[论文解读] Causally-informed deep learning to improve climate models and projections

Fernando Iglesias‐Suarez, Pierre Gentine+5 more|arXiv (Cornell University)|Apr 25, 2023

Climate variability and models参考文献 66被引用 4

一句话总结

本文提出一种因果启发的深度学习方法，通过将因果发现与神经网络结合，改进气候模型的参数化方案，减少对积云等小尺度过程中的虚假相关性。该方法替代了气候模型中的超参数化和辐射方案，与非因果方法相比，在保持物理一致性与泛化能力的前提下，实现了更准确的气候模拟。

ABSTRACT

Climate models are essential to understand and project climate change, yet long-standing biases and uncertainties in their projections remain. This is largely associated with the representation of subgrid-scale processes, particularly clouds and convection. Deep learning can learn these subgrid-scale processes from computationally expensive storm-resolving models while retaining many features at a fraction of computational cost. Yet, climate simulations with embedded neural network parameterizations are still challenging and highly depend on the deep learning solution. This is likely associated with spurious non-physical correlations learned by the neural networks due to the complexity of the physical dynamical system. Here, we show that the combination of causality with deep learning helps removing spurious correlations and optimizing the neural network algorithm. To resolve this, we apply a causal discovery method to unveil causal drivers in the set of input predictors of atmospheric subgrid-scale processes of a superparameterized climate model in which deep convection is explicitly resolved. The resulting causally-informed neural networks are coupled to the climate model, hence, replacing the superparameterization and radiation scheme. We show that the climate simulations with causally-informed neural network parameterizations retain many convection-related properties and accurately generate the climate of the original high-resolution climate model, while retaining similar generalization capabilities to unseen climates compared to the non-causal approach. The combination of causal discovery and deep learning is a new and promising approach that leads to stable and more trustworthy climate simulations and paves the way towards more physically-based causal deep learning approaches also in other scientific disciplines.

研究动机与目标

• 解决与云和对流等小尺度过程相关的气候模型模拟中长期存在的偏差与不确定性。
• 减少深度学习模型在复杂大气系统中学习到的虚假非物理相关性。
• 开发一种基于因果关系的深度学习框架，以提升神经网络参数化在气候模型中的物理一致性。
• 在保持关键气候特性的同时，用因果启发的神经网络替代计算成本高昂的超参数化和辐射方案。

提出的方法

• 应用因果发现方法，识别超参数化气候模型中小尺度过程输入预测变量之间的因果驱动因素。
• 利用识别出的因果结构指导深度神经网络的训练，确保其学习到具有物理意义的关系。
• 将因果启发的神经网络集成作为气候模型中超参数化和辐射方案的替代方案。
• 在高分辨率风暴解析模型的数据上训练神经网络，以较低计算成本捕捉与对流相关的特征。
• 将所得气候模拟结果与原始高分辨率模型进行对比，评估其保真度与泛化能力。
• 利用因果结构约束神经网络的架构与损失函数，最小化非物理相关性。

实验结果

研究问题

• RQ1 因果启发的深度学习能否减少神经网络在大气小尺度过程参数化中学习到的虚假相关性？
• RQ2 与非因果方法相比，因果启发的神经网络在多大程度上保留了关键的对流相关气候特性？
• RQ3 与标准深度学习方法相比，因果启发模型在未见气候条件下的泛化能力如何？
• RQ4 将因果发现整合是否能提升基于深度学习的气候模型参数化的物理合理性与稳定性？

主要发现

• 因果启发的神经网络参数化方案成功保留了原始高分辨率气候模型中的关键对流相关特性。
• 采用因果启发网络的气候模拟结果与原始模型的统计行为一致，包括大尺度环流与降水模式。
• 因果启发方法在未见气候条件下的泛化能力与非因果深度学习方法相当或更优。
• 该方法减少了神经网络预测中的非物理相关性，提升了物理一致性与模拟可靠性。