Articles | Volume 6, issue 2
https://doi.org/10.5194/ascmo-6-223-2020
https://doi.org/10.5194/ascmo-6-223-2020
22 Dec 2020
 | 22 Dec 2020

A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5

Katherine Dagon, Benjamin M. Sanderson, Rosie A. Fisher, and David M. Lawrence

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Latest update: 24 Feb 2024
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Short summary
Uncertainties in land model projections are important to understand in order to build confidence in Earth system modeling. In this paper, we introduce a framework for estimating uncertain land model parameters with machine learning. This method increases the computational efficiency of this process relative to traditional hand tuning approaches and provides objective methods to assess the results. We further identify key processes and parameters that are important for accurate land modeling.