Climate model uncertainty and trend detection of regional sea level projections in the open ocean and coastal zone

Abstract

Projections of future sterodynamic sea level change from global climate models are associated with different sources of uncertainty. From a scientific, societal and policy-making perspective, it is relevant to both understand and reduce uncertainty in projections of climate change. Here, we review recent findings which describe, and shed light on, climate model uncertainty focusing particularly on two types of model uncertainty that contribute to the currently large spread in dynamical sea level patterns (i.e., regional sea level relative to the global mean). These uncertainties are: (1) intermodel uncertainty due to differences in models’ responses in a warming climate and (2) internal model variability due to an individual model’s own climate variability. On timescales longer than about 50 years from now, anthropogenic sterodynamic (dynamic plus global mean) sea level trends from middle- and high-end forcing scenarios will be larger than internal model variability. By 2100, these anthropogenic trends will also be larger than intermodel uncertainty when global mean thermosteric sea level rise and/or melting contributions from land ice are considered along with dynamic sea level changes. Furthermore, we discuss projections of future coastal sea level from the perspective of global climate models as well as from downscaled efforts based on regional climate models. Much knowledge and understanding has been achieved in the last decade from intermodel experiments and studies of sea level process-based model; here, the prospects for improving coastal sea level and reducing sea level uncertainty are discussed.

Laure Zanna

Professor of Mathematics & Atmosphere/Ocean Science [She/Her]

My research interests include Climate Dynamics, Physical Oceanography and Data Science.