Geo Intelligence.

RESEARCH & SOURCES

Samudra: An AI Global Ocean Emulator for Climate

Geophysical Research Letters ·

Paper authors:Surya Dheeshjith · Adam Subel · Alistair Adcroft · Julius Busecke · Carlos Fernandez‐Granda · Shubham Gupta · Laure Zanna

DOI: 10.1029/2024GL114318

AI reading guide

AI summaryLearns from ocean-model data at multiple depths to emulate temperature, salinity, currents and sea-surface height. Long integrations are stable in the study, while responses to trends in external forcing remain limited in amplitude.

This reading guide is not the authors’ abstract. Consult the original source.

This record provides source metadata. A detailed editorial analysis has not yet been published.

Read the original source ↗

Related research

Research articleNatureCAS Tier 1 · Top (2025)

Authors Cristian Bodnar · Wessel P. Bruinsma · Ana Lucic · Megan Stanley · Anna Allen · Johannes Brandstetter · Patrick Garvan · Maik Riechert · Jonathan A. Weyn · Haiyu Dong · Jayesh K. Gupta · Kit Thambiratnam · Alexander T. Archibald · Chun-Chieh Wu · Elizabeth Heider · Max Welling · Richard E. Turner · Paris Perdikaris

AI summaryPretrains on diverse geophysical data and adapts to forecasting weather, ocean waves, air quality and tropical cyclone tracks, exploring a foundation model for multiple Earth-system tasks.

AuroraFoundation modelsMultitask forecasting
Details & notesRead original ↗
PerspectiveNature Reviews Earth & EnvironmentCAS Tier 1 · Top (2025)

Authors Chaopeng Shen · Alison P. Appling · Pierre Gentine · Toshiyuki Bandai · Hoshin Gupta · Alexandre Tartakovsky · Marco Baity-Jesi · Fabrizio Fenicia · Daniel Kifer · Li Li · Xiaofeng Liu · Wei Ren · Yi Zheng · Ciaran J. Harman · Martyn Clark · Matthew Farthing · Dapeng Feng · Praveen Kumar · Doaa Aboelyazeed · Farshid Rahmani · Yalan Song · Hylke E. Beck · Tadd Bindas · Dipankar Dwivedi · Kuai Fang · Marvin Höge · Chris Rackauckas · Binayak Mohanty · Tirthankar Roy · Chonggang Xu · Kathryn Lawson

AI summaryReviews how differentiable computation can jointly train physical components and neural networks to support process understanding, parameter estimation and geoscientific prediction.

Differentiable modellingPhysical constraintsHydrological modelling
Details & notesRead original ↗
PerspectiveCommunications Earth & EnvironmentCAS Tier 1 · Top (2025)

Authors Xiao Xiang Zhu · Zhitong Xiong · Yi Wang · Adam J. Stewart · Konrad Heidler · Yuanyuan Wang · Zhenghang Yuan · Thomas Dujardin · Qingsong Xu · Yilei Shi

AI summaryProposes 11 features of an ideal Earth foundation model, including multisensor integration, awareness of location and scale, and physical consistency, and discusses evaluation and research directions.

Earth observationFoundation modelsEvaluation
Details & notesRead original ↗
Research articleNatureCAS Tier 1 · Top (2025)

Authors Dmitrii Kochkov · Janni Yuval · Ian Langmore · Peter Norgaard · Jamie Smith · Griffin Mooers · Milan Klöwer · James Lottes · Stephan Rasp · Peter Düben · Sam Hatfield · Peter Battaglia · Alvaro Sanchez-Gonzalez · Matthew Willson · Michael P. Brenner · Stephan Hoyer

AI summaryCombines a differentiable dynamical core with neural-network parameterisations to study weather forecasting and climate simulation, including physical consistency over long integrations.

NeuralGCMDifferentiable modelsPhysics and AI
Details & notesRead original ↗