对流解析区域气候模式对青藏高原降水模拟能力的研究

以中国科学院区域气候-环境重点实验室研制的区域环境集成系统模式(RIEMS 2.0)为基础，采用中国科学院资源与环境数据中心提供的植被类型数据和北京师范大学提供的中国土壤质地数据，以及美国地质调查局提供的月植被覆盖度资料，进行模式本地化，从而建成了青藏高原对流解析区域气候模式．利用该模式对青藏高原进行了2001—2018年连续积分模拟，重点考察了区域气候模式在水平分辨率为9 km条件下对青藏高原降水模拟能力，结果表明:1）模式能够较好地模拟年、雨季降水的空间分布特征以及不同区域降水年变化，同时，模式模拟降水较观测偏多，偏差为13.01%~39.95%；区域气候模式模拟青藏高原降水较国际耦合模式“比较计划第六阶段（CMIP6）”45个全球模式模拟试验结果的年降水空间分布和强度有明显提高，并且更加接近观测值．2）模式能够较好地模拟出年降水时间和4个不同等级降水事件空间分布，特别是5~10、10~20、>20 mm这3个不同等级降水时间接近观测值．3）模式能够较好地模拟出青藏高原不同区域候平均降水随时间演变，降水强度除半干旱藏南地区较观测明显偏多外，对其他地区模式模拟的降水都非常接近观测值，同时与观测值之间相关系数为0.901~0.981，都通过99%置信度检验，与观测值之间的均方根误差为0.37~0.99 mm·d?1，其中对于极度干旱的柴达木地区候平均降水也能够较好地模拟出来，相关系数达到0.919；对青藏高原西南的南羌塘地区模拟最好，相关系数达到0.981．4）该研究表明采用青藏高原对流解析区域气候模式进行动力降尺度后，解决青藏高原等地区缺乏长时间序列高时空分辨率的气象数据集的瓶颈问题,为青藏高原气候和环境未来变化、生态安全屏障建设等提供坚实可靠的科学数据基础． 

Convection-permitting regional climate model to simulate precipitation in the Qinghai-Tibet Plateau

Regional Integrated Environmental Model System, developed by the Key Laboratory of Regional Climate-Environment of the Chinese Academy of Sciences, was applied to high-resolution vegetation type data provided by the Resources and Environment Data Center of the Chinese Academy of Sciences, Chinese soil texture data provided by Beijing Normal University, monthly vegetation coverage data provided by the US Geological Survey．The convection-permitting regional climate model over the Qinghai-Tibet Plateau was thus established．The model was used for continuous simulation of the Qinghai-Tibet Plateau region from 2001-2018, focusing on precipitation with a horizontal resolution of 9 km．For precipitation, the model was found to reproduce rather well the spatial distribution and seasonal cycle, simulated precipitation was an overestimation, with annual biase from 13.01%-39.95% in different regions．The model was found to simulate spatial distribution of annual precipitation number days and 4 different levels of precipitation number days．The model could reproduce time evolution of pentad mean precipitation in different regions in the Qinghai-Tibet Plateau, with correlation coefficient of 0.901-0.981, at significance level of 1%．The lack in long-term sequence high-temporal resolution meteorological datasets for the Qinghai-Tibet Plateau is therefore overcome by convection-permitting regional climate model．This work will help to understand future changes in climate and environment in the Qinghai-Tibet Plateau．