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The Predictability of Ocean Environments that Contributed to the 2020/21 Extreme Cold Events in China:2020/21 La Niña and 2020 Arctic Sea Ice Loss 被引量:2

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摘要 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 Niña 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 Niña 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 Niña 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.
出处 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2022年第4期658-675,共18页 大气科学进展(英文版)
基金 supported by the Key Research Program of Frontier Sciences,CAS (Grant No. ZDBS-LY-DQC010) the National Natural Science Foundation of China (Grant Nos. 41876012 and 41861144015,42175045) the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No.XDB42000000).
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  • 1彭京备,郑飞,范方兴,陈红,郎咸梅,詹艳玲,林朝晖,张庆云,林壬萍,李超凡,马洁华,田宝强,包庆,穆松宁,宗海锋,王磊,段晚锁,周天军.2022年汛期气候趋势预测与展望[J].气候与环境研究,2022,27(4):547-558. 被引量:2
  • 2范方兴,郑飞,彭京备,陈红,郎咸梅,詹艳玲,马洁华,李超凡,包庆,胡帅,董啸,田宝强,王磊,穆松宁,宗海锋,段晚锁,林朝晖,张庆云,周天军.2023年汛期气候趋势预测与展望[J].气候与环境研究,2023,28(4):450-460.

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