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.
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.
the Creative Commons Attribution 4.0 License.
22 Dec 2020
| 22 Dec 2020
A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5
National Center for Atmospheric Research, Boulder, CO, USA
Benjamin M. Sanderson
National Center for Atmospheric Research, Boulder, CO, USA
CERFACS, Toulouse, France
Rosie A. Fisher
National Center for Atmospheric Research, Boulder, CO, USA
CERFACS, Toulouse, France
David M. Lawrence
National Center for Atmospheric Research, Boulder, CO, USA
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Latest update: 16 Oct 2025
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.
Uncertainties in land model projections are important to understand in order to build confidence...
