IPCC AR4气候多模式对云南特征气温变化的模拟预估——滇西南区案例分析(1960-2050年)(英文)

Projection of IPCC AR4 Coupled Climate Models for Multitimescale Temperature Variation in Yunnan: A Case Study on Southwest Yunnan from 1960 to 2050

本文针对云南地区,以滇西南区为案例,利用研究区20个气象观测站点实测逐日平均气温分析了1961-2007年各特征气温(年均气温、干季平均气温、以及湿季平均气温)的变化趋势特征;并采用IPCC AR4气候模式集成的21世纪气候变化模拟试验的预估结果,针对滇西南区各特征气温,分别就不同排放情景下的模拟结果在该区的适用性进行了详细验证和分析。主要结果如下:(1)1961-2007年间,研究区各特征气温变化表现出了非常显著的上升趋势,MK统计值均通过了95%的置信度检验。(2)标准化均方根误差和MK趋势验证的结果表明,基准期内特征气温(尤其是年均气温和干季气温)的模拟值距平变化与实测值的距平变化趋势特征非常相似,因此利用IPCC AR4气候模式的试验结果模拟研究区三种排放情景下各特征气温的变化趋势具有一定的可信度,且针对年均气温及干季平均气温的模拟效果要比湿季平均气温好。(3)未来40年内,该区各特征气温将明显上升,并且年均气温上升趋势比干季、湿季平均气温更为显著;不同排放情景下各特征气温将表现出不同的升温率:2020s期间SRESA2高排放情景下的特征气温升温率将小于SRESB1低排放情景,除此之外,各特征气温在SRESA1B中等排放情景下的升温率将最大,在SRESA2高排放情景下其次,在SRESB1低排放情景下将最小。文中对IPCC AR4气候模式在云南地区的适用性及其影响因素进行了进一步的探讨。

Based on daily mean temperature records from 1961 to 2007 at 20 meteorological sites in Southwest Yunnan, and the surface temperature simulated by IPCC AR4 Climate Models, a quantitative examination was undertaken into the characteristics of multi-timescale temperature （AMT, DMT and WMT） variation in Southwest Yunnan. The simulation abilities of the models were also evaluated with the normalized root mean square error （NRMSE） and Mann-Kendal test statistic methods. Temperatures show remarkable increasing trend from 1961 to 2007, with the Mann-Kendall test statistic passing 95% confidence verification. The result of the NRMSE analysis shows that the simulated temperature anomaly variations are more similar to observed ones especially for AMT and DMT, and the projected result （anomalies） of IPCC AR4 climate models can be used for predicting the trends in multi-timescale temperature variation in Southwest Yunnan in the next 40 years under the three emission scenarios, which has better simulating effect on AMT and DMT than WMT. Over the next 40 years the temperature will continue to rise, with annual mean temperature showing a more remarkable rising trend than that of the dry and wet seasons. Temperature anomalies exhibit different increasing rates under different emission scenarios： During the 2020s the rising rates of multi-timescale temperature anomalies in a high greenhouse gases emissions scenario （SRESA2） are smaller than those under a low emission scenario （SRESB1）. Except that, the rate of increase in temperature anomalies are the highest in the intermediate emissions scenario （SRESA1B）, followed by those in SRESA2, and those in low emissions scenario （SRESB1） are the lowest. The reason of different simulating effects on WMT from AMT and DMT was also discussed.