Hacking Kilometer-Scale Models: A Participative Model for Climate Information
Abstract:
Abstract In May 2025, nearly 700 participants from all around the world coalesced at 10 regional nodes and a few satellite nodes to take part in a global hackathon of kilometer-scale (horizontal grid spacing < 10 km) regional and global Earth system models. Exciting science is emerging from these efforts, ranging across novel model analysis, new ways of integrating with satellite data, and emulation with machine learning. New technologies were trialed that enable the community to work in new and complementary ways to democratize access to global information at a local scale from a set of the world鈥檚 highest-resolution climate models. The hackathon demonstrated how exascale data can be organized to be accessible to anyone. Fundamentally, the community could apply these techniques and technologies to move toward more participative models for coproduction and delivery of diverse sources of climate information for climate scientists and citizens alike. Significance Statement This report documents the results of a global hackathon for kilometer-scale atmospheric models held in May 2025. Nearly 700 participants at 10 nodes around the world were able to produce more than a 1000 plots from the most advanced global and regional atmospheric models. New tools were used to access and analyze these large datasets. Success was enabled by 1) using data at the right scale, 2) the ability to access only needed data, and 3) a common open-source analysis platform to analyze data without copying it. The community could apply these techniques and technologies widely to move toward more participative models for the delivery of climate information.From stable online coupling to decade-long climate simulations: A machine learning parameterization for cloud microphysics in ICON
CloudFlow: a flow matching model to generate high-resolution cloud structures
Abstract:
Our limited understanding of clouds is the dominant source of uncertainty in future climate predictions. Understanding how changing atmospheric environmental conditions constrain cloud organisational patterns and their radiative effects is key to understanding their impact on future climate change. We present CloudFlow, a flow matching model that is able to generate high-resolution cloud structures conditioned on coarse-scale atmospheric conditions. Our model generates realistic cloud structures that match the spectra and distributions of the original high-resolution scenes. CloudFlow introduces a new modeling regime to study how atmospheric environmental conditions impact cloud morphologies which will contribute to an improved understanding of cloud feedbacks, the cloud response to a changing climate and its effect on climate itself.