Articles | Volume 16, issue 3
The Cryosphere, 16, 1007–1030, 2022
https://doi.org/10.5194/tc-16-1007-2022
The Cryosphere, 16, 1007–1030, 2022
https://doi.org/10.5194/tc-16-1007-2022

Research article 15 Mar 2022

Research article | 15 Mar 2022

Evaluation of Northern Hemisphere snow water equivalent in CMIP6 models during 1982–2014

Kerttu Kouki et al.

Data sets

GlobSnow v3.0 snow water equivalent (SWE) Kari Luojus, Jouni Pulliainen, Matias Takala, Juha Lemmetyinen, and Mikko Moisander https://doi.org/10.1594/PANGAEA.911944

Climate Data Dashboard of the ESA Climate Change Initiative European Space Agency https://climate.esa.int/en/odp/#/dashboard

MERRA-2 tavg1_2d_lnd_Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Land Surface Diagnostics V5.12.4 (M2T1NXLND) Global Modeling and Assimilation Office (GMAO) https://doi.org/10.5067/RKPHT8KC1Y1T

MERRA-2 tavgM_2d_slv_Nx: 2d,Monthly mean,Time-Averaged,Single-Level,Assimilation,Single-Level Diagnostics V5.12.4 (M2TMNXSLV) Global Modeling and Assimilation Office (GMAO) https://doi.org/10.5067/AP1B0BA5PD2K

GPCC Full Data Monthly Product Version 2018 Udo Schneider, Andreas Becker, Peter Finger, Anja Meyer-Christoffer, and Markus Ziese https://doi.org/10.5676/DWD_GPCC/FD_M_V2018_050

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Short summary
We analyze state-of-the-art climate models’ ability to describe snow mass and whether biases in modeled temperature or precipitation can explain the discrepancies in snow mass. In winter, biases in precipitation are the main factor affecting snow mass, while in spring, biases in temperature becomes more important, which is an expected result. However, temperature or precipitation cannot explain all snow mass discrepancies. Other factors, such as models’ structural errors, are also significant.