Detection and quantification of thin sea ice for climate monitoring in the Arctic

Sea ice plays an important role in the climatic system, being a factor affecting the global energy budget as well as the oceanic circulation. It is also an indicator variable of climate change. Considering various types of sea ice, thin ice is of particular interest as it is most vulnerable to summer sea ice melt, and it allows for more heat transfer.

Earth observation methods for retrieval of sea ice concentration are accurate and well validated for multi-year ice and consolidated first-year ice, but are less reliable and accurate for the youngest stages of sea ice development. Standard passive microwave algorithms for sea ice concentration, for instance, tend to confuse thin ice with a mixture of thick ice and open water. Algorithms making use of other types of sensors for observing young, seasonal sea ice are therefore valuable.

In the ThinIce project we aim to develop an automatic processing chain for estimating thin sea ice thickness, using optical data from the MODIS sensor, and atmosphere data from the ERA project. The algorithm is based on the works of Yu and Rothrock (1996), in which a model of the heat balance of the ice surface is used to establish the relation between ice thickness and other parameters (in particular surface temperature). A number of empirical models are employed to describe the thermal processes on the ice surface.