利用对比学习方法理解扰动参数集合的敏感性

中文摘要 AI 辅助

扰动参数集合（PPEs）揭示了物理参数如何影响气候模拟，但在多变量、空间结构化的输出中解释参数敏感性仍然具有挑战性，尤其是在根据观测数据校准模型时。我们开发了一种可解释的对比学习模型，将5个月平均的云和辐射场映射到一个共享表示空间。我们在两个100成员的社区大气模型版本6（CAM6）PPE的场上训练该模型，这些PPE跨越34个参数，仅在暖雨微物理方案上有所不同：KK2000（默认的体微物理方案）和TAU-ML（一种bin微物理方案的神经网络模拟器）。学习到的表示以超过94%的线性分类准确率将两个PPE分开，同时保留了由参数扰动引起的季节变化和集合离散度。在共享表示空间中，卫星观测的表示与PPE位于相同的低维流形上，但在北半球春季和秋季与PPE的偏离最为显著。在表示空间中，TAU-ML PPE与观测的距离比KK2000更小。积分梯度归因突出了副热带低云区域、北半球和南半球风暴路径区域以及热带对流区域对PPE与观测差异的贡献。区域归因与云微物理、边界层湍流和深对流相关的参数相关性最强。这些结果表明，气候场的可解释表示可以将模型差异归因于特定的变量、区域、季节和物理参数。

英文摘要

Perturbed parameter ensembles (PPEs) reveal how physics parameters affect climate simulations, but interpreting parameter sensitivities across multivariate, spatially structured outputs remains challenging, particularly when calibrating models against observations. We develop an explainable contrastive learning model that maps 5 monthly cloud and radiation fields into a shared representation space. We train the model on the fields of two 100-member Community Atmosphere Model version 6 (CAM6) PPEs, spanning 34 parameters, that only differ in the warm rain microphysics scheme: KK2000, the default bulk microphysics scheme, and TAU-ML, a neural network emulator of a bin microphysics scheme. The learned representations separates two PPEs with over 94\% linear classification accuracy while preserving the seasonal variability and ensemble spread due to parameter perturbations. In the shared representation space, the representations of satellite observations occupy the same low-dimensional manifold as the PPEs but are displaced from them most strongly during boreal spring and autumn. TAU-ML PPE has a lower distance to observations compared to KK2000 in the representation space. Integrated Gradients attributions highlights the contributions in subtropical low-cloud regions, Northern and Southern Hemisphere storm track regions, and tropical convection regions to differences between PPEs and observations. Regional attributions correlate most strongly with parameters associated with cloud microphysics, boundary layer turbulence, and deep convection. These results demonstrate that explainable representations of climate fields can attribute model differences to specific variables, regions, seasons, and physical parameters.

发表机构

• NSF National Center for Atmospheric Research（NSF国家大气研究中心）
• Columbia University（哥伦比亚大学）
• NASA Goddard Institute for Space Studies（NASA戈达德空间研究所）

机构由 AI 辅助整理，请以论文原文为准。