期刊
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
卷 127, 期 7, 页码 -出版社
AMER GEOPHYSICAL UNION
DOI: 10.1029/2021JD036286
关键词
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资金
- National Natural Science Foundation of China [91837205]
- Major Science and Technology Project of Gansu Province [20ZD7FA005]
- National Key R&D Program of China [2020YFA0608404]
This study investigates the coupling of soil moisture and precipitation over the Tibetan Plateau, revealing significant spatial differences in the relationship between spring soil moisture and summer precipitation. The study also highlights the link between summer precipitation biases and the failure of models to capture soil moisture-precipitation coupling.
The impact of soil moisture (SM) and precipitation coupling (SM-P-C) on the subsequent climate at daily to seasonal scales over the Tibetan Plateau (TP) is a critical issue. In this study, the spring SM and summer precipitation coupling (SSM-SP-C) were investigated using multi-reanalysis and validated with 13 global climate models (GCMs) in Coupled Model Intercomparison Project Phase 5, and its relationship with the wet bias in GCMs was explored. The results reveal that SSM-SP-C shows an east-west reversal, with positive coupling in eastern and central TP and negative coupling in the western TP. The spring surface sensible heat has significantly negative effects on the strength of SSM-SP-C in eastern and central TP, while significantly positive effects in western TP; whereas, the SM shows the reverse effects in eastern and western TP. The concurrent SM-P-C over TP shows a positive trend from spring to summer, but coupling strength has significant spatial differences. The 13 GCMs ensemble mean underestimates the positive SSM-SP-C in eastern TP and can't reproduce the negative SSM-SP-C in western TP. The wet bias of summer precipitation is significantly associated with the model's failure in capturing SSM-SP-C. This study highlights the impacts of SM-P-C on subsequent climate in the alpine cold region and provides a potential way for reducing precipitation biases by improving SM-P-C parameterizations in GCMs.
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