Abstract

Sea ice is an important indicator of change in the global climate system. The extent and concentration of Arctic sea ice cover has been measured with satellite retrievals from passive microwave sensors since 1978, providing a 40-year long record. Unfortunately, similarly long-term records of sea ice thickness are not available. Basin-scale thickness retrievals from satellite altimeters only go back to the early 2000s and represent a rather short record for examination of decadal changes. Moreover, satellite altimetry observations are poor near the coast, making observations in narrow channels and straits difficult.

Sea ice concentration, age, and floe size have been observed and recorded in ice charts by national ice services for decades. These ice charts are created for shipping and offshore construction purposes and have rarely been used for scientific reasons. As sea ice thickness is related to ice concentration, age and floe size, we can use these charts to create a sea ice thickness proxy product.

I’m creating a machine learning model that predicts sea ice thickness from information in the Canadian Ice Service ice charts. This model is trained on recent ice charts and CryoSat-2 sea ice thickness observations (2011-2021). We apply the model to estimate sea ice thickness from older ice charts, going back to the 1990s for the Canadian Arctic. This will create the longest record of large-scale Canadian Arctic sea ice thickness to date. The proxy-sea ice thickness product will be used to study long-term trends and variability in one of the fastest changing regions of the Arctic. This model also predicts sea ice thickness in areas that cannot be measured well by satellite altimetry, for example channels in the Canadian Arctic Archipelago.