Climate models, such as Earth system models (ESMs), are crucial for simulating future climate change based on projected Shared Socioeconomic Pathways (SSP) greenhouse gas emissions scenarios. While ESMs are sophisticated and invaluable, machine learning-based emulators trained on existing simulation data can project additional climate scenarios much faster and are computationally efficient. However, they often lack generalizability and interpretability. This work delves into the potential of causal representation learning, specifically the \emph{Causal Discovery with Single-parent Decoding} (CDSD) method, which could render climate model emulation efficient \textit{and} interpretable. We evaluate CDSD on multiple climate datasets, focusing on emissions, temperature, and precipitation. Our findings shed light on the challenges, limitations, and promise of using CDSD as a stepping stone towards more interpretable and robust climate model emulation.

翻译：气候模型，例如地球系统模型（ESMs），对于基于共享社会经济路径（SSP）温室气体排放情景预测未来气候变化至关重要。尽管ESMs先进且价值巨大，但基于现有模拟数据训练的机器学习仿真器能够以更快的速度预测更多气候情景，且计算效率更高。然而，这些仿真器往往缺乏泛化能力和可解释性。本研究深入探讨了因果表示学习的潜力，特别是名为“基于单亲解码的因果发现”（Causal Discovery with Single-parent Decoding, CDSD）的方法，该方法有望实现高效且可解释的气候模型仿真。我们在多个气候数据集上评估了CDSD，重点关注排放、温度和降水。研究结果揭示了将CDSD作为迈向更可解释、更稳健的气候模型仿真的基石所面临的挑战、局限性与前景。