|Title||Uncertainty in 21st century CMIP5 sea level projections|
|Publication Type||Journal Article|
|Year of Publication||2014|
|Authors||Little, Christopher M., Horton Radley M., Kopp Robert E., Oppenheimer Michael, and Yip Stan|
|Journal||Journal of Climate|
|Keywords||Climate models, Climate prediction, Regression analysis, risk assessment, sea level, Societal impacts|
The Coupled Model Intercomparison Project Phase 5 “representative concentration pathways” (CMIP5 RCP) simulations quantify the response of the climate system to different natural and anthropogenic forcing scenarios. These simulations differ due to: 1) forcing; 2) the representation of the climate system in atmosphere-ocean general circulation models (AOGCMs); and 3) the presence of unforced (internal) variability. Global and local sea level rise projections derived from these simulations, and the differentiation (“emergence”) of RCPs, depend upon the relative magnitude of these sources of uncertainty at different lead times. Here, we partition uncertainty in CMIP5 projections of sea level, at a global and local scale, using a 164-member ensemble of 21st century simulations. Local projections at New York City (NYSL) are highlighted. The partition between model uncertainty, scenario uncertainty, and internal variability in global mean sea level (GMSL) is qualitatively consistent with that of surface air temperature, with model uncertainty dominant for most of the 21st century. Locally, model uncertainty is dominant through 2100, with maxima in the North Atlantic and the Arctic Ocean. The model spread is driven largely by 5 of the 16 AOGCMs in the ensemble; these models exhibit outlying behavior in all RCPs and in both GMSL and NYSL. The magnitude of internal variability varies widely by location and across models, leading to differences of several decades in the local emergence of RCPs. The AOGCM spread, and its sensitivity to model exclusion and/or weighting, has important implications for sea level assessments, especially if a local risk management approach is utilized.