Impact of the Asian–Pacific Oscillation on the interannual variability of rainy season onset date in Southwest China

Wen, Dayong; Zhang, Jiawei; Cao, Jie

doi:10.1007/s00382-022-06149-9

Cited by 6 publications

(1 citation statement)

References 36 publications

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“…Ha et al (2019) [17] found that the weakening of the South Asian monsoon due to warming SST in the northern Indian Ocean resulted in reduced moisture transport from the Bay of Bengal to the Yunnan-Guizhou Plateau, leading to a significant decrease in summer precipitation from 2003 to 2012 compared to 1993-2002. Wen et al (2022) [18] found that the positive phase of the Western Pacific Oscillation enhanced abnormal low-level cyclones and anticyclones, causing an earlier onset of the rainy season in the Yunnan-Guizhou Plateau. Consequently, SSTAs in regions such as the northern Indian Ocean, western Pacific, and central-eastern Pacific have a significant impact on summer precipitation anomalies in the Yunnan-Guizhou region.…”

Section: Introductionmentioning

confidence: 99%

Predicting Summer Precipitation Anomalies in the Yunnan–Guizhou Plateau Using Spring Sea-Surface Temperature Anomalies

Tuo,

Qiao,

Liu

et al. 2024

Atmosphere

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By constructing a correlation network between global sea surface temperature anomalies (SSTAs) and summer precipitation anomalies in the Yunnan–Guizhou Plateau, key SST regions influencing summer precipitation anomalies in the Yunnan–Guizhou Plateau were selected. It was found that spring SSTAs in the Bay of Bengal, southwestern Atlantic, and eastern Pacific are crucial for influencing summer precipitation anomalies in the Yunnan–Guizhou Plateau. Setting SSTAs from these three regions as predictor variables 3 months in advance, we constructed multiple linear regression (MLR), ridge regression (RR), and lasso regression (LR) models to predict summer precipitation anomalies over the Yunnan–Guizhou region. The training phase involved data spanning from 1961 to 2005, which aimed to predict precipitation anomalies in the Yunnan–Guizhou Plateau for the period extending from 2006 to 2022. Based on MLR, RR, and LR models, the correlations between predicted values and observed summer precipitation anomalies in Yunnan–Guizhou were 0.48, 0.46, and 0.46, respectively. These values were all higher than the correlation coefficients of the NCC_CSM model’s predicted and observed values. Additionally, its performance in predicting summer precipitation anomalies over the Yunnan–Guizhou region, based on key SST regions, was assessed using performance metrics such as anomaly correlation coefficient (ACC), anomaly sign consistency rate (PC), and trend anomaly comprehensive score (PS score). The average ACC of MLR, RR, and LR models was higher than that of the NCC_CSM model’s predictions. For MLR, RR, LR, and NCC_CSM models, the PCs exceeding 50% of the year were 14, 14, 11, and 10, respectively. Furthermore, the average PS score for predicting summer precipitation anomalies over the Yunnan–Guizhou region using MLR, RR, and LR was approximately 73 points; 8 higher than the average PS score of the NCC_CSM model. Therefore, predicting summer precipitation anomalies over the Yunnan–Guizhou region based on key SST regions is of great significance for improving the prediction skills of precipitation anomalies in this region.

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