Gan Xu, Huijie Shi, Shoujuan Shu, Xuesong Chen, Jiabei Gu, Weijun Li
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引用次数: 0
Abstract
China faces the Northwest Pacific with the world's most active tropical cyclones (TCs). Whether and how the warming of the “Roof of the World”, the Tibetan Plateau (TP), influences the environmental field and precipitation of landfalling TC (LTC) remains unclear. In this study, a data-driven objective classification reveals that the key environmental field influencing the LTC precipitation in China is closely related to the TP-related high pressure. The precipitation rate of LTC in China exhibits an overall increasing trend over the past 43 years under TP warming. However, the trend of the precipitation rate depends on environmental fields. For LTCs affecting southeastern China, the South Asian High (SAH) intensifies and extends eastward, the Western Pacific Subtropical High (WPSH) shifts westward but weakens, stabilizing the atmosphere and reducing precipitation rate. For LTCs impacting southern China, the SAH and WPSH strengthen, increasing atmospheric instability and enhancing precipitation rate.
期刊介绍:
Atmospheric Science Letters (ASL) is a wholly Open Access electronic journal. Its aim is to provide a fully peer reviewed publication route for new shorter contributions in the field of atmospheric and closely related sciences. Through its ability to publish shorter contributions more rapidly than conventional journals, ASL offers a framework that promotes new understanding and creates scientific debate - providing a platform for discussing scientific issues and techniques.
We encourage the presentation of multi-disciplinary work and contributions that utilise ideas and techniques from parallel areas. We particularly welcome contributions that maximise the visualisation capabilities offered by a purely on-line journal. ASL welcomes papers in the fields of: Dynamical meteorology; Ocean-atmosphere systems; Climate change, variability and impacts; New or improved observations from instrumentation; Hydrometeorology; Numerical weather prediction; Data assimilation and ensemble forecasting; Physical processes of the atmosphere; Land surface-atmosphere systems.