A dual model for emulation of thermosteric and dynamic sea-level change

Article

Abstract

Future thermosteric and dynamic sea-level changes are often projected by process-based climate models. Emulation of such computationally expensive models helps enable model intercomparison over a range of forcing scenarios and thus enables additional analysis of sea-level rise projection uncertainty. Current emulation methods use linear response functions to estimate global mean sea-level response. Here, we introduce a novel dual model to emulate global mean thermosteric sea-level rise that incorporates short- and long-term responses to climate forcing. This nonlinear response function outperforms existing linear response functions over six illustrative general circulation models and the four representative concentration pathways. To emulate dynamic sea-level projections, we introduce a linear pattern scaling model that relates regional sea-level changes to global mean thermosteric sea-level rise. Pattern scaling is shown to reproduce strongly forced sea-level trends. Our results demonstrate effective emulation of global and regional sea-level rise, which can facilitate the consideration of sea-level rise projection uncertainty critical to the analysis of sea-level rise hazard.

Notes

Acknowledgements

We acknowledge the World Climate Research Programmes Working Group on Coupled Modeling, which is responsible for CMIP, and we thank the institutes for the climate models (listed in Fig. 2 of this paper) for producing and making available their model output. The U.S. 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 for CMIP5. We also thank the developers of the MAGICC6 reduced complexity climate model. In addition, the capital and operating support from the Office of the Provost to T.L.s Multi-Hazard Sustainability (HazSus) Research Group is gratefully acknowledged.

Supplementary material

10584_2018_2198_MOESM1_ESM.docx (8.3 mb)
ESM 1 (DOCX 8485 kb)

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© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Civil, Construction and Environmental EngineeringMarquette UniversityMilwaukeeUSA

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