摘要
The Lancang–Mekong River basin(LMRB) is under increasing threat from global warming. In this paper, the projection of future climate in the LMRB is explored by focusing on the temperature change and extreme temperature events. First, the authors evaluate the bias of temperature simulated by the Weather Research and Forecasting model. Then, correction is made for the simulation by comparing with observation based on the non-parametric quantile mapping using robust empirical quantiles(RQUANT) method. Furthermore, using the corrected model results, the future climate projections of temperature and extreme temperature events in this basin during 2016–35, 2046–65, and 2080–99 are analyzed. The study shows that RQUANT can effectively reduce the bias of simulation results. After correction, the simulation can capture the spatial features and trends of mean temperature over the LMRB, as well as the extreme temperature events. Besides, it can reproduce the spatial and temporal distributions of the major modes. In the future, the temperature will keep increasing, and the warming in the southern basin will be more intense in the wet season than the dry season. The number of extreme high-temperature days exhibits an increasing trend, while the number of extreme low-temperature days shows a decreasing trend. Based on empirical orthogonal function analysis, the dominant feature of temperature over this basin shows a consistent change. The second mode shows a seesaw pattern.
研究目的:澜沧江-湄公河流域以农业生产为主要经济来源,气候变化会对该地区人民的生产生活及生命财产安全造成严重的影响,对此流域21世纪的温度和极端温度事件进行预估,可以为灾害防控和政策制定提供科学性参考。创新要点:利用分位数订正法对WRF降尺度数据进行订正,对澜沧江-湄公河流域21世纪的温度变化进行预估。研究方法:运用分位数订正法对模拟和预估数据进行订正,与观测数据进行对比评估,在此基础上对2016–35, 2046–65和2080–99的温度进行预估。重要结论:21世纪澜沧江-湄公河流域温度呈现持续升高的趋势。干季时,北部高海拔地区升温速度较快;湿季时,下游地区升温明显。干湿季最主要的空间模态为全区一致型;干季的第二空间模态为东西反向变化,湿季为南北反向变化。极端高温日数增加,流域下游增加较快;极端低温日数减少,2016–35年的减少最为明显。
基金
This work was supported by the External Cooperation Program of Bureau of International Co-operation,Chinese Academy of Sciences[grant number GJHZ1729]
the Key Program of the Natural Science Foundation of Yunnan Province of China[grant number 2016FA041].
