Probabilistic parameterisation of the surface mass balance–elevation feedback in regional climate model simulations 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 Brussels, 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 present a new parameterisation that relates surface mass balance (SMB: the sum of surface accumulation and surface ablation) to changes in surface elevation of the Greenland ice sheet (GrIS) for the MAR (Modèle Atmosphérique Régional: Fettweis, 2007) regional climate model. The motivation is to dynamically adjust SMB as the GrIS evolves, allowing us to force ice sheet models with SMB simulated by MAR while incorporating the SMB–elevation feedback, without the substantial technical challenges of coupling ice sheet and climate models. This also allows us to assess the effect of elevation feedback uncertainty on the GrIS contribution to sea level, using multiple global climate and ice sheet models, without the need for additional, expensive MAR simulations.
We estimate this relationship separately below and above the equilibrium line altitude (ELA, separating negative and positive SMB) and for regions north and south of 77° N, from a set of MAR simulations in which we alter the ice sheet surface elevation. These give four "SMB lapse rates", gradients that relate SMB changes to elevation changes. We assess uncertainties within a Bayesian framework, estimating probability distributions for each gradient from which we present best estimates and credibility intervals (CI) that bound 95% of the probability. Below the ELA our gradient estimates are mostly positive, because SMB usually increases with elevation: 0.56 (95% CI: −0.22 to 1.33) kg m−3 a−1 for the north, and 1.91 (1.03 to 2.61) kg m−3 a−1 for the south. Above the ELA, the gradients are much smaller in magnitude: 0.09 (−0.03 to 0.23) kg m−3 a−1 in the north, and 0.07 (−0.07 to 0.59) kg m−3 a−1 in the south, because SMB can either increase or decrease in response to increased elevation.
Our statistically founded approach allows us to make probabilistic assessments for the effect of elevation feedback uncertainty on sea level projections (Edwards et al., 2014).