Reduced Cloud Cover Errors in a Hybrid AI-Climate Model Through Equation Discovery And Automatic Tuning

TL;DR

This paper introduces a hybrid AI-climate model that uses symbolic regression for interpretable cloud parameterizations and automatic tuning to significantly reduce biases in cloud cover predictions, enhancing climate model accuracy and robustness.

Contribution

It presents a novel two-step approach combining symbolic regression and automatic calibration to improve cloud cover predictions in climate models with interpretable machine learning.

Findings

Significant bias reduction in cloud cover over Southern Ocean and subtropical regions.

Model remains robust under increased surface warming (+4K).

Demonstrates effectiveness of interpretable ML and automatic tuning in climate modeling.

Abstract

Cloud-related parameterizations remain a leading source of uncertainty in climate projections. Although machine learning holds promise for Earth system models (ESMs), many data-driven parameterizations lack interpretability, physical consistency, and smooth integration into ESMs. Here, a two-step method is presented to improve a climate model with data-driven parameterizations. First, we incorporate a physically consistent cloud cover parameterization -- derived from storm-resolving simulations via symbolic regression, preserving interpretability while enhancing accuracy -- into the ICON global atmospheric model. Second, we apply the gradient-free Nelder-Mead optimizer to automatically recalibrate the hybrid model against Earth observations, tuning in nested stages (2-, 7-, 30- and 365-day runs) to ensure stability and tractability. The tuned hybrid model substantially reduces long-standing biases in cloud cover -- particularly over the Southern Ocean (by 75%) and subtropical stratocumulus regions (by 44%) -- and remains robust under +4K surface warming. These results demonstrate that interpretable machine-learned parameterizations, paired with practical tuning, can efficiently and transparently strengthen ESM fidelity.