High-skill members in the subseasonal forecast ensemble of extreme cold events in East Asia

Abstract

Subseasonal forecasting of extreme events is crucial for early warning systems. However, the forecast skills for extreme events are limited. Taking the extreme cold events in January 2018 as a specific example, and analyzing the 34 extreme cold events in East Asia from 1998 to 2020, the authors evaluated the forecast skills of the ECMWF model ensemble members on subseasonal time scales. The results show that while the ensemble mean has limited skills for forecasting extreme cold events at the 3-week lead time, some individual members demonstrate high forecast skills. For most extreme cold events, there are >10 % of members among the total ensembles that can well predict the rapid temperature transitions at the 14-day lead time. This highlights the untapped potential of the ECMWF model to forecast extreme cold events on subseasonal time scales. High-skill ensemble members rely on accurate predictions of atmospheric circulation patterns (500-hPa geopotential height, mean sea level pressure) and key weather systems, including the Ural Blocking and Siberian High, that influence extreme cold events.

摘要

极端事件次季节预报对防灾减灾保障社会经济安全具有重要意义. 本研究针对东亚地区极端低温事件的次季节预报难题, 通过分析1998–2020年34起东亚地区极端低温事件, 并重点关注2018年1月中国东北地区极端低温事件, 系统评估不同版本ECMWF模式集合成员之间的预报性能. 提前3周的模式集合平均预报性能存在局限, 但不同集合成员的预报技巧存在差异. 部分成员具有高预报技巧, 约10 %的高技巧成员能提前14天捕捉气温快速转折的过程. 研究指出集合成员是否具有高预报技巧依赖于对大气环流演变特征的合理预报. 该发现为极端冷事件次季节预报评估和后期订正提供了新视角, 凸显挖掘集合成员预报潜力的重要性, 并为提升次季节时间尺度预警能力提供了理论支撑.