Articles | Volume 20, issue 7
https://doi.org/10.5194/bg-20-1313-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-20-1313-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Apr 2023
Research article |
| 06 Apr 2023
| 06 Apr 2023
Towards an ensemble-based evaluation of land surface models in light of uncertain forcings and observations
Vivek K. Arora
CORRESPONDING AUTHOR
Canadian Centre for Climate Modelling and Analysis, Climate Research
Division, Environment Canada, Victoria, BC, Canada
Christian Seiler
Climate Processes Section, Climate Research Division, Environment and
Climate Change Canada, Toronto, ON, Canada
Libo Wang
Climate Processes Section, Climate Research Division, Environment and
Climate Change Canada, Toronto, ON, Canada
Sian Kou-Giesbrecht
Canadian Centre for Climate Modelling and Analysis, Climate Research
Division, Environment Canada, Victoria, BC, Canada
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Cited
17 citations as recorded by crossref.
- The impacts of modelling prescribed vs. dynamic land cover in a high-CO2 future scenario – greening of the Arctic and Amazonian dieback S. Kou-Giesbrecht et al. https://doi.org/10.5194/bg-21-3339-2024
- Impacts of benchmarking choices on inferred model skill of the Arctic–Boreal terrestrial carbon cycle J. Poe et al. https://doi.org/10.1088/2752-664X/adacee
- NDVI joint process-based models drive a learning ensemble model for accurately estimating cropland net primary productivity (NPP) Z. Wang et al. https://doi.org/10.3389/fenvs.2023.1304400
- On the predictability of turbulent fluxes from land: PLUMBER2 MIP experimental description and preliminary results G. Abramowitz et al. https://doi.org/10.5194/bg-21-5517-2024
- An inter-comparison of inverse models for estimating European CH4 emissions E. Ioannidis et al. https://doi.org/10.5194/essd-18-167-2026
- Improving physiological simulations in seasonally dry tropical forests with limited measurements I. e Silva et al. https://doi.org/10.1007/s00704-024-05050-1
- Vegetation biogeography is a main source of uncertainty in modelling the land carbon cycle R. Zhao et al. https://doi.org/10.1038/s41467-025-67636-1
- A three-quantile bias correction with spatial transfer for the correction of simulated European river runoff to force ocean models S. Hagemann et al. https://doi.org/10.5194/os-20-1457-2024
- Identical hierarchy of physical drought types for climate change signals and uncertainty P. Hosseinzadehtalaei et al. https://doi.org/10.1016/j.wace.2023.100573
- Multi-fold increase in rainforest tipping risk beyond 1.5–2 °C warming C. Singh et al. https://doi.org/10.5194/esd-15-1543-2024
- Atmospheric forcing uncertainty contributes to divergent estimates of China’s terrestrial carbon dynamics Y. Cheng et al. https://doi.org/10.1088/1748-9326/ae2af8
- Global Sensitivity Analysis of the Historical Carbon Sink across Biomes R. Deepak et al. https://doi.org/10.1080/07055900.2025.2552952
- OpenBench: a land model evaluation system Z. Wei et al. https://doi.org/10.5194/gmd-18-6517-2025
- Mapping of ESA's Climate Change Initiative land cover data to plant functional types for use in the CLASSIC land model L. Wang et al. https://doi.org/10.5194/bg-20-2265-2023
- Effects of land surface model resolution on soil moisture and wildfire simulations using Community Land Model version 5 – Biogeochemistry H. Seo et al. https://doi.org/10.1016/j.jhydrol.2025.134085
- Comparative analysis of JRA-3Q and JRA-55 reanalysis datasets as forcing for land surface model: implications for hydrological processes Z. Wei et al. https://doi.org/10.1016/j.jhydrol.2026.135616
- Improving terrestrial carbon flux simulations with machine learning and global Earth observations C. Seiler https://doi.org/10.5194/esd-17-651-2026
17 citations as recorded by crossref.
- The impacts of modelling prescribed vs. dynamic land cover in a high-CO2 future scenario – greening of the Arctic and Amazonian dieback S. Kou-Giesbrecht et al. https://doi.org/10.5194/bg-21-3339-2024
- Impacts of benchmarking choices on inferred model skill of the Arctic–Boreal terrestrial carbon cycle J. Poe et al. https://doi.org/10.1088/2752-664X/adacee
- NDVI joint process-based models drive a learning ensemble model for accurately estimating cropland net primary productivity (NPP) Z. Wang et al. https://doi.org/10.3389/fenvs.2023.1304400
- On the predictability of turbulent fluxes from land: PLUMBER2 MIP experimental description and preliminary results G. Abramowitz et al. https://doi.org/10.5194/bg-21-5517-2024
- An inter-comparison of inverse models for estimating European CH4 emissions E. Ioannidis et al. https://doi.org/10.5194/essd-18-167-2026
- Improving physiological simulations in seasonally dry tropical forests with limited measurements I. e Silva et al. https://doi.org/10.1007/s00704-024-05050-1
- Vegetation biogeography is a main source of uncertainty in modelling the land carbon cycle R. Zhao et al. https://doi.org/10.1038/s41467-025-67636-1
- A three-quantile bias correction with spatial transfer for the correction of simulated European river runoff to force ocean models S. Hagemann et al. https://doi.org/10.5194/os-20-1457-2024
- Identical hierarchy of physical drought types for climate change signals and uncertainty P. Hosseinzadehtalaei et al. https://doi.org/10.1016/j.wace.2023.100573
- Multi-fold increase in rainforest tipping risk beyond 1.5–2 °C warming C. Singh et al. https://doi.org/10.5194/esd-15-1543-2024
- Atmospheric forcing uncertainty contributes to divergent estimates of China’s terrestrial carbon dynamics Y. Cheng et al. https://doi.org/10.1088/1748-9326/ae2af8
- Global Sensitivity Analysis of the Historical Carbon Sink across Biomes R. Deepak et al. https://doi.org/10.1080/07055900.2025.2552952
- OpenBench: a land model evaluation system Z. Wei et al. https://doi.org/10.5194/gmd-18-6517-2025
- Mapping of ESA's Climate Change Initiative land cover data to plant functional types for use in the CLASSIC land model L. Wang et al. https://doi.org/10.5194/bg-20-2265-2023
- Effects of land surface model resolution on soil moisture and wildfire simulations using Community Land Model version 5 – Biogeochemistry H. Seo et al. https://doi.org/10.1016/j.jhydrol.2025.134085
- Comparative analysis of JRA-3Q and JRA-55 reanalysis datasets as forcing for land surface model: implications for hydrological processes Z. Wei et al. https://doi.org/10.1016/j.jhydrol.2026.135616
- Improving terrestrial carbon flux simulations with machine learning and global Earth observations C. Seiler https://doi.org/10.5194/esd-17-651-2026
Saved (final revised paper)
Latest update: 23 Jun 2026
Short summary
The behaviour of natural systems is now very often represented through mathematical models. These models represent our understanding of how nature works. Of course, nature does not care about our understanding. Since our understanding is not perfect, evaluating models is challenging, and there are uncertainties. This paper illustrates this uncertainty for land models and argues that evaluating models in light of the uncertainty in various components provides useful information.
The behaviour of natural systems is now very often represented through mathematical models....
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