Data mining tools for abrupt climate change
Updating our knowledge on abrupt climate change
Around 240 BC, Eratosthenes was the first scientist to estimate the size of the Earth by measuring the distance of shadows cast in a well. This project builds on his legacy, using repeat satellite imagery to detect shadows from mountains cast on glaciers to estimate glacier elevation change. Glaciers and ice caps are sensitive indicators of climate change; although they store a freshwater volume equivalent that is two orders of magnitude smaller than that of ice sheets, they contribute ~1/3 to present sea-level rise. Yet, their contribution in the recent past has the largest uncertainty among sea-level rise components. In-situ observations of glacier mass balance are limited, and adequate remote sensing techniques to obtain worldwide, repeat observations of small and medium-size glaciers are currently unavailable. Satellite altimetry (e.g. ICESat, CryoSat-2) offers a means to recover elevation changes of larger glaciers but spatial and temporal coverage of small and medium-sized glaciers is highly incomplete due to resolution and sampling issues; gravimetry (GRACE) provides the integrated mass balance over large areas (>100 000 km2), but cannot isolate individual glaciers. Consequently, the historical and current mass balance of small and medium-size glaciers remains largely unconstrained. Using shadows cast on a glacier surface from surrounding mountain tops, tracked between sun-synchronous satellite imagery, we will derive a unique, dense record of multi-temporal elevation differences on glaciers, with global coverage, dating from the early 1970s to present, addressing this pressing lack of data. These data will provide new insights in the historical volumetric evolution of small glaciers, and deliver essential information for development and improvement of glacier models. The scale and data volume require a multi-disciplinary collaboration of glaciologists and eScientists. The established workflow will be open-source, which eases the adaption of large-volume analytics in other domains.
In this project, we detect shadows casted by mountain tops in satellite imagery of glaciers. Changes in these shadows are used to estimate changes in glacier height. This new, unconventional approach provides unprecedented information about the health of small glaciers, and improves water discharge forecasting, sealevel predictions and glacier models.