Understanding and predicting climate variability is vital if we are to properly prepare for the impact of climate change in an increasingly warmer world, including rising sea level as a result of melting ice and iceberg discharge. Fortunately, technological developments mean that 1) our numerical models of the cryospheric and climate systems are increasingly able to capture their inherent complexity, and 2) we are able to acquire much more detailed observations of our polar regions by satellite than ever before. This also brings an important challenge however: how can we extract the maximum possible meaning from these data while minimizing the increase in uncertainty that added volume/complexity/heterogeneity brings?
In this session we invite submissions on research that applies Data Science techniques to answer research questions in Glaciology and Polar Climate studies. This includes, but is not limited to, studies using machine learning and AI, advanced statistics (e.g. extreme value analysis or changepoint methods), surrogate modelling (emulators), network analysis and innovative software/computing solutions. These could be applied to any, or any combination of, data sources including remote sensing, numerical model output and field/ground/lab observations. We are particularly interested in contributors interested in a wider discussion about Data Science and its application in Climate and Cryospheric research and in contributions which reveal new insight that would not be possible using traditional methods.
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