GrSMBMIP: intercomparison of the modelled 1980–2012 surface mass balance over the Greenland Ice Sheet

Xavier Fettweis,Willem Jan Van De Berg,Charlotte Lang,Sebastian H Mernild,Uwe Mikolajewicz,Michiel Van Den Broeke,Glen E Liston,Heiko Goelzer,Marco Tedesco,Kameswarrao Modali,Akihiro Hashimoto,Philippe Huybrechts,Jan Streffing,Constantijn J Berends,Jan T M Lenaerts,Edward Hanna,Michalea D King,Jason E Box,Leo Van Kampenhout,Bert Wouters,Alison Delhasse,Tobias Zolles,Peter L Langen,Gerrit Lohmann,Andreas Born,Florian Ziemen,Christoph Kittel,Jonathan C Ryan,Charles Amory,Teruo Aoki,Paul Gierz,Koji Fujita,David J Wilton ,A Smith ,Brice Nöel ,Michio Niwano ,Marie‐Luise Kapsch ,Ruth Mottram ,R S W Van De Wal ,Uta Krebs‐Kanzow ,Stefan Höfer

doi:10.5194/tc-14-3935-2020

Abstract

Abstract. Observations and models agree that the Greenland Ice Sheet (GrIS) surface mass balance (SMB) has decreased since the end of the 1990s due to an increase in meltwater runoff and that this trend will accelerate in the future. However, large uncertainties remain, partly due to different approaches for modelling the GrIS SMB, which have to weigh physical complexity or low computing time, different spatial and temporal resolutions, different forcing fields, and different ice sheet topographies and extents, which collectively make an inter-comparison difficult. Our GrIS SMB model intercomparison project (GrSMBMIP) aims to refine these uncertainties by intercomparing 13 models of four types which were forced with the same ERA-Interim reanalysis forcing fields, except for two global models. We interpolate all modelled SMB fields onto a common ice sheet mask at 1 km horizontal resolution for the period 1980–2012 and score the outputs against (1) SMB estimates from a combination of gravimetric remote sensing data from GRACE and measured ice discharge; (2) ice cores, snow pits and in situ SMB observations; and (3) remotely sensed bare ice extent from MODerate-resolution Imaging Spectroradiometer (MODIS). Spatially, the largest spread among models can be found around the margins of the ice sheet, highlighting model deficiencies in an accurate representation of the GrIS ablation zone extent and processes related to surface melt and runoff. Overall, polar regional climate models (RCMs) perform the best compared to observations, in particular for simulating precipitation patterns. However, other simpler and faster models have biases of the same order as RCMs compared with observations and therefore remain useful tools for long-term simulations or coupling with ice sheet models. Finally, it is interesting to note that the ensemble mean of the 13 models produces the best estimate of the present-day SMB relative to observations, suggesting that biases are not systematic among models and that this ensemble estimate can be used as a reference for current climate when carrying out future model developments. However, a higher density of in situ SMB observations is required, especially in the south-east accumulation zone, where the model spread can reach 2 m w.e. yr−1 due to large discrepancies in modelled snowfall accumulation.

Highlights

Mass loss from the Greenland Ice Sheet (GrIS) has been accelerating since the 1990s (Enderlin et al, 2014; Mouginot et al, 2019; Hanna et al, 2020; IMBIE2, 2020)
In comparison with surface mass balance (SMB) derived from ice cores, both Positive degree day (PDD) models perform the best
This paper describes the methodology and results of the GrIS SMB Model Intercomparison Project (GrSMBMIP): a novel effort that intercompares GrIS SMB fields produced using five regional climate models (RCMs), four Energy balance models (EBMs), two PDDs and two General circulation models (GCMs)

Summary

Introduction

Mass loss from the Greenland Ice Sheet (GrIS) has been accelerating since the 1990s (Enderlin et al, 2014; Mouginot et al, 2019; Hanna et al, 2020; IMBIE2, 2020). Since the end of the 1990s, the models suggest that the surface melt has almost doubled, reaching record melt volume in the summers of 2012 and 2019, while the snowfall accumulation has remained approximately constant (Noël et al, 2019; Lenaerts et al, 2019; Tedesco and Fettweis, 2020) This recent GrIS SMB decrease – largely driven by the increase in meltwater runoff (Van den Broeke et al, 2016; Fettweis et al, 2017; Lenaerts et al, 2019; IPCC, 2019) – has been caused by Arctic amplification, a state change in the North Atlantic Oscillation and increased Greenland Blocking events in summer (Fettweis et al, 2013b; Delhasse et al, 2018; Hanna et al, 2018; Hahn et al, 2020), which raise the average temperatures (Screen and Simmonds, 2010), reduce the cloudiness (Hofer et al, 2017) and enhance the melt–albedo feedback (Box et al, 2012; Ryan et al, 2019; Noël et al, 2019). SMB-related processes are one of the main uncertainties in future projections of the GrIS contribution to sea level rise as the ice sheet retreats in a warmer climate (Goelzer et al, 2013; van den Broeke et al, 2017; Hofer et al, 2019)

Results

Discussion

Conclusion