Dynamical Downscaling Simulation and Future Projection of Extreme Precipitation Activities in Taiwan during the Mei-Yu Seasons

摘要

By using the Weather Research and Forecasting (denoted as WRF) model driven by two super-high-resolution global models, High Resolution Atmospheric Model (denoted as HiRAM) and Meteorological Research Institute Atmospheric General Circulation Model (denoted as MRI), this study investigates the dynamical downscaling simulation and projection of extreme precipitation activities (including intensity and frequency) in Taiwan during the Mei-Yu seasons (May and June). The analyses focus on two time period simulations: the present-day (1979-2003, historical run) and the future (2075-2099, RCP8.5 scenario). For the present-day simulation, our results show that the bias of HiRAM and MRI in simulating the extreme precipitation activities over Taiwan can be reduced after dynamical downscaling by using the WRF model. For the future projections, both the dynamical downscaling models (i.e., HiRAM-WRF and MRI-WRF) project that extreme precipitation will become more frequent and more intense over western Taiwan but less frequent and less intense over eastern Taiwan. The east-west contrast in the projected changes in extreme precipitation in Taiwan are found to be a local response to the enhancement of southwesterly monsoonal flow over the coastal regions of South China, which leads to an increase in water vapor convergence over the windward side (i.e., western Taiwan) and a decrease in water vapor convergence over the leeward side (i.e., eastern Taiwan). Further examinations of the significance of the projected changes in extreme precipitation that affect the agriculture regions of Taiwan show that the southwestern agriculture regions will be affected by extreme precipitation events more frequently and more intensely than the other subregions. This finding highlights the importance of examining regional differences in the projected changes in extreme precipitation over the complex terrain of East Asia.