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Drivers of Eurasian Spring Snow-Cover Variability

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  • 1 Key Laboratory of Alpine Ecology, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China
  • 2 University of Chinese Academy of Sciences, Beijing, China
  • 3 CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing, China
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Abstract

The variability of spring snow cover over Eurasia can have notable impacts on the current and following season climate, but the causes of it are poorly understood. This study investigates the potential drivers and the associated physical processes for the first two empirical orthogonal function (EOF) modes of the Eurasian spring snow-cover variability during 1967–2018, which are characterized by a continent-wide coherent pattern and a west–east dipole structure, respectively. Analyses show that the spring surface air temperature and snowfall are the direct factors influencing the two modes. We further examined the contributions to the snow-cover variability of atmospheric teleconnection patterns, sea surface temperature (SST) anomalies, and variations of Arctic sea ice during spring. The results indicate that circulation anomalies associated with the Arctic Oscillation, Polar–Eurasian pattern, and West Pacific pattern can partly explain the formation of the EOF1 mode, while the EOF2 mode has a close relationship with the East Atlantic–Western Russia pattern. In addition, a horseshoe-like monopole structure of SST anomalies over the North Atlantic plays an important role in regulating the EOF2 mode by inducing a wave train circulation. Moreover, the EOF2 mode is also affected by anomalous circulations induced by the sea ice anomalies in the Barents–Kara Seas. An empirical model using these drivers satisfactorily reproduced the temporal variations of the two EOF modes, implying that our results can substantially improve comprehension of the variability of Eurasian spring snow cover.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-20-0413.s1.

© 2021 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: Tao Wang, twang@itpcas.ac.cn

Abstract

The variability of spring snow cover over Eurasia can have notable impacts on the current and following season climate, but the causes of it are poorly understood. This study investigates the potential drivers and the associated physical processes for the first two empirical orthogonal function (EOF) modes of the Eurasian spring snow-cover variability during 1967–2018, which are characterized by a continent-wide coherent pattern and a west–east dipole structure, respectively. Analyses show that the spring surface air temperature and snowfall are the direct factors influencing the two modes. We further examined the contributions to the snow-cover variability of atmospheric teleconnection patterns, sea surface temperature (SST) anomalies, and variations of Arctic sea ice during spring. The results indicate that circulation anomalies associated with the Arctic Oscillation, Polar–Eurasian pattern, and West Pacific pattern can partly explain the formation of the EOF1 mode, while the EOF2 mode has a close relationship with the East Atlantic–Western Russia pattern. In addition, a horseshoe-like monopole structure of SST anomalies over the North Atlantic plays an important role in regulating the EOF2 mode by inducing a wave train circulation. Moreover, the EOF2 mode is also affected by anomalous circulations induced by the sea ice anomalies in the Barents–Kara Seas. An empirical model using these drivers satisfactorily reproduced the temporal variations of the two EOF modes, implying that our results can substantially improve comprehension of the variability of Eurasian spring snow cover.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-20-0413.s1.

© 2021 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: Tao Wang, twang@itpcas.ac.cn

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