Articles | Volume 15, issue 5
https://doi.org/10.5194/gmd-15-1913-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-1913-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
08 Mar 2022
Methods for assessment of models |
| 08 Mar 2022
| 08 Mar 2022
Emulation of high-resolution land surface models using sparse Gaussian processes with application to JULES
Department of Mathematical Sciences, University of Exeter, Exeter EX4 4QF, UK
Anna B. Harper
Department of Mathematical Sciences, University of Exeter, Exeter EX4 4QF, UK
Daniel Williamson
Department of Mathematical Sciences, University of Exeter, Exeter EX4 4QF, UK
Peter Challenor
Department of Mathematical Sciences, University of Exeter, Exeter EX4 4QF, UK
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Cited
16 citations as recorded by crossref.
- Systematic underestimation of type-specific ecosystem process variability in the Community Land Model v5 over Europe C. Poppe Terán et al. 10.5194/gmd-18-287-2025
- Mechanistic microclimate models and plant pest risk modelling J. Mosedale et al. 10.1007/s10340-024-01777-y
- Feature Calibration for Computer Models W. Xu et al. 10.1137/24M163253X
- Emulating grid-based forest carbon dynamics using machine learning: an LPJ-GUESS v4.1.1 application C. Natel et al. 10.5194/gmd-18-4317-2025
- Emulator-based calibration of a dynamic grassland model using recurrent neural networks and Hamiltonian Monte Carlo V. Aakula et al. 10.1016/j.eja.2025.127769
- Advances in land surface forecasting: a comparison of LSTM, gradient boosting, and feed-forward neural networks as prognostic state emulators in a case study with ecLand M. Wesselkamp et al. 10.5194/gmd-18-921-2025
- Spatiotemporal patterns of gross primary productivity and the response to climate variability in the subtropical ecosystem of Eastern China R. Wu et al. 10.1016/j.ecolmodel.2025.111292
- Microclimate, an important part of ecology and biogeography J. Kemppinen et al. 10.1111/geb.13834
- A Global Sensitivity Analysis of Parameter Uncertainty in the CLASSIC Model R. S. N. et al. 10.1080/07055900.2024.2396426
- Calibrating a large-domain land/hydrology process model in the age of AI: the SUMMA CAMELS emulator experiments M. Farahani et al. 10.5194/hess-29-4515-2025
- Exploring the potential of history matching for land surface model calibration N. Raoult et al. 10.5194/gmd-17-5779-2024
- Towards the assimilation of atmospheric CO2 concentration data in a land surface model using adjoint-free variational methods S. Beylat et al. 10.5194/gmd-18-7501-2025
- Effects of land surface model resolution on soil moisture and wildfire simulations using Community Land Model version 5 – Biogeochemistry H. Seo et al. 10.1016/j.jhydrol.2025.134085
- Global Sensitivity Analysis of the Future Land Carbon Sink R. Deepak et al. 10.1080/07055900.2025.2540430
- Economic impacts of extreme climate events: Policies and practices N. Abbas Abuzied 10.1016/j.envdev.2025.101350
- Constraining the carbon cycle in JULES-ES-1.0 D. McNeall et al. 10.5194/gmd-17-1059-2024
16 citations as recorded by crossref.
- Systematic underestimation of type-specific ecosystem process variability in the Community Land Model v5 over Europe C. Poppe Terán et al. 10.5194/gmd-18-287-2025
- Mechanistic microclimate models and plant pest risk modelling J. Mosedale et al. 10.1007/s10340-024-01777-y
- Feature Calibration for Computer Models W. Xu et al. 10.1137/24M163253X
- Emulating grid-based forest carbon dynamics using machine learning: an LPJ-GUESS v4.1.1 application C. Natel et al. 10.5194/gmd-18-4317-2025
- Emulator-based calibration of a dynamic grassland model using recurrent neural networks and Hamiltonian Monte Carlo V. Aakula et al. 10.1016/j.eja.2025.127769
- Advances in land surface forecasting: a comparison of LSTM, gradient boosting, and feed-forward neural networks as prognostic state emulators in a case study with ecLand M. Wesselkamp et al. 10.5194/gmd-18-921-2025
- Spatiotemporal patterns of gross primary productivity and the response to climate variability in the subtropical ecosystem of Eastern China R. Wu et al. 10.1016/j.ecolmodel.2025.111292
- Microclimate, an important part of ecology and biogeography J. Kemppinen et al. 10.1111/geb.13834
- A Global Sensitivity Analysis of Parameter Uncertainty in the CLASSIC Model R. S. N. et al. 10.1080/07055900.2024.2396426
- Calibrating a large-domain land/hydrology process model in the age of AI: the SUMMA CAMELS emulator experiments M. Farahani et al. 10.5194/hess-29-4515-2025
- Exploring the potential of history matching for land surface model calibration N. Raoult et al. 10.5194/gmd-17-5779-2024
- Towards the assimilation of atmospheric CO2 concentration data in a land surface model using adjoint-free variational methods S. Beylat et al. 10.5194/gmd-18-7501-2025
- Effects of land surface model resolution on soil moisture and wildfire simulations using Community Land Model version 5 – Biogeochemistry H. Seo et al. 10.1016/j.jhydrol.2025.134085
- Global Sensitivity Analysis of the Future Land Carbon Sink R. Deepak et al. 10.1080/07055900.2025.2540430
- Economic impacts of extreme climate events: Policies and practices N. Abbas Abuzied 10.1016/j.envdev.2025.101350
- Constraining the carbon cycle in JULES-ES-1.0 D. McNeall et al. 10.5194/gmd-17-1059-2024
Latest update: 31 Oct 2025
Short summary
We have adapted machine learning techniques to build a model of the land surface in Great Britain. The model was trained using data from a very complex land surface model called JULES. Our model is faster at producing simulations and predictions and can investigate many different scenarios, which can be used to improve our understanding of the climate and could also be used to help make local decisions.
We have adapted machine learning techniques to build a model of the land surface in Great...
