Improving E3SM Land Model Photosynthesis Parameterization via Satellite SIF, Machine Learning, and Surrogate Modeling

Improving E3SM Land Model Photosynthesis Parameterization via Satellite SIF, Machine Learning, and Surrogate Modeling

Abstract The parameterization of key photosynthesis parameters is one of the key uncertain sources in modeling ecosystem gross primary productivity (GPP). Solar‐induced chlorophyll fluorescence (SIF) offers a good proxy for GPP since it marks the actual process of photosynthesis; while machine learn...

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Bibliographic Details
Main Authors: Anping Chen, Daniel Ricciuto, Jiafu Mao, Jiawei Wang, Dan Lu, Fandong Meng
Format: Article
Language:English
Published: American Geophysical Union (AGU) 2023-04-01
Series:Journal of Advances in Modeling Earth Systems
Subjects:
data‐model assimilation
solar‐induced chlorophyll fluorescence (SIF)
photosynthetic parameters
surrogate model
Exascale Earth System Model Land Model (ELM)
Online Access:https://doi.org/10.1029/2022MS003135

Internet

https://doi.org/10.1029/2022MS003135

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