Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 5
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.
https://doi.org/10.5194/gmd-15-1913-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 5
Methods for assessment of models
 | 
08 Mar 2022
Methods for assessment of models |  | 08 Mar 2022

Emulation of high-resolution land surface models using sparse Gaussian processes with application to JULES

Evan Baker, Anna B. Harper, Daniel Williamson, and Peter Challenor

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Cited articles

AppEEARS Team: Application for Extracting and Exploring Analysis Ready Samples (AppEEARS), Ver. 2.35., NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota, USA, https://lpdaacsvc.cr.usgs.gov/appeears, last access: 12 February 2020. a
Baker, E., Harper, A., Williamson, D., and Challenor, P.: Gross Primary Productivity simulations of Great Britain for emulation, 2001–2010 from JULES land surface model, NERC Environmental Information Data Centre [data set], https://doi.org/10.5285/789bea37-0450-4822-9857-3dc848feb937, 2021. a
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011. a
Binois, M., Gramacy, R. B., and Ludkovski, M.: Practical heteroscedastic gaussian process modeling for large simulation experiments, J. Comput. Graph. Stat., 27, 808–821, 2018. a
Blyth, E. M., Martinez-de la Torre, A., and Robinson, E. L.: Trends in evapotranspiration and its drivers in Great Britain: 1961 to 2015, Prog. Phys. Geog., 43, 666–693, 2019. a, b, c, d, e
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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.
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