Subseasonal Prediction of Land Cold Extremes in Boreal Wintertime

Baoqiang Xiang, Y. Qiang Sun, Jan Huey Chen, Nathaniel C. Johnson, Xianan Jiang

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

Abstract

Subseasonal climate prediction has emerged as a top forecast priority but remains a great challenge. Subseasonal extreme prediction is even more difficult than predicting the time-mean variability. Here we show that the wintertime cold extremes, measured by the frequency of extreme cold days (ECDs), are skillfully predicted by the European Centre for Medium-Range Weather Forecasts (ECMWF) model 2–4 weeks in advance over a large fraction of the Northern Hemisphere land region. The physical basis for such skill in predicting ECDs is primarily rooted in predicting a small subset of leading empirical orthogonal function (EOF) modes of ECDs identified from observations, including two modes in Eurasia (North Atlantic Oscillation and Eurasia Meridional Dipole mode) and three modes in North America (North Pacific Oscillation, Pacific-North America teleconnection mode, and the North America Zonal Dipole mode). It is of interest to note that these two modes in Eurasia are more predictable than the three leading modes in North America mainly due to their longer persistence. The source of predictability for the leading EOF modes mainly originates from atmospheric internal modes and the land-atmosphere coupling. All these modes are strongly coupled to dynamically coherent planetary-scale atmospheric circulations, which not only amplify but also prolong the surface air temperature anomaly, serving as a source of predictability at subseasonal timescales. The Eurasian Meridional Dipole mode is also tied to the lower-boundary snow anomaly, and the snow-atmosphere coupling helps sustain this mode and provides a source of predictability.

Original languageEnglish
Article numbere2020JD032670
JournalJournal of Geophysical Research: Atmospheres
Volume125
Issue number13
DOIs
StatePublished - Jul 16 2020

Keywords

  • extremes
  • land-atmosphere coupling
  • predictability sources
  • subseasonal prediction
  • surface air temperature

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