https://doi.org/10.1130/G46585.1

Manuscript received 31 May 2019 Revised manuscript received 30 September 2019 Manuscript accepted 13 October 2019

© 2019 The Authors. Gold Open Access: This paper is published under the terms of the CC-BY license. Published online 22 November 2019

Postglacial erosion of bedrock surfaces and deglaciation timing: New insights from the massif (western ) Benjamin Lehmann1*, Frédéric Herman1, Pierre G. Valla2,3, Georgina E. King1, Rabiul H. Biswas1, Susan Ivy-Ochs4, Olivia Steinemann4 and Marcus Christl4 1Institute of Earth Surface Dynamics, University of Lausanne, CH-1015 Lausanne, Switzerland 2Institut des Sciences de la Terre (ISTerre), University of Grenoble Alpes, University of Savoie Mont Blanc, Centre National de Recherche Scientifique (CNRS)–Institut de Recherche pour le Développement (IRD)–Institut Français des Sciences et Technologies des Transports, de l’Aménagement et des Réseaux (IFSTTAR), 38000 Grenoble, France 3Institute of Geological Sciences and Oeschger Center for Climate Research, University of Bern, 3012 Bern, Switzerland 4Laboratory of Ion Beam Physics, ETH Zürich, Otto-Stern-Weg 5, 8093 Zurich, Switzerland

ABSTRACT ture variation and snow cover and their effects Since the Last Glacial Maximum, ∼20 k.y. ago, Alpine have retreated and thinned. on the evolution of bedrock surfaces over diur- This transition exposed bare bedrock surfaces that could then be eroded by a combination nal to decadal time scales (e.g., Łoziński, 1909; of debuttressing or local frost cracking and weathering. Quantification of the respective Matsuoka and Murton, 2008). However, bridging contributions of these processes is necessary to understand the links between long-term the temporal gap between these erosion estimates climate and erosion in mountains. Here, we quantified the erosion histories of postglacial remains challenging, in part because of the sto- exposed bedrock in glacial valleys. Combining optically stimulated luminescence and ter- chastic nature of geomorphic processes (Koppes restrial cosmogenic nuclide (TCN) surface exposure dating, we estimated the erosion rate and Montgomery, 2009; Ganti et al., 2016). of bedrock surfaces at time scales from 101 to 104 yr. Bedrock surfaces sampled from the To address these issues, we adopted a new flanks of the Mer de Glace (, European Alps) revealed erosion rates that method that combined optically stimulated lumi- vary from 3.5 ± 1.2 × 10−3 mm/yr to 4.3 ± 0.6 mm/yr over ∼500 m of elevation, with a nega- nescence (OSL) and TCN surface exposure dat- tive correlation between erosion rate and elevation. The observed spatial variation in erosion ing (Sohbati et al., 2018; Lehmann et al., 2019). rates, and their high values, reflect morphometric (elevation and surface slope) and climatic In this study, we applied this methodology to (temperature and snow cover) controls. Furthermore, the derived erosion rates can be used investigate how erosion rates have evolved over to correct the timing of deglaciation based on TCN data, potentially suggesting very rapid time scales of 101–104 yr on bedrock surfaces of ice thinning during the Gschnitz stadial. the Mer de Glace (Mont Blanc massif, European Alps) and how morphometric and climatic fac- INTRODUCTION of periglacial processes during ­interglacial pe- tors control their evolution. Then, we addressed To understand the long-term evolution of riods (Burbank et al., 1996; Ballantyne,­ 2002; how the variability in erosion rates can be used Alpine landscapes, the respective contributions Scherler, 2015). Yet, the rate at which bare-bed- to correct TCN exposure ages, leading to very of surface erosion, sediment production, and rock surfaces weather and erode during intergla- different possible scenarios of ice thinning dur- sediment transport must be quantified. During cials remains poorly quantified (e.g., Colman, ing the Gschnitz stadial (a period of regional- the Quaternary period, the alternation between 1981; Zimmerman et al., 1994; André, 2002; ly extensive advance in the European glacial and interglacial periods has modulated Nicholson, 2008; Kirkbride and Bell, 2010). Alps, temporally between the breakdown of the the efficiency of glacial, fluvial, and hillslope The erosion of hillslopes in periglacial envi- Last Glacial Maximum piedmont lobes and the processes (Koppes and Montgomery, 2009). In ronments is governed by a combination of land- ­beginning of the Bølling warm interval). that context, changes in bedrock morphology sliding, rock shattering, and weathering (e.g., and corresponding sediment delivery have been Anderson and Anderson, 2010). During the last STUDY SITE related to glacier extent, and glacial erosion is decades, the development of terrestrial cosmogen- We collected samples along two elevation often thought to be the most efficient erosional ic nuclide (TCN) methods, mainly using in situ– profiles at the Mer de Glace Fig. 1( ). Six bed- and sediment transport mechanism in mountain produced 10Be in quartz crystals, has improved rock surfaces were sampled below the Mont environments (e.g., Hallet et al., 1996; Brozović our ability to quantify bedrock surface erosion Blanc Tête de Trélaporte (MBTP sample sites, et al., 1997; Montgomery, 2002; Mitchell and over time scales from 104 to 106 yr, assuming west side of the glacier, from 2545 to 2094 m Montgomery, 2006; Egholm et al., 2009; Herman that erosion occurs steadily through time (Balco above sea level [masl]; Fig. 1), and three bed- et al., 2013; Herman and Champagnac, 2016). et al., 2008; von Blanckenburg and Willenbring, rock surfaces were sampled below the Ai- Recent studies have also revealed the importance 2014; Hippe, 2017). Over modern time scales, guille du Moine (MBAM sample sites, east geomorphologists working on frost cracking have side of the glacier, ranging from 2447 to 2259 *E-mail: [email protected] also highlighted the feedbacks between tempera- masl; Fig. 1). All surfaces were from the same

CITATION: Lehmann, B., et al., 2020, Postglacial erosion of bedrock surfaces and deglaciation timing: New insights from the Mont Blanc massif (western Alps): Geology, v. 48, p. 139–144, https://doi.org/10.1130/G46585.1.

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Downloaded from https://pubs.geoscienceworld.org/gsa/geology/article-pdf/48/2/139/4926750/139.pdf by guest on 13 February 2020 A C 6.93°E 6.95°E ­phenocrystalline granitic lithology of the Mont Switzerland 2000 Blanc massif, and selected sampling sites can all be classified as glacially eroded bedrock 1 B 45.92° N surfaces (see the GSA Data Repository ). The 2500 surfaces are rough and exhibit a weathered Trélaporte France texture without glacial striations. All studied bedrock surfaces were located between the ice- Italy 3000 surface elevations of the Little Ice Age (LIA) B and the Last Glacial Maximum (LGM; Cout- terand and Buoncristiani, 2006; Vincent et al., 2014) and were therefore likely deglaciated 45.91° N C sometime between ca. 20–18 ka (Wirsig et al., 2016) and A.D. 1850. Moine 3000 METHODS 10 2500 We measured both Be concentrations (e.g., Gosse and Phillips, 2001; Ivy-Ochs and Briner, 2014) and OSL profiles (Sohbati et al., Mont-Blanc 4810 0 4 km 01500 000 m

NN45.90°N 2012) on exposed granitic rock samples (see D the Data Repository). The 10Be concentrations 2800 Trélaporte profile Moine profile provided us with constraints on the time since trimline the rocks were exposed to cosmic rays and 2600 trimline MBTP1 with a temporal framework for the possible MBTP2 MBAM1 erosion histories since exposure. OSL profiles 2400 MBAM2 constrained the erosion history since the rock MBTP11 MBAM3 exposure to light following ice decay (Lehm- MBTP5 ann et al., 2019). Note that it is the differ- 2200 MBTP9 LIA 10 Elevation [masl] MBTP6 ence in sensitivity between Be and OSL that LIA 2000 makes it possible to quantify surface erosion rate histories over short (<102 yr) and long SSW NNESW NE (>104 yr) time scales (Sohbati et al., 2018; 1800 0 500 1000 1500 2000 2500 Lehmann et al., 2019). Horizontal distance [m] The evolution in time of the OSL bleaching front into a rock surface depends on exposure age, Figure 1. Study sites sampled along Mer de Glace glacier (Mont Blanc massif, European surface erosion, electron trapping and detrapping Alps) at (A) regional, (B) massif, and (C) local scale. Blue area shows extent of Mer de Glace determined from aerial images in 2004 CE (Rabatel et al., 2016). Red lines depict two vertical profiles, Trélaporte and Moine, along which bedrock surfaces were sampled (each colored 1GSA Data Repository item 2020042, supplemen- dot represents a specific sample; round and triangle dots are from the Trélaporte and Moine tal details on sample preparation, measurement, and profiles, respectively). (D) Topographic cross sections of Trélaporte and Moine vertical profiles analysis, as well as a sample list with their character- along the Mer de Glace and corresponding sampled surfaces (MB—Mont Blanc, TP—Trélaporte, istics and measured 10Be concentrations, calibration AM—Aiguille du Moine). Gray lines represent elevation of Last Glacial Maximum (LGM) trim- details, and luminescence signal, is available online lines (Coutterand and Buoncristiani, 2006); blue lines represent Little Ice Age (LIA) elevation at http://www.geosociety.org/datarepository/2020/, or (Vincent et al., 2014); masl—meters above sea level. on request from [email protected].

ABC

Figure 2. Schematic representation of four different erosion scenarios through time (A,B) and their resulting luminescence signal (C), 10 where t0 is uncorrected Be exposure age, tS is onset time of erosion (yr), tC is corrected exposure age, and ε is erosion rate (mm/yr). Note that luminescence plots in C are not model outputs but drawings, with the aim of conceptualizing how the experiments were designed.

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Downloaded from https://pubs.geoscienceworld.org/gsa/geology/article-pdf/48/2/139/4926750/139.pdf by guest on 13 February 2020 (bleaching) rates, and athermal loss (Lehmann tC = the corrected exposure age. Note that tC was were sliced such that a depth and an OSL sig- et al., 2019, their equation 1). While the latter three estimated by combining OSL and TCN data. In nal could be attributed to each rock slice (green variables can be constrained from laboratory mea- turn, various solutions for the erosion rate history dots in Figs. 3A–3C; Tables DR3–DR5 in the

surements, the erosion history is unknown. Here, (tS-ε pairs) and tC were inferred; high erosion rates Data Repository). The OSL signal was bleached we constrained the erosion history by performing and durations that did not fit the observed 10Be con- near the surface and reached a plateau at depth. a joint inversion of the OSL and 10Be data for each centration data (Lal, 1991) were excluded from the As a reference profile, a model was computed sample. In this inversion, we assumed that erosion parameter search (forbidden zone in Figs. 3D–3F; (Lehmann et al., 2019, their equation 2) using

rates evolved as step functions from no erosion to see the Data Repository). t0 and considering no erosion (dashed black line erosion going forward in time (Fig. 2; Lehmann in Figs. 3A–3C). Erosion rate histories were in- et al., 2019). A reference OSL profile was estab- RESULTS AND DISCUSSION ferred from paired bedrock surface OSL signals lished by taking the TCN exposure age with no In Figures 3A–3C, we show the OSL mea- and 10Be concentrations (Figs. 3D–3F and 4A).

erosion correction (t0). The inferred erosion his- surements (i.e., infrared stimulated lumines- The most likely solutions (red lines in Figs. 3A– tory included an erosion rate (ε in Fig. 2), and two cence at 50 °C) for samples MBTP1, MBTP2, 3C and yellow areas in Figs. 3D–3F) were de- 4 times: tS = the time at which erosion began, and and MBTP11. Three individual cores per sample termined by testing 10 pairs of both ε and tS (combination of 100 values of both parameters) in log space. ADThe inferred erosion rates varied between 3.5 ± 1.2 × 10−3 mm/yr and 4.3 ± 0.6 mm/yr (Fig. 4A), with high erosion rates (>0.1 mm/ yr) over time scales from 101 to 103 yr and low erosion rates (<0.1 mm/yr) over longer time scales, 103 to 104 yr. Such a variation is not due to lithological changes, since the bedrock is uni- form. Biological controls are also likely to be minor because of the high elevation; vegetation is not a major component of the environment, and lichen cover does not differ significantly along the profiles. We assumed that these sur- faces were mainly affected by grain-by-grain BEerosion because the character of the glacially eroded bedrock surfaces is preserved; there is no evidence of rockfall scars or surface spalling. The inferred erosion rates are one to two orders of magnitude greater than previously observed rates for bedrock surfaces globally. Portenga and Bierman (2011) compiled 10Be ero- sion rates of outcropping bedrock surfaces and found a mean erosion rate of ∼1.2 × 10−2 mm/ yr. Studies using in situ 10Be and 26Al exhibited a maximum mean bare-bedrock erosion rate of ∼7.6 × 10−3 mm/yr in arid western North Ameri- can summits (Small et al., 1997). Part of this CFdisagreement is likely due to the time scale over which TCN erosion rates are averaged, which is typically 104–106 yr. Using reference surfaces such as ice-polished quartz veins preserved on glacially eroded bedrock surfaces, erosion rates of 0.1 × 10−3 to 10 × 10−3 mm/yr have been mea- sured, depending of the lithology and the loca- tion (André, 2002; Nicholson, 2008; Kirkbride and Bell, 2010). In contrast, the intensities of ero- sion rates observed in this study are comparable to other erosion processes such as debris flows and rockfalls (Norton et al., 2010) or subglacial erosion (e.g., Herman et al., 2015, 2018). Although the observed trend may suggest Figure 3. (A–C) Luminescence profiles and inversion results, where green dots represent mea- that the most recently exposed surfaces are more sured infrared stimulated luminescence (IRSL50) profiles for samples MBTP1, MBTP2, and MBTP11 prone to erosion, we note that the apparent de- (MBTP—Mont Blanc Tête de Trélaporte), respectively, from the Mont Blanc massif, European Alps. crease in erosion rates with increasing time is Dashed black lines represent reference profiles, taking terrestrial cosmogenic nuclide (TCN) common to most techniques that are used to con- exposure age with no erosion correction (t ); red lines represent inferred fits where likelihood is 0 strain erosion rates (e.g., Koppes and Montgom- greater 0.95; tcmax represents the maximum corrected TCN exposure age. (D–F) Probability distri- butions inverted from respective plots A–C. Forbidden zone defines the range of solutions with ery, 2009; Ganti et al., 2016). Such a relation- high erosion rates and durations that is unable to predict observed 10Be concentration. ship is thought to reflect a bias caused by the

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Downloaded from https://pubs.geoscienceworld.org/gsa/geology/article-pdf/48/2/139/4926750/139.pdf by guest on 13 February 2020 A Distribution of the erosion rate in time B Distribution of the erosion rate with elevation are less shielded by snow cover and thus experi- 1 101 10 ence more time in the FCW and are exposed to more freeze-thaw cycles. The snow cover pattern surely has different effects on TCN and OSL sig- 0 100 10 nals. By considering a snow cover of 50 cm for 6 mo per year (Wirsig et al., 2016), the TCN cor- rection accounts for a change of between 5.4% MBTP1 -1 10-1 10 and 6.6% (for MBTP5, 20.2 ± 0.9–21.3 ± 0.9 MBTP2 MBAM1 ka; and MBAM2, 12.1 ± 0.5–12.9 ± 0.5 ka). The MBAM2 snow cover effect for OSL dating is accounted MBTP11 -2 -2 for in the calibration, assuming that calibration 10 MBAM3 10 Bedrock surface erosion [mm/yr] MBTP5 Bedrock surface erosion [mm/yr] surfaces have experienced the same snow cover MBTP9 as the other surfaces (Lehmann et al., 2018). MBTP6 2 -3 r = 0.53 We also speculate that surfaces in the vicin- 10-3 10 100 101 102 103 104 2000 2100 2200 2300 2400 2500 2600 ity of the glacier are influenced by cold kata- Bedrock surface erosion time ts [yr] Elevation [masl] batic winds coming from the glacier (∼50 m for alpine glaciers; Oerlemans and Grisogono, C Distribution of the erosion rate with slope D Distribution of the TCN ages with elevation 101 2600 2002), which in turn promote frost cracking at Non corrected age lower elevation. A final explanation could be Range of corr. age that bedrock surfaces are damaged under the 2500 glacier in a way that decreases with the depth in 100 the rock due to the difference in ice load (e.g., 2400 Leith et al., 2014). This area of inherited damage facilitates rapid subaerial weathering until much 10-1 2300 less susceptible rocks with fewer microcracks are encountered, which in turn erode much less Elevation [masl] rapidly. -2 2200 10 We show in Figure 4D that TCN exposure Bedrock surface erosion [mm/yr] ages increase with elevation (Table DR2). If ero- 2100 r2 = 0.22 sion is ignored, we observe a correlation be- 10-3 0102030405060708090 5101520 tween age and elevation on the Trélaporte pro- Slope [°] TCN exposure age [ka] file with age from 16.6 ± 0.6 ka to 6.6 ± 0.9 ka (MBTP1 and MBTP6, respectively). The outlier Figure 4. (A) Inverted values of bedrock surface erosion rate ε (mm/yr) and erosion onset MBTP5 (20.2 ± 0.8 ka) is potentially located time tS (yr) for Trélaporte and Moine samples (Mont Blanc, European Alps). (B) Distribution of ε with elevation (meters above sea level [masl]). (C) Distribution of ε with surface slope [°]. in a glacial erosion shadow of the topography, (D) Distribution of 10Be surface exposure ages with elevation, where circles represent erosion exhibiting inheritance from previous exposure. uncorrected ages, and lines show all possible ages corrected with specific inverted values of For the Moine profile, the three sites were freed and t presented in panel A. Gray area in D shows range of corrected terrestrial cosmogenic ε S from the glacier at the same time, with noncor- nuclide (TCN) exposure ages older than the corrected age at the highest elevation (16.6 ± 0.6 ka to 134.1 ± 5.8 ka; Table DR2 [see footnote 1]), which are not physically plausible because a rected TCN ages of ca. 12 ka (Fig. 4D; Table low-altitude sample cannot be exposed before a high-altitude sample. DR2). These results agree with known degla- ciation scenarios for the Alps (Ivy-Ochs, 2015; stochastic nature of geomorphic processes (e.g., decrease in erosion rates with elevation is also Wirsig et al., 2016; Protin et al., 2019). Indeed, Koppes and Montgomery, 2009; Schumer and too pronounced to be explained by chemical ice-surface lowering starting at 16.6 ± 0.6 ka at Jerolmack, 2009; Sadler and Jerolmack, 2015; weathering alone. Assuming a 3 °C difference an elevation of 2550 masl coincides with TCN Ganti et al., 2016). over the 451 m of elevation, the difference in dating from the southern side of the Mont Blanc A second result is the apparent decrease in reaction rates accommodates only 2% of the massif (Wirsig et al., 2016). However, TCN ex- erosion rate with elevation (r2 = 0.53; Fig. 4B). observed change (using an activation energy of posure ages are different when the full range The highest erosion rates were observed at the 60 kJ/mol; Lasaga et al., 1994). of estimated erosion is included. Any correc- lowest elevation. This decrease in erosion rate is In Figure 4C, we investigated the relation- tion of TCN exposure ages older than the cor- opposite to what is expected for frost cracking ship between the erosion rates and surface rected age at the highest elevation (16.6 ± 0.6 for this elevation range (Anderson, 1998; Hales slope measured at the outcrop scale. A positive ka to 134.1 ± 5.8 ka; Fig. 4D; Table DR2) is and Roering, 2007). Frost cracking predicts high correlation between erosion rates and surface not physically plausible because a low-altitude erosion rates within the frost cracking window slopes was observed, although the correlation surface cannot be exposed before a high-altitude (FCW), which would be at high altitude here. was weaker (r2 = 0.22) than with elevation. This one (gray area in Fig. 4D). In the most extreme Using modern records, a rock surface at 1800 implies that stagnant water on the bedrock sur- scenario, the ages may indicate extremely rapid, masl spends 8% of the year in the FCW (−3 °C face may not have a primary effect on setting near-instantaneous deglaciation at these sites. to −8 °C; Matsuoka and Murton, 2008), or 14% erosion rate. An alternative possibility is snow and 21% at 2400 and 3200 masl, respectively. cover. The surfaces at high elevation with flat- CONCLUSIONS This trend also holds for the Younger Dryas– ter slopes experience higher solid precipitation By combining OSL and TCN surface expo- early Holocene transition, assuming the tem- and periods of snow cover during yearly cycles, sure dating, we quantified erosion-rate histories perature was 4.5 °C less than today (3.6–5.5 °C, which maintain the rock surface at 0 °C, and in of postglacial exposed bedrock in glacial val- Protin et al., 2019) and that the summer-winter turn suppress the efficiency of frost cracking. leys at time scales from 101 to 104 yr. Bedrock difference was similar to today. The observed Surfaces at lower elevations with steeper slopes surfaces sampled from the flanks of the Mer

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Downloaded from https://pubs.geoscienceworld.org/gsa/geology/article-pdf/48/2/139/4926750/139.pdf by guest on 13 February 2020 de Glace revealed erosion rates that vary from Burbank, D.W., Leland, J., Fielding, E., Anderson, Koppes, M., and Montgomery, D., 2009, The relative 3.5 ± 1.2 × 10−3 mm/yr to 4.3 ± 0.6 mm/yr over R.S., Brozović, N., Reid, M.R., and Duncan, efficacy of fluvial and glacial erosion over modern C., 1996, Bedrock incision, rock uplift and to orogenic timescales: Nature Geoscience, v. 2, p. 451 m of elevation, with an anticorrelation be- threshold hillslopes in the northwestern Hima- 644–647, https://doi.org/10.1038/ngeo616​ . tween erosion rate and elevation. 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In some cases, erosion is so large that one Egholm, D., Nielsen, S., Pedersen, V.K., and Le- 2018, Investigation of OSL surface exposure dat- can accommodate a scenario in which thinning semann, J.-E., 2009, Glacial effects limiting ing to reconstruct post-LIA glacier fluctuations in was very rapid and thus deglaciation was es- ­mountain height: Nature, v. 460, p. 884–887, the (Mer de Glace, Mont Blanc mas- sentially instantaneous. The difference between https://doi.org/10.1038/nature08263. sif): Quaternary Geochronology, v. 44, p. 63–74, Ganti, V., von Hagke, C., Scherler, D., Lamb, M.P., https://doi​.org/10.1016/j.quageo.2017.12.002. these two end-member scenarios leads to impor- Fischer, W.W., and Avouac, J.P., 2016, Time scale Lehmann, B., Herman, F., Valla, P.G., King, G.R., tant implications for paleoclimate reconstruc- bias in erosion rates of glaciated landscapes: and Biswas, R.H., 2019, Evaluating post-glacial tions, and the potential controls of precipitation Science Advances, v. 2, p. e1600204, https://doi​ bedrock erosion and surface exposure duration 10 and temperature on the regional climate of the .org/10.1126/sciadv.1600204. by coupling in-situ OSL and Be dating: Earth Gosse, J.C., and Phillips, F.M., 2001, Terrestrial Surface Dynamics Discussions, v. 7, p. 1–38, western European Alps. in situ cosmogenic nuclides: Theory and ap- https://doi​.org/10.5194/esurf-2018-97. plication: Quaternary Science Reviews, v. 20, Leith, K., Moore, J.R., Amann, F., and Loew, S., 2014, ACKNOWLEDGMENTS p. 1475–1560, https://doi​.org/10.1016/S0277- Sub-glacial extensional fracture development and This work was supported by the Swiss-AlpArray 3791(00)00171-2. implications for Alpine valley evolution: Journal SINERGIA project (CRSII2_154434/1) and project Hales, T.C., and Roering, J.J., 2007, Climatic con- of Geophysical Research–Earth Surface, v. 119, PP00P2_170559 (Valla) funded by the Swiss National trols on frost cracking and implications for the p. 62–81, https://doi.org/10.1002/2012JF002691​ . Science Foundation (SNFS). King acknowledges evolution of bedrock landscapes: Journal of Łoziński, M.W., 1909, Über die mechanische Verwit- support from project PZ00P2_167960. We thank P.-H. Geophysical Research, v. 112, F02033, https:// terung der Sandsteine im gemassibten Klima. Blard for sharing the code of the CREp calculator, doi​.org/10.1029/2006JF000616. Académie des sciences de cracovie, Bulletin In- and D. Six and C. Vincent for GLACIOCLIM Alps Hallet, B., Hunter, L., and Bogen, J., 1996, Rates of ternationale, Classe de Science, Mathématiques data availability. We thank A. Cogez, N. Gribenski, erosion and sediment evacuation by glaciers: A et Naturelles, v. 1, p. 1–25. (English translation F. Mettra, D. Fabel, and A. Lang for their constructive review of field data and their implications: Global by Mrozek Teresa, 1992, On the mechanical inputs; S. Coutterand for expertise in the Quaternary and Planetary Change, v. 12, p. 213–235, https:// weathering of sandstones in temperate climates, of the Mont-Blanc massif and for help during the doi​.org/10.1016/0921-8181(95)00021-6. in Evans, D.J.A., ed., Cold Climate Landforms: sampling campaign; and N. Stalder, J. González Herman, F., and Champagnac, J.-D., 2016, Plio-Pleis- Chichester, UK, Wiley, p. 119–134.) ­Holguera, G. Bustarret, U. Nanni, and S. Vivero tocene increase of erosion rates in mountain belts Matsuoka, N., and Murton, J., 2008, Frost weathering: for their support during field excursions. J. El Kadi, in response to climate change: Terra Nova, v. 28, Recent advances and future directions: Perma- M. Faria, and K. Häring are thanked for laboratory p. 2–10, https://doi​.org/10.1111/ter.12186. frost and Periglacial Processes, v. 19, p. 195–210, support. We thank K. Hippe, R.S. 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