Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 14, issue 5
Articles | Volume 14, issue 5
https://doi.org/10.5194/gmd-14-2371-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-2371-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 5
Model evaluation paper
 | 
03 May 2021
Model evaluation paper |  | 03 May 2021

CLASSIC v1.0: the open-source community successor to the Canadian Land Surface Scheme (CLASS) and the Canadian Terrestrial Ecosystem Model (CTEM) – Part 2: Global benchmarking

Christian Seiler, Joe R. Melton, Vivek K. Arora, and Libo Wang

Data sets

CLASSIC v1.0: the open-source community successor to the Canadian Land Surface Scheme (CLASS) and the Canadian Terrestrial Ecosystem Model (CTEM) - Part 2: Global Benchmarking C. Seiler, J. R. Melton, V. K. Arora, and L. Wang https://doi.org/10.5281/zenodo.4596546

Model code and software

CLASSIC v1.0: the open-source community successor to the Canadian Land Surface Scheme (CLASS) and the Canadian Terrestrial Ecosystem Model (CTEM) - Part 2: Global Benchmarking C. Seiler, J. R. Melton, V. K. Arora, and L. Wang https://doi.org/10.5281/zenodo.4596546

AMBER: Automated Model Benchmarking R Package, rpackage version 1.0.3 C. Seiler https://cseiler.shinyapps.io/ShinyAmber

The Canadian Land Surface Scheme including Biogeochemical Cycles (Version 1.0) J. R. Melton, V. Arora, E. Wisernig-Cojoc,C. Seiler, M. Fortier, E. Chan, and L. Teckentrup https://doi.org/10.5281/zenodo.3522407

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Short summary
This study evaluates how well the CLASSIC land surface model reproduces the energy, water, and carbon cycle when compared against a wide range of global observations. Special attention is paid to how uncertainties in the data used to drive and evaluate the model affect model skill. Our results show the importance of incorporating uncertainties when evaluating land surface models and that failing to do so may potentially misguide future model development.
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