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Ridge Regression Prediction Model for Temperatures of South China in May
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摘要: 该文研究5月华南气温变化特征及其成因, 寻找海温前兆信号并探讨其影响气温的可能物理机制, 建立5月华南气温的多元岭回归预测模型。结果表明: 5月华南气温异常偏高(偏低)年表现为乌拉尔山、东亚的异常反气旋(气旋)环流, 以及贝加尔湖附近的异常气旋(反气旋)环流, 使东亚经向环流减弱(加强), 冷空气活动减弱(加强); 同时副热带高压在华南地区异常西伸(东退)和西南风减弱(加强)。海温前兆信号主要为前冬北大西洋三极子型、印度洋全区一致型, 其中北大西洋海温前兆信号的相关性最强。北大西洋海温前兆信号为正(负)位相时, 通过欧亚遥相关波列使经向环流减弱(增强)和冷空气活动减弱(加强), 同时副热带高压在华南一带西伸(东退), 有利于华南地区气温偏高(低)。利用前冬前兆信号所建立的5月气温多元岭回归预测模型, 拟合效果较好并对异常年份有较好的预测能力。Abstract: The temperature variability of South China in May is investigated, identifying precursor signals in sea surface temperatures (SST) and exploring the potential physical processes influencing these variations. A ridge regression prediction model has been developed. The analysis reveals that during years with anomalously high (low) temperature in May, there are observed anticyclonic (cyclonic) circulations over the Ural Mountains and East Asia, along with anomalous cyclonic (anticyclonic) circulations near Lake Baikal. These conditions weaken (strengthen) the East Asian meridional circulation, reducing (intensifying) cold air activity. Concurrently, the subtropical high abnormally extends westward (retreats eastward) in the South China region, while the southwest winds weaken (strengthen).The key precursor SST signals for temperature anomalies in May are identified, primarily from the North Atlantic tripole pattern in the preceding winter and the basin-wide variability pattern in the Indian Ocean. Among these, SST signal of the North Atlantic Ocean shows the strongest correlation. When the North Atlantic Ocean SST precursor signal is in a positive (or negative) phase, it influences the meridional circulation to weaken (or strengthen) and reduces (or intensifies) cold air activity through the Eurasian teleconnection wave train. Simultaneously, the subtropical high extends westward (or retreats eastward) in South China, resulting in higher (or lower) temperature.The multivariate ridge regression prediction model for temperature in May, developed using precursor signals from the preceding winter, demonstrates good fitting results and predictive capability for anomalous years. The model's performance is validated through various statistical tests, including mean squared error (MSE) and correlation coefficients, which demonstrate its robustness and accuracy in predicting temperature anomalies in May. Results indicate that the ridge regression model offers a significant advantage over traditional multiple linear regression models in this context. The model's predictive power is particularly remarkable in capturing the overall trends and variations of temperature in May, although it exhibits some limitations in predicting extreme values. The research provides valuable insights into the climate dynamics of South China and offers a reliable tool for enhancing the accuracy of short-term climate forecasts in the region, and underscores the importance of considering large-scale climate signals, such as the North Atlantic Tripole and Indian Ocean Basin-wide variability, in the development of predictive models for regional climate anomalies. By incorporating these signals, the model can better account for the complex interactions between different climate systems, leading to more accurate and reliable forecasts. This approach not only enhances our understanding of the factors influencing temperature in South China in May, but also provides a framework for future research and operational forecasting efforts aimed at mitigating impacts of climate variability.
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图 1 1961—2022年5月华南平均气温距平(柱状) 及其10年以上低通滤波变化曲线(实线)(灰色虚线为1倍标准差线)
Fig. 1 Anomalies of temperature in May from 1961 to 2022 (the bar) and 10-year lowpass filter temperatures (the solid line)(the grey dashed line denotes one standard deviation)
图 2 气温异常年的大气环流和海温合成图
(黄色点和紫色箭头分别表示高度场和风场达到0.1显著性水平，下同) (a)异常偏高年500 hPa高度场标准化距平与合成风场，(b)异常偏低年500 hPa高度场标准化距平与合成风场，(c)异常偏高年850 hPa高度场标准化距平与合成风场, (d)异常偏低年850 hPa高度场标准化距平与合成风场, (e)异常偏高年海温标准化距平，(f)异常偏低年海温标准化距平
Fig. 2 Composite map of atmospheric circulation and sea surface temerature(SST) in abnormal temperature years
(the yellow dot and the purple arrow denote the height and wind fields passing the test of 0.1 level, similarly hereinafter) (a)500 hPa height normalized anomaly and resultant wind field in years of abnormally high temperature, (b)500 hPa height normalized anomaly and resultant wind field in years of abnormally low temperature, (c)850 hPa height normalized anomaly and resultant wind field in years of abnormally high temperature, (d)850 hPa height normalized anomaly and resultant wind field in years of abnormally low temperature, (e)SST standardization anomaly in years of abnormally high temperature, (f)SST standardization anomaly in years of abnormally low temperature
图 3 5月华南平均气温与前冬12月—当年5月的逐月海温相关系数
(矩形框为筛选的前兆信号区域)
Fig. 3 Correlation coefficients between average temperature in South China in May and monthly SST from previous Dec to May of current year
(the rectangular box denotes the selected precursor signal region)
图 4 大西洋前兆因子与5月500 hPa高度场(填色)、风场(箭头) (a)及850 hPa高度场(填色)、风场(箭头) (b)的相关系数
Fig. 4 Correlation coefficients of the Atlantic SST factor to 500 hPa height (the shaded), wind field (the arrow) (a) and 850 hPa height (the shaded), wind field (the arrow) (b)
图 5 5月华南气温距平的1961—2005年拟合(黑色实线)、2006—2022年回报(黑色虚线) 与观测距平(灰色实线) 对比
Fig. 5 Ridge regression fitting (the black solid line) in 1961-2005, prediction (the black dashed line) in 2006-2022, and comparison with observed temperature anomalies (the grey solid line) for temperature in South China in May
表 1 不同拟合样本量对应的岭回归模型
Table 1 Ridge regression model corresponding to different numbers of fitting samples and testing
训练拟合样本量 回报期样本量 最优α 拟合期相关系数 拟合期均方误差 回报期相关系数 回报期均方误差 50 12 1.38 0.470 0.426 0.520 1.147 49 13 1.45 0.469 0.433 0.522 1.073 48 14 1.31 0.491 0.427 0.470 1.013 47 15 1.31 0.491 0.436 0.485 0.946 46 16 1.33 0.488 0.444 0.485 0.894 45 17 1.38 0.497 0.402 0.499* 0.843 44 18 1.47 0.499 0.403 0.462 0.983 43 19 1.42 0.506 0.408 0.449 0.929 42 20 1.57 0.494 0.404 0.456* 0.941 41 21 1.68 0.492 0.403 0.452* 0.940 40 22 1.69 0.488 0.413 0.451* 0.902 39 23 1.71 0.486 0.424 0.446* 0.864 38 24 1.97 0.443 0.430 0.520* 0.860 注：*表示达到0.05显著性水平。 
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