Subseasonal prediction of land cold extremes in boreal wintertime
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.- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2020
- Bibcode:
- 2020AGUFMA192...08X
- Keywords:
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- 3322 Land/atmosphere interactions;
- ATMOSPHERIC PROCESSES;
- 3339 Ocean/atmosphere interactions;
- ATMOSPHERIC PROCESSES;
- 3362 Stratosphere/troposphere interactions;
- ATMOSPHERIC PROCESSES;
- 3373 Tropical dynamics;
- ATMOSPHERIC PROCESSES