Articles | Volume 13, issue 11
https://doi.org/10.5194/gmd-13-5389-2020
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, Martin Leduc, Antti-Ilari Partanen, H. Damon Matthews, and Alex Rogers

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Cited articles

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Cheng, L., Trenberth, K. E., Fasullo, J., Boyer, T., Abraham, J., and Zhu, J.: Improved estimates of ocean heat content from 1960 to 2015, Sci. Adv., 3, e1601545, https://doi.org/10.1126/sciadv.1601545, 2017. 
Fordham, D. A., Wigley, T. M. L., Watts, M. J., and Brook, B. W.: Strengthening forecasts of climate change impacts with multi-model ensemble averaged projections using MAGICC/SCENGEN 5.3, Ecography 35, 4–8, https://doi.org/10.1111/j.1600-0587.2011.07398.x, 2012. 
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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.