Advancing global sea ice prediction capabilities using a fully coupled climate model with integrated machine learning.

William Gregory, Mitchell Bushuk, Yong-Fei Zhang, Alistair Adcroft, Laure Zanna, Colleen McHugh, Liwei Jia

Abstract read

In one paragraph

Article in Science advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

William GregoryAtmospheric and Oceanic Sciences Program, Princeton University, Princeton, NJ 08540, USA.ORCID 0000-0001-8176-1642

Mitchell BushukNOAA, Geophysical Fluid Dynamics Laboratory, Princeton, NJ 08540, USA.ORCID 0000-0002-0063-1465

Yong-Fei ZhangEarth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA.

Alistair AdcroftAtmospheric and Oceanic Sciences Program, Princeton University, Princeton, NJ 08540, USA.ORCID 0000-0001-9413-1017

Laure ZannaCourant Institute of Mathematical Sciences, New York University, New York, NY 10012, USA.ORCID 0000-0002-8472-4828

Colleen McHughScience Applications International Corporation, Reston, VA 20190, USA.ORCID 0000-0003-0560-3225

Liwei JiaNOAA, Geophysical Fluid Dynamics Laboratory, Princeton, NJ 08540, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We showcase a hybrid modeling framework that embeds machine learning (ML) inference into the Geophysical Fluid Dynamics Laboratory Seamless System for Prediction and Earth System Research (SPEAR) climate model for online sea ice bias correction during a set of global fully coupled 1-year retrospective forecasts. We compare two hybrid versions of SPEAR to understand the importance of exposing ML models to coupled ice-atmosphere-ocean feedbacks before implementation into fully coupled simulations: Hybrid

Identifiers

PMID41477855

DOI10.1126/sciadv.ady8957

PMCPMC12757041

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.