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

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

doi:https://doi.org/10.5194/gmd-14-2371-2021

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

Download

Final revised paper (published on 03 May 2021)
Preprint (discussion started on 28 Oct 2020)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

- Printer-friendly version

- Supplement

RC1: 'Review of "CLASSIC v1.0...Part 2: Global Benchmarking" by Seiler et al.', Anonymous Referee #1, 16 Dec 2020
- AC1: 'Reply on RC1', Christian Seiler, 09 Feb 2021
RC2: 'Review of the manuscript by Christian Seiler and coworkers', Anonymous Referee #2, 26 Feb 2021
- AC2: 'Reply on RC2', Christian Seiler, 11 Mar 2021

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Christian Seiler on behalf of the Authors (13 Mar 2021) Author's response Author's tracked changes Manuscript

ED: Publish as is (22 Mar 2021) by Gerd A. Folberth

AR by Christian Seiler on behalf of the Authors (24 Mar 2021)

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.