用于校正 CMIP6 级地球系统模型偏差的深度学习
Deep learning for bias-correcting CMIP6-class Earth system models
AI总结：
提出基于物理约束 cGAN 的后处理方法，可同时校正 CMIP6 级 ESM 降水的局地频率分布与空间模式偏差，并在极端降水空间间歇性上显著优于现有方法。
摘要：
在地球系统模型（ESMs）中准确表征降水，对于可靠预测人为全球变暖带来的生态和社会经济影响至关重要。然而，产生降水的各种过程之间存在复杂的跨尺度相互作用，建模难度很大，从而在 ESM 输出场中引发潜在的强烈偏差，尤其是在极端事件方面。最先进的偏差校正方法只能在每个独立网格单元局部处理模拟频率分布中的误差。改善 ESM 输出中不符合实际的空间模式需要空间上下文信息，而这在此前尚无法实现。在此，我们表明，一种基于物理约束生成对抗网络（cGANs）的后处理方法，能够同时校正最先进的 CMIP6 级 ESM 在局部频率分布和空间模式中的偏差。尽管我们的方法对局部频率分布的改善效果与黄金标准偏差调整框架相当，但它在空间模式校正方面显著优于任何现有方法，尤其是在降水极端事件特有的空间间歇性方面。
英文摘要：
The accurate representation of precipitation in Earth system models (ESMs) is crucial for reliable projections of the ecological and socioeconomic impacts in response to anthropogenic global warming. The complex cross-scale interactions of processes that produce precipitation are challenging to model, however, inducing potentially strong biases in ESM fields, especially regarding extremes. State-of-the-art bias correction methods only address errors in the simulated frequency distributions locally at every individual grid cell. Improving unrealistic spatial patterns of the ESM output, which would require spatial context, has not been possible so far. Here, we show that a post-processing method based on physically constrained generative adversarial networks (cGANs) can correct biases of a state-of-the-art, CMIP6-class ESM both in local frequency distributions and in the spatial patterns at once. While our method improves local frequency distributions equally well as gold-standard bias-adjustment frameworks, it strongly outperforms any existing methods in the correction of spatial patterns, especially in terms of the characteristic spatial intermittency of precipitation extremes.
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