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A Statistical Prediction for East Asian Winter Monsoon Based on Sea-ice-air System
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摘要: 东亚冬季风对东亚冬季的天气气候具有重要影响, 对其预测研究是冬季气候预测的关键问题。已有东亚冬季风强度指数(ISA)与东北冬季气温在年际、年代际尺度具有显著且稳定的相关关系, 但ISA的前兆信号在20世纪90年代末发生年代际转变, 对ISA的预测效果转差。在海-冰-气系统重新寻找影响ISA的前兆因子, 分析其与东亚冬季风的可能关联, 构建统计预测方法并开展交叉检验。结果表明:20世纪80年代后ISA与前期热带太平洋马蹄型结构的海温、墨西哥湾流区海温、平流层欧亚中高纬环流型呈显著正相关, 与巴伦支海海冰密集度呈显著负相关。以上前兆信号可通过冬季大气环流、海陆热力差等途径影响ISA, 预测模型拟合效果较好, 试报期间(2012—2022年)与实况的符号一致率达81.8%(9/11), 可用于当前年代际背景下东亚冬季风强度预测。Abstract: East Asian winter monsoon (EAWM) is one of the most crucial circulation systems in the Northern Hemisphere during winter, significantly influencing the weather and climate of East Asia. Therefore, predicting EAWM variations is considered as a key issue in winter climate prediction. The EAWM intensity index, as defined by Liu Shi (ISA) has shown a strong and consistent correlation with the interannual and interdecadal variations of winter temperature in Northeast China. However, the precursors influencing the EAWM (ISA) changed significantly with the decadal shift of the EAWM in the late 1990s. Predictions of EAWM have become less effective, and it is necessary to identify new predictors. Therefore, correlation analysis is conducted to identify the key factors influencing ISA based on the sea-ice-air system using reanalysis data produced by the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR), as well as optimum interpolation SST V2 data from the National Oceanic and Atmospheric Administration (NOAA). EAWM precursor factors are established and their possible interactions are discussed. Factors are used to construct a statistical prediction model using multiple linear regression method, which is evaluated through cross-validation. Results reveal a significant positive correlation between ISA and the horseshoe-shaped sea surface temperature (SST) pattern over the tropical Pacific autumn, as well as SST over the Gulf Stream and the Eurasian mid-high latitude circulation pattern in stratosphere. ISA shows a stronger and more consistent negative correlation with the sea ice concentration of the Barents Sea than that of the Kara Sea and Laptev Sea. These precursors influence ISA through land/sea thermal differences, winter atmospheric circulation patterns such as the East Asian trough, Ural blocking, and the East Asian subtropical westerly jet. The aforementioned prediction model demostrates a good fit and can be utilized to predict EAWM intensity under the current interdecadal background, with a consistency in the anomaly sign rate of 81.8% (9/11) during 11-year hindcast from 2012 to 2022. An analysis of two years of prediction failures reveals that the winter Arctic Oscillation (AO) forecasts, as well as the abrupt transition of the AO from autumn to winter, should be considered in the EAWM prediction process.
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图 1 1961—2022年ISA(a)和ISH(b)与东亚大陆冬季平均2 m气温相关分布
(·表示达到0.01显著性水平)
Fig. 1 Correlation of winter 2 m temperature over East Asia to ISA(a) and ISH(b) during 1961-2022
(· denotes passing the test of 0.01 level)
图 2 1991—2020年ISA与9—10月SST(a)及10月500 hPa位势高度(b)相关分布
(·表示达到0.05显著性水平)
Fig. 2 Correlation of ISA to SST in Sep-Oct(a) and 500 hPa height in Oct(b) during 1991-2020
(· denotes passing the test of 0.05 level)
图 3 1982—2011年ISA与9—10月SST相关分布
(·表示达到0.05显著性水平)
Fig. 3 Correlation of ISA to SST in Sep-Oct during 1982-2011
(· denotes passing the test of 0.05 level)
图 4 1982—2011年ISA与9—10月海冰密集度相关分布
(·表示达到0.05显著性水平)
Fig. 4 Correlation of ISA to sea ice concentration in Sep-Oct during 1982-2011
(· denotes passing the test of 0.05 level)
图 5 1961—1990年(a)、1982—2011年(b)ISA与9—10月150 hPa位势高度相关分布
(·表示达到0.05显著性水平)
Fig. 5 Correlation of ISA to 150 hPa height in Sep-Oct during 1961-1990(a) and 1982-2011(b)
(· denotes passing the test of 0.05 level)
图 6 1982—2011年ISST与冬季SST(a)和SLP(b)相关分布
(·表示达到0.05显著性水平)
Fig. 6 Correlation of ISA to SST(a) and SLP(b) in winter during 1982-2011
(· denotes passing the test of 0.05 level)
图 7 1982—2011年ISST4与冬季SLP(a)和500 hPa位势高度(b)相关分布
(·表示达到0.05显著性水平)
Fig. 7 Correlation of ISST4 to SLP(a) and 500 hPa height(b) in winter during 1982-2011
(· denotes passing the test of 0.05 level)
图 8 1982—2011年-IBR与冬季SLP(a)、500 hPa位势高度(b)和200 hPa纬向风(c)相关分布
(·表示达到0.05显著性水平)
Fig. 8 Correlation of -IBR to SLP(a), 500 hPa height(b) and 200 hPa zonal wind(c) in winter during 1982-2011
(· denotes passing the test of 0.05 level)
图 9 1982—2011年IZ150与冬季SLP(a)和150 hPa位势高度(b)相关分布
(·表示达到0.05显著性水平)
Fig. 9 Correlation of IZ150 to SLP(a) and 150 hPa height(b) in winter during 1982-2011
(· denotes passing the test of 0.05 level)
表 1 1982—2011年关键区海温指数及其与ISA的相关系数和符号一致率
Table 1 Correlation coefficients and consistency of the anomaly sign between ISA and key SST indices during 1982-2011
海温指数 范围 相关系数 符号一致率/% ISST1 黑潮延伸区(28°~36°N，170°E~172°W) 0.51Δ 70 ISST2 新西兰东北侧(32°~24°S，176°E~170°W) 0.49Δ 80 ISST3 Niño区(6°~14°N，130°~154°W) -0.56Δ 83 ISST4 墨西哥湾区(34°~40°N，64°~72°W) 0.66Δ 77 注：Δ表示相关系数达到0.05显著性水平。 
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表 2 各预测因子与ISA的相关系数、符号一致率
Table 2 Correlation and consistency of the anomaly sign between ISA and prediction factors
预测因子 与ISA的相关系数(1982—2011年) 符号一致率(1982—2011年)/% 符号一致率(2012—2022年)/% ISST 0.58ΔΔ 76.7 63.6 ISST4 0.66ΔΔ 73.3 63.6 IZ150 0.51Δ 76.7 72.7 IBR -0.63ΔΔ 73.3 72.7 注：Δ和ΔΔ分别表示相关系数达到0.05和0.01显著性水平。
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