weather Research and Forecasting (WRF) atmospheric model and the gauge-based parameter-elevation regressions on independent slopes model (PRISM) dataset for different mountains ranges of the US (e.g., Gutmann et al, 2012; Silverman et al, 2013; Jing et al, 2017)
Summary
Accurate estimation of winter precipitation stored as snow in mountainous terrain is critical for many applications. Winter precipitation in mountainous terrain presents a large spatial and temporal variability influenced by topography at different scales (Mott et al, 2018). Microphysical processes, such as the seeder-feeder mechanism (Mott et al, 2014) and preferential deposition of snowfall (Lehning et al, 2008) enhance the spatial variability of solid precipitation (Gerber et al, 2019). This multiscale variability represents a challenge in obtaining reliable precipitation dataset in mountainous terrain
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