Journal article

Synthesizing long-term sea level rise projections - the MAGICC sea level model v2.0

Alexander Nauels, Malte Meinshausen, Matthias Mengel, Katja Lorbacher, Tom ML Wigley

GEOSCIENTIFIC MODEL DEVELOPMENT | COPERNICUS GESELLSCHAFT MBH | Published : 2017

Abstract

Sea level rise (SLR) is one of the major impacts of global warming; it will threaten coastal populations, infrastructure, and ecosystems around the globe in coming centuries. Well-constrained sea level projections are needed to estimate future losses from SLR and benefits of climate protection and adaptation. Process-based models that are designed to resolve the underlying physics of individual sea level drivers form the basis for state-of-The-Art sea level projections. However, associated computational costs allow for only a small number of simulations based on selected scenarios that often vary for different sea level components. This approach does not sufficiently support sea level impact..

View full abstract

Grants

Awarded by Australian Research Council (ARC) Future Fellowship


Awarded by Australian Research Council


Funding Acknowledgements

We acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output (CMIP5 models used in the present study are listed in Table 1 of this paper; see also http: //cmip-pcmdi.llnl.gov/cmip5/docs/CMIP5_modeling_groups.pdf). For CMIP 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. We would especially like to thank B. Marzeion, X. Fettweis, F. Nick, S. Ligtenberg, A. Levermann, and Y. Wada for providing the calibration data used in this study. The authors would also like to acknowledge C. Hay and J. Church and the CSIRO for making available their GMSL reconstruction/reanalysis datasets. M. Meinshausen receives the Australian Research Council (ARC) Future Fellowship Grant FT130100809. T. M. L. Wigley is supported by the Australian Research Council under Discovery Grant DP130103261.