Collaborative Research: Testing latest Pleistocene and Holocene glacial chronologies in the western United States with cosmogenic-nuclide and radiocarbon dating and models

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Small alpine glaciers are sensitive to climate, and projections of future global warming indicate that many alpine glaciers throughout the world will shrink considerably, if not disappear altogether. This award will address two fundamental questions relevant to understanding the history and controls of alpine glaciation in the western U.S.: (1) What is the age of moraines deposited by glaciers that occur immediately downstream to those deposited during the Little Ice Age ~300 years ago? (2) How have changes in large-scale controls of climate through the last ~10,000 years (Holocene) affected glacier fluctuations? Methods to be employed to accomplish these goals will involve systematically dating cirque-glacier moraines throughout the western U.S. using in-situ cosmogenic radionuclides and radiocarbon techniques. A hierarchical climate and glacier modeling strategy will be employed to evaluate the effects of changing large-scale climate controls through the Holocene (ice sheets, insolation, greenhouse gases) on glacier mass balance and resulting fluctuations. This research strategy provides an outstanding approach to applying the geologic record to constrain the sensitivity of alpine glaciers to radiative forcing. Broader Impact. Understanding the sensitivity of alpine glaciers to radiative forcing is of great interest with regard to landscape appearance, slope stability, water resources, sediment loading to rivers, and ecosystem health. The modeling approaches developed here will be applicable (outside of this project) to improve assessments of future anthropogenic impacts on alpine glaciation, with implications for sea-level, water resources, and societal concerns over the aesthetic qualities of glaciers. This study includes a strong element of inter-agency cooperation, with contributions for modeling supported by the USGS. This proposal will directly contribute to human resources by including the training, education, and research contributions of one Ph.D. student, with strong cross-training in elements of data acquisition, data analysis, and modeling.