Zhang Yunkai, Du Juan, Ding Yibo, Wu Lingling, Ao Tianqi
State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource & Hydropower, Sichuan University, No. 24 South Section 1, Yihuan Road, Chengdu, 610065, China.
Yellow River Engineering Consulting Co., Ltd, Zhengzhou, 450003, China.
Sci Rep. 2024 Sep 28;14(1):22428. doi: 10.1038/s41598-024-73741-w.
Selecting appropriate global climate models (GCMs) is crucial for minimizing uncertainty in regional climate projections under future scenarios. Previous studies have predominantly assessed the modeling capability of GCMs for regional precipitation climatology and its long-term patterns based on annual and seasonal precipitation data. Building upon these, we primally evaluated the performance of five GCMs from phase 3b of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b) in simulating precipitation concentration and its variations in the Southwest River Basin (SWRB) of China using the precipitation concentration index (PCI). The results indicate that: (1) The 5 GCMs generally capture the spatial distribution of annual average precipitation in the SWRB but significantly overestimate its magnitude, with a maximum regional average deviation of 207.80 mm. Furthermore, all models tend to overestimate the overall drying trend in the SWRB and show limited capability in simulating interdecadal variations of annual precipitation. (2) While the 5 GCMs reasonably simulate the spatial distribution of annual average PCI in the SWRB, they tend to overestimate its values, with a maximum regional average deviation of 1.54. Additionally, their simulation performance in capturing PCI trends and interdecadal variations is also limited. (3) The 5 GCMs tend to overestimate seasonal precipitation in the SWRB, with the best simulation performance for the distribution of autumn precipitation, followed by spring and summer, and the poorest for winter. Significant differences exist in the simulation performance of the models for seasonal precipitation proportions, which result in discrepancies in the models' representation of PCI. Moreover, the models' poor simulation performance of PCI trends is partly due to their inadequate modeling of trends in seasonal precipitation proportions. The findings will contribute to laying the foundation for meteorological hydrological research and water resource management in the SWRB.
选择合适的全球气候模型(GCMs)对于将未来情景下区域气候预测的不确定性降至最低至关重要。以往的研究主要基于年降水量和季节降水量数据,评估了GCMs对区域降水气候学及其长期模式的模拟能力。在此基础上,我们首先利用降水集中度指数(PCI),评估了跨部门影响模型相互比较项目(ISIMIP3b)3b阶段的五个GCMs在中国西南河流域(SWRB)模拟降水集中度及其变化的性能。结果表明:(1)这5个GCMs总体上捕捉到了SWRB年平均降水量的空间分布,但显著高估了其量级,区域平均最大偏差为207.80毫米。此外,所有模型都倾向于高估SWRB的整体干旱趋势,并且在模拟年降水量的年代际变化方面能力有限。(2)虽然这5个GCMs合理地模拟了SWRB年平均PCI的空间分布,但它们倾向于高估其值,区域平均最大偏差为1.54。此外,它们在捕捉PCI趋势和年代际变化方面的模拟性能也有限。(3)这5个GCMs倾向于高估SWRB的季节性降水,对秋季降水分布的模拟性能最佳,其次是春季和夏季,冬季最差。各模型在季节性降水比例的模拟性能上存在显著差异,这导致了模型在PCI表示上的差异。此外,模型对PCI趋势的模拟性能较差,部分原因是它们对季节性降水比例趋势的模拟不足。这些发现将有助于为SWRB的气象水文研究和水资源管理奠定基础。