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

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.

Modeling the Greenland englacial stratigraphy

“…Future simulations and in particular model calibration will need to prioritize the realism of the SMB simulation, because the large uncertainties in accumulation eclipse the impact of dynamics and our ability to constrain the corresponding parameters. We plan to use a much more reliable SMB scheme in the future (Born et al, 2019;Fettweis et al, 2020;Zolles and Born, 2020), but these simulations would still depend on uncertain past climates. Here, the isochronal model offers an alternative that is independent from climate simulations.…”

Brief Communication: Reduction of the future Greenland ice sheet surface melt with the help of solar geoengineering

“…The radiative scheme of MARv3.11 has been adapted to deal with the GHG concentrations and the solar constant time series which have been used to constrain CNRM-ESM2-1. We refer to Kravitz et al (2016) andO'Neill et al (2016) for the description of the scenarios used here and to Fettweis et al (2020) about the MAR presentation and evaluation. Compared to MAR forced by the ERA5 reanalysis over 1981-2010, the biases of SMB as well as of the near-surface summer temperature over the GrIS simulated by MAR forced by CNRM-ESM2-1 using the historical simulation are not statistically significant (see Fig.…”

“…This means that the CNRM-ESM2-1-based future MAR projections are not impacted by significant biases over the current climate as discussed in Fettweis et al (2020). Finally, it is important to note that MAR is not coupled with an ice sheet model as in Le Clec'h et al (2019) and then that the present-day ice-sheet topography and extent are used here during the whole simulation.…”

“…The Greenland ice sheet (GrIS) is projected to contribute several centimetres to global mean sea-level rise until 2100, mainly as a result of the projected surface meltwater runoff increase due to Global Warming (Hofer et al 2020, Goelzer et al, 2020. Knowing that both Antarctic and Greenland ice sheets are already losing mass more in line with the extreme highemission scenarios from IPCC AR5 (Slater et al, 2020), the most direct way to reduce the sea level rise contribution from Greenland is to reduce our Greenhouse Gases (GHG) emissions, as there is a factor of 3 between the Greenland ice sheet surface melt in an extreme high-emission world (ssp585) vs in a scenario more closely aligned to the Paris Agreement (ssp126) (Goelzer et al, 2020). One possibility to mitigate sea level rise in a scenario where we would otherwise overshoot the global warming limits set out in the Paris Agreement is the employment of solar geoengineering measures (Tilmes et al, 2020).…”

“…Until now, the precise impact of such solar geoengineering measures on the future Greenland ice sheet surface melt remains highly uncertain because it was evaluated only with global models run at too coarse spatial resolution not resolving the ablation zone and using very simple snow models (Irvine et al, 2018;Moore et al;. As shown in Fettweis et al (2020), the polar regional climate models offer a unique opportunity to refine these estimates with a polar-oriented sophisticated physics, a full representation of the snow-atmosphere interactions as well as a spatial resolution adequate to explicitly resolve the narrow GrIS ablation zone . Moreover, regional models enable us to explore local impacts from geoengineering measures with unchanged boundary conditions.…”