Geosci. Model Dev., 10, 2495–2524, 2017 https://doi.org/10.5194/gmd-10-2495-2017 © Author(s) 2017. This work is distributed under the Creative Commons Attribution 3.0 License. Synthesizing long-term sea level rise projections – the MAGICC sea level model v2.0 Alexander Nauels1,2, Malte Meinshausen1,2,3, Matthias Mengel3, Katja Lorbacher1, and Tom M. L. Wigley4,5 1Australian-German Climate and Energy College, The University of Melbourne, Parkville 3010, Victoria, Australia 2Department of Earth Sciences, The University of Melbourne, Parkville 3010, Victoria, Australia 3Potsdam Institute for Climate Impact Research (PIK), Telegrafenberg, 14473 Potsdam, Germany 4The Environment Institute and School of Biological Sciences, The University of Adelaide, Adelaide, SA 5005, Australia 5Climate and Global Dynamics Division, National Center for Atmospheric Research, Boulder, CO 80307-3000, USA Correspondence to: Alexander Nauels ([email protected]) Received: 31 August 2016 – Discussion started: 5 October 2016 Revised: 21 June 2017 – Accepted: 22 June 2017 – Published: 30 June 2017 Abstract. Sea level rise (SLR) is one of the major impacts of models. The land water storage component replicates recent global warming; it will threaten coastal populations, infras- hydrological modeling results. For 2100, we project 0.35 to tructure, and ecosystems around the globe in coming cen- 0.56 m (66 % range) total SLR based on the RCP2.6 scenario, turies. Well-constrained sea level projections are needed to 0.45 to 0.67 m for RCP4.5, 0.46 to 0.71 m for RCP6.0, and estimate future losses from SLR and benefits of climate pro- 0.65 to 0.97 m for RCP8.5. These projections lie within the tection and adaptation. Process-based models that are de- range of the latest IPCC SLR estimates. SLR projections for signed to resolve the underlying physics of individual sea 2300 yield median responses of 1.02 m for RCP2.6, 1.76 m level drivers form the basis for state-of-the-art sea level pro- for RCP4.5, 2.38 m for RCP6.0, and 4.73 m for RCP8.5. The jections. However, associated computational costs allow for MAGICC sea level model provides a flexible and efficient only a small number of simulations based on selected sce- platform for the analysis of major scenario, model, and cli- narios that often vary for different sea level components. mate uncertainties underlying long-term SLR projections. It This approach does not sufficiently support sea level im- can be used as a tool to directly investigate the SLR implica- pact science and climate policy analysis, which require a tions of different mitigation pathways and may also serve as sea level projection methodology that is flexible with re- input for regional SLR assessments via component-wise sea gard to the climate scenario yet comprehensive and bound level pattern scaling. by the physical constraints provided by process-based mod- els. To fill this gap, we present a sea level model that em- ulates global-mean long-term process-based model projec- tions for all major sea level components. Thermal expansion 1 Introduction estimates are calculated with the hemispheric upwelling- diffusion ocean component of the simple carbon-cycle cli- Global sea level has increased by around 0.2 m since the be- mate model MAGICC, which has been updated and cali- ginning of the 20th century and will continue to rise during brated against CMIP5 ocean temperature profiles and ther- the 21st century and far beyond (Church and White, 2011; mal expansion data. Global glacier contributions are esti- Church et al., 2013a). This will have wide-ranging impacts mated based on a parameterization constrained by transient for coastal regions around the globe and therefore requires and equilibrium process-based projections. Sea level contri- careful monitoring. The total sea level signal is the sum of bution estimates for Greenland and Antarctic ice sheets are several individual sea level components, the main ones be- derived from surface mass balance and solid ice discharge ing thermal expansion, global glacier melt, Greenland and parameterizations reproducing current output from ice-sheet Antarctic ice-sheet mass loss, and land water storage changes (Church et al., 2013a). Over the coming centuries, the mag- Published by Copernicus Publications on behalf of the European Geosciences Union. 2496 A. Nauels et al.: The MAGICC sea level model nitude of total sea level rise (SLR) will strongly depend of global-mean temperature, were introduced together with on the amount of anthropogenic greenhouse gases (GHGs) early approaches to model thermal expansion based on emitted to the atmosphere during the 21st century and the simplified ocean processes (Gornitz et al., 1982). Gener- corresponding physical responses of the major SLR drivers ally, SEMs establish statistical relationships between ob- (Horton et al., 2014). Future GHG emissions are therefore served/reconstructed global-mean temperature or radiative a main uncertainty source when trying to project SLR tra- forcing changes and observed/reconstructed global-mean sea jectories. SLR uncertainties are further increased by struc- level changes. Assuming that such relationships do not tural differences of the underlying process-based models for change in the future, they are used to estimate future SLR the individual SLR contributions and limited process under- from projected global temperature/forcing changes (Rahm- standing, like the behavior of polar ice shelves in a warm- storf, 2007; Vermeer and Rahmstorf, 2009; Jevrejeva et al., ing world (Nicholls and Cazenave, 2010). To assess major 2010; Kopp et al., 2016). Therefore, these SEMs do not cal- parts of these scenario and model uncertainties, we extend culate sea level by resolving the underlying physical pro- the widely used simple carbon-cycle climate model MAG- cesses. This approach generated considerable scientific de- ICC (Meinshausen et al., 2011a, 2009; Wigley et al., 2009; bate and was not included in latest IPCC estimates (Or- Wigley and Raper, 2001) to comprehensively model global lic and Pasaric, 2013; Storch et al., 2008; Church et al., SLR. This MAGICC sea level model has been designed to 2013a). The computational efficiency of this method, how- emulate the behavior of process-based sea level projections ever, made it attractive to applied research questions, like in- presented in the fifth IPCC Assessment Report (Church et al., vestigating the global-mean SLR response for different cli- 2013a), with thorough calibrations for each major sea level mate targets (Schaeffer et al., 2012). Recently, this method component. It is intended to serve as an efficient and flexible has been developed further and was applied to individual tool for the assessment of multi-centennial global SLR. In the sea level components (Mengel et al., 2016). SLR projections following section, we motivate and explain the key concepts are also provided based on expert elicitations (Horton et al., underlying the MAGICC sea level model. Section2 covers 2014). Furthermore, sea level expert judgments have been the detailed model description and Sect.3 provides key re- combined with statistical models synthesizing sea level pro- sults. In Sect.4, we discuss the capabilities of the presented jections for individual components (Kopp et al., 2014). Other sea level emulator and shine a first light on potential applica- studies have used an extended suite of methods, analyzing tions. paleoclimatic archives, modeling parts of the SLR response with a reduced complexity model, and deriving future projec- Motivation tions for land-ice contribution-based semi-empirical consid- erations (Clark et al., 2016). The growing efforts in the sea Future sea level is modeled with varying degrees of com- level modeling community to provide fully transparent and plexity. Process-based modeling represents the physically freely available model code are reflected by the recent intro- most comprehensive but also computationally most expen- duction of a transparent, simple model framework to estimate sive approach to project SLR. It is based on Atmosphere– regional sea levels (Wong et al., 2017). Previous MAGICC Ocean General Circulation Models (AOGCMs) and spe- versions also provided SLR estimates based on simplified pa- cialized glacier, ice-sheet and groundwater models that dy- rameterizations for selected components (Wigley and Raper, namically simulate sea level changes resulting from natu- 1987, 1992, 2005; Wigley, 1995). ral and anthropogenic forcings. The main sea level output Here, we adopt an approach of deriving a total sea level from AOGCMs is the thermosteric ocean response, mostly response by emulating existing process-based projections diagnosed with post-simulation adjustments to compensate for individual sea level components (Perrette et al., 2013; Boussinesq approximation effects (Griffies and Greatbatch, Schleussner et al., 2016). Future sea level dynamics is syn- 2012). Process-based glacier and ice-sheet models are gener- thesized by calibrating simplified parameterizations to the ally run separately or “offline” and receive important bound- selected complex model projections for all major sea level ary conditions either from observational data, AOGCMs, or contributions. Progress in the understanding of individual sea regional climate model input (Rae et al., 2012; Pattyn et al., level processes and the availability of revised future sea level 2012). Due to the complexity of the physical processes re- contributions require sea level emulators to be updated reg- quired