The seasonal cycle of snow and sea ice is a fundamental feature of the Arctic climate system. In the Northern Hemisphere, about 55 million km² of sea ice and snow undergo complete melt and freeze processes every year. Because snow and sea ice are much brighter (higher albedo) than the underlying surface, their presence reduces absorption of incoming solar energy at high latitudes. Therefore, changes of the sea-ice and snow cover have a large impact on the Arctic climate and possibly at lower latitudes. One of the most important determining factors of the seasonal snow and sea-ice cover is the timing of the seasonal melt-freeze transitions. Hence, in order to better understand Arctic climate variability, it is key to continuously monitor these transitions.

This thesis presents an algorithm for obtaining melt-freeze transitions using scatterometers over both the land and sea-ice domains. These satellite-borne instruments emit radiation at microwave wavelengths and measure the returned signal. Several scatterometers are employed: QuikSCAT (1999–2009), ASCAT (2009–present), and OSCAT (2009–present). QuikSCAT and OSCAT operate at Ku-band (λ=2.2 cm) and ASCAT at C-band (λ=5.7 cm), resulting in slightly different surface interactions. This thesis discusses these dissimilarities over the Arctic sea-ice domain, and juxtaposes the time series of seasonal melt-freeze transitions from the three scatterometers and compares them with other, independent datasets.

The interactions of snow and sea ice with other components of the Arctic climate system are complex. Models are commonly employed to disentangle these interactions. But this hinges upon robust and well-formulated models, reached by perpetual testing against observations. This thesis also presents an evaluation of how well eleven state-of-the-art global climate models reproduce the Arctic sea-ice cover and the summer length—given by the melt-freeze transitions—using surface observations of air temperature.