CDF-T方法在站点尺度日降水预估中的应用

Projection of the Daily Precipitation Using CDF-T Method at Meteorological Observation Site Scale

基于1961-2015年上海降水观测数据和8个全球气候模式GCMs模拟的日降水量数据,采用累计概率分布函数构建转换模型CDF-T建立了站点尺度日降水量的统计降尺度模型。结果表明,降尺度模型显著改善了GCMs对降水日数偏多、降水强度偏低和降水量偏少的模拟结果。与利用全年日降水序列建模结果相比,利用汛期日降水序列建模更好地刻画了汛期降水的累计概率分布曲线,同时提高了汛期总降水量、降水强度和年平均暴雨日数、暴雨量、暴雨强度的均值和变化趋势的降尺度效果。模型对较长年份的暴雨重现期订正效果更佳。与当代(2006-2015年)气候相比,2016-2095年上海降水呈现以下特征:全年和汛期总降水量和降水强度增加,降水日数减少,未来可能出现更多的旱涝年;汛期降水极端性增强,暴雨降水均值和极端值均增加;50年以上重现期的年最大日降水量未来呈前40年减少后40年增加的变化。CDF-T模型为站点尺度气候变化影响评估和未来预估提供降尺度技术和基础气候数据。

Based on climate change scenarios derived from 8 GCMs （Global Climate Models） and daily precipi- tation data during the period of 1961-2015 in Shanghai, a cumulative distribution function-transform （CDF-T） model was developed to downscale the daily precipitation on the meteorological observation site scale. The results showed that this downscaling method can improve the simulation results, which has more rain days, lower pre- cipitation intensity and less precipitation. It shows that using the daily data in flood season to develop downscaling model can improve the CDF curve, the total amount and intensity of precipitation in flood season compared with that using whole-year daily data. Similarly, this method can improve the correlation of the observed and correct mean value of the days, amount and intensity of the rainstorm as well as the daily maximum precipitation in lon- ger return periods. For the period of 2016 - 2095, it was found that the precipitation and its intensity will in- crease, while the rainy days both for the whole year and flood seasons will decrease in Shanghai, compared with the current stage （2006 -2015）. There is likely to have more drought and flood events and intensify extreme rainfall events with the increased average and extreme values of rainstorm. The daily maximum precipitation of the recurrence intervals over 50 years will decrease in the former 40 years and increase in the later 40 years in the future. Consequently, the downscaling model of CDF-T can be applied in meteorological observation site scale and provide the downscaling method and climate data for climate change projection and assessment.