用概率集成学习提升高亚洲未来降水预测精度
Refined climatologies of future precipitation over High Mountain Asia using probabilistic ensemble learning
- 采用专家混合模型融合13个气候模式的月度降水数据
- 相比平均法提升32%，单模型提升254%预测精度
- 揭示西喜马拉雅干旱冬季、藏东南湿冬新趋势
高亚洲（HMA）是极地以外全球冰冻水储量最集中的地区，为超过19亿人提供水源。降水是该区域未来水文模拟的最大不确定性来源。本文提出一种概率机器学习框架，通过专家混合（MoE）方法整合13个参与协调区域降尺度实验（CORDEX）的区域气候模型在高亚洲的月度降水数据，有效捕捉模型的时空偏差，从而生成更可靠的降水分布预测。该模型基于网格化历史降水数据训练与验证，相比等权平均提升32%，相比任意单一成员提升254%。进而用于生成2036–2065年（近未来）和2066–2095年（远未来）在RCP4.5与RCP8.5情景下的降水预估。相较于以往估算，MoE预测西喜马拉雅与喀喇昆仑夏季更湿润、冬季更干旱，而青藏高原、横断山脉及东南西藏冬季更湿润。
原文摘要 · Abstract (English)
High Mountain Asia (HMA) holds the highest concentration of frozen water outside the polar regions, serving as a crucial water source for more than 1.9 billion people. Precipitation represents the largest source of uncertainty for future hydrological modelling in this area. In this study, we propose a probabilistic machine learning framework to combine monthly precipitation from 13 regional climate models developed under the Coordinated Regional Downscaling Experiment (CORDEX) over HMA via a mixture of experts (MoE). This approach accounts for seasonal and spatial biases within the models, enabling the prediction of more faithful precipitation distributions. The MoE is trained and validated against gridded historical precipitation data, yielding 32% improvement over an equally-weighted average and 254% improvement over choosing any single ensemble member. This approach is then used to generate precipitation projections for the near future (2036-2065) and far future (2066-2095) under RCP4.5 and RCP8.5 scenarios. Compared to previous estimates, the MoE projects wetter summers but drier winters over the western Himalayas and Karakoram and wetter winters over the Tibetan Plateau, Hengduan Shan, and South East Tibet.
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