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Updated: Jan 7, 2026

Using Generative Art to Convey Past and Future Climate Transitions

Using Generative Art to Convey Past and Future Climate Transitions

Published on: March 31, 2023

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

William Gregory¹, Mitchell Bushuk², Yong-Fei Zhang³

¹Atmospheric and Oceanic Sciences Program, Princeton University, Princeton, NJ 08540, USA.

Science Advances

|January 1, 2026

Summary

This study introduces a hybrid climate model integrating machine learning (ML) for sea ice bias correction. Training ML with coupled feedbacks significantly improves Arctic and Antarctic sea ice forecasts.

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Published on: October 4, 2024

Area of Science:

Climate science
Machine learning applications
Geophysical fluid dynamics

Background:

Numerical climate models like the Geophysical Fluid Dynamics Laboratory Seamless System for Prediction and Earth System Research (SPEAR) face challenges with sea ice prediction accuracy.
Machine learning (ML) offers potential for improving climate model performance, but its integration requires careful consideration of training data and environmental feedbacks.

Purpose of the Study:

To develop and evaluate a hybrid modeling framework embedding ML for online sea ice bias correction within the SPEAR climate model.
To assess the impact of exposing ML models to coupled ice-atmosphere-ocean feedbacks (Hybrid_CPL) versus training without these feedbacks (Hybrid_IO) on forecast accuracy.

Main Methods:

A hybrid modeling framework was created by integrating ML inference into the SPEAR climate model.
Two versions were compared: Hybrid_CPL (trained with coupled feedbacks) and Hybrid_IO (trained without coupled feedbacks).
Global, fully coupled 1-year retrospective forecasts were conducted to evaluate performance.

Main Results:

Hybrid_CPL systematically reduced seasonal forecast errors in the Arctic compared to the original SPEAR model.
Hybrid_CPL considerably reduced Antarctic sea ice forecast errors from May to December, showing over a twofold error reduction for 4- to 6-month lead times.
Hybrid_IO exhibited out-of-sample behavior, leading to potential Southern Ocean feedback chains and unrealistic ice-free Antarctic summers.

Conclusions:

Machine learning can demonstrably enhance numerical sea ice prediction capabilities.
Exposing ML models to coupled ice-atmosphere-ocean processes during training is crucial for ensuring their generalization and reliable performance in fully coupled climate simulations.