Geoscientific Model Development www.geosci-model-dev.net 1991-959X 1991-9603 3 1 2010 10.5194/gmd-3-293-2010 http://www.geosci-model-dev.net/3/293/2010/ http://www.geosci-model-dev.net/3/293/2010/gmd-3-293-2010.html http://www.geosci-model-dev.net/3/293/2010/gmd-3-293-2010.pdf 293 307 2010-04-12 Modeling the statistical distributions of cosmogenic exposure dates from moraines P. J. Applegate patrick.applegate@natgeo.su.se N. M. Urban B. J. C. Laabs K. Keller R. B. Alley Department of Geosciences, Pennsylvania State University, University Park, Pennsylvania, 16801, USA Department of Geological Sciences, State University of New York at Geneseo, Geneseo, New York, 14454, USA now at: Department of Physical Geography and Quaternary Geology, Stockholm University, 106 91 Stockholm, Sweden Geomorphic process modeling allows us to evaluate different methods for estimating moraine ages from cosmogenic exposure dates, and may provide a means to identify the processes responsible for the excess scatter among exposure dates on individual moraines. Cosmogenic exposure dating is an elegant method for estimating the ages of moraines, but individual exposure dates are sometimes biased by geomorphic processes. Because exposure dates may be either "too young" or "too old," there are a variety of methods for estimating the ages of moraines from exposure dates. In this paper, we present Monte Carlo-based models of moraine degradation and inheritance of cosmogenic nuclides, and we use the models to examine the effectiveness of these methods. The models estimate the statistical distributions of exposure dates that we would expect to obtain from single moraines, given reasonable geomorphic assumptions. The model of moraine degradation is based on prior examples, but the inheritance model is novel. The statistical distributions of exposure dates from the moraine degradation model are skewed toward young values; in contrast, the statistical distributions of exposure dates from the inheritance model are skewed toward old values. Sensitivity analysis shows that this difference is robust for reasonable parameter choices. Thus, the skewness can help indicate whether a particular data set has problems with inheritance or moraine degradation. 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