← Back to all news
arXiv Machine Learning By Bosong Zhang, Timothy M. Merlis

The Equilibrium Response of Atmospheric Machine-Learning Models to Uniform Sea Surface Temperature Warming

Read the original on arXiv Machine Learning →

arXiv:2510. 02415v3 Announce Type: replace-cross Abstract: Machine learning models for the global atmosphere that are capable of producing stable, multi-year simulations of Earth's climate have recently been developed.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv AI
Jul 7

Evaluating Skill and Stability of ArchesWeather and ArchesWeatherGen under Multi-Decadal Climate Simulations

arXiv:2605. 29976v2 Announce Type: replace-cross Abstract: We evaluate the climate simulation capabilities of ArchesWeather and ArchesWeatherGen, two machine learning models originally trained for weather forecasting and evaluated up to a 10-day lead time.

By Renu Singh, Robert Brunstein, Antonia Jost, Yana Hasson, Thomas Rackow, Claire Monteleoni, Christian Lessig, Guillaume Couairon
benchmarks
More like this →
arXiv Machine Learning
Jul 14

How Can Machine Learning Emulators Best Support Climate Science?

arXiv:2603. 22320v3 Announce Type: replace Abstract: For decades, physics-based climate models have been used to provide insights for climate decision-making.

By Luca Schmidt, Nina Effenberger, Vitus Benson, Philine L. Bommer, Robert Brunstein, Mikel N. Legasa, Maxim Samarin, Maybritt Schillinger
More like this →
arXiv Machine Learning
Aug 12

Deep Learning-Based Statistical Downscaling of Sea Surface Temperature Using a Residual Corrective Neural Network

arXiv:2608. 10022v1 Announce Type: cross Abstract: The large-scale oceanic and atmospheric forecasts provided by global climate models typically lack sufficient resolution to accurately capture the response of the coastal ocean to atmospheric forcing and coastal circulation that drive fine-scale SST variability.

By Onkar Jadhav, Tim French, Ivica Janekovic, Nicole L. Jones, Matthew Rayson
More like this →
arXiv Machine Learning
Sep 25

Lightweight Probabilistic Downscaling from a Deterministic Base Model

The paper introduces lightweight probabilistic downscaling models that build on a modified U‑Net backbone, adapting two recent machine learning techniques from weather forecasting. Using a two‑stage training curriculum—deterministic pretraining followed by probabilistic fine‑tuning—the authors evaluate their models on the CORDEX‑ML‑Bench suite for daily maximum temperature and precipitation in the Alps, New Zealand, and South Africa. The results show that this approach outperforms the current state‑of‑the‑art in RMSE, offering a more computationally efficient method for generating fine‑resolution regional climate data.

By Joseph McLean, Tiffany Vlaar, Sigrid Passano Hellan, Linus Ericsson
benchmarks
More like this →