A dual model for emulation of thermosteric and dynamic sea-level change
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.
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.
This work was supported in part by the Committee on Research at Marquette University, under the Regular Research Grant and the Summer Faculty Fellowship awarded to the Principal Investigator T.L.—corresponding (second) author. The Regular Research Grant provides research assistantship support for the first author M.A.T., along with the Research Leaders Fellowship from the Opus College of Engineering.
- Bentsen M, Bethke I, Debernard JB, Iversen T, Kirkevg A, Seland, Drange H, Roelandt C, Seierstad IA, Hoose C, Kristjnsson JE (2013) The Norwegian Earth System Model, NorESM1-M part 1: description and basic evaluation of the physical climate. Geosci Model Dev 6(3):687–720. https://doi.org/10.5194/gmd-6-687-2013 CrossRefGoogle Scholar
- Bilbao RAF, Gregory JM, Bouttes N (2015) Analysis of the regional pattern of sea level change due to ocean dynamics and density change for 1993-2099 in observations and CMIP5 AOGCMs. Clim Dynam 1–20. https://doi.org/10.1007/s00382-015-2499-z
- IPCC (2013) Climate Change 2013: the physical science basis. In: Stocker TF, Gin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp.Google Scholar
- Jones CD, Hughes JK, Bellouin N, Hardiman SC, Jones GS, Knight J, Liddicoat S, O’Connor FM, Andres RJ, Bell C, Boo KO, Bozzo A, Butchart N, Cadule P, Corbin KD, Doutriaux-Boucher M, Friedlingstein P, Gornall J, Gray L, Halloran PR, Hurtt G, Ingram WJ, Lamarque JF, Law RM, Meinshausen M, Osprey S, Palin EJ, Parsons Chini L, Raddatz T, Sanderson MG, Sellar AA, Schurer A, Valdes P, Wood N, Woodward S, Yoshioka M, Zerroukat M (2011) The HadGEM2-ES implementation of CMIP5 centennial simulations. Geosci Model Dev 4(3):543–570. https://doi.org/10.5194/gmd-4-543-2011 CrossRefGoogle Scholar
- Kuhlbrodt T, Gregory J (2012) Ocean heat uptake and its consequences for the magnitude of sea level rise and climate change. Geophys Res Lett 39. https://doi.org/10.1029/2012GL052952
- Levermann A, Winkelmann R, Nowicki S, Fastook JL, Frieler K, Greve R, Hellmer HH, Martin MA, Meinshausen M, Mengel M, Payne AJ, Pollard D, Sato T, Timmermann R, Wang WL, Bindschadler RA (2014) Projecting Antarctic ice discharge using response functions from SeaRISE ice-sheet models. Earth Syst Dynam 5(2):271–293. https://doi.org/10.5194/esd-5-271-2014 CrossRefGoogle Scholar
- MATLAB (2013) Statistics Toolbox 8.1. The MathWorks, Inc., NatickGoogle Scholar
- Meinshausen M, Smith S, Calvin K, Daniel J, Kainuma M, Lamarque JF, Matsumoto K, Montzka S, Raper S, Riahi K, Thomson A, Velders G, Vuuren D (2011) The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Clim Chang 109(1/2):213–241. https://doi.org/10.1007/s10584-011-0156-z CrossRefGoogle Scholar
- Nowicki S, Bindschadler RA, Abe-Ouchi A, Aschwanden A, Bueler E, Choi H, Fastook J, Granzow G, Greve R, Gutowski G, Herzfeld U, Jackson C, Johnson J, Khroulev C, Larour E, Levermann A, Lipscomb WH, Martin MA, Morlighem M, Parizek BR, Pollard D, Price SF, Ren D, Rignot E, Saito F, Sato T, Seddik H, Seroussi H, Takahashi K, Walker R, Wang WL (2013) Insights into spatial sensitivities of ice mass response to environmental change from the SeaRISE ice sheet modeling project II: Greenland. J Geophys Res-Earth 118(2):1025–1044. https://doi.org/10.1002/jgrf.20076 CrossRefGoogle Scholar
- Schmidt GA, Kelley M, Nazarenko L, Ruedy R, Russell GL, Aleinov I, Bauer M, Bauer SE, Bhat MK, Bleck R, Canuto V, Chen YH, Cheng Y, Clune TL, Del Genio A, de Fainchtein R, Faluvegi G, Hansen JE, Healy RJ, Kiang NY, Koch D, Lacis AA, LeGrande AN, Lerner J, Lo KK, Matthews EE, Menon S, Miller RL, Oinas V, Oloso AO, Perlwitz JP, Puma MJ, Putman WM, Rind D, Romanou A, Sato M, Shindell DT, Sun S, Syed RA, Tausnev N, Tsigaridis K, Unger N, Voulgarakis A, Yao MS, Zhang J (2014) Configuration and assessment of the GISS ModelE2 contributions to the CMIP5 archive. J Adv Model Earth Syst 6(1):141–184. https://doi.org/10.1002/2013MS000265 CrossRefGoogle Scholar
- Seiji Y, Yukimasa A, Masahiro H, Tomonori S, Hiromasa Y, Mikitoshi H, Taichu YT, Eiki S, Hiroyuki T, Makoto D, Ryo M, Shoukichi Y, Atsushi O, Hideyuki N, Tsuyoshi K, Tomoaki O, Akio K (2012) A new global climate model of the Meteorological Research Institute: MRI-CGCM3|model description and basic performance|. J Meteorol Soc Jpn 90A:23–64. https://doi.org/10.2151/jmsj.2012-A02 CrossRefGoogle Scholar
- Watanabe S, Hajima T, Sudo K, Nagashima T, Takemura T, Okajima H, Nozawa T, Kawase H, Abe M, Yokohata T, Ise T, Sato H, Kato E, Takata K, Emori S, Kawamiya M (2011) MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments. Geosci Model Dev 4:845–872. https://doi.org/10.5194/gmd-4-845-2011 CrossRefGoogle Scholar
- Webster M, Forest C, Reilly J, Babiker M, Kicklighter D, Mayer M, Prinn R, Sarofim M, Sokolov A, Stone P, Wang C (2003) Uncertainty analysis of climate change and policy response. Clim Chang 61(3):295–320. https://doi.org/10.1023/B:CLIM.0000004564.09961.9f CrossRefGoogle Scholar