Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

IF value: 5.240
IF5.240
IF 5-year value: 5.768
IF 5-year
5.768
CiteScore value: 8.9
CiteScore
8.9
SNIP value: 1.713
SNIP1.713
IPP value: 5.53
IPP5.53
SJR value: 3.18
SJR3.18
Scimago H <br class='widget-line-break'>index value: 71
Scimago H
index
71
h5-index value: 51
h5-index51
Preprints
https://doi.org/10.5194/gmd-2020-294
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-294
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: model evaluation paper 28 Oct 2020

Submitted as: model evaluation paper | 28 Oct 2020

Review status
This preprint is currently under review for the journal GMD.

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 Seiler1, Joe R. Melton1, Vivek K. Arora2, and Libo Wang3 Christian Seiler et al.
  • 1Climate Processes Section, Climate Research Division, Environment and Climate Change Canada, Victoria, BC, Canada
  • 2Canadian Centre for Climate Modelling and Analysis, Climate Research Division, Environment and Climate Change Canada, Victoria, BC, Canada
  • 3Climate Processes Section, Climate Research Division, Environment and Climate Change Canada, North York, ON, Canada

Abstract. The Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC) is an open source community model designed to address research questions that explore the role of the land surface in the global climate system. Here we evaluate how well CLASSIC reproduces the energy, water, and carbon cycle when forced with quasi-observed meteorological data. Model skill scores summarize how well model output agrees with observation-based reference data across multiple statistical metrics. A lack of agreement may be due to deficiencies in the model, its forcing data, and/or reference data. To address uncertainties in the forcing we evaluate an ensemble of CLASSIC runs that is based on three meteorological data sets. To account for observational uncertainty, we compute benchmark skill scores that quantify the level of agreement among independent reference data sets. The benchmark scores demonstrate what score values a model may realistically achieve given the uncertainties in the observations. Our results show that uncertainties associated with the forcing and observations are considerably large. For instance, for 10 out of 19 variables assessed in this study, the sign of the bias changes depending on what forcing and reference data are used. Benchmark scores are much lower than expected, implying large observational uncertainties. Model and benchmark score values are mostly similar, indicating that CLASSIC performs well when considering observational uncertainty. Using the difference between model and benchmark scores as a measure of performance shows that model skill increases in the following order: fractional area burned, runoff, soil heat flux, leaf area index, net shortwave radiation, net ecosystem exchange, above-ground biomass, gross primary productivity, surface albedo, snow water equivalent, net surface radiation, sensible heat flux, net longwave radiation, latent heat flux, and ecosystem respiration. Our results will serve as a baseline for guiding and monitoring future CLASSIC development.

Christian Seiler et al.

Interactive discussion

Status: open (until 01 Jan 2021)
Status: open (until 01 Jan 2021)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement

Christian Seiler et al.

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 Christian Seiler, Joe R. Melton, Vivek K. Arora, and Libo Wang https://doi.org/10.5281/zenodo.4010681

Model code and software

The Canadian Land Surface Scheme including Biogeochemical Cycles Joe R. Melton, Vivek Arora, Eduard Wisernig-Cojoc, Christian Seiler, Matthew Fortier, Ed Chan, and Lina Teckentrup https://doi.org/10.5281/zenodo.3522407

Executable research compendia (ERC)

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 https://doi.org/10.5281/zenodo.4010681

Christian Seiler et al.

Viewed

Total article views: 211 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
150 56 5 211 4 4
  • HTML: 150
  • PDF: 56
  • XML: 5
  • Total: 211
  • BibTeX: 4
  • EndNote: 4
Views and downloads (calculated since 28 Oct 2020)
Cumulative views and downloads (calculated since 28 Oct 2020)

Viewed (geographical distribution)

Total article views: 134 (including HTML, PDF, and XML) Thereof 134 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved

No saved metrics found.

Discussed

No discussed metrics found.
Latest update: 01 Dec 2020
Publications Copernicus
Download
Short summary
This study evaluates how well the CLASSIC Land Surface Model reproduces the energy, water, and carbon cycle when compared to 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.
This study evaluates how well the CLASSIC Land Surface Model reproduces the energy, water, and...
Citation