Numerical modeling of the dynamics of the Mer de Glace glacier, French Alps: comparison with past observations and forecasting of near-future evolution

Abstract

Abstract. Alpine glaciers are shrinking and rapidly loosing mass in a warming climate. Glacier modeling is required to assess the future consequences of these retreats on water resources, the hydropower industry and risk management. However, the performance of such ice flow modeling is generally difficult to evaluate because of the lack of long-term glaciological observations. Here, we assess the performance of the Elmer/Ice full Stokes ice flow model using the long dataset of mass balance, thickness change, ice flow velocity and snout fluctuation measurements obtained between 1979 and 2015 on the Mer de Glace glacier, France. Ice flow modeling results are compared in detail to comprehensive glaciological observations over 4 decades including both a period of glacier expansion preceding a long period of decay. To our knowledge, a comparison to data at this detail is unprecedented. We found that the model accurately reconstructs the velocity, elevation and length variations of this glacier despite some discrepancies that remain unexplained. The calibrated and validated model was then applied to simulate the future evolution of Mer de Glace from 2015 to 2050 using 26 different climate scenarios. Depending on the climate scenarios, the largest glacier in France, with a length of 20 km, could retreat by 2 to 6 km over the next 3 decades.

The S2M meteorological and snow cover reanalysis over the French mountainous areas, description and evaluation (1958–2020)

“…The S2M dataset was used as a reference for evaluating snow cover simulations driven by the NWP model AROME (Queno et al, 2016;Vionnet et al, 2019). SCM and S2M reanalyses have been used in a number of studies addressing glacier mass balance in the French Alps (Gerbaux et al, 2005;Réveillet et al, 2018;Bolibar et al, 2020;Peyaud et al, 2020) or hydrological simulation of alpine mountainous catchments (Lafaysse et al, 2011 (Lafaysse et al, 2014;Verfaillie et al, 2017). This methods leads to the provision of meteorological forcing driving files corresponding to future climate time series, on the same geometry and data format as S2M reanalysis forcing files, enabling homogeneous post-processing including running SURFEX/ISBA-Crocus for natural (Verfaillie et al, 2018) and managed snow in ski resorts.…”

Glacier tourism without ice: Envisioning future adaptations in a melting world

“…With around 400,000 visitors yearly, the Montenvers site is one of the most-visited glaciers in France (Figure 2). It is also clearly threatened by climate change, and by 2050, it may no longer be visible from the Montenvers viewpoint (Peyaud et al, 2020). Salim and Ravanel (2020) demonstrated that visitors come to the site to witness the glacier before it disappears.…”

“…For example, the Aletsch Glacier will likely no longer be visible from the vantage point by 2100 (Jouvet and Huss, 2019; see Figure 1). Moreover, Peyaud et al (2020) proposed a model for the retreat of the Mer de Glace that suggests that by the 2050s, the glacier will no longer be visible from the Montenvers vantage point (see Figure 2). Accordingly, this paper assessed the pathways glacier tourism stakeholders could take to enhance the visitor experience when the glaciers are almost or entirely gone.…”

Validating ensemble historical simulations of Upernavik Isstrøm (1985–2019) using observations of surface velocity and elevation

“…The recent increase in satellite observations, mainly of surface elevation and velocities, provides new opportunities to investigate the performance of ice-sheet models on decadal timescales. Several studies have undertaken comparisons between historical model outputs and observational data, examining specific cases such as the effects of alterations on the ice front position (Bondzio and others, 2017;Haubner and others, 2018), grounding line retreat (Joughin and others, 2019;Åkesson and others, 2021) and variations in surface mass balance (SMB) (Peyaud and others, 2020). Nevertheless, establishing suitable quantitative measurements for categorising and distinguishing model results poses a challenge.…”