基于国际耦合模式比较计划第五阶段(Coupled Model Intercomparison Project Phase 5,以下简称CMIP5)28个模式的数值模拟结果和1981~2010年华东和上海气温和降水观测数据,评估了该28个气候模式对华东和上海气温和降水的模拟能力,并预估了RCP4.5(Representative Concentration Pathway 4.5)情景下上海2021~2030年极端气温和降水气候的变化趋势和不确定性。结果表明:与观测值相比,模式对华东和上海年平均气温的模拟大多均值偏高、方差偏低;对年总降水量的模拟大多均值偏高,但方差以华东偏高、上海偏低为主;26个模式的气温变化趋势和12个模式的降水变化趋势与观测值相同。选出8个模式的预估结果表明:与2001~2010年相比, 2021~2030年上海冬天极端低温的出现日数(冷夜日数)呈减少趋势,不确定性最小;夏天暖夜日数呈增加的趋势,不确定性较小;其他极端气温事件的变化趋势则存在较大的不确定性,冷夜指标的不确定性最大。强降水发生日数和强降水的强度都呈现增加的趋势,且不确定性较小。
英文摘要:
Based on climate and climate change scenarios simulated by 28 CMIP5 (Coupled Model Intercomparison Project Phase 5) models and observations of temperature and precipitation, the authors evaluate the performance of the 28 CMIP5 models in simulating temperature and precipitation in East China and Shanghai. Future changes in extreme temperature and precipitation events under the RCP4.5 (Representative Concentration Pathways 4.5) scenario are presented and uncertainties are assessed for the time period from 2021 to 2030. Compared with observations, annual mean temperature is overestimated while the variance is underestimated by most of the CMIP5 models in both East China and Shanghai. Analysis of precipitation indicates that the annual mean total precipitation simulated by most of the CMIP5 models are higher than that from observations, whereas the variance is higher in East China and lower in Shanghai than that from observations. For the historical simulation of climate change in Shanghai, the temperature trends simulated by 26 models and precipitation trends by 12 models are similar to the observation during 1986-2005. Based on the above results, eight CMIP5 global climate models have been selected to predict future extreme event changes in Shanghai. The results show that the number of extreme low temperature events (cold nights) is likely to decrease with the lowest level of uncertainty, while the number of extreme warm nights is likely to increase with a lower level of uncertainty. Large uncertainties exist in the projection of other climate variable indices, which may also change in the future. In particular, great uncertainties are found in the prediction of cold night index change. Both the frequency and intensity of heavy rain events are predicted to increase and uncertainties in such predictions are low.