Several consecutive extreme cold events impacted China during the first half of winter 2020/21, breaking the low-temperature records in many cities. How to make accurate climate predictions of extreme cold events is still an urgent issue. The synergistic effect of the warm Arctic and cold tropical Pacific has been demonstrated to intensify the intrusions of cold air from polar regions into middle-high latitudes, further influencing the cold conditions in China. However, climate models failed to predict these two ocean environments at expected lead times. Most seasonal climate forecasts only predicted the 2020/21 La Ni?a after the signal had already become apparent and significantly underestimated the observed Arctic sea ice loss in autumn 2020 with a 1–2 month advancement. In this work, the corresponding physical factors that may help improve the accuracy of seasonal climate predictions are further explored. For the 2020/21 La Ni?a prediction, through sensitivity experiments involving different atmospheric–oceanic initial conditions, the predominant southeasterly wind anomalies over the equatorial Pacific in spring of 2020 are diagnosed to play an irreplaceable role in triggering this cold event. A reasonable inclusion of atmospheric surface winds into the initialization will help the model predict La Ni?a development from the early spring of 2020. For predicting the Arctic sea ice loss in autumn 2020, an anomalously cyclonic circulation from the central Arctic Ocean predicted by the model, which swept abnormally hot air over Siberia into the Arctic Ocean, is recognized as an important contributor to successfully predicting the minimum Arctic sea ice extent.

Fei ZHENG;Ji-Ping LIU;Xiang-Hui FANG;Mi-Rong SONG;Chao-Yuan YANG;Yuan YUAN;Ke-Xin LI;Jiang ZHU

International Center for Climate and Environment Science(ICCES),Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,China;Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters,Nanjing University of Information Science& Technology,Nanjing 210044,China;Department of Atmospheric and Environmental Sciences University at Albany,State University of New York,Albany,NY 12222,USA;Department of Atmospheric and Oceanic Sciences& Institute of Atmospheric Sciences,Fudan University,Shanghai 200438,China;State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics(LASG),Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,China;School of Atmospheric Sciences,Sun Yat-sen University,Zhuhai 519082,China;National Climate Center,Beijing 100081,China;University of Chinese Academy of Sciences,Beijing 100049,China9Beijing Municipal Climate Center,Beijing 100089,China

大气科学进展（英文版）

[1]Fei ZHENG;Ji-Ping LIU;Xiang-Hui FANG;Mi-Rong SONG;Chao-Yuan YANG;Yuan YUAN;Ke-Xin LI;Jiang ZHU-.The Predictability of Ocean Environments that Contributed to the 2020/21 Extreme Cold Events in China: 2020/21 La Ni?a and 2020 Arctic Sea Ice Loss)[J].大气科学进展（英文版）,2022(04):658-672

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