Understanding climate dynamics requires going beyond correlations in observational data to uncover their underlying causal process. Latent drivers, such as atmospheric processes, play a critical role in temporal dynamics, while direct causal influences also exist among geographically proximate observed variables. Traditional Causal Representation Learning (CRL) typically focuses on latent factors but overlooks such observable-to-observable causal relations, limiting its applicability to climate analysis. In this paper, we introduce a unified framework that jointly uncovers (i) causal relations among observed variables and (ii) latent driving forces together with their interactions. We establish conditions under which both the hidden dynamic processes and the causal structure among observed variables are simultaneously identifiable from time-series data. Remarkably, our guarantees hold even in the nonparametric setting, leveraging contextual information to recover latent variables and causal relations. Building on these insights, we propose CaDRe (Causal Discovery and Representation learning), a time-series generative model with structural constraints that integrates CRL and causal discovery. Experiments on synthetic datasets validate our theoretical results. On real-world climate datasets, CaDRe not only delivers competitive forecasting accuracy but also recovers visualized causal graphs aligned with domain expertise, thereby offering interpretable insights into climate systems.

翻译：理解气候动力学需要超越观测数据中的相关性，以揭示其潜在的因果过程。潜在驱动因素（如大气过程）在时间动态中起着关键作用，而地理上邻近的观测变量之间也存在直接的因果影响。传统的因果表示学习（CRL）通常专注于潜在因素，但忽视了此类可观测变量之间的因果关系，限制了其在气候分析中的适用性。本文提出了一种统一框架，能够联合揭示（i）观测变量之间的因果关系，以及（ii）潜在驱动力及其相互作用。我们建立了从时间序列数据中同时识别隐藏动态过程和观测变量间因果结构的条件。值得注意的是，我们的保证甚至在非参数设定下也成立，通过利用上下文信息来恢复潜在变量和因果关系。基于这些见解，我们提出了CaDRe（因果发现与表示学习），这是一个具有结构约束的时间序列生成模型，它整合了CRL和因果发现。在合成数据集上的实验验证了我们的理论结果。在真实世界的气候数据集上，CaDRe不仅提供了具有竞争力的预测准确性，还恢复了与领域专业知识相符的可视化因果图，从而为气候系统提供了可解释的见解。