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

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

doi:https://doi.org/10.5194/ascmo-6-223-2020

Articles | Volume 6, issue 2

https://doi.org/10.5194/ascmo-6-223-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/ascmo-6-223-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 6, issue 2

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

Supplement

https://doi.org/10.5194/ascmo-6-223-2020-supplement

Data sets

CLM5 Perturbed Parameter Ensembles Katherine Dagon https://doi.org/10.5065/9bcc-4a87

katiedagon/CLM5_ParameterUncertainty: Publication release Katherine Dagon https://doi.org/10.5281/zenodo.4302690

ESCOMP/CTSM: Update documentation for release-clm5.0 branch, and fix issues with no-anthro surface dataset creation, Version release-clm5.0.34 CTSM Development Team https://doi.org/10.5281/zenodo.3779821

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.