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The predictability of snow depth at the North Hemisphere originated from persistence and oceanic forcing

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Abstract

As a vital source of the climate change predictability, the snow depth predictability originates from its own persistence and the external forcing factors. In order to investigate the root of snow depth predictability at the North Hemisphere, this study conducted an ensemble of 20 simulations spanning 50 years with the Community Earth System Model (CESM). With a regression model constructed via the canonical correlation analysis method, we analyzed the temporal and spatial distribution characteristics of snow depth predictability on the global scale, as well as the effects of snow depth persistence and sea surface temperature (SST) on snow depth predictability. The results show that the predictability due to snow depth persistence depends on both season and location. The persistence of snow depth can reach more than 3 months in high latitude region. After considering the SST forcing, the predictability is increased in many parts of the Northern Hemisphere, such as northern North America, Europe, and central Siberia. The areas where SST significantly influences snow depth predictability mainly overlap the snow cover transition zones. We further investigated the possible pathways of the impact of SST on snow depth predictability, and found that in North America and Europe, SST improves the predictability mainly through affecting the surface temperature, while in central Siberia and eastern Europe, the pathway also includes snowfall and shortwave radiation, respectively. Additionally, we conducted a similar analysis with three other climate models from the Atmospheric Model Intercomparison Project phase 6 (AMIP6), and the results can also verify the conclusions of CESM ensemble simulations.

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Data availability

The AMIP6 model results can be achieved from https://esgf-node.llnl.gov/search/cmip6/, the JRA-55 Reanalysis data can be download at http://search.diasjp.net/en/dataset/JRA55, the CESM model code can be achieved from https://svn-ccsm-models.cgd.ucar.edu/cesm1/release_tags/cesm1_2_2/, and the datasets used for running this model can be downloaded at https://svn-ccsm-inputdata.cgd.ucar.edu/trunk/inputdata/.

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Acknowledgements

The authors thank the support of the National Natural Science Foundation of China (42088101, 41625019, 42021004 and 41905075). We also thank the editor and the anonymous reviewers for their comments, which greatly helped improve the manuscript.

Funding

This work was funded by the National Natural Science Foundation of China (42088101, 41625019, 42021004 and 41905075).

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Authors and Affiliations

  1. Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/International Joint Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center On Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and Technology (NUIST), Nanjing, China

    Siguang Zhu, Xuan Dong, Yajing Qi, Jiangfeng Wei & Haishan Chen

  2. School of Atmospheric Sciences, NUIST, Nanjing, 210044, China

    Siguang Zhu, Xuan Dong, Yajing Qi, Jiangfeng Wei & Haishan Chen

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  1. Siguang Zhu
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Correspondence to Haishan Chen.

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Zhu, S., Dong, X., Qi, Y. et al. The predictability of snow depth at the North Hemisphere originated from persistence and oceanic forcing. Clim Dyn 60, 945–958 (2023). https://doi.org/10.1007/s00382-022-06356-4

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