Articles | Volume 15, issue 5
Geosci. Model Dev., 15, 1913–1929, 2022
https://doi.org/10.5194/gmd-15-1913-2022
Geosci. Model Dev., 15, 1913–1929, 2022
https://doi.org/10.5194/gmd-15-1913-2022

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 et al.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on gmd-2021-205', Astrid Kerkweg, 13 Aug 2021
  • RC1: 'Comment on gmd-2021-205', Anonymous Referee #1, 15 Sep 2021
  • RC2: 'Comment on gmd-2021-205', Anonymous Referee #2, 25 Nov 2021
  • AC1: 'Comment on gmd-2021-205', Evan Baker, 27 Jan 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Evan Baker on behalf of the Authors (27 Jan 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (29 Jan 2022) by Hisashi Sato
RR by Anonymous Referee #2 (30 Jan 2022)
ED: Publish as is (31 Jan 2022) by Hisashi Sato
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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.