Climate models play a critical role in understanding and projecting climate change. Due to their complexity, their horizontal resolution of about 40-100 km remains too coarse to resolve processes such as clouds and convection, which need to be approximated via parameterizations. These parameterizations are a major source of systematic errors and large uncertainties in climate projections. Deep learning (DL)-based parameterizations, trained on data from computationally expensive short, high-resolution simulations, have shown great promise for improving climate models in that regard. However, their lack of interpretability and tendency to learn spurious non-physical correlations result in reduced trust in the climate simulation. We propose an efficient supervised learning framework for DL-based parameterizations that leads to physically consistent models with improved interpretability and negligible computational overhead compared to standard supervised training. First, key features determining the target physical processes are uncovered. Subsequently, the neural network is fine-tuned using only those relevant features. We show empirically that our method robustly identifies a small subset of the inputs as actual physical drivers, therefore removing spurious non-physical relationships. This results in by design physically consistent and interpretable neural networks while maintaining the predictive performance of unconstrained black-box DL-based parameterizations.

翻译：气候模型在理解和预测气候变化方面发挥着关键作用。由于其复杂性，当前气候模型约40-100公里的水平分辨率仍过于粗糙，无法解析云和对流等过程，这些过程需要通过参数化进行近似处理。这些参数化方案是气候预测中系统性误差和巨大不确定性的主要来源。基于深度学习（DL）的参数化方法，通过计算成本高昂的短期高分辨率模拟数据进行训练，在改进气候模型方面展现出巨大潜力。然而，其缺乏可解释性及易学习虚假非物理相关性的特点，导致对气候模拟结果的信任度降低。我们提出了一种高效的监督学习框架，用于构建基于深度学习的参数化方案，该方法能够生成具有物理一致性的模型，在保持标准监督训练计算开销可忽略的前提下，显著提升模型可解释性。首先，我们识别决定目标物理过程的关键特征；随后，仅使用这些相关特征对神经网络进行微调。我们通过实证研究表明，该方法能稳健地识别出输入数据中的小型子集作为实际物理驱动因子，从而消除虚假的非物理关联。由此产生的神经网络在保持无约束黑箱式深度学习参数化方案预测性能的同时，实现了结构层面的物理一致性与可解释性。