“Global Climate Change and Water System Impacts” 系列主题学术讲座（讲座议程）

报告题目：A cascading bias correction method for global climate model simulated multi-year precipitation variability

报  告 人：Prof. Allan Frei

邀  请 人：陈杰 教授

时      间：2021年11月30日（星期二）晚上9:00

地      点： Zoom会议（ID:  873 912 5358）

               密码：123456

报告人简介：  

Allan Frei is a climatologist whose research interests have focused on the potential impact of climate change on hydrology, including snow cover and extreme storms and floods. Recent projects include an investigation of potential impacts of climate change on New York City's water supply in collaboration with the New York City Department of Environmental Protection, which manages the city's water supply system.

报告简介：

The use of Global Climate Model (GCM) precipitation simulations typically requires corrections for precipitation biases at sub-grid spatial scales, typically at daily or monthly timescales. However, over many regions GCMs underestimate of the magnitudes of multi-year precipitation extremes in the observed climate, resulting in a likely underestimation of the magnitudes of multi-year precipitation extremes in future scenarios. The objective of this study is to propose a method to extract from GCMs scenarios of multi-year precipitation extremes over time horizons of decades to one century that are more realistic than raw climate model results over particular regions. This proposed correction method is analogous to widely used bias correction methods, except that it is applied to variability at longer time scales than previous implementations (i.e. multi-year rather than daily or monthly). A case study of a precipitation over a basin from the New York City water supply system demonstrates the potential magnitude of the underestimation of multi-year precipitation using uncorrected GCM scenarios, and the potential impact of the correction on multi-year extreme hydrological extremes. Overall, it is a practical, conceptually simple approach meant for water supply system impact studies, but can be used for any impact studies that require more realistic multi-year extreme precipitation extreme scenarios.

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