学习气候模型中强迫对温度影响的分层因果表示
Learning Hierarchical Causal Representations of the Effects of Forcings on Temperature in Climate Models
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中文总结 AI 辅助
本研究提出一种分层因果表示学习框架，用于气候模型海表温度模拟，显式建模内部变率与强迫响应，准确预测温度演变并展现物理真实的扰动响应。
中文摘要 AI 辅助
机器学习（ML）模拟器提供了一种快速且成本效益高的方法，在基于地球系统模型预测训练后模拟气候变化情景。然而，这些数据驱动方法的黑箱性质限制了其输出的可用性和可信度，尤其是作为因果归因工具的用途。在此，我们开发了一个分层因果表示学习框架，应用于最先进的全球气候模型的海表温度场。作为对先前工作的关键进展，我们的框架明确建模了由内部气候变率引起的大气动力相互作用，以及由大气温室气体和气溶胶浓度变化引起的强迫响应。当在未来的气候变化情景上训练时，我们的方法准确预测了长期全球平均和区域温度演变，并在未见过的情景上评估时，显示出对温室气体和气溶胶浓度扰动的物理上真实的响应。我们的结果强调了因果表示学习框架在推进气候模型模拟方面的潜力。
英文摘要
Machine learning (ML) emulators provide a fast and cost-effective method to simulate climate change scenarios after being trained on Earth System Models projections. However, the black-box nature of those data-driven approaches limit the usability and trustworthiness of their outputs and in particular their use as causal attribution tools. Here, we develop a hierarchical causal representation learning framework applied to sea surface temperature fields from a state-of-the-art global climate model. As a key advance over previous work, our framework explicitly models both atmospheric dynamical interactions arising from internal climate variability and forced responses due to changes in atmospheric greenhouse gas and aerosol concentrations. When trained on future climate change scenarios, our method accurately predicts the long-term global mean and regional temperature evolution and shows physically realistic responses to perturbations in greenhouse gas and aerosol concentrations when evaluated on unseen scenarios. Our results underline the potential of causal representation learning frameworks for advancing climate model emulation.
发表机构
- Technical University of Munich（慕尼黑工业大学）
- Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
- McGill University & Mila（麦吉尔大学与Mila）
- Intel Labs（英特尔实验室）
机构由 AI 辅助整理，请以论文原文为准。
