58 citations · 67 across the 11 of their papers we have counts for
9 papers · 1 filter
ClimateBench v2.0: Probabilistic Climate Model Benchmarking
Duncan Watson-Parris, Willa Tobin, Aytaç Paçal +32
We present ClimateBench v2, a standardized protocol for evaluating climate models on diagnostics expected to be informative for their skill in projecting mid-century regional tempe…
HClimRep-Ocean: A Global Ocean Emulator on an Unstructured Mesh
Kacper Nowak, Aleksei Koldunov, Nikolay Koldunov +7
Machine-learning (ML) emulators for atmospheric processes have advanced rapidly in recent years, transforming weather forecasting. Although early ML ocean forecasting models now ex…
FESOM2-JAX v1.0: a differentiable shadow of the ocean-sea-ice model FESOM2, cast onto GPUs
Nikolay V. Koldunov, Sergey Danilov, Suvarchal Cheedela +9
We present FESOM2-JAX, a Python re-implementation of the Finite-volumE Sea ice-Ocean Model (FESOM2) in JAX. The model retains the unstructured-mesh, cell-vertex finite-volume formu…
Machine learning is revolutionizing weather forecasting -- the next step is a change in how we work
Peter Dueben, Peter Bauer, Oliver Fuhrer +2
Following the success of machine learning in producing weather predictions with competitive skill compared to complex traditional systems, this article shifts attention from foreca…
CMIP-Forge: An Agentic System that Retrieves, Computes, and Self-Reviews Climate Science
Dmitrii Pantiukhin, Boris Shapkin, Ivan Kuznetsov +2
The Coupled Model Intercomparison Project Phase 6 (CMIP6) has generated thousands of peer-reviewed publications documenting model configurations, evaluation procedures, emergent co…
An Ocean Model Ported by a Large Language Model: Experience and Lessons from FESOM2 (Fortran to C to C++/Kokkos)
Nikolay V. Koldunov, Suvarchal K. Cheedela, Sergey Danilov +3
Large language models (LLMs) can translate and modify source code, and have been shown to do so for codes of different complexity. Whether they can port a complete, production geop…
AIMIP Phase 1: systematic evaluations of AI weather and climate models
Brian Henn, Christopher S. Bretherton, Nikolay Koldunov +18
We present the AI weather and climate model intercomparison project (AIMIP), phase 1. Drawing from the rich tradition of intercomparisons in climate model development, we specify a…
Robustness of AI-based weather forecasts in a changing climate
Thomas Rackow, Nikolay Koldunov, Christian Lessig +9
Data-driven machine learning models for weather forecasting have made transformational progress in the last 1-2 years, with state-of-the-art ones now outperforming the best physics…
Emerging AI-based weather prediction models as downscaling tools
Nikolay Koldunov, Thomas Rackow, Christian Lessig +5
The demand for high-resolution information on climate change is critical for accurate projections and decision-making. Presently, this need is addressed through high-resolution cli…