Effect of uncertainty in surface mass balance–elevation feedback on projections of the future sea level contribution of the Greenland ice sheet
- 1Department of Geographical Sciences, University of Bristol, Bristol BS8 1SS, UK
- 2Department of Geography, University of Liege, Laboratory of Climatology (Bat. B11), Allée du 6 Août, 2, 4000 Liège, Belgium
- 3Laboratoire de Glaciologie et Géophysique de l'Environnement, UJF – Grenoble 1/CNRS, 54, rue Molière BP 96, 38402 Saint-Martin-d'Hères Cedex, France
- 4Institut Universitaire de France, Paris, France
- 5Earth System Sciences & Departement Geografie, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussel, Belgium
- 6NCAS-Climate, Department of Meteorology, University of Reading, Reading, UK
- 7Met Office Hadley Centre, Exeter, UK
- 8Fluid Dynamics and Solid Mechanics Group, Los Alamos National Laboratory, T3 MS B216, Los Alamos, NM 87545, USA
- 9Department of Scientific Computing, Florida State University, 400 Dirac Science Library, Tallahassee, FL 32306, USA
Abstract. We apply a new parameterisation of the Greenland ice sheet (GrIS) feedback between surface mass balance (SMB: the sum of surface accumulation and surface ablation) and surface elevation in the MAR regional climate model (Edwards et al., 2014) to projections of future climate change using five ice sheet models (ISMs). The MAR (Modèle Atmosphérique Régional: Fettweis, 2007) climate projections are for 2000–2199, forced by the ECHAM5 and HadCM3 global climate models (GCMs) under the SRES A1B emissions scenario.
The additional sea level contribution due to the SMB–elevation feedback averaged over five ISM projections for ECHAM5 and three for HadCM3 is 4.3% (best estimate; 95% credibility interval 1.8–6.9%) at 2100, and 9.6% (best estimate; 95% credibility interval 3.6–16.0%) at 2200. In all results the elevation feedback is significantly positive, amplifying the GrIS sea level contribution relative to the MAR projections in which the ice sheet topography is fixed: the lower bounds of our 95% credibility intervals (CIs) for sea level contributions are larger than the "no feedback" case for all ISMs and GCMs.
Our method is novel in sea level projections because we propagate three types of modelling uncertainty – GCM and ISM structural uncertainties, and elevation feedback parameterisation uncertainty – along the causal chain, from SRES scenario to sea level, within a coherent experimental design and statistical framework. The relative contributions to uncertainty depend on the timescale of interest. At 2100, the GCM uncertainty is largest, but by 2200 both the ISM and parameterisation uncertainties are larger. We also perform a perturbed parameter ensemble with one ISM to estimate the shape of the projected sea level probability distribution; our results indicate that the probability density is slightly skewed towards higher sea level contributions.