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

A computationally efficient method for probabilistic local warming projections constrained by history matching and pattern scaling, demonstrated by WASP-LGRTC-1.0
A computationally efficient method for probabilistic local warming projections constrained by history matching and pattern scaling, demonstrated by WASP-LGRTC-1.0
Climate projections are made using a hierarchy of models of different complexities and computational efficiencies. While the most complex climate models contain the most detailed representations of many physical processes within the climate system, both parameter space exploration and Integrated Assessment Modelling require the increased computational efficiency of reduced-complexity models. This study presents a computationally efficient method for generating probabilistic projections of local warming across the globe, using a pattern scaling approach derived from the Climate Model Intercomparison Project phase 5 (CMIP5) ensemble, that can be coupled to any efficient model ensemble simulation of global mean surface warming. While the method can project local warming for arbitrary future scenarios, using it for scenarios with peak global mean warming ≤ 2°C is problematic due to the large uncertainties involved. First, global mean warming is projected using a 103-member ensemble of history-matched simulations with an example reduced complexity Earth system model: the Warming Acidification and Sea-level Projector (WASP). The ensemble-projection of global mean warming from this WASP ensemble is then converted into local warming projections using a pattern scaling analysis from the CMIP5 archive, considering both the mean and uncertainty of the Local to Global Ratio of Temperature Change (LGRTC) spatial patterns from the CMIP5 ensemble for high-end and mitigated scenarios. The LGRTC spatial pattern is assessed for scenario dependence in the CMIP5 ensemble using RCP2.6, RCP4.5 and RCP8.5, and spatial domains are identified where the pattern scaling is useful across a variety of arbitrary scenarios. The computational efficiency of our WASP/LGRTC model approach makes it ideal for future incorporation into an Integrated Assessment Model framework, or efficient assessment of multiple scenarios. We utilise an emergent relationship between warming and future cumulative carbon emitted in our simulations to present an approximation tool making local warming projections from total future carbon emitted.
1991-9603
5389–5399
Goodwin, Philip
87dbb154-5c39-473a-8121-c794487ee1fd
Leduc, Martin
ce88159e-a99b-404a-bf32-c5cccc89b68f
Partanen, Antti-Ilari
3589336d-1136-44ff-877f-328eefe8ffe2
Matthews, H. Damon
6c21b5f5-e32b-46db-91b5-121068e69cac
Rogers, Alex
64c6e362-a2ec-428c-9835-5d9757f8dea5
Goodwin, Philip
87dbb154-5c39-473a-8121-c794487ee1fd
Leduc, Martin
ce88159e-a99b-404a-bf32-c5cccc89b68f
Partanen, Antti-Ilari
3589336d-1136-44ff-877f-328eefe8ffe2
Matthews, H. Damon
6c21b5f5-e32b-46db-91b5-121068e69cac
Rogers, Alex
64c6e362-a2ec-428c-9835-5d9757f8dea5

Goodwin, Philip, Leduc, Martin, Partanen, Antti-Ilari, Matthews, H. Damon and Rogers, Alex (2020) A computationally efficient method for probabilistic local warming projections constrained by history matching and pattern scaling, demonstrated by WASP-LGRTC-1.0. Geoscientific Model Development, 13 (11), 5389–5399, [5389-2020]. (doi:10.5194/gmd-13-5389-2020).

Record type: Article

Abstract

Climate projections are made using a hierarchy of models of different complexities and computational efficiencies. While the most complex climate models contain the most detailed representations of many physical processes within the climate system, both parameter space exploration and Integrated Assessment Modelling require the increased computational efficiency of reduced-complexity models. This study presents a computationally efficient method for generating probabilistic projections of local warming across the globe, using a pattern scaling approach derived from the Climate Model Intercomparison Project phase 5 (CMIP5) ensemble, that can be coupled to any efficient model ensemble simulation of global mean surface warming. While the method can project local warming for arbitrary future scenarios, using it for scenarios with peak global mean warming ≤ 2°C is problematic due to the large uncertainties involved. First, global mean warming is projected using a 103-member ensemble of history-matched simulations with an example reduced complexity Earth system model: the Warming Acidification and Sea-level Projector (WASP). The ensemble-projection of global mean warming from this WASP ensemble is then converted into local warming projections using a pattern scaling analysis from the CMIP5 archive, considering both the mean and uncertainty of the Local to Global Ratio of Temperature Change (LGRTC) spatial patterns from the CMIP5 ensemble for high-end and mitigated scenarios. The LGRTC spatial pattern is assessed for scenario dependence in the CMIP5 ensemble using RCP2.6, RCP4.5 and RCP8.5, and spatial domains are identified where the pattern scaling is useful across a variety of arbitrary scenarios. The computational efficiency of our WASP/LGRTC model approach makes it ideal for future incorporation into an Integrated Assessment Model framework, or efficient assessment of multiple scenarios. We utilise an emergent relationship between warming and future cumulative carbon emitted in our simulations to present an approximation tool making local warming projections from total future carbon emitted.

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Accepted/In Press date: 27 September 2020
e-pub ahead of print date: 9 November 2020
Published date: 9 November 2020
Additional Information: Funding Information: Acknowledgements. Philip Goodwin acknowledges funding from UK NERC grant NE/N009789/1 and combined UK Government Department of BEIS and UK NERC grant NE/P01495X/1. Martin Leduc thanks Ouranos and Concordia University. Antti-Ilari Par-tanen was supported by Emil Aaltonen Foundation, The Fonds de recherche du Quebec – Nature et technologies (grant no. 200414), Concordia Institute for Water, Energy and Sustainable Systems (CI-WESS), and Academy of Finland (grant no. 308365). We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups (listed in Table S1 in the Supplement) for producing and making available their model output. For CMIP the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. Funding Information: Financial support. This research has been supported by the Natu- Funding Information: ral Environment Research Council (grant nos. NE/N009789/1 and NE/P01495X/1), the Emil Aaltonen Foundation (grant no. 200414) and the Academy of Finland (grant no. 308365). Publisher Copyright: © Author(s) 2020.

Identifiers

Local EPrints ID: 444194
URI: http://eprints.soton.ac.uk/id/eprint/444194
ISSN: 1991-9603
PURE UUID: f7bb0afc-2e1b-4519-8090-178bd74d2ea2
ORCID for Philip Goodwin: ORCID iD orcid.org/0000-0002-2575-8948

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Date deposited: 01 Oct 2020 16:31
Last modified: 15 Jun 2022 01:42

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Contributors

Author: Philip Goodwin ORCID iD
Author: Martin Leduc
Author: Antti-Ilari Partanen
Author: H. Damon Matthews
Author: Alex Rogers

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