Vol.35, NO.2, 2024
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Article Navigation >  Journal of Applied Meteorological Science  > 2024  >  35(2): 168-181
Shao Qiduo, Tu Gang, Bueh Cholaw, et al. A statistical prediction for East Asian winter monsoon based on sea-ice-air system. J Appl Meteor Sci, 2024, 35(2): 168-181. DOI:  10.11898/1001-7313.20240204.
Citation: Shao Qiduo, Tu Gang, Bueh Cholaw, et al. A statistical prediction for East Asian winter monsoon based on sea-ice-air system. J Appl Meteor Sci, 2024, 35(2): 168-181. DOI:  10.11898/1001-7313.20240204.
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A Statistical Prediction for East Asian Winter Monsoon Based on Sea-ice-air System

DOI: 10.11898/1001-7313.20240204
  • 1.

    Institute of Meteorological Sciences of Jilin Province Jilin Provincial Key Laboratory of Changbai Mountain Meteorology & Climate Change, Laboratory of Research for Middle-high Latitude Circulation Systems and East Asian Monsoon, Changchun 130062

  • 2.

    International Centerfor Climate and Environment Sciences, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029

  • 3.

    Jilin Meteorological Bureau, Changchun 130062

  • Received Date: 2023-11-15
  • Rev Recd Date: 2024-01-15
  • Publish Date: 2024-03-27
    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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  • 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)

    DownLoad: Full-Size Img PowerPoint

    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)

    DownLoad: Full-Size Img PowerPoint

    Fig. 3  Correlation of ISA to SST in Sep-Oct during 1982-2011

    (· denotes passing the test of 0.05 level)

    DownLoad: Full-Size Img PowerPoint

    Fig. 4  Correlation of ISA to sea ice concentration in Sep-Oct during 1982-2011

    (· denotes passing the test of 0.05 level)

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    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)

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    Fig. 6  Correlation of ISA to SST(a) and SLP(b) in winter during 1982-2011

    (· denotes passing the test of 0.05 level)

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    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)

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    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)

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    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)

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    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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    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。
    DownLoad: Download CSV
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    • Received : 2023-11-15
    • Accepted : 2024-01-15
    • Published : 2024-03-27
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