科研之友
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摘要
A unique approach for downscaling daily precipitation extremes from historical analogues is presented. While various analogue methods have been developed for the purpose of downscaling local climate data, few have placed an emphasis on downscaling daily extremes. Unlike previous approaches, the new method utilizes a two-step procedure in which the occurrence of extreme precipitation on a given target day is first determined based on the observed probability of extreme precipitation on that day's closest historical analogue days. Then, if extreme precipitation occurred on the selected analogue day, the historical precipitation observations associated with the analogue day are used to ascribe precipitation amounts on the corresponding target day. The method is developed and tested for a very strict definition of extreme precipitation (partial duration series events), as well as a more lenient definition of extreme precipitation (95th percentile of non-zero precipitation events). The analogue approach is more skillful than climatology at identifying the occurrence of both partial duration series (PDS) and 95th percentile events. In both cases, the analogue method slightly underestimates the observed occurrence of extreme precipitation. Return period precipitation amounts estimated from the downscaled PDS are similar to, but generally lower than those calculated from observed PDS. Over the entire study domain (157 stations in New York State and surrounding regions of adjacent states and Canada), the median difference between downscaled and observed 5-year (100-year) return period precipitation amounts is less than 5% (10%). These median differences are smaller than those obtained from historical dynamically downscaled climate model simulations.
- 出版日期2016-3-30
