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.

应用因果表示学习中的单亲解码方法（CDSD）对多个气候数据集进行评估，重点关注排放、温度和降水，揭示使用 CDSD 作为更可解释和稳健的气候模型的跳板的挑战、限制和前景。