Evaluation of Remote Sensing and Reanalysis Snow Depth Datasets over the Northern Hemisphere during 1980–2016

Lin Xiao,Tao Che,Liyun Dai

doi:10.3390/rs12193253

Abstract

Snow cover is a key parameter of the climate system and its significant seasonal and annual variability have significant impacts on the surface energy balance and global water circulation. However, current snow depth datasets show large inconsistencies and uncertainties, which limit their applications in climate change projections and hydrological processes simulations. In this study, a comprehensive assessment of five hemispheric snow depth datasets was carried out against ground observations from 43,391 stations. The five snow depth datasets included three remote sensing datasets, i.e., Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), Advanced Microwave Scanning Radiometer-2 (AMSR2), Global Snow Monitoring for Climate Research (GlobSnow), and two reanalysis datasets, i.e., ERA-Interim and the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). Assessment results imply that the spatial distribution of GlobSnow and ERA-Interim exhibit overall better agreements with ground observations than other datasets. GlobSnow and ERA-Interim exhibit less uncertainty during the snow accumulation and ablation periods, respectively. In plain and forested regions, GlobSnow, ERA-Interim and MERRA-2 show better performances, while in mountain and forested mountain areas, GlobSnow exhibits the best performance. AMSR-E and AMSR2 agree better with ground observations in shallow snow condition (0–10 cm), while MERRA-2 shows more satisfying performance when snow depth exceeds 50 cm. These systematic and integrated understanding of the five representative snow depth datasets provides information on data selection and data refinement, as well as data fusion, which is our next work of interest.

Highlights

Snow cover is the most widely distributed and most dynamic component of the cryosphere, and its significant seasonal and annual variability have notable influences on the global water circulation and surface energy balance [1,2,3]
The spatial and temporal characteristics of the five snow depth datasets were first intercompared, and evaluated against ground observations through multiple metrics (BIAS, root mean square error (RMSE), relative error (RE), R), and the results are described respectively
Consistencies among the five datasets are that snow cover is mainly distributed in mid-high latitudes over the Northern Hemisphere, and deep snow is generally distributed in Alaska, Northern Canada, and Northern Eurasia

Summary

Introduction

Snow cover is the most widely distributed and most dynamic component of the cryosphere, and its significant seasonal and annual variability have notable influences on the global water circulation and surface energy balance [1,2,3]. Snow cover is a sensitive indicator that responds to the climate system, but is an important feedback variable that amplifies global warming [7,8,9]. According to the fifth assessment report (AR5) of the Intergovernmental Panel on Climate Change (IPCC), there is very high confidence that the spring snow cover extent over the Northern Hemisphere has decreased since the mid-20th century, but only a lower data accuracy of snow depth than that of snow cover extent was noted [17]. In the Special Report “The Ocean and Cryosphere in a Changing Climate” of IPCC AR6, change in snow depth over the Northern Hemisphere during the 20th century had still not been clarified [18]

Results

Discussion

Conclusion