Boulder cosmogenic exposure ages as constraints for glacial chronologiesJakob Heyman1, Arjen P Stroeven1, Jon Harbor2, Marc W Caffee3

(1) Department of Physical Geography and Quaternary Geology, Stockholm University (2) Department of Earth and Atmospheric Sciences, Purdue University(3) Department of Physics / PRIME Lab, Purdue University

jakob.heyman@natgeo.su.se

RejuvenationP

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Post-glacial domain

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ogenicreduction

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Deglaciation age (ka)

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Measured exposure age n = 1067 boulders

Model exposure age envelope

Data overlapFull dataset: 88%Max age >5 ka: 95%

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Indi

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Data overlapFull dataset: 87%Min age >5 ka: 97%

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Measured datan = 288 boulder groups20-point running mean

Model datan = 28,800 boulder groups500-point running mean

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Boulder group max exposure age (ka)

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standard deviation (ka)

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10Be exposure age - reconstructed deglaciation age (ka)

Glacial bouldersn = 387

13%

26%

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Glacial boulders (relict area)n = 228

Prior exposure

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iation

age

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ExhumationErosionBurial

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eInheritanceExposure

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iation

age

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Exposure

a Ideal case

b Prior exposure

c Post-glacial shielding

Inheritance evaluation

All boulders from recent glaciers have exposure ages <4 ka (Fig. 2f) indicating that none of these boulders experienced significant prior exposure.

For the palaeo-ice sheet exposure ages we have quantified the geological uncertainty by sub-tracting independent deglaciation reconstruction ages (Fig. 3) based primarily on 14C ages (Dyke et al. 2003; Gyllencreutz et al. 2007; Kleman et al. 2008). The palaeo-ice sheet dataset was split into boulders from relict areas preserved under non-erosive ice and boulders from glacially modified landscapes (based on geomorphology). Only 4% of the boulders from glacially modified landscapes have exposure ages >10 ka older than that the deglacial age of the surface indicating limited prior exposure.

Shielding evaluation

The Tibetan Plateau boulders were organized in groups representing discrete glacial deposits (mostly single moraines). To simulate post-glacial shielding we used a simple surface degradation Monte Carlo model with one time-dependent exponential boulder exhum-ation rate for all boulders. The degradation model captures the main characteristics of the measured data remarkably well (Fig. 4) indicating that post-glacial shielding is an important factor for the apparent exposure age.

Summary

• Quantification of cosmogenic inheritance indicate limited importance of prior exposure.

• Boulder exhumation simulation indicate that post-glacial shielding is an important factor. The relative importance of post-glacial shiel- ding increases with deglaciation age (Fig. 5). • Glacial boulder exposure ages should, in the absence of other evidence, be viewed as minimum limiting deglaciation ages.

Introduction

Cosmogenic exposure dating greatly enhances our ability to define glacial chronologies. However, two principal geological factors yield erroneous inferred ages (Fig. 1):

1. Prior exposure (inheritance) yields exposure ages that are too old2. Post-glacial shielding yields exposure ages that are too young

Here we evaluate these two options with the aim of helping interpretation strategies for datasets with wide exposure age disparity.

Methodology

We have compiled three extensive boulder 10Be exposure age datasets (Fig. 2) for meta analysis:

• Tibetan Plateau (1123 boulders)

• Northern Hemisphere palaeo-ice sheets (615 boulders)

• Recent glaciers (186 boulders)

All exposure ages have been re-calculated using the CRONUS online calculator (Balco et al. 2008) version 2.2, reconciling measurements performed using various 10Be standards (Nishiizumi et al. 2007). The exposure ages presented here are derived from the CRONUS Lm production rate scaling scheme.

Fig. 1. Principle of prior exposure and post-glacial shielding and resulting apparent exposure ages. (a) In the ideal case the sample is completely shielded prior to glaciation and continuously exposed following deglaciation. (b) Prior exposure yields exposure ages exceeding the deglaciation age. (c) Post-glacial shielding yields exposure ages younger than the deglaciation age.

Fig. 5. Cosmogenic exposure age duality related to deglaciation age.

Fig. 4. Measured and simulated Tibetan Plateau exposure age data from multiple-boulder groups (≥2 boulders per group). (a) Individual boulder exposure age data. (b) Boulder group exposure age spread. Increasing age spread with group minimum and maximum exposure age indicate an increasing uncertainty with deglaciation age.

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Glacial boulder(relict area)

Fig. 2. Distribution and apparent exposure ages of the glacial boulder exposure age datasets from the Tibetan Plateau (a,d), the northern Hemisphere palaeo-ice sheets (b,c,e), and recent glaciers (f).

Fig. 3. Quantification of geological unceratinty for the palaeo-ice sheet dataset (Fig. 2b,c,e). Most of the boulders, in particular from non-relict areas, have exposure ages fairly close to the corresponding reconstructed deglaciation age.

ReferencesBalco G, Stone JO, Lifton NA, Dunai TJ, 2008. A complete and easily accessible

means of calculating surface exposure ages or erosion rates from 10Be and 26Al measurements. Quaternary Geochronology 3, 174-195.

Dyke AS, Moore A, Robinson L, 2003. Deglaciation of North America. Geological Survey of Canada Open File 1574.

Gyllencreutz R, Mangerud J, Svendsen J-I, Lohne Ø, 2007. DATED – a GIS-based reconstruction and dating database of the Eurasian deglaciation. Geological Survey of Finland Special Paper 46, 113-120.

Kleman J, Jansson KN, Hättestrand C, De Angelis H, Stroeven AP, Glasser NF, Alm G, Borgström I, 2008. The Laurentide ice sheet from the last interglacial to the LGM – a reconstruction based on integration of geospatial and stratigraphical data. Geophysical Research Abstracts 10, EGU2008-A-11657.

Nishiizumi K, Imamura M, Caffee MW, Southon JR, Finkel RC, McAninch J, 2007. Absolute calibration of 10Be AMS standards. Nuclear Instruments and Methods in Physics Research B 258, 403-413.