考虑季节内变化的我国北方冬季气候统计-动力预测研究

Intraseasonal variability (ISV) of winter climate over northern China is more significant than ever, occurring alternative frigid and extreme warm weather in winter, resulting in serious haze and meteorology disasters. For instance, in North China, more frequent cold air activity results in below than normal temperature in December in 2015. However, the temperature increased rapidly in January in 2015 resulting in severe haze pollution over North China. So far, as regards to winter climate prediction over northern China, we focus on the winter seasonal mean climate prediction (December-January-February), whereas little focus on the ISV climate prediction. Therefore, we should consider the ISV to forecast the winter climate over northern China. So far, most related researches on ISV of summer climate and winter precipitation over southern China have been carried out, whereas little research focus on ISV of winter climate over northern China. Although the tropical Madden Julian Oscillation (MJO) is served as one of main prediction sources of ISV of climate over China, it cannot capture the actually ISV of climate in China, especially for winter climate over northern China. The most of the state-of-the-art of climate dynamical models show a limited skill in prediction of the ISO. Therefore, in the project, here, ISV denotes the sub-seasonal with the focus on month-to-month variability in winter, we will analyze the ISV of winter climate over northern China, to identify the key processes and mechanisms; to investigate the ability of the state-of-the-art coupled models including the sub-seasonal-to-seasonal prediction project (S2S) coupled models on prediction of ISV of winter climate over northern China, to identify new prediction sources from high latitude such as sea ice, snow cover, and extra-tropical atmospheric modes, etc.; based on the above understanding of ISV, we will attempt to develop effective statistical, dynamical, hybrid of statistical and dynamical prediction models for winter climate over northern China. The project is expected to contribute to improve the level of accuracy in prediction of winter climate in northern China in the future.

我国北方冬季经常出现气候季节内变化显著和大幅度冷暖交替的情况，造成严重雾霾和气象灾害。如2014年12月华北气温偏低，冷空气频繁，而2015年1月则气温偏高，中重度雾霾天气多次出现。过去关于北方冬季气候预测研究大多只关注冬季（12-2月）平均，对于季节内变化的预测研究甚少，因此，非常需要研究考虑季节内气候异常差异的冬季气候预测。关于季节内振荡的研究此前大多关注南方，尽管已揭示热带大气低频振荡是季节内变化主要成因和预测源，但不能完全解释我国特别是我国北方冬季季节内的变化。本项目将研究我国北方冬季气候季节内变化的事实和机理；分析评估国内外先进耦合模式对我国北方冬季气候季节内变化的预测能力，揭示热带外可能的新预测源（海冰、积雪、大气模态等）；进而研究考虑我国北方季节内变化的冬季气候统计、动力及二者结合的预测模型，开展实时预测，期望提升我国冬季气候预测水平，为推动气候预测业务进步奠定科学基础。

中国北方冬季气候常常发生剧烈的季节内反转现象，造成严重的气象灾害，给气候预测带来更大挑战。围绕该前沿科学问题，本项目深入研究了近几十年中国北方冬季气候及相联气候系统异常的季节内差异的事实和机理，据此系统评估了模式可预测性和预测来源，进而研制了考虑季节内变化的中国北方冬季气候及相联气候系统的预测模型，并开展实时预测。目前取得的重要研究成果：（1）揭示了中国及其北方典型区域冬季气温月际转折的基本特征，给出了中太平洋型ENSO、北极海冰等影响的物理机制；（2）指出12月/1月西伯利亚高压强度转折频次自2000年后显著增加及成因，揭示了2020/21年冬季西伯利亚高压强度季节内转折的机制及对东亚气温转折的影响；（3）揭示了冬季北半球平流层极涡季节内−年际变化特征、机制及对欧亚气温的影响；（4）指出了亚洲中高纬地表气温和北太平洋涛动的年际变率在冬季不同月份存在明显差异，且北极海冰对此有重要贡献；（5）揭示了北半球中高纬气候系统对欧亚冬季极端与平均气候、春夏植被的影响及机制；（6）指出了动力模式对中国冬季气温及相联气候系统异常季节内转折的预测能力有限，以及动力模式对它们的预测效能在不同月份或不同阶段（前冬、后冬）的差异及成因；（7）基于以上观测事实、机理和模式预测效能的深入认知，研制了中国冬季气候及相联气候系统的季节平均、不同月份或不同阶段的动力和统计结合预测模型；（8）将研究成果应用于国家业务部门，开展实时预测，取得较好效果。本项目的开展加深了对中国北方冬季气候异常季节内差异机理和模式预测效能的认知，研制了中国冬季气候的高效预测模型，为国家防灾减灾提供科学参考。目前，已发表文章55篇（SCI 41篇），第1和第2标注49篇（第1标注30篇）。项目负责人范可教授入选国家“万人计划”科技领军人才，荣获“庆祝中华人民共和国成立70周年”纪念章、2020年国务院政府特殊津贴，入选2020年全球前2%顶尖科学家榜单等。培养研究生10名，多人荣获国家奖学金、中国科学院大学以及省级优秀毕业生等荣誉。