Snow Depth Trends from CMIP6 Models Conflict with Observational Evidence

Xinyue Zhong aKey Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, China

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Tingjun Zhang bKey Laboratory of Western China’s Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, China

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Shichang Kang cState Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, China
dUniversity of Chinese Academy of Sciences, Beijing, China

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Jian Wang aKey Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, China
eJiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, China

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Abstract

In this study, we compiled a high-quality, in situ observational dataset to evaluate snow depth simulations from 22 CMIP6 models across high-latitude regions of the Northern Hemisphere over the period 1955–2014. Simulated snow depths have low accuracy (RMSE = 17–36 cm) and are biased high, exceeding the observed baseline (1976–2005) on average (18 ± 16 cm) across the study area. Spatial climatological patterns based on observations are modestly reproduced by the models (normalized root-mean-square deviations of 0.77 ± 0.20). Observed snow depth during the cold season increased by about 2.0 cm over the study period, which is approximately 11% relative to the baseline. The models reproduce decreasing snow depth trends that contradict the observations, but they all indicate a precipitation increase during the cold season. The modeled snow depths are insensitive to precipitation but too sensitive to air temperature; these inaccurate sensitivities could explain the discrepancies between the observed and simulated snow depth trends. Based on our findings, we recommend caution when using and interpreting simulated changes in snow depth and associated impacts.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Xinyue Zhong, xyzhong@lzb.ac.cn

Abstract

In this study, we compiled a high-quality, in situ observational dataset to evaluate snow depth simulations from 22 CMIP6 models across high-latitude regions of the Northern Hemisphere over the period 1955–2014. Simulated snow depths have low accuracy (RMSE = 17–36 cm) and are biased high, exceeding the observed baseline (1976–2005) on average (18 ± 16 cm) across the study area. Spatial climatological patterns based on observations are modestly reproduced by the models (normalized root-mean-square deviations of 0.77 ± 0.20). Observed snow depth during the cold season increased by about 2.0 cm over the study period, which is approximately 11% relative to the baseline. The models reproduce decreasing snow depth trends that contradict the observations, but they all indicate a precipitation increase during the cold season. The modeled snow depths are insensitive to precipitation but too sensitive to air temperature; these inaccurate sensitivities could explain the discrepancies between the observed and simulated snow depth trends. Based on our findings, we recommend caution when using and interpreting simulated changes in snow depth and associated impacts.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Xinyue Zhong, xyzhong@lzb.ac.cn
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