Articles | Volume 13, issue 11
Geosci. Model Dev., 13, 5389–5399, 2020
https://doi.org/10.5194/gmd-13-5389-2020
Geosci. Model Dev., 13, 5389–5399, 2020
https://doi.org/10.5194/gmd-13-5389-2020

Model description paper 09 Nov 2020

Model description paper | 09 Nov 2020

A computationally efficient method for probabilistic local warming projections constrained by history matching and pattern scaling, demonstrated by WASP–LGRTC-1.0

Philip Goodwin et al.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by P. Goodwin on behalf of the Authors (11 May 2020)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (04 Jun 2020) by James Annan
RR by Anonymous Referee #2 (22 Jun 2020)
ED: Publish subject to minor revisions (review by editor) (21 Aug 2020) by James Annan
AR by P. Goodwin on behalf of the Authors (26 Aug 2020)  Author's response    Manuscript
ED: Publish as is (27 Sep 2020) by James Annan
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Short summary
Numerical climate models are used to make projections of future surface warming for different pathways of future greenhouse gas emissions, where future surface warming will vary from place to place. However, it is so expensive to run complex models using supercomputers that future projections can only be produced for a small number of possible future emissions pathways. This study presents an efficient climate model to make projections of local surface warming using a desktop computer.