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Ocean-atmosphere interactions and multi-scale climate variability in a changing climate
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Ocean Salinity as a Predictor of Summer Rainfall over East Asian Monsoon Region
P-P2-01-S
Biao CHEN* , 1. State Key Laboratory of Tropical Oceanography (LTO), South China Sea Institute of Oceanology (SCSIO), Chinese Academy of Science, Guangzhou
Huiling QIN, 3. School of Atmospheric Sciences, and Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Sun Yat-sen University, Guangzhou
Guixing CHEN, 3. School of Atmospheric Sciences, and Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Sun Yat-sen University, Guangzhou
Huijie XUE, 1. State Key Laboratory of Tropical Oceanography (LTO), South China Sea Institute of Oceanology (SCSIO), Chinese Academy of Science, Guangzhou
Presenter Email: chenbiao@scsio.ac.cn |
| The Sea Surface Salinity (SSS) can vary largely as a result of evaporation-precipitation difference, indicating the source or sink of regional/global water vapor. In this study, we identify a close relationship between the spring SSS in the Northwest Pacific and the summer rainfall of the East Asian Monsoon Region (EAMR) during 1980-2017. Analysis results suggest that the SSS-rainfall link may involve the coupled ocean-atmosphere-land processes with a two-stage evolution. In spring, evaporation and water vapor flux divergence were enhanced in some years over the Northwest Pacific where an anomalous atmospheric anticyclone was established and a high SSS was well observed. As a result, the convergence of water vapor flux and the soil moisture over the EAMR were strengthened. The change in spring soil moisture over the EAMR then modulated the subsequent large-scale atmospheric circulation and the regional water cycle, which contributed to the variation of summer rainfall. The high correlations among the above factors suggest that the signals of water cycle can be preserved for up to three months, implying a predictability of EAMR rainfall using SSS data. Using a random forest regression algorithm, we further evaluate the relative importance of SSS in predicting summer rainfall compared to other climate indices. As the SSS is now monitored routinely by satellite, it may serve as a good metrics for measuring the water cycle and predicting the EAMR rainfall. |
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