arXiv (All)
open access
Rescene: stable century-long climate emulation from a frozen weather model
Machine-learning weather models now match or exceed operational numerical prediction in the medium range, but when they are run freely for years they drift, blow up or lose their variability, and dedicated climate emulators are expensive to train. Here we present Rescene, a 0.4 M-parameter wrapper t
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- The Equilibrium Response of Atmospheric Machine-Learning Models to Uniform Sea Surface Temperature Warming#1386941
- SaWaM WRF physics parameterization scheme combination #7 (RUN7)#1082654
- SaWaM WRF physics parameterization scheme combination #1 (RUN1)#1082647
- Learning Hierarchical Causal Representations of the Effects of Forcings on Temperature in Climate Models#1105554
- Regional Climate Model Emulation with Diffusion Approaches: What is the Added Value of Generative Machine Learning?#271839
Record · ID 1386341
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive