敖康顺,耿新,张文君,王林. 2024. 东亚冬季风指数对我国冬季气候变化表征能力的对比分析[J]. 气象学报, (0):-, doi:[doi]
东亚冬季风指数对我国冬季气候变化表征能力的对比分析
Investigation of the Efficacy of East Asian Winter Monsoon Indices in Capturing Winter Climate Change over China
投稿时间:2023-07-28  修订日期:2023-11-17
DOI:
中文关键词:  东亚冬季风指数,年际变化,年代际变化,趋势变化,ENSO,北极涛动
英文关键词:East  Asian winter  monsoon index, Interannual  variability, Interdecadal  variability, Trend  change, ENSO, Arctic  Oscillation
基金项目:
作者单位邮编
敖康顺 南京信息工程大学气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心 210044
耿新* 南京信息工程大学气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心 210044
张文君 南京信息工程大学气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心 210044
王林 中国科学院大气物理研究所季风系统研究中心 100029
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中文摘要:
      [资料和方法]利用1951-2022年ERA5再分析大气环流资料和国家气候中心全国站点气温和降水资料,[目的]将33个常用的东亚冬季风(EAWM)指数划分为海陆差异类、高压特征类、大槽特征类、低层风场类、中高层风场类和综合类六类,按类别对比分析了它们的线性变化趋势和年际、年代际变化特征,并就各指数对我国冬季气温、降水时空变化的表征能力以及与厄尔尼诺-南方涛动(ENSO)、北极涛动(AO)等气候系统主要内部变率的关系进行了评估分析。[结论]结果显示:1)在趋势变化方面,我国冬季气候暖湿化特征明显,但仅大槽特征类和综合类指数反映出季风的减弱趋势,其余类型指数则多呈现微弱的增强趋势,表明EAWM各子成员对当前全球变暖的响应存在差异;2)在年际、年代际变化方面,EAWM指数主要表现为准4a、准8a和准16a的周期振荡,基本都能刻画出1980年代中后期EAWM的年代际减弱,对于2000年代中期EAWM的年代际增强,考虑了南北气压差的海陆差异类指数以及高压特征类、大槽特征类和中高层风场类指数能较好表征;3)在反映我国冬季气温变率的能力方面,除低层风场类指数外,各类指数表现良好,尤其是高压特征类指数的表征能力最佳,而在降水变率方面,高压特征类指数的代表性较差,低层风场类指数的指示意义最好;4)在与气候系统主要内部变率的关系方面,大多数指数能较好反映ENSO与EAWM之间的关系,其中低层风场类指数的表征能力最好。而在反映AO与EAWM之间的关系上,则是高压特征类和大槽特征类指数的表现更佳。[结果]总体而言,除趋势变化存在较大差异外,各类EAWM指数能够一致性地反映我国冬季气候变化的主要特征,但不同类别指数所表征的侧重点有所差异。因此,在分析EAWM相关科学问题时中应根据研究目的选择合适的指数。
英文摘要:
      Based on the ERA5 atmospheric reanalysis data and the stational surface air temperature (SAT) and precipitation data from the Chinese National Climate Center during the 1951-2022 period, 33 East Asian winter monsoon (EAWM) indices are selected and categorized into six types, namely the land-sea sea-level pressure (SLP) difference indices, Siberian high indices, East Asian trough indices, low-level wind indices, mid- to high-level wind indices, and synthetic indices. The characteristics of their long-term linear trends, interannual and interdecadal variabilities are then analyzed, and their abilities to represent the spatio-temporal variability of the winter SAT and precipitation in China and their relationships with the major internal variabilities of the climate system, such as the El Ni?o-Southern Oscillation (ENSO) and the Arctic Oscillation (AO), are also evaluated. The results are shown as follows. 1) In terms of the long-term trend, although China"s winter climate is characterized by an obvious warming and wetting trend, only the East Asian trough and the synthetic indices exhibit a significant weakening trend, while the rest of the indices basically show a weak strengthening trend, suggesting some differences in the response of different sub-members of the EAWM to the current global warming. 2) On the interannual and interdecadal variability, the EAWM indices mainly show periodic oscillation characteristics at quasi-4a, quasi-8a and quasi-16a. Most of the indices can well capture the interdecadal weakening of the EAWM around the mid to late 1980s. Meanwhile, the Siberian high indices, the East Asian through indices, the mid- to high-level wind indices and the land-sea SLP difference indices considering the north-south SLP difference show a good representation of the interdecadal strengthening of the EAWM occurring in the mid-2000s. 3) All types of indices, except the low-level wind indices, perform well in reflecting the SAT ?variability in China. In particular, the indices based on the Siberian high show the best performance. The winter precipitation variability in China is best described by the low-level wind indices, while the Siberian high indices show relatively poor representation. 4) Regarding the relationship between the major internal variability of the climate system and the EAWM, most of the indices show excellent performance in reflecting the ENSO-EAWM relationship, among which the low-level wind indices show the best ability. The AO-EAWM relationship is best reflected by the Siberian high and the East Asian trough indices. In general, except for the considerable differences in the long-term trends, all types of EAWM indices can consistently reflect the main characteristics of winter climate change in China. However, different categories of indices emphasize different aspects. Therefore, appropriate indices should be selected according to the purpose of the study in analyzing scientific issues related to EAWM.
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