Advancing Sea Ice Predictability in E3SM with Machine Learning

Focal area(s): To improve predictions of sea ice in E3SM we propose to develop a hierarchy of data-driven models using observational and simulation data to investigate the most important Earth system drivers of sea ice variability and loss, develop surrogates that build on the reduced parameter spac...

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Bibliographic Details
Main Authors: Peterson, Kara, Davis, Warren, Peterson, Matt, Nichol, J. Jake, Chowdhary, Kenny, D'Elia, Marta
Language:unknown
Published: 2022
Subjects:
58 GEOSCIENCES
54 ENVIRONMENTAL SCIENCES
97 MATHEMATICS AND COMPUTING
Sea ice
Online Access:http://www.osti.gov/servlets/purl/1769655
https://www.osti.gov/biblio/1769655
https://doi.org/10.2172/1769655
Description
Summary:Focal area(s): To improve predictions of sea ice in E3SM we propose to develop a hierarchy of data-driven models using observational and simulation data to investigate the most important Earth system drivers of sea ice variability and loss, develop surrogates that build on the reduced parameter space of important drivers, and, where appropriate, couple machine learning models with standard PDE models to capture important physical behavior at different scales. This work falls under Focal Area 2. Predictive modeling through the use of AI techniques.

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