利用第五次国际耦合模式比较计划(the Fifth Phase of Coupled Model Inter-comparison Project, CMIP5)的8个模式在高浓度排放路径RCP8. 5下的输出资料对青藏高原(下称高原) 21世纪未来气候变化进行预测,基于水汽收支方程对高原局地地表水通量P-E(降水-蒸发)变化进行热动力过程分解,求取平均环流(动力因子Mean Circulation Dynamic,MCD)、水汽辐合项(热动力因子,Thermal Dynamic, TH)等对P-E通量变化的相对贡献率,建立大尺度环流变化和高原局地气候变化的定量关系,探讨高原未来气候变化的热动力成因。研究结果表明:(1)高原未来整体变暖湿,与历史参考时期1986- 2005年相比,21世纪末P-E通量增加17. 9%,增湿梯度呈西北-东南向分布,以高原东南部林木分布区增加最显著;(2)在高原湿季(5-9月,也即高原植被生长季)内,因平均环流变化导致的水汽输送变化是高原未来变湿的主要原因,贡献了约53%的P-E通量增加,这与气候变暖后Hadley环流下沉支和中高纬西风环流的极向扩展有关;热动力因子贡献了12% P-E通量的增加,对高原未来的整体变湿贡献相对较小,但在三江源区热动力贡献较大,这与该区未来植被覆盖增加,植被对气候变化的正反馈加强有关。值得注意的是,受CMIP5多模式分辨率粗糙、模拟性能在高原地区差异较大等的影响,分析结果存在一定不确定性,结论比较初步,未来使用分辨率更高、物理过程更完善的模式,结合统计方法提高预测精度可进一步改善研究结果。
英文摘要:
Projected climate changes (indicated by P-E) in the Qinghai-Tibetan Plateau (QTP) in 21st century are accessed by 8 coupled climate models from the fifth Phase of the Coupled Model Inter-comparison Project (CMIP5),the possible dynamic and thermodynamic effects of large-scale general circulation on the QTP climate change are investigated based on the moisture budget equation. Results indicated the QTP is projected to be much warmer and wetter than historical period in future,with P-E increased by 17. 9% in the wet season of May to September (or vegetation growing season) in the last 20 years of 21st century (from 2080 to 2099) under RCP8. 5. Dynamic effects of mean flow change related to poleward expansion of Hadley cell are considered as the dominating factor of projected P-E increase,which contributes to 53% increment of P-E. Thermodynamic effects associated with specific humidity change contribute to 12% P-E increase. In the Three River Source (TRS) region where the most significant greening has been found in the QTP under RCP8. 5,the positive feedback of vegetation to future climate change favor the region moisten. The uncertainty in our results highlight the need for understanding the interaction between land surface and regional climate,particularly incorporation more complicated vegetation-climate interactions mechanisms into the models to better quantify the vegetation feedback on climate change.