Contrastive learning clarifies cloud model differences and observations
Understanding Perturbed Parameter Ensemble Sensitivities Using A Contrastive Learning Approach
Artificial Intelligence
Summary
Climate models have many settings that affect their weather predictions, but it is hard to understand how these settings influence complex weather features. The authors used a machine learning tool to create simple summaries of many climate patterns, which helps to compare two versions of a climate model and real satellite data. Their approach shows which regions, seasons, and weather variables differ between models and observations, linking these to specific model settings. This helps scientists see where models match reality and where they need improvement.
What this means in practice
- •For climate model developers: Identify which physical parameters and regions cause differences between model versions and observations to guide model improvement.
- •For weather data analysts: Use learned representations to compare multivariate climate data sets efficiently and highlight key seasonal or spatial discrepancies.
Authors
Da Fan, David John Gagne, Gregory S Elsaesser, Brian Medeiros, Addisu G Semie, Qingyuan Yang, Akila Sampath, Subashree Venkatasubramanian
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.