Deep Learning of Systematic Sea Ice Model Errors From Data Assimilation Increments

Deep Learning of Systematic Sea Ice Model Errors From Data Assimilation Increments

Abstract Data assimilation is often viewed as a framework for correcting short‐term error growth in dynamical climate model forecasts. When viewed on the time scales of climate however, these short‐term corrections, or analysis increments, can closely mirror the systematic bias patterns of the dynam...

Full description

Bibliographic Details
Main Authors: William Gregory, Mitchell Bushuk, Alistair Adcroft, Yongfei Zhang, Laure Zanna
Format: Article
Language:English
Published: American Geophysical Union (AGU) 2023-10-01
Series:Journal of Advances in Modeling Earth Systems
Subjects:
machine learning
data assimilation
sea ice
climate modeling
prediction
parameterization
Online Access:https://doi.org/10.1029/2023MS003757

Internet

https://doi.org/10.1029/2023MS003757

Similar Items