Developing data assimilation methods for Glaciology

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Data assimilation seeks to optimally combine observational data with numerical (computer) model output to determine the best estimate of the state of a system. This proposed project seeks to develop and apply data assimilation techniques for use in Glaciology (the study of glaciers and ice-sheets). The application of data assimilation methods is well established within other Earth Science disciplines, particularly Atmospheric and Ocean Sciences. For example, it is essential for weather forecasting. However, at present the development of data assimilation tools for glaciological research has been limited. Scholars have yet to realize the full potential of the mathematical methods of data assimilation to advance glaciological modelling capabilities. Without such methods, we are left with an inadequate analysis that fails to utilize all available information at our disposal. Over the last decade increased resources have been dedicated to improving observational coverage and model development within Glaciology. This proposed work seeks to make best use of current and future observational data and state-of-the-art ice-flow models. Research output from this proposed project will result in novel applications of data assimilation theory and play an important role in improving ice-sheet modelling capabilities. Improving our understanding of glacier and ice-sheet dynamics is essential for monitoring and predicting changes in Arctic, subarctic and alpine areas of Canada. This includes reducing uncertainty in sea-level rise estimates, assessing risk associated with glacier-related hazards, as well as improving knowledge of changes to glacier runoff and its effect on fresh water supply. These are pressing research problems of global significance.