Arktis är den plats där den globala uppvärmningen är som mest märkbar på hela jorden (Arktiska rådet). Därför är Arktis ett intressant område att studera ur klimatsynpunkt. Enligt Gore (2006) så kan en ökning av jordens medeltemperatur på 1,5 °C leda till att de ekvatoriala områdena får en temperaturökning på 0,5 °C medan temperaturen vid Arktis kan stiga med hela 6 °C. De senaste 30 åren har medeltemperaturen vid Arktis stigit med drygt en grad per decennium (Anisimov, 2007) medan den globala medeltemperaturen för samma period endast stigit med ca 0.15 grader per decennium. Arktis tros ha varit isfritt under en värmeperiod för ca 6000 – 8500 år sedan (Founder, 2011) men det som är unikt med den aktuella situationen är att avsmältningen går så snabbt. Mycket snabbare än naturliga cykler (Gore, 2006) och det råder inom forskarvärlden ingen tvekan om att det är de antropogena utsläppen av växthusgaser som är orsaken. Frågeställningen som ligger till grund för den här rapporten är att ta reda på vilket årtal som Arktis kommer att vara isfritt. Detta görs genom att undersöka hur den Arktiska isutsträckningen minskat under perioden 1979-2011.  Isdata till beräkningarna i den här rapporten är tagna från National Snow and Ice Data Center, Boulder, Colorado USA, och värdena för temperaturerna kommer från National Aeronautics and Space Administration (NASA). Genom att med minstakvadratanpassning göra en linjär approximation av varje års minsta värde av isutsträckningen vid Arktis för perioden 1979-2011 erhålls att Arktis kommer att vara isfritt år 2062. / The Arctic is the place where global warming is most significant in the whole world (Arctic Council). That is why the Arctic is an interesting area to study from a climate perspective. According to Gore (2006), an increase in global temperature of 1.5 °C might lead to an increase of 0.5 °C in the equatorial regions while the Arctic could receive a teperature rise by as much as 6 °C . During the past 30 years, average temperatures in the Arctic have risen by more then one degree per decade (Anisimov, 2007) while the global average teperature for the same period only increased by about 0.15 degrees per decade. Arctic is believed to have been ice-free during a heating period for about 6000 - 8500 years ago (Founder, 2011) but what is unique about the current situation is that the melting is so quickly. Much faster than natural cycles (Gore, 2006) and there is within the scientific community no doubt that the anthropogenic emissions of greenhouse gases is the cause. The issue which forms the basis for this report has been to investigate how the Arctic extent decreased over the period 1979-2011 and then to attempt to determine a year for when the Arctic will be ice free. Ice data to the calculations in this report are taken from the National Snow and Ice Data Center, Boulder, Colorado USA, and the temperature values comes from the National Aeronautics and Space Administration (NASA). By making a linear approximation with the least square method of each year’s minimum value of the Arctic sea ice extent for the period 1979-2011 it is obtained that the Arctic sea will be ice free by the year of 2062.

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The sea ice extent change and variability of the Canadian Arctic Archipelago (CAA) are quite different compared to the Arctic as a whole due to its unique geographic settings. In this thesis, the sea ice retreat processes, the connection with other Arctic regions, and the linkages to the surface radiation flux in the CAA are examined. The sea ice retreat processes in the CAA follow a four-phase process: a slow ice melt phase that usually lasts until early June (phase 1); a quick melt phase with large daily sea ice extent change which lasts close to half-a-month (phase 2); a slow melt phase that looks like slow sea ice melt or even a small ice increase that lasts another half-a-month (phase 3); and a steady ice decrease phase (phase 4). With the help of Moderate-Resolution Imaging Spectroradiometer (MODIS) data, it is identified that the quick melt in phase 2 is actually melt ponding, with melt ponds being falsely identified as open water by passive microwave. A simplified data assimilation method is then developed to improve the passive microwave sea ice concentration estimation by fusion with MODIS ice surface temperature data. The ice concentration from the analysis is found to improve the original passive microwave sea ice concentration estimation, with the largest improvements during sea ice melt. The sea ice retreat patterns in the CAA region are correlated with the sea ice retreat patterns in other regions of the Arctic. A decision tree classifier is designed to segment the sea ice retreat patterns in the CAA into several classes and classification maps are generated. These maps are effective in identifying the geographic locations that have large changes in the sea ice retreat patterns through the years. The daily progressions of the surface radiation components are described in detail. Due to the lack of multiple reflection, the percentage of shortwave radiation at the top of atmosphere that reaches the surface is influenced by the form of melt ponds over ice surface. The roles that each surface radiation component plays in forcing sea ice retreat are different in different years.

Because the earth's cryosphere influences global weather patterns and climate, the scientific community has had great interest in monitoring this important region. Microwave remote sensing has proven to be a useful tool in estimating sea and glacial ice surface characteristics with both scatterometers and radiometers exhibiting high sensitivity to important ice properties. This dissertation presents an array of studies focused on extracting key surface features from multisensor microwave data sets. First, several enhanced resolution image reconstruction issues are addressed. Among these are the optimization of the scatterometer image reconstruction (SIR) algorithm for NASA scatterometer (NSCAT) data, an analysis of Ku-band azimuthal modulation in Antarctica, and inter-sensor European Remote Sensing Satellite (ERS) calibration. Next, various methods for the removal of atmospheric distortions in image reconstruction of passive radiometer observations are considered. An automated algorithm is proposed which determines the spatial extent of sea ice in the Arctic and Antarctic regions from NSCAT data. A multisensor iterative sea ice statistical classification method which adapts to the temporally varying signatures of ice types is developed. The sea ice extent and classification algorithms are adopted for current SeaWinds scatterometer data sets. Finally, the automated inversion of large-scale forward electromagnetic scattering of models is considered and used to study the temporal evolution of the scattering properties of polar sea ice.