Treatment of Key Aerosol and Cloud Processes in Earth System Models – Recommendations from the FORCeS Project
Tellus B: Chemical and Physical Meteorology Stockholm University Press 78:1 (2026) 1-66
Calibration of climate model parameterizations using Bayesian experimental design
Machine Learning: Earth IOP Publishing 2:1 (2026) 015003-015003
A generative likelihood framework for high-resolution climate model evaluation
Environmental Data Science Cambridge University Press (CUP) 5 (2026)
Abstract:
Abstract Next-generation high-resolution (km-scale) climate models promise unprecedented accuracy in climate projections, but realizing their potential requires robust methods to quantify how well simulations align with real-world observations. Average-based metrics conventionally used for climate model evaluation ignore the physics encoded in the fine-scale structures of km-scale simulations. To overcome this limitation, we propose a novel, statistically principled evaluation methodology based on the likelihood function of a generative image model. Our method provides a continuous similarity metric derived from the likelihood distribution of observation and simulation snapshots, which can redefine the evaluation, intercomparison, and parameter tuning of high-resolution climate models. We demonstrate the applicability and interpretability of this method by evaluating convective clouds simulated by two state-of-the-art global km-scale models, using their outgoing infrared radiation fields. This work establishes a scalable pathway toward observation-based evaluation of next-generation climate simulations.Effects of convective intensity and organisation on the structure and lifecycle of deep convective clouds
Atmospheric Chemistry and Physics (ACP) Discussions European Geosciences Union (2025)
A climatology of meteorological droughts in New England, Australia, 1880–2022
Journal of Southern Hemisphere Earth Systems Science CSIRO Publishing 75:3 (2025) null-null