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

Goodwin, Philip; Leduc, Martin; Partanen, Antti-Ilari; Matthews, H. Damon; Rogers, Alex

doi:https://doi.org/10.5194/gmd-13-5389-2020

Articles | Volume 13, issue 11

https://doi.org/10.5194/gmd-13-5389-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-13-5389-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 13, issue 11

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, Martin Leduc, Antti-Ilari Partanen, H. Damon Matthews, and Alex Rogers

Supplement

https://doi.org/10.5194/gmd-13-5389-2020-supplement

Model code and software

WASP-ESM/WASP_Earth_System_Model: WASP_LGRTC_v1.0.0 release August 2020 Philip Goodwin, Martin Leduc, Antti-Ilari Partanen, H. Damon Matthews and Alex Rogers https://doi.org/10.5281/zenodo.4001523

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.