Understanding Perturbed Parameter Ensemble Sensitivities Using A Contrastive Learning Approach

2026-09-24

AI Summary

This study addresses the challenge of interpreting climate model parameter sensitivity within multivariate spatial outputs, particularly when calibrating against observations. We propose an interpretable framework integrating contrastive learning with integrated gradients to map cloud radiative fields simulated by CAM6 into a shared representation space, enabling fine-grained sensitivity analysis from climate fields to physical parameters, regions, and seasons. Experimental results demonstrate that ensemble classification accuracy exceeds 94%. Furthermore, the framework successfully quantifies the superiority of the neural network microphysics scheme (TAU-ML) over the default parameterization while precisely identifying critical influencing parameters and geographic regions. By bridging deep interpretability methods with climate simulation diagnostics, this work establishes a novel paradigm for evaluating and attributing sensitivities in complex climate models.

Abstract

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.

Problem

• Perturbed parameter ensembles
• Parameter sensitivity
• Climate simulations
• Model calibration
• Multivariate spatial outputs

Innovation

• Contrastive Learning
• Perturbed Parameter Ensembles
• Explainable AI
• Integrated Gradients
• Climate Model Calibration