Abstract

Abstract. Seasonal snow cover of the Northern Hemisphere (NH) is a major factor in the global climate system, which makes snow cover an important variable in climate models. Previously, substantial uncertainties have been reported in NH snow water equivalent (SWE) estimates. A recent bias-correction method significantly reduces the uncertainty of NH SWE estimation, which enables a more reliable analysis of the climate models' ability to describe the snow cover. We have intercompared NH SWE estimates between CMIP6 (Coupled Model Intercomparison Project Phase 6) models and observation-based SWE reference data north of 40∘ N for the period 1982–2014 and analyzed with a regression approach whether model biases in temperature (T) and precipitation (P) could explain the model biases in SWE. We analyzed separately SWE in winter and SWE change rate in spring. For SWE reference data, we used bias-corrected SnowCCI data for non-mountainous regions and the mean of Brown, MERRA-2 and Crocus v7 data for the mountainous regions. The SnowCCI SWE data are based on satellite passive microwave radiometer data and in situ snow depth data. The analysis shows that CMIP6 models tend to overestimate SWE; however, large variability exists between models. In winter, P is the dominant factor causing SWE discrepancies especially in the northern and coastal regions. T contributes to SWE biases mainly in regions, where T is close to 0∘ C in winter. In spring, the importance of T in explaining the snowmelt rate discrepancies increases. This is to be expected, because the increase in T is the main factor that causes snow to melt as spring progresses. Furthermore, it is obvious from the results that biases in T or P cannot explain all model biases either in SWE in winter or in the snowmelt rate in spring. Other factors, such as deficiencies in model parameterizations and possibly biases in the observational datasets, also contribute to SWE discrepancies. In particular, linear regression suggests that when the biases in T and P are eliminated, the models generally overestimate the snowmelt rate in spring.

Highlights

  • Seasonal snow cover of the Northern Hemisphere (NH) is an important factor of the global climate system

  • We studied through linear regression analysis how the snow water equivalent (SWE) bias in February depends on the precipitation (P ) bias and temperature (T ) biases, summed over the three preceding months from November to January: SWE = βT Tcum + βP Pcum + C, (1)

  • The SWE distribution is similar for the multi-model mean and SWE reference data, the models overestimate SWE in several regions, which are mostly located in the northern parts of the study area in northeastern Canada, northeastern Siberia, and Eurasia around 90◦ E

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Summary

Introduction

Seasonal snow cover of the Northern Hemisphere (NH) is an important factor of the global climate system. The seasonal snow cover greatly influences surface albedo and, the Earth’s energy balance (Callaghan et al, 2011; Flanner et al, 2011; Qu and Hall, 2005; Trenberth and Fasullo, 2009). This makes snow cover an important variable in climate models (Derksen and Brown, 2012; Loth et al, 1993). Snow cover significantly affects the hydrological cycle at high latitudes and in mountainous regions (Barnett et al, 2005; Bormann et al, 2018; Callaghan et al, 2011; Douville et al, 2002). Snow is the largest freshwater storage, and about one-sixth of the world’s population is dependent on meltwater from snow (Barnett et al, 2005; Hall et al, 2008)

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