A state-of-the-art numerical model shows that the advance of glaciers in a cooling climate depends strongly on the pre-existing landscape, and that glacial erosion paves the way for greater glacial extent in the future. See Letter p.206
The growth of glaciers reflects the balance between the accumulation of snow and its loss through melting. Given the close association between altitude and temperature1, the elevation and morphology of the valley floor on which a glacier forms are key determinants of a glacier's size and longevity. Glacial erosion has carved the spectacular alpine landscapes of many mountain ranges, and these are characterized by extensive glacial valley floors at elevations close to the long-term snowline2. These landscapes contrast strongly with their precursors, which are generally steeper, narrower valleys sculpted by rivers (Fig. 1). On page 206 of this issue, Pedersen and Egholm3 use a state-of-the-art numerical model of glacier dynamics to quantify, for the first time, the stark contrast in glacier development between modern alpine mountain ranges and those that came before them. They compare the legacy of numerous glaciations during the Pleistocene epoch (between about 2.5 million and 10,000 years ago) with the landscapes that would have been present at the onset of these glaciations.
It has long been recognized that glaciers and their surroundings share an intimate, coupled relationship4,5. Shaded aspects and large areas at high elevation promote glacier growth. In turn, glacial erosion strongly modifies the underlying landscape, widening valley floors and making hill slopes steeper. However, given the efficiency of glaciers at reworking and removing the evidence left by previous glacial cycles, direct insight into the relationship between glaciers and topography during the initial phases of late Cenozoic glaciation (between about 2.5 million and 1 million years ago) has remained frustratingly elusive.
Numerical modelling offers the opportunity to explore glacier growth during initial glaciation. Pedersen and Egholm demonstrate an almost linear relationship between the degree of climate cooling and ice volume when glaciers develop in fluvial landscapes. However, for landscapes previously occupied and sculpted by glaciers, the equivalent relationship is highly nonlinear. Once the climate cools sufficiently, glaciers that descend onto the wide, shallow valley floor carved by preceding glacial occupations expand markedly.
Pedersen and Egholm also explore the full evolution of modern glacial landscapes by driving a simplified version of their numerical model — that is, without fluvial or hill-slope erosion, or active tectonics — with temperature fluctuations representing the past 2 million years. The mid-Pleistocene transition (MPT) about 950,000 years ago marked a shift from 40,000-year glacial cycles accompanied by symmetrical periods of cooling and warming to the protracted cooling and rapid warming of the more recent 100,000-year glacial cycles. The authors find much more extensive and erosive glaciers after the MPT than during the previous 1 million years. This result is consistent with geological evidence for accelerated glacial erosion after the MPT6,7. However, as Pedersen and Egholm show, this accelerated erosion is not simply a consequence of the changing climate; faster glacial erosion post-MPT was preconditioned by the landscape modifications made by the preceding, smaller-scale glaciations.
Pedersen and Egholm3 have taken on a considerable challenge. Numerical modelling of landscape evolution resulting from glacial erosion is a complex, multi-faceted problem. As such, the first models of glacial landscape evolution have emerged only within the past 15 years8. Egholm and co-authors3,9 have led the field and achieved compellingly realistic results by introducing ice-dynamics formulations that are more appropriate to valley glaciers — which descend the relatively steep downstream gradient of the valley floor and interact with the valley sides —than earlier simplifications borrowed from ice-sheet models.
Many considerations are involved in any attempt to numerically model glaciers and glacial erosion. The amount of snow that falls is strongly influenced by local climate, and is sensitive to changes in atmospheric moisture content, temperature and prevailing wind direction. Falling snow can be redistributed by wind10 and avalanching before it reaches the glacier surface. Glacial-ice melt is influenced by temperature change, shading and rock fall from the valley sides. Glacial ice deforms under its own weight, and can also slide on its bed if liquid water is present. However, at this point, this generic model still only describes ice accumulating, deforming and melting. How would this moving ice (and water) erode the landscape?
As was recently noted11, models of glacial landscape evolution, including Pedersen and Egholm's, still rely on simple, empirical relationships between glacier sliding velocity and glacial erosion rate, supported by modest field-data sets. There is little clear connection between these numerical relationships and the quarrying of large bedrock blocks from the glacier bed, which in most circumstances represents the primary process of glacial erosion. The liquid water in the subglacial hydrological network is also known to have a key role in glacial erosion, but determining the appropriate numerical formulation for the role of subglacial water is in its infancy12. Further progress will require numerical models that are robustly supported by a combination of careful observations of modern glaciers13 and thermochronological evidence for how glacial landscapes have evolved over the longer timescales7 that are explored numerically here. It will also require driving models with more-realistic climate representations10,14 than those currently considered, and will probably depend on continuing advances in computer power.
Stone, P. H. & Carlson, J. H. J. Atmos. Sci. 36, 415–423 (1979).
Brozović, N., Burbank, D. W. & Meigs, A. J. Science 276, 571–574 (1997).
Pedersen, V. K. & Egholm, D. L. Nature 493, 206–210 (2013).
Johnson, W. D. J. Geol. 12, 569–578 (1904).
Gilbert, G. K. J. Geol. 12, 579–588 (1904).
Haeuselmann, P., Granger, D. E., Jeannin, P.-Y. & Lauritzen, S.-E. Geology 35, 143–146 (2007).
Valla, P. G., Shuster, D. L. & van der Beek, P. A. Nature Geosci. 4, 688–692 (2011).
Braun, J., Zwartz, D. & Tomkin, J. H. Ann. Glaciol. 28, 282–290 (1999).
Egholm, D. L., Knudsen, M. F., Clark, C. D. & Lesemann, J. E. J. Geophys. Res. 116, F02012 (2011).
Anders, A. M., Roe, G. H., Montgomery, D. R. & Hallet, B. Geology 36, 479–482 (2008).
Iverson, N. R. Geology 40, 679–682 (2012).
Herman, F., Beaud, F., Champagnac, J.-D., Lemieux, J.-M. & Sternai, P. Earth Planet. Sci. Lett. 310, 498–508 (2011).
Riihimaki, C. A., MacGregor, K. R., Anderson, R. S., Anderson, S. P. & Loso, M. G. J. Geophys. Res. 110, F03003 (2005).
Rowan, A. V., Plummer, M. A., Brocklehurst, S. H., Jones, M. A. & Schultz, D. M. Geology http://dx.doi.org/10.1130/G33829.1 (2012).
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