Pith Number

pith:ONFNJHF4

pith:2026:ONFNJHF43JRIXY2JJNLB6MRUGL

not attested not anchored not stored refs pending

AIMIP Phase 1: systematic evaluations of AI weather and climate models

Antonia Jost, Brian Henn, Christian Lessig, Christopher S. Bretherton, Dale Durran, Dmitrii Kochkov, Guillaume Couairon, Ignacio Lopez-Gomez, Janni Yuval, Kyle Joseph Chen Hall, Maria J. Molina, Nathaniel Cresswell-Clay, Nikolay Koldunov, Noah Brenowitz, Oliver Watt-Meyer, Peter Manshausen, Renu Singh, Robert Brunstein, Stephan Hoyer, Troy Arcomano, Yana Hasson

AI weather and climate models simulate historical climate and forcing responses as well as conventional physically-based models, though some underestimate warming trends and diverge in out-of-sample tests.

arxiv:2605.06944 v2 · 2026-05-07 · physics.ao-ph

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{ONFNJHF43JRIXY2JJNLB6MRUGL}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

We find that the AI models are able to simulate the historical climate and response to forcing as well as a conventional physically-based model, but some AI models underestimate historical warming trends, and their predictions diverge in the out-of-sample generalization tests.

C2weakest assumption

That training solely against historical reanalysis data under the stated constraints, combined with the five chosen evaluation criteria, is sufficient to assess and build trust in the models' reliability for climate applications.

C3one line summary

AIMIP Phase 1 shows AI models simulate historical climate and El Niño responses as well as traditional models, though some underestimate trends and diverge in generalization tests, with a public dataset released for further checks.

Formal links

1 machine-checked theorem link

Cited by

1 paper in Pith

Probabilistic Seasonal Streamflow Forecasting Across California's Sierra Nevada Watersheds with Agentic AI

Receipt and verification

First computed	2026-05-20T02:05:44.687216Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

734ad49cbcda628be3494b561f323432e1f61525237b79b44272a5baf3ccc41d

Aliases

arxiv: 2605.06944 · arxiv_version: 2605.06944v2 · doi: 10.48550/arxiv.2605.06944 · pith_short_12: ONFNJHF43JRI · pith_short_16: ONFNJHF43JRIXY2J · pith_short_8: ONFNJHF4

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/ONFNJHF43JRIXY2JJNLB6MRUGL \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 734ad49cbcda628be3494b561f323432e1f61525237b79b44272a5baf3ccc41d

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "89464a08ba745777660d7909494ca52fc657c7888c7071749d3924a5877c0774",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "physics.ao-ph",
    "submitted_at": "2026-05-07T21:04:05Z",
    "title_canon_sha256": "8cef03696c862f2a1a07072dcdc314b9c13597e2e8353a3b30c693809ef77337"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.06944",
    "kind": "arxiv",
    "version": 2
  }
}