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Repository files navigation

DocsPyPIModel checkpointsOpen In Colab

Logo for the ACE Project

Ai2 Climate Emulator

Ai2 Climate Emulator (ACE) is a fast machine learning model that simulates global atmospheric variability in a changing climate over time scales ranging from hours to centuries. This repository contains the fme python package which can be used to train, run and evaluate weather and climate AI models such as ACE. It also contains the data processing scripts and model configurations used in recent papers published by the Ai2 Climate Modeling group.

Installation

pip install fme

Documentation

See complete documentation here and a quickstart guide here.

Model checkpoints

Pretrained model checkpoints are available in the ACE Hugging Face collection.

Papers

The following papers described models trained using code in this repository.

  • "ACE: A fast, skillful learned global atmospheric model for climate prediction" (link)
  • "Application of the Ai2 Climate Emulator to E3SMv2's global atmosphere model, with a focus on precipitation fidelity" (link)
  • "ACE2: Accurately learning subseasonal to decadal atmospheric variability and forced responses" (link)
  • "ACE2-SOM: Coupling an ML Atmospheric Emulator to a Slab Ocean and Learning the Sensitivity of Climate to Changed CO2" (link)
  • "Applying the ACE2 Emulator to SST Green's Functions for the E3SMv3 Global Atmosphere Model" (link)
  • "SamudrACE: Fast and Accurate Coupled Climate Modeling with 3D Ocean and Atmosphere Emulators" (link)
  • "HiRO-ACE: Fast and skillful AI emulation and downscaling trained on a 3 km global storm-resolving model" (link)
  • "FloeNet: A mass-conserving global sea ice emulator that generalizes across climates" (link)
  • "AIMIP Phase 1: systematic evaluations of AI weather and climate models" (link)
  • "Disentangling the effects of sea surface temperature and CO2 in global machine learned weather-climate emulators" (link)
  • "Stochastic Emulation of a Fully Coupled Preindustrial E3SMv3 Simulation" (link)

⚠️ Important migration notice

This repository had a breaking history change on the main branch in December 2025 as part of our transition to open development. If you have an existing clone from before this migration, you will need to take action.

See MIGRATION.md for complete instructions.

  • If you have no local work to preserve: delete your local clone and re-clone the repository
  • If you have local branches or commits: follow the detailed migration steps in MIGRATION.md

About

Ai2 Climate Emulator: fast machine learning models for weather and climate prediction

ai2-climate-emulator.readthedocs.io/en/latest/

Resources

Readme
Apache-2.0 license

Contributing

Contributing
Activity
Custom properties

Stars

240 stars

Watchers

6 watching

Forks

50 forks
Report repository

Releases

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
Repository files navigation
DocsPyPIModel checkpointsOpen In Colab
Logo for the ACE Project
Ai2 Climate Emulator
Ai2 Climate Emulator (ACE) is a fast machine learning model that simulates global atmospheric variability in a changing climate over time scales ranging from hours to centuries. This repository contains the fme python package which can be used to train, run and evaluate weather and climate AI models such as ACE. It also contains the data processing scripts and model configurations used in recent papers published by the Ai2 Climate Modeling group.
Installation
pip install fme

Documentation
See complete documentation here and a quickstart guide here.
Model checkpoints
Pretrained model checkpoints are available in the ACE Hugging Face collection.
Papers
The following papers described models trained using code in this repository.
  • "ACE: A fast, skillful learned global atmospheric model for climate prediction" (link)
  • "Application of the Ai2 Climate Emulator to E3SMv2's global atmosphere model, with a focus on precipitation fidelity" (link)
  • "ACE2: Accurately learning subseasonal to decadal atmospheric variability and forced responses" (link)
  • "ACE2-SOM: Coupling an ML Atmospheric Emulator to a Slab Ocean and Learning the Sensitivity of Climate to Changed CO2" (link)
  • "Applying the ACE2 Emulator to SST Green's Functions for the E3SMv3 Global Atmosphere Model" (link)
  • "SamudrACE: Fast and Accurate Coupled Climate Modeling with 3D Ocean and Atmosphere Emulators" (link)
  • "HiRO-ACE: Fast and skillful AI emulation and downscaling trained on a 3 km global storm-resolving model" (link)
  • "FloeNet: A mass-conserving global sea ice emulator that generalizes across climates" (link)
  • "AIMIP Phase 1: systematic evaluations of AI weather and climate models" (link)
  • "Disentangling the effects of sea surface temperature and CO2 in global machine learned weather-climate emulators" (link)
  • "Stochastic Emulation of a Fully Coupled Preindustrial E3SMv3 Simulation" (link)
⚠️ Important migration notice
This repository had a breaking history change on the main branch in December 2025 as part of our transition to open development. If you have an existing clone from before this migration, you will need to take action.
See MIGRATION.md for complete instructions.
  • If you have no local work to preserve: delete your local clone and re-clone the repository
  • If you have local branches or commits: follow the detailed migration steps in MIGRATION.md
About
Ai2 Climate Emulator: fast machine learning models for weather and climate prediction
ai2-climate-emulator.readthedocs.io/en/latest/
Resources
Readme
Apache-2.0 license
Contributing
Contributing
Activity
Custom properties
Stars
240 stars
Watchers
6 watching
Forks
50 forks
Report repository
Releases
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
DocsPyPIModel checkpointsOpen In Colab
Logo for the ACE Project
Ai2 Climate Emulator
Ai2 Climate Emulator (ACE) is a fast machine learning model that simulates global atmospheric variability in a changing climate over time scales ranging from hours to centuries. This repository contains the fme python package which can be used to train, run and evaluate weather and climate AI models such as ACE. It also contains the data processing scripts and model configurations used in recent papers published by the Ai2 Climate Modeling group.
Installation
pip install fme

Documentation
See complete documentation here and a quickstart guide here.
Model checkpoints
Pretrained model checkpoints are available in the ACE Hugging Face collection.
Papers
The following papers described models trained using code in this repository.
  • "ACE: A fast, skillful learned global atmospheric model for climate prediction" (link)
  • "Application of the Ai2 Climate Emulator to E3SMv2's global atmosphere model, with a focus on precipitation fidelity" (link)
  • "ACE2: Accurately learning subseasonal to decadal atmospheric variability and forced responses" (link)
  • "ACE2-SOM: Coupling an ML Atmospheric Emulator to a Slab Ocean and Learning the Sensitivity of Climate to Changed CO2" (link)
  • "Applying the ACE2 Emulator to SST Green's Functions for the E3SMv3 Global Atmosphere Model" (link)
  • "SamudrACE: Fast and Accurate Coupled Climate Modeling with 3D Ocean and Atmosphere Emulators" (link)
  • "HiRO-ACE: Fast and skillful AI emulation and downscaling trained on a 3 km global storm-resolving model" (link)
  • "FloeNet: A mass-conserving global sea ice emulator that generalizes across climates" (link)
  • "AIMIP Phase 1: systematic evaluations of AI weather and climate models" (link)
  • "Disentangling the effects of sea surface temperature and CO2 in global machine learned weather-climate emulators" (link)
  • "Stochastic Emulation of a Fully Coupled Preindustrial E3SMv3 Simulation" (link)
⚠️ Important migration notice
This repository had a breaking history change on the main branch in December 2025 as part of our transition to open development. If you have an existing clone from before this migration, you will need to take action.
See MIGRATION.md for complete instructions.
  • If you have no local work to preserve: delete your local clone and re-clone the repository
  • If you have local branches or commits: follow the detailed migration steps in MIGRATION.md
About
Ai2 Climate Emulator: fast machine learning models for weather and climate prediction
ai2-climate-emulator.readthedocs.io/en/latest/
Resources
Readme
Apache-2.0 license
Contributing
Contributing
Activity
Custom properties
Stars
240 stars
Watchers
6 watching
Forks
50 forks
Report repository
Releases
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
DocsPyPIModel checkpointsOpen In Colab
Logo for the ACE Project
Ai2 Climate Emulator
Ai2 Climate Emulator (ACE) is a fast machine learning model that simulates global atmospheric variability in a changing climate over time scales ranging from hours to centuries. This repository contains the fme python package which can be used to train, run and evaluate weather and climate AI models such as ACE. It also contains the data processing scripts and model configurations used in recent papers published by the Ai2 Climate Modeling group.
Installation
pip install fme

Documentation
See complete documentation here and a quickstart guide here.
Model checkpoints
Pretrained model checkpoints are available in the ACE Hugging Face collection.
Papers
The following papers described models trained using code in this repository.
  • "ACE: A fast, skillful learned global atmospheric model for climate prediction" (link)
  • "Application of the Ai2 Climate Emulator to E3SMv2's global atmosphere model, with a focus on precipitation fidelity" (link)
  • "ACE2: Accurately learning subseasonal to decadal atmospheric variability and forced responses" (link)
  • "ACE2-SOM: Coupling an ML Atmospheric Emulator to a Slab Ocean and Learning the Sensitivity of Climate to Changed CO2" (link)
  • "Applying the ACE2 Emulator to SST Green's Functions for the E3SMv3 Global Atmosphere Model" (link)
  • "SamudrACE: Fast and Accurate Coupled Climate Modeling with 3D Ocean and Atmosphere Emulators" (link)
  • "HiRO-ACE: Fast and skillful AI emulation and downscaling trained on a 3 km global storm-resolving model" (link)
  • "FloeNet: A mass-conserving global sea ice emulator that generalizes across climates" (link)
  • "AIMIP Phase 1: systematic evaluations of AI weather and climate models" (link)
  • "Disentangling the effects of sea surface temperature and CO2 in global machine learned weather-climate emulators" (link)
  • "Stochastic Emulation of a Fully Coupled Preindustrial E3SMv3 Simulation" (link)
⚠️ Important migration notice
This repository had a breaking history change on the main branch in December 2025 as part of our transition to open development. If you have an existing clone from before this migration, you will need to take action.
See MIGRATION.md for complete instructions.
  • If you have no local work to preserve: delete your local clone and re-clone the repository
  • If you have local branches or commits: follow the detailed migration steps in MIGRATION.md
About
Ai2 Climate Emulator: fast machine learning models for weather and climate prediction
ai2-climate-emulator.readthedocs.io/en/latest/
Resources
Readme
Apache-2.0 license
Contributing
Contributing
Activity
Custom properties
Stars
240 stars
Watchers
6 watching
Forks
50 forks
Report repository
Releases
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
DocsPyPIModel checkpointsOpen In Colab
Logo for the ACE Project
Ai2 Climate Emulator
Ai2 Climate Emulator (ACE) is a fast machine learning model that simulates global atmospheric variability in a changing climate over time scales ranging from hours to centuries. This repository contains the fme python package which can be used to train, run and evaluate weather and climate AI models such as ACE. It also contains the data processing scripts and model configurations used in recent papers published by the Ai2 Climate Modeling group.
Installation
pip install fme

Documentation
See complete documentation here and a quickstart guide here.
Model checkpoints
Pretrained model checkpoints are available in the ACE Hugging Face collection.
Papers
The following papers described models trained using code in this repository.
  • "ACE: A fast, skillful learned global atmospheric model for climate prediction" (link)
  • "Application of the Ai2 Climate Emulator to E3SMv2's global atmosphere model, with a focus on precipitation fidelity" (link)
  • "ACE2: Accurately learning subseasonal to decadal atmospheric variability and forced responses" (link)
  • "ACE2-SOM: Coupling an ML Atmospheric Emulator to a Slab Ocean and Learning the Sensitivity of Climate to Changed CO2" (link)
  • "Applying the ACE2 Emulator to SST Green's Functions for the E3SMv3 Global Atmosphere Model" (link)
  • "SamudrACE: Fast and Accurate Coupled Climate Modeling with 3D Ocean and Atmosphere Emulators" (link)
  • "HiRO-ACE: Fast and skillful AI emulation and downscaling trained on a 3 km global storm-resolving model" (link)
  • "FloeNet: A mass-conserving global sea ice emulator that generalizes across climates" (link)
  • "AIMIP Phase 1: systematic evaluations of AI weather and climate models" (link)
  • "Disentangling the effects of sea surface temperature and CO2 in global machine learned weather-climate emulators" (link)
  • "Stochastic Emulation of a Fully Coupled Preindustrial E3SMv3 Simulation" (link)
⚠️ Important migration notice
This repository had a breaking history change on the main branch in December 2025 as part of our transition to open development. If you have an existing clone from before this migration, you will need to take action.
See MIGRATION.md for complete instructions.
  • If you have no local work to preserve: delete your local clone and re-clone the repository
  • If you have local branches or commits: follow the detailed migration steps in MIGRATION.md
About
Ai2 Climate Emulator: fast machine learning models for weather and climate prediction
ai2-climate-emulator.readthedocs.io/en/latest/
Resources
Readme
Apache-2.0 license
Contributing
Contributing
Activity
Custom properties
Stars
240 stars
Watchers
6 watching
Forks
50 forks
Report repository
Releases
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
DocsPyPIModel checkpointsOpen In Colab
Logo for the ACE Project
Ai2 Climate Emulator
Ai2 Climate Emulator (ACE) is a fast machine learning model that simulates global atmospheric variability in a changing climate over time scales ranging from hours to centuries. This repository contains the fme python package which can be used to train, run and evaluate weather and climate AI models such as ACE. It also contains the data processing scripts and model configurations used in recent papers published by the Ai2 Climate Modeling group.
Installation
pip install fme

Documentation
See complete documentation here and a quickstart guide here.
Model checkpoints
Pretrained model checkpoints are available in the ACE Hugging Face collection.
Papers
The following papers described models trained using code in this repository.
  • "ACE: A fast, skillful learned global atmospheric model for climate prediction" (link)
  • "Application of the Ai2 Climate Emulator to E3SMv2's global atmosphere model, with a focus on precipitation fidelity" (link)
  • "ACE2: Accurately learning subseasonal to decadal atmospheric variability and forced responses" (link)
  • "ACE2-SOM: Coupling an ML Atmospheric Emulator to a Slab Ocean and Learning the Sensitivity of Climate to Changed CO2" (link)
  • "Applying the ACE2 Emulator to SST Green's Functions for the E3SMv3 Global Atmosphere Model" (link)
  • "SamudrACE: Fast and Accurate Coupled Climate Modeling with 3D Ocean and Atmosphere Emulators" (link)
  • "HiRO-ACE: Fast and skillful AI emulation and downscaling trained on a 3 km global storm-resolving model" (link)
  • "FloeNet: A mass-conserving global sea ice emulator that generalizes across climates" (link)
  • "AIMIP Phase 1: systematic evaluations of AI weather and climate models" (link)
  • "Disentangling the effects of sea surface temperature and CO2 in global machine learned weather-climate emulators" (link)
  • "Stochastic Emulation of a Fully Coupled Preindustrial E3SMv3 Simulation" (link)
⚠️ Important migration notice
This repository had a breaking history change on the main branch in December 2025 as part of our transition to open development. If you have an existing clone from before this migration, you will need to take action.
See MIGRATION.md for complete instructions.
  • If you have no local work to preserve: delete your local clone and re-clone the repository
  • If you have local branches or commits: follow the detailed migration steps in MIGRATION.md
About
Ai2 Climate Emulator: fast machine learning models for weather and climate prediction
ai2-climate-emulator.readthedocs.io/en/latest/
Resources
Readme
Apache-2.0 license
Contributing
Contributing
Activity
Custom properties
Stars
240 stars
Watchers
6 watching
Forks
50 forks
Report repository
Releases
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
DocsPyPIModel checkpointsOpen In Colab
Logo for the ACE Project
Ai2 Climate Emulator
Ai2 Climate Emulator (ACE) is a fast machine learning model that simulates global atmospheric variability in a changing climate over time scales ranging from hours to centuries. This repository contains the fme python package which can be used to train, run and evaluate weather and climate AI models such as ACE. It also contains the data processing scripts and model configurations used in recent papers published by the Ai2 Climate Modeling group.
Installation
pip install fme

Documentation
See complete documentation here and a quickstart guide here.
Model checkpoints
Pretrained model checkpoints are available in the ACE Hugging Face collection.
Papers
The following papers described models trained using code in this repository.
  • "ACE: A fast, skillful learned global atmospheric model for climate prediction" (link)
  • "Application of the Ai2 Climate Emulator to E3SMv2's global atmosphere model, with a focus on precipitation fidelity" (link)
  • "ACE2: Accurately learning subseasonal to decadal atmospheric variability and forced responses" (link)
  • "ACE2-SOM: Coupling an ML Atmospheric Emulator to a Slab Ocean and Learning the Sensitivity of Climate to Changed CO2" (link)
  • "Applying the ACE2 Emulator to SST Green's Functions for the E3SMv3 Global Atmosphere Model" (link)
  • "SamudrACE: Fast and Accurate Coupled Climate Modeling with 3D Ocean and Atmosphere Emulators" (link)
  • "HiRO-ACE: Fast and skillful AI emulation and downscaling trained on a 3 km global storm-resolving model" (link)
  • "FloeNet: A mass-conserving global sea ice emulator that generalizes across climates" (link)
  • "AIMIP Phase 1: systematic evaluations of AI weather and climate models" (link)
  • "Disentangling the effects of sea surface temperature and CO2 in global machine learned weather-climate emulators" (link)
  • "Stochastic Emulation of a Fully Coupled Preindustrial E3SMv3 Simulation" (link)
⚠️ Important migration notice
This repository had a breaking history change on the main branch in December 2025 as part of our transition to open development. If you have an existing clone from before this migration, you will need to take action.
See MIGRATION.md for complete instructions.
  • If you have no local work to preserve: delete your local clone and re-clone the repository
  • If you have local branches or commits: follow the detailed migration steps in MIGRATION.md
About
Ai2 Climate Emulator: fast machine learning models for weather and climate prediction
ai2-climate-emulator.readthedocs.io/en/latest/
Resources
Readme
Apache-2.0 license
Contributing
Contributing
Activity
Custom properties
Stars
240 stars
Watchers
6 watching
Forks
50 forks
Report repository
Releases
Used by
Contributors
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, 'i');
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Ai2 Climate Emulator
Ai2 Climate Emulator (ACE) is a fast machine learning model that simulates global atmospheric variability in a changing climate over time scales ranging from hours to centuries. This repository contains the fme python package which can be used to train, run and evaluate weather and climate AI models such as ACE. It also contains the data processing scripts and model configurations used in recent papers published by the Ai2 Climate Modeling group.
Installation
pip install fme

Documentation
See complete documentation here and a quickstart guide here.
Model checkpoints
Pretrained model checkpoints are available in the ACE Hugging Face collection.
Papers
The following papers described models trained using code in this repository.
  • "ACE: A fast, skillful learned global atmospheric model for climate prediction" (link)
  • "Application of the Ai2 Climate Emulator to E3SMv2's global atmosphere model, with a focus on precipitation fidelity" (link)
  • "ACE2: Accurately learning subseasonal to decadal atmospheric variability and forced responses" (link)
  • "ACE2-SOM: Coupling an ML Atmospheric Emulator to a Slab Ocean and Learning the Sensitivity of Climate to Changed CO2" (link)
  • "Applying the ACE2 Emulator to SST Green's Functions for the E3SMv3 Global Atmosphere Model" (link)
  • "SamudrACE: Fast and Accurate Coupled Climate Modeling with 3D Ocean and Atmosphere Emulators" (link)
  • "HiRO-ACE: Fast and skillful AI emulation and downscaling trained on a 3 km global storm-resolving model" (link)
  • "FloeNet: A mass-conserving global sea ice emulator that generalizes across climates" (link)
  • "AIMIP Phase 1: systematic evaluations of AI weather and climate models" (link)
  • "Disentangling the effects of sea surface temperature and CO2 in global machine learned weather-climate emulators" (link)
  • "Stochastic Emulation of a Fully Coupled Preindustrial E3SMv3 Simulation" (link)
⚠️ Important migration notice
This repository had a breaking history change on the main branch in December 2025 as part of our transition to open development. If you have an existing clone from before this migration, you will need to take action.
See MIGRATION.md for complete instructions.
  • If you have no local work to preserve: delete your local clone and re-clone the repository
  • If you have local branches or commits: follow the detailed migration steps in MIGRATION.md
About
Ai2 Climate Emulator: fast machine learning models for weather and climate prediction
ai2-climate-emulator.readthedocs.io/en/latest/
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Apache-2.0 license
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