Evaluating statistical consistency for the ocean component of earth system models using physics informed convolutional autoencoder

Abstract Model verification is a crucial step in the development and optimization of Earth system models (ESMs). Recently, a deep learning approach in evaluating statistical consistency for the atmosphere component of ESMs (A-ESM-DCT) has been proven effective in model verification. However, due to...

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Bibliographic Details
Main Authors:	Yangyang Yu, Shaoqing Zhang, Haohuan Fu, Dexun Chen, Yishuai Jin, Yang Gao, Xiaopei Lin, Zhao Liu, Xiaojing Lv, Yunlong Fei, Kaidi Wang
Format:	Article
Language:	English
Published:	Nature Portfolio 2025-05-01
Series:	Scientific Reports
Subjects:	Consistency test approach for the ocean models Physics-informed neural networks Earth system model ensemble simulations
Online Access:	https://doi.org/10.1038/s41598-025-03092-7
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Evaluating statistical consistency for the ocean component of earth system models using physics informed convolutional autoencoder

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