用贝叶斯滤波构建可解释的气候模拟器，提升长期预测稳定性。
Causal Climate Emulation with Bayesian Filtering
- 基于因果表示学习与贝叶斯滤波，实现可解释的气候模拟。
- 在合成数据和两个主流气候模型上均准确捕捉气候动态。
- 适合关注气候建模可解释性与长期预测的研究者。
传统气候模型依赖复杂的耦合方程组模拟地球系统中的物理过程，但计算成本极高，限制了气候变化预测及成因影响分析。机器学习有望快速模拟气候模型数据，但现有方法难以融入基于物理的因果关系。本文提出一种基于因果表示学习的可解释气候模型模拟器，设计了一种新型贝叶斯滤波方法，实现稳定的长期自回归模拟。我们在一个真实合成数据集以及两个广泛应用的气候模型数据上验证了该模拟器对气候动力学的准确学习能力，并评估了各组件的重要性。
原文摘要 · Abstract (English)
Traditional models of climate change use complex systems of coupled equations to simulate physical processes across the Earth system. These simulations are highly computationally expensive, limiting our predictions of climate change and analyses of its causes and effects. Machine learning has the potential to quickly emulate data from climate models, but current approaches are not able to incorporate physically-based causal relationships. Here, we develop an interpretable climate model emulator based on causal representation learning. We derive a novel approach including a Bayesian filter for stable long-term autoregressive emulation. We demonstrate that our emulator learns accurate climate dynamics, and we show the importance of each one of its components on a realistic synthetic dataset and data from two widely deployed climate models.
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