Understanding and predicting climate variability is vital if we are to properly prepare for the impact of climate change in an increasingly warmer world, for example rising sea level as a result of melting ice. 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. Our ability efficiently store, process, share and analyse the vast amounts of data that are produced (~Pb yearly) by the modelling and remote sensing communities is a pre-requisite in order to 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 how new tools developed in data sciences can be applied to questions relating to climate and the cryosphere and to help us address the great challenges that our society is facing in a rapidly changing planet. These include, but are not limited to, advanced statistics (e.g. extreme value analysis or changepoint methods), surrogate modelling (emulators), data assimilation, machine learning, 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 particularly welcome submissions from 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.