Postglacial erosion of bedrock surfaces and deglaciation timing: New insights from the Mont Blanc massif (western Alps)

(1)

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

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

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.

Postglacial erosion of bedrock surfaces and deglaciation

timing: New insights from the Mont Blanc massif (western Alps)

Benjamin Lehmann¹*, Frédéric Herman¹, Pierre G. Valla^2,3, Georgina E. King¹, Rabiul H. Biswas¹, Susan Ivy-Ochs⁴, Olivia Steinemann⁴ and Marcus Christl⁴

1 Institute of Earth Surface Dynamics, University of Lausanne, CH-1015 Lausanne, Switzerland

2 Institut 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

3 Institute of Geological Sciences and Oeschger Center for Climate Research, University of Bern, 3012 Bern, Switzerland

4 Laboratory of Ion Beam Physics, ETH Zürich, Otto-Stern-Weg 5, 8093 Zurich, Switzerland ABSTRACT

Since the Last Glacial Maximum, ∼20 k.y. ago, Alpine glaciers have retreated and thinned.

This transition exposed bare bedrock surfaces that could then be eroded by a combination of debuttressing or local frost cracking and weathering. Quantification of the respective contributions of these processes is necessary to understand the links between long-term climate and erosion in mountains. Here, we quantified the erosion histories of postglacial exposed bedrock in glacial valleys. Combining optically stimulated luminescence and ter- restrial cosmogenic nuclide (TCN) surface exposure dating, we estimated the erosion rate of bedrock surfaces at time scales from 10¹ to 10⁴ yr. Bedrock surfaces sampled from the flanks of the Mer de Glace (Mont Blanc massif, European Alps) revealed erosion rates that 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- tive correlation between erosion rate and elevation. The observed spatial variation in erosion rates, and their high values, reflect morphometric (elevation and surface slope) and climatic (temperature and snow cover) controls. Furthermore, the derived erosion rates can be used to correct the timing of deglaciation based on TCN data, potentially suggesting very rapid ice thinning during the Gschnitz stadial.

INTRODUCTION

To understand the long-term evolution of Alpine landscapes, the respective contributions of surface erosion, sediment production, and sediment transport must be quantified. During the Quaternary period, the alternation between glacial and interglacial periods has modulated the efficiency of glacial, fluvial, and hillslope processes (Koppes and Montgomery, 2009). In that context, changes in bedrock morphology and corresponding sediment delivery have been related to glacier extent, and glacial erosion is often thought to be the most efficient erosional and sediment transport mechanism in mountain environments (e.g., Hallet et al., 1996; Brozović et al., 1997; Montgomery, 2002; Mitchell and Montgomery, 2006; Egholm et al., 2009; Herman et al., 2013; Herman and Champagnac, 2016).

Recent studies have also revealed the importance

of periglacial processes during interglacial periods (Burbank et al., 1996; Ballantyne, 2002;

Scherler, 2015). Yet, the rate at which bare-bedrock surfaces weather and erode during intergla- cials remains poorly quantified (e.g., Colman, 1981; Zimmerman et al., 1994; André, 2002;

Nicholson, 2008; Kirkbride and Bell, 2010).

The erosion of hillslopes in periglacial environments is governed by a combination of land- sliding, rock shattering, and weathering (e.g., Anderson and Anderson, 2010). During the last decades, the development of terrestrial cosmogenic nuclide (TCN) methods, mainly using in situ–

produced ¹⁰Be in quartz crystals, has improved our ability to quantify bedrock surface erosion over time scales from 10⁴ to 10⁶ yr, assuming that erosion occurs steadily through time (Balco et al., 2008; von Blanckenburg and Willenbring, 2014; Hippe, 2017). Over modern time scales, geomorphologists working on frost cracking have also highlighted the feedbacks between tempera-

ture variation and snow cover and their effects on the evolution of bedrock surfaces over diur- nal to decadal time scales (e.g., Łoziński, 1909;

Matsuoka and Murton, 2008). However, bridging the temporal gap between these erosion estimates remains challenging, in part because of the stochastic nature of geomorphic processes (Koppes and Montgomery, 2009; Ganti et al., 2016).

To address these issues, we adopted a new method that combined optically stimulated luminescence (OSL) and TCN surface exposure dating (Sohbati et al., 2018; Lehmann et al., 2019).

In this study, we applied this methodology to investigate how erosion rates have evolved over time scales of 10¹–10⁴ yr on bedrock surfaces of the Mer de Glace (Mont Blanc massif, European Alps) and how morphometric and climatic factors control their evolution. Then, we addressed how the variability in erosion rates can be used to correct TCN exposure ages, leading to very different possible scenarios of ice thinning during the Gschnitz stadial (a period of regional- ly extensive glacier advance in the European Alps, temporally between the breakdown of the Last Glacial Maximum piedmont lobes and the beginning of the Bølling warm interval).

STUDY SITE

We collected samples along two elevation profiles at the Mer de Glace (Fig. 1). Six bedrock surfaces were sampled below the Mont Blanc Tête de Trélaporte (MBTP sample sites, west side of the glacier, from 2545 to 2094 m above sea level [masl]; Fig. 1), and three bedrock surfaces were sampled below the Ai- guille du Moine (MBAM sample sites, east side of the glacier, ranging from 2447 to 2259 masl; Fig. 1). All surfaces were from the same

*E-mail: lehmann.benj@gmail.com

Published online 22 November 2019

(2)

phenocrystalline granitic lithology of the Mont Blanc massif, and selected sampling sites can all be classified as glacially eroded bedrock surfaces (see the GSA Data Repository¹). The surfaces are rough and exhibit a weathered texture without glacial striations. All studied bedrock surfaces were located between the ice- surface elevations of the Little Ice Age (LIA) and the Last Glacial Maximum (LGM; Cout- terand and Buoncristiani, 2006; Vincent et al., 2014) and were therefore likely deglaciated sometime between ca. 20–18 ka (Wirsig et al., 2016) and A.D. 1850.

METHODS

We measured both ¹⁰Be concentrations (e.g., Gosse and Phillips, 2001; Ivy-Ochs and Briner, 2014) and OSL profiles (Sohbati et al., 2012) on exposed granitic rock samples (see the Data Repository). The ¹⁰Be concentrations provided us with constraints on the time since the rocks were exposed to cosmic rays and with a temporal framework for the possible erosion histories since exposure. OSL profiles constrained the erosion history since the rock exposure to light following ice decay (Lehm- ann et al., 2019). Note that it is the difference in sensitivity between ¹⁰Be and OSL that makes it possible to quantify surface erosion rate histories over short (<10² yr) and long (>10⁴ yr) time scales (Sohbati et al., 2018;

Lehmann et al., 2019).

The evolution in time of the OSL bleaching front into a rock surface depends on exposure age, surface erosion, electron trapping and detrapping

1GSA Data Repository item 2020042, supplemen- tal details on sample preparation, measurement, and analysis, as well as a sample list with their character- istics and measured ¹⁰Be concentrations, calibration details, and luminescence signal, is available online at http://www.geosociety.org/datarepository/2020/, or on request from editing@geosociety.org.

500

0 1000 m

Moine Trélaporte

2500 2500

3000 3000

3000 3000 2000

2000 6.93°E

45.91°N

6.95°E

45.92°N45.90°N

C

0

N 4 km N

Mont-Blanc 4810 Chamonix

B

A

B

C

France

Italy Switzerland

0 500 1000 1500 2000 2500

Horizontal distance [m]

1800 2000 2200 2400 2600 2800

Elevation [masl]

MBTP1 MBTP2

MBTP11 MBTP5

MBTP9 MBTP6

MBAM1 MBAM3

MBAM2

Trélaporte profile Moine profile

SSW NNE SW NE

trimline

LIA

D

Figure 1. Study sites sampled along Mer de Glace glacier (Mont Blanc massif, European 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 dot represents a specific sample; round and triangle dots are from the Trélaporte and Moine profiles, respectively). (D) Topographic cross sections of Trélaporte and Moine vertical profiles along the Mer de Glace and corresponding sampled surfaces (MB—Mont Blanc, TP—Trélaporte, AM—Aiguille du Moine). Gray lines represent elevation of Last Glacial Maximum (LGM) trim- lines (Coutterand and Buoncristiani, 2006); blue lines represent Little Ice Age (LIA) elevation (Vincent et al., 2014); masl—meters above sea level.

A B C

Figure 2. Schematic representation of four different erosion scenarios through time (A,B) and their resulting luminescence signal (C), where t0 is uncorrected ¹⁰Be exposure age, tS is onset time of erosion (yr), tC is corrected exposure age, and εε 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.

(3)

(bleaching) rates, and athermal loss (Lehmann et al., 2019, their equation 1). While the latter three variables can be constrained from laboratory measurements, the erosion history is unknown. Here, we constrained the erosion history by performing a joint inversion of the OSL and ¹⁰Be data for each sample. In this inversion, we assumed that erosion rates evolved as step functions from no erosion to erosion going forward in time (Fig. 2; Lehmann et al., 2019). A reference OSL profile was estab- lished by taking the TCN exposure age with no erosion correction (t₀). The inferred erosion history included an erosion rate (ε in Fig. 2), and two times: t_S= the time at which erosion began, and

tC = the corrected exposure age. Note that tC was estimated by combining OSL and TCN data. In turn, various solutions for the erosion rate history (t_S-ε pairs) and t_C were inferred; high erosion rates and durations that did not fit the observed ¹⁰Be concentration data (Lal, 1991) were excluded from the parameter search (forbidden zone in Figs. 3D–3F;

see the Data Repository).

RESULTS AND DISCUSSION

In Figures 3A–3C, we show the OSL measurements (i.e., infrared stimulated luminescence at 50 °C) for samples MBTP1, MBTP2, and MBTP11. Three individual cores per sample

were sliced such that a depth and an OSL signal could be attributed to each rock slice (green dots in Figs. 3A–3C; Tables DR3–DR5 in the Data Repository). The OSL signal was bleached near the surface and reached a plateau at depth.

As a reference profile, a model was computed (Lehmann et al., 2019, their equation 2) using t0 and considering no erosion (dashed black line in Figs. 3A–3C). Erosion rate histories were inferred from paired bedrock surface OSL signals and ¹⁰Be concentrations (Figs. 3D–3F and 4A).

The most likely solutions (red lines in Figs. 3A–

3C and yellow areas in Figs. 3D–3F) were determined by testing 10⁴ pairs of both ε and t_S (combination of 100 values of both parameters) in log space.

The 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 surfaces were mainly affected by grain-by-grain erosion 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 erosion 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 disagreement 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 measured, depending of the lithology and the loca- tion (André, 2002; Nicholson, 2008; Kirkbride and Bell, 2010). In contrast, the intensities of erosion 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 that the most recently exposed surfaces are more prone to erosion, we note that the apparent decrease in erosion rates with increasing time is common to most techniques that are used to con- strain erosion rates (e.g., Koppes and Montgom- ery, 2009; Ganti et al., 2016). Such a relation- ship is thought to reflect a bias caused by the

A D

B E

C F

Figure 3. (A–C) Luminescence profiles and inversion results, where green dots represent mea- sured infrared stimulated luminescence (IRSL50) profiles for samples MBTP1, MBTP2, and MBTP11 (MBTP—Mont Blanc Tête de Trélaporte), respectively, from the Mont Blanc massif, European Alps.

Dashed black lines represent reference profiles, taking terrestrial cosmogenic nuclide (TCN) exposure age with no erosion correction (t0); red lines represent inferred fits where likelihood is greater 0.95; t_cmax 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 high erosion rates and durations that is unable to predict observed ¹⁰Be concentration.

(4)

stochastic nature of geomorphic processes (e.g., Koppes and Montgomery, 2009; Schumer and Jerolmack, 2009; Sadler and Jerolmack, 2015;

Ganti et al., 2016).

A second result is the apparent decrease in erosion rate with elevation (r² = 0.53; Fig. 4B).

The highest erosion rates were observed at the lowest elevation. This decrease in erosion rate is opposite to what is expected for frost cracking for this elevation range (Anderson, 1998; Hales and Roering, 2007). Frost cracking predicts high erosion rates within the frost cracking window (FCW), which would be at high altitude here.

Using modern records, a rock surface at 1800 masl spends 8% of the year in the FCW (−3 °C to −8 °C; Matsuoka and Murton, 2008), or 14%

and 21% at 2400 and 3200 masl, respectively.

This trend also holds for the Younger Dryas–

early Holocene transition, assuming the temperature was 4.5 °C less than today (3.6–5.5 °C, Protin et al., 2019) and that the summer-winter difference was similar to today. The observed

decrease in erosion rates with elevation is also too pronounced to be explained by chemical weathering alone. Assuming a 3 °C difference over the 451 m of elevation, the difference in reaction rates accommodates only 2% of the observed change (using an activation energy of 60 kJ/mol; Lasaga et al., 1994).

In Figure 4C, we investigated the relation- ship between the erosion rates and surface slope measured at the outcrop scale. A positive correlation between erosion rates and surface slopes was observed, although the correlation was weaker (r²= 0.22) than with elevation. This implies that stagnant water on the bedrock surface may not have a primary effect on setting erosion rate. An alternative possibility is snow cover. The surfaces at high elevation with flat- ter slopes experience higher solid precipitation and periods of snow cover during yearly cycles, which maintain the rock surface at 0 °C, and in turn suppress the efficiency of frost cracking.

Surfaces at lower elevations with steeper slopes

are less shielded by snow cover and thus experience 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 signals. By considering a snow cover of 50 cm for 6 mo per year (Wirsig et al., 2016), the TCN correction accounts for a change of between 5.4%

and 6.6% (for MBTP5, 20.2 ± 0.9–21.3 ± 0.9 ka; and MBAM2, 12.1 ± 0.5–12.9 ± 0.5 ka). The snow cover effect for OSL dating is accounted for in the calibration, assuming that calibration surfaces have experienced the same snow cover as the other surfaces (Lehmann et al., 2018).

We also speculate that surfaces in the vicin- ity of the glacier are influenced by cold kata- batic winds coming from the glacier (∼50 m for alpine glaciers; Oerlemans and Grisogono, 2002), which in turn promote frost cracking at lower elevation. A final explanation could be that bedrock surfaces are damaged under the glacier in a way that decreases with the depth in the rock due to the difference in ice load (e.g., Leith et al., 2014). This area of inherited damage facilitates rapid subaerial weathering until much less susceptible rocks with fewer microcracks are encountered, which in turn erode much less rapidly.

We show in Figure 4D that TCN exposure ages increase with elevation (Table DR2). If erosion is ignored, we observe a correlation between age and elevation on the Trélaporte profile with age from 16.6 ± 0.6 ka to 6.6 ± 0.9 ka (MBTP1 and MBTP6, respectively). The outlier MBTP5 (20.2 ± 0.8 ka) is potentially located in a glacial erosion shadow of the topography, exhibiting inheritance from previous exposure.

For the Moine profile, the three sites were freed from the glacier at the same time, with noncor- rected TCN ages of ca. 12 ka (Fig. 4D; Table DR2). These results agree with known deglaciation scenarios for the Alps (Ivy-Ochs, 2015;

Wirsig et al., 2016; Protin et al., 2019). Indeed, ice-surface lowering starting at 16.6 ± 0.6 ka at an elevation of 2550 masl coincides with TCN dating from the southern side of the Mont Blanc massif (Wirsig et al., 2016). However, TCN exposure ages are different when the full range of estimated erosion is included. Any correction of TCN exposure ages older than the corrected age at the highest elevation (16.6 ± 0.6 ka to 134.1 ± 5.8 ka; Fig. 4D; Table DR2) is not physically plausible because a low-altitude surface cannot be exposed before a high-altitude one (gray area in Fig. 4D). In the most extreme scenario, the ages may indicate extremely rapid, near-instantaneous deglaciation at these sites.

CONCLUSIONS

By combining OSL and TCN surface exposure dating, we quantified erosion-rate histories of postglacial exposed bedrock in glacial valleys at time scales from 10¹ to 10⁴ yr. Bedrock surfaces sampled from the flanks of the Mer

Bedrock surface erosion time ts [yr]

A Distribution of the erosion rate in time

MBTP1 MBTP2 MBAM1 MBAM2 MBTP11 MBAM3 MBTP5 MBTP9 MBTP6

Bedrock surface erosion [mm/yr]

10⁰ 10¹

10^-1

10^-2

10^-3

10⁰ 10¹ 10² 10³ 10⁴ 2000 2100 2200 2300 2400 2500 2600

Elevation [masl]

B Distribution of the erosion rate with elevation

10⁰ 10¹

10^-1

10^-2

10^-3

Slope [°]

C Distribution of the erosion rate with slope

0 10 20 30 40 50 60 70 80 90

10⁰ 10¹

10^-1

10^-2

10^-3

r² = 0.53

r² = 0.22

5 10 15 20

2100 2200 2300 2400 2500 2600

Elevation [masl]

D Distribution of the TCN ages with elevation

TCN exposure age [ka]

Non corrected age Range of corr. age

Figure 4. (A) Inverted values of bedrock surface erosion rate εε (mm/yr) and erosion onset time t_S (yr) for Trélaporte and Moine samples (Mont Blanc, European Alps). (B) Distribution of εε with elevation (meters above sea level [masl]). (C) Distribution of εε with surface slope [°].

(D) Distribution of ¹⁰Be surface exposure ages with elevation, where circles represent erosion uncorrected ages, and lines show all possible ages corrected with specific inverted values of εε^{and t}S presented in panel A. Gray area in D shows range of corrected terrestrial cosmogenic 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 low-altitude sample cannot be exposed before a high-altitude sample.

(5)

de Glace revealed erosion rates that vary from 3.5 ± 1.2 × 10⁻³ mm/yr to 4.3 ± 0.6 mm/yr over 451 m of elevation, with an anticorrelation between erosion rate and elevation. We conclude that a combination of climatic (temperature and snow cover) and morphometric (elevation and surface slope) factors control the variation in erosion rates. Finally, we found that the OSL- derived erosion rates can be used to correct the timing of deglaciation age based on TCN data.

Without such a correction, our data would have suggested progressive thinning after 16.6 ± 0.6 ka. In some cases, erosion is so large that one can accommodate a scenario in which thinning was very rapid and thus deglaciation was es- sentially instantaneous. The difference between these two end-member scenarios leads to impor- tant implications for paleoclimate reconstruc- tions, and the potential controls of precipitation and temperature on the regional climate of the western European Alps.

ACKNOWLEDGMENTS

This work was supported by the Swiss-AlpArray SINERGIA project (CRSII2_154434/1) and project PP00P2_170559 (Valla) funded by the Swiss National Science Foundation (SNFS). King acknowledges support from project PZ00P2_167960. We thank P.-H.

Blard for sharing the code of the CREp calculator, and D. Six and C. Vincent for GLACIOCLIM Alps data availability. We thank A. Cogez, N. Gribenski, F. Mettra, D. Fabel, and A. Lang for their constructive inputs; S. Coutterand for expertise in the Quaternary of the Mont-Blanc massif and for help during the sampling campaign; and N. Stalder, J. González Holguera, G. Bustarret, U. Nanni, and S. Vivero for their support during field excursions. J. El Kadi, M. Faria, and K. Häring are thanked for laboratory support. We thank K. Hippe, R.S. Anderson, and one anonymous reviewer, whose relevant remarks helped to improve this manuscript.

REFERENCES CITED

Anderson, R.S., 1998, Near-surface thermal profiles in alpine bedrock: Implications for the frost weathering of rock: Arctic and Alpine Research, v. 30, p. 362–372, https://doi .org/10.2307/1552008.

Anderson, R.S., and Anderson, S.P., 2010, Geo- morphology: The Mechanics and Chemistry of Landscapes: Cambridge, UK, Cambridge University Press, https://doi .org/10.1017/

CBO9780511794827.

André, M.-F., 2002, Do periglacial landscapes evolve under periglacial conditions?: Geomorphology, v. 52, p. 149–164, https://doi .org/10.1016/S0169- 555X(02)00255-6.

Balco, G., Stone, J.O., Lifton, N.A., Dunai, T.J., Lif- ton, A., and Dunai, T.J., 2008, A complete and easily accessible means of calculating surface exposure ages or erosion rates from ¹⁰Be and

26Al measurements: Quaternary Geochronol- ogy, v. 3, p. 174–195, https://doi .org/10.1016/

j.quageo.2007.12.001.

Ballantyne, C.K., 2002, Paraglacial geomorphology: Quaternary Science Reviews, v. 21, p. 1935–2017, https://doi .org/10.1016/S0277- 3791(02)00005-7.

Brozović, N., Burbank, D.W., and Meigs, A.J., 1997, Climatic limits on landscape development in the northwestern Himalaya: Science, v. 276, p. 571–574, https://doi .org/10.1126/

science.276.5312.571.

Burbank, D.W., Leland, J., Fielding, E., Anderson, R.S., Brozović, N., Reid, M.R., and Duncan, C., 1996, Bedrock incision, rock uplift and threshold hillslopes in the northwestern Hima- layas: Nature, v. 379, p. 505–510, https://doi .org/10.1038/379505a0.

Colman, S.M., 1981, Rock-weathering rates as functions of time: Quaternary Research, v. 15, p. 250–264, https://doi.org/10.1016/0033- 5894(81)90029-6.

Coutterand, S., and Buoncristiani, J.-F., 2006, Paléo- géographie du dernier maximum glaciaire du Pléis- tocène récent de la région du massif du Mont Blanc, France: Quaternaire: Revue de l’Association Fran- çaise pour l’Étude du Quaternaire, v. 17, p. 35–43.

Egholm, D., Nielsen, S., Pedersen, V.K., and Le- semann, J.-E., 2009, Glacial effects limiting mountain height: Nature, v. 460, p. 884–887, https://doi.org/10.1038/nature08263.

Ganti, V., von Hagke, C., Scherler, D., Lamb, M.P., Fischer, W.W., and Avouac, J.P., 2016, Time scale bias in erosion rates of glaciated landscapes:

Science Advances, v. 2, p. e1600204, https://doi .org/10.1126/sciadv.1600204.

Gosse, J.C., and Phillips, F.M., 2001, Terrestrial in situ cosmogenic nuclides: Theory and ap- plication: Quaternary Science Reviews, v. 20, p. 1475–1560, https://doi .org/10.1016/S0277- 3791(00)00171-2.

Hales, T.C., and Roering, J.J., 2007, Climatic controls on frost cracking and implications for the evolution of bedrock landscapes: Journal of Geophysical Research, v. 112, F02033, https://

doi .org/10.1029/2006JF000616.

Hallet, B., Hunter, L., and Bogen, J., 1996, Rates of erosion and sediment evacuation by glaciers: A review of field data and their implications: Global and Planetary Change, v. 12, p. 213–235, https://

doi .org/10.1016/0921-8181(95)00021-6.

Herman, F., and Champagnac, J.-D., 2016, Plio-Pleis- tocene increase of erosion rates in mountain belts in response to climate change: Terra Nova, v. 28, p. 2–10, https://doi .org/10.1111/ter.12186.

Herman, F., Seward, D., Valla, P.G., Carter, A., Kohn, B., Willett, S.D., and Ehlers, T.A., 2013, World- wide acceleration of mountain erosion under a cooling climate: Nature, v. 504, p. 423–426, https://doi .org/10.1038/nature12877.

Herman, F., Beyssac, O., Brughelli, M., Lane, S.T., Lep- rince, S., Adatte, T., Lin, J.Y.Y., Avouac, J.-P., and Cox, S.C., 2015, Erosion by an Alpine glacier: Sci- ence, v. 350, p. 193–195, https://doi .org/10.1126/

science.aab2386.

Herman, F., Braun, J., Deal, E., and Prasicek, G., 2018, The response time of glacial erosion: Journal of Geophysical Research–

Earth Surface, v. 123, p. 801–817, https://doi .org/10.1002/2017JF004586.

Hippe, K., 2017, Constraining processes of landscape change with combined in situ cosmogenic

14C-¹⁰Be analysis: Quaternary Science Reviews, v. 173, p. 1–19, https://doi .org/10.1016/j.quascirev.2017.07.020.

Ivy-Ochs, S., 2015, Glacier variations in the European Alps at the end of the last glaciation: Cuardernos de Investigacion Geográfica, v. 41, p. 295–315, https://doi .org/10.18172/cig.2750.

Ivy-Ochs, S., and Briner, J.P., 2014, Dating disap- pearing ice with cosmogenic nuclides: Elements, v. 10, p. 351–356, https://doi .org/10.2113/gselements.10.5.351.

Kirkbride, M.P., and Bell, C.M., 2010, Edge-roundness of boulders of Torridonian Sandstone (northwest Scotland): Applications for relative dating and implications for warm and cold climate weathering rates: Boreas, v. 39, p. 187–198, https://doi .org/10.1111/j.1502-3885.2009.00131.x.

Koppes, M., and Montgomery, D., 2009, The relative efficacy of fluvial and glacial erosion over modern to orogenic timescales: Nature Geoscience, v. 2, p.

644–647, https://doi .org/10.1038/ngeo616.

Lal, D., 1991, Cosmic ray labeling of erosion surfaces: In situ nuclide production rates and erosion models: Earth and Planetary Science Letters, v. 104, p. 424–439, https://doi .org/10.1016/0012- 821X(91)90220-C.

Lasaga, A.C., Soler, J.M., Ganor, J., Burch, T.E., and Nagy, K.L., 1994, Chemical weathering rate laws and global geochemical cycles: Geochimica et Cosmochimica Acta, v. 58, p. 2361–2386, https://

doi .org/10.1016/0016-7037(94)90016-7.

Lehmann, B., Valla, P.G., King, G.E., and Herman, F., 2018, Investigation of OSL surface exposure dating to reconstruct post-LIA glacier fluctuations in the French Alps (Mer de Glace, Mont Blanc massif): Quaternary Geochronology, v. 44, p. 63–74, https://doi .org/10.1016/j.quageo.2017.12.002.

Lehmann, B., Herman, F., Valla, P.G., King, G.R., and Biswas, R.H., 2019, Evaluating post-glacial bedrock erosion and surface exposure duration by coupling in-situ OSL and ¹⁰Be dating: Earth Surface Dynamics Discussions, v. 7, p. 1–38, https://doi .org/10.5194/esurf-2018-97.

Leith, K., Moore, J.R., Amann, F., and Loew, S., 2014, Sub-glacial extensional fracture development and implications for Alpine valley evolution: Journal of Geophysical Research–Earth Surface, v. 119, p. 62–81, https://doi .org/10.1002/2012JF002691.

Łoziński, M.W., 1909, Über die mechanische Verwit- terung der Sandsteine im gemassibten Klima.

Académie des sciences de cracovie, Bulletin In- ternationale, Classe de Science, Mathématiques et Naturelles, v. 1, p. 1–25. (English translation by Mrozek Teresa, 1992, On the mechanical weathering of sandstones in temperate climates, in Evans, D.J.A., ed., Cold Climate Landforms:

Chichester, UK, Wiley, p. 119–134.)

Matsuoka, N., and Murton, J., 2008, Frost weathering:

Recent advances and future directions: Perma- frost and Periglacial Processes, v. 19, p. 195–210, https://doi .org/10.1002/ppp.620.

Mitchell, S.G., and Montgomery, D.R., 2006, In- fluence of a glacial buzzsaw on the height and morphology of the Cascade Range in central Washington State, USA: Quaternary Research, v. 65, p. 96–107, https://doi .org/10.1016/

j.yqres.2005.08.018.

Montgomery, D.R., 2002, Valley formation by fluvial and glacial erosion: Geology, v. 30, p. 1047–1050, https://doi .org/10.1130/0091- 7613(2002)030<1047:VFBFAG>2.0.CO;2.

Nicholson, D.T., 2008, Rock control on microweath- ering of bedrock surfaces in a periglacial environment: Geomorphology, v. 101, p. 655–665, https://doi .org/10.1016/j.geomorph.2008.03.009.

Norton, K.P., von Blanckenburg, F., and Kubik, P.W., 2010, Cosmogenic nuclide–derived rates of diffu- sive and episodic erosion in the glacially sculpted upper Rhone Valley, Swiss Alps: Earth Surface Processes and Landforms, v. 35, p. 651–662, https://doi .org/10.1002/esp.1961.

Oerlemans, J., and Grisogono, B., 2002, Glacier winds and parameterisation of the related surface heat fluxes: Tellus A: Dynamic Meteorology and Oceanography, v. 54, p. 440–452, https://doi .org/10.1034/j.1600-0870.2002.201398.x.

Portenga, E.W., and Bierman, P.R., 2011, Understand- ing Earth’s eroding surface with ¹⁰Be: GSA To- day, v. 21, no. 8, p. 4–10, https://doi .org/10.1130/

G111A.1.

Protin, M., Schimmelpfennig, I., Mugnier J.-L., Rava- nel, L., Le Roy, M., Deline, P., Favier, V., Buon- cristiani, J.-F., and Consortium A.S.T.E.R. Team, 2019, Climatic reconstruction for the Younger

(6)

Dryas/early Holocene transition and the Little Ice Age based on paleo-extents of Argentière glacier (French Alps): Quaternary Science Reviews, v. 221, p. 105863, https://doi .org/10.1016/j.quascirev.2019.105863.

Rabatel, A., Dedieu, J.P., and Vincent, C., 2016, Spa- tio-temporal changes in glacier-wide mass bal- ance quantified by optical remote sensing on 30 glaciers in the French Alps for the period 1983–

2014: Journal of Glaciology, v. 62, p. 1153–1166, https://doi .org/10.1017/jog.2016.113.

Sadler, P.M., and Jerolmack, D.J., 2015, Scaling laws for aggradation, denudation and progradation rates: The case for time-scale invariance at sediment sources and sinks, in Smith, D.G., et al., eds., Strata and Time: Probing the Gaps in Our Understanding: Geological Society [London]

Special Publication 404, p. 69–88, https://doi .org/10.1144/SP404.7.

Scherler, D., 2015, Climatic limits to headwall retreat in the Khumbu Himalaya, eastern Nepal: Geolo- gy, v. 42, p. 1019–1022, https://doi .org/10.1130/

G35975.1.

Schumer, R., and Jerolmack, D.J., 2009, Real and apparent changes in sediment deposi- tion rates through time: Journal of Geophysi- cal Research, v. 114, F00A06, https://doi .org/10.1029/2009JF001266.

Small, E.E., Anderson, R.S., Repka, J.L., and Finkel, R., 1997, Erosion rates of alpine bedrock sum- mit surfaces deduced from in situ ¹⁰Be and ²⁶Al:

Earth and Planetary Science Letters, v. 150, p. 413–425, https://doi .org/10.1016/S0012- 821X(97)00092-7.

Sohbati, R., Jain, M., and Murray, A., 2012, Surface exposure dating of non-terrestrial bodies using optically stimulated luminescence: A new method: Icarus, v. 221, p. 160–166, https://doi .org/10.1016/j.icarus.2012.07.017.

Sohbati, R., Liu, J., Jain, M., Murray, A., Egholm, D., Paris, R., and Guralnik, B., 2018, Cen- tennial- to millennial-scale hard rock erosion rates deduced from luminescence-depth profiles: Earth and Planetary Science Letters, v. 493, p. 218–230, https://doi .org/10.1016/

j.epsl.2018.04.017.

Vincent, C., Harter, M., Gilbert, A., Berthier, E., and Six, D., 2014, Future fluctuations of Mer de Glace, French Alps, assessed using a parameter- ized model calibrated with past thickness changes: Annals of Glaciology, v. 55, p. 15–24, https://

doi .org/10.3189/2014AoG66A050.

von Blanckenburg, F., and Willenbring, J.K., 2014, Cosmogenic nuclides: Dates and rates of Earth- surface change: Elements, v. 10, p. 341–346, https://doi .org/10.2113/gselements.10.5.341.

Wirsig, C., Zasadni, J., Christl, M., Akçar, N., and Ivy-Ochs, S., 2016, Dating the onset of LGM ice surface lowering in the High Alps: Quaternary Science Reviews, v. 143, p. 37–50, https://doi .org/10.1016/j.quascirev.2016.05.001.

Zimmerman, S.G., Evenson, E.B., Gosse, J.C., and Erskine, C.P., 1994, Extensive boulder erosion resulting from a range fire on the type-Pinedale moraines, Fremont Lake, Wyoming: Quater- nary Research, v. 42, p. 255–265, https://doi .org/10.1006/qres.1994.1076.

Printed in USA