Predicting climate anomalies:A real challenge |
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| Authors: | Huijun Wang Yongjiu Dai Song Yang Tim Li Jingjia Luo Bo Sun Mingkeng Duan Jiehua Ma Zhicong Yin Yanyan Huang |
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| Affiliation: | Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster,Ministry of Education,Nanjing University of Information Science and Technology,Nanjing,China;School of Atmospheric Sciences,San Yat-sen University & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),Zhuhai,China;Nansen-Zhu International Research Center,Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing,China;Climate Change Research Center,Chinese Academy of Sciences,Beijing,China,School of Atmospheric Sciences,San Yat-sen University & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),Zhuhai,China,Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster,Ministry of Education,Nanjing University of Information Science and Technology,Nanjing,China;International Pacific Research Center and Department of Atmospheric Sciences,School of Ocean and Earth Science and Technology,University of Hawaii at Manoa,Honolulu,USA,Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster,Ministry of Education,Nanjing University of Information Science and Technology,Nanjing,China,Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster,Ministry of Education,Nanjing University of Information Science and Technology,Nanjing,China;School of Atmospheric Sciences,San Yat-sen University & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),Zhuhai,China;Nansen-Zhu International Research Center,Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing,China,Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster,Ministry of Education,Nanjing University of Information Science and Technology,Nanjing,China;School of Atmospheric Sciences,San Yat-sen University & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),Zhuhai,China,Nansen-Zhu International Research Center,Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing,China |
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| Abstract: | In recent decades, the damage and economic losses caused by climate change and extreme climate events have been increasing rapidly. Although scientists all over the world have made great efforts to understand and predict climatic variations, there are still several major problems for improving climate prediction. In 2020, the Center for Climate System Prediction Research (CCSP) was established with support from the National Natural Science Foundation of China. CCSP aims to tackle three scientific problems related to climate prediction—namely, El Niño–Southern Oscillation (ENSO) prediction, extended-range weather forecasting, and interannual-to-decadal climate prediction—and hence provide a solid scientific basis for more reliable climate predictions and disaster prevention. In this paper, the major objectives and scientific challenges of CCSP are reported, along with related achievements of its research groups in monsoon dynamics, land–atmosphere interaction and model development, ENSO variability, intraseasonal oscillation, and climate prediction. CCSP will endeavor to tackle key scientific problems in these areas.摘要过去几十年, 气候变化和极端气候事件造成的经济损失和灾害显著增加. 虽然全球的科学家在理解和预测气候变异方面做出了巨大的努力, 但当前在气候预测领域仍然存在几个重大难题. 2020年, 依托于国家自然科学基金基础科学中心项目的气候系统预测研究中心 (CCSP) 成立了, 该中心旨在应对和处理气候预测领域的三大科学难题: 厄尔尼诺-南方涛动 (ENSO) 预测, 延伸期天气预报, 年际-年代际气候预测, 并为更加准确的气候预测和更加有效的灾害防御提供科学依据. 因此, 本文介绍了CCSP的主要目标和面对的科学挑战, 回顾了CCSP在季风动力过程, 陆-气相互作用和模式开发, ENSO变率, 季节内振荡, 气候预测等方面已取得的重要研究成果. 未来CCSP将继续致力于解决上述领域的关键科学问题. |
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| Keywords: | Center for climate system prediction research(CCSP) Monsoon dynamics Land surface model ENSO dynamics Extended-range forecasting Interannual-to-decadal prediction |
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