Semiempirical and process-based global sea level projections

posted Jun 20, 2014, 1:57 AM by Aslak Grinsted
We review the two main approaches to estimating sea level rise over the coming century: physically plausible models of reduced complexity that exploit statistical relationships between sea level and climate forcing, and more complex physics-based models of the separate elements of the sea level budget. Previously, estimates of future sea level rise from semiempirical models were considerably larger than those from process-based models. However, we show that the most recent estimates of sea level rise by 2100 using both methods have converged, but largely through increased contributions and uncertainties in process-based model estimates of ice sheets mass loss. Hence, we focus in this paper on ice sheet flow as this has the largest potential to contribute to sea level rise. Progress has been made in ice dynamics, ice stream flow, grounding line migration, and integration of ice sheet models with high-resolution climate models. Calving physics remains an important and difficult modeling issue. Mountain glaciers, numbering hundreds of thousands, must be modeled by extensive statistical extrapolation from a much smaller calibration data set. Rugged topography creates problems in process-based mass balance simulations forced by regional climate models with resolutions 10–100 times larger than the glaciers. Semiempirical models balance increasing numbers of parameters with the choice of noise model for the observations to avoid overfitting the highly autocorrelated sea level data. All models face difficulty in separating out non-climate-driven sea level rise (e.g., groundwater extraction) and long-term disequilibria in the present-day cryosphere-sea level system.

Moore, J. C., A. Grinsted, T. Zwinger, and S. Jevrejeva (2013), Semiempirical and process-based global sea level projections, Rev. Geophys., 51, 484–522, doi:10.1002/rog.20015.