Browsing by Author "Pekkala, Minttu"

There has been a major decreasing trend in the amount of sea ice in the Arctic since the 1990s. The Arctic amplification (AA) warms the Arctic climate as a result of the global warming, strengthened by the ice albedo feedback loop. The ice albedo feedback loop is caused by melting of snow and ice surfaces. Melting of snow and ice causes changes to the surface albedo, which is a measure of the amount of incident solar radiation that is reflected. Melting snow and ice surface types and revealed open water or terrain have significantly lower albedo than the original snow and ice surfaces. Therefore more radiation is absorbed, which has a warming effect. CLARA-A3 dataset is analyzed in this thesis. Surface albedo (SAL) and top of atmosphere (ToA) albedo values are compared. Data from June and July of the years 2012 and 2014 are analyzed. The objective is to check the consistency of these data records. The surface albedo values are also modelled with the Simplified Model for Atmospheric Correction (SMAC) to further validate the data. The relationship between SAL and ToA is also studied. This is achieved by analysing snow and ice optical properties, interaction of solar radiation with Earth’s atmosphere and the effect of illumination and viewing geometry. The results of data analysis indicate consistency between the observed values for SAL and ToA albedo differences within the observed period. The results are also in line with predictions made based on previous studies on the seasonal trends in the Arctic albedo. Furthermore results modelled with SMAC show dependency with the observed results and thereby validate the data. However data from June 2012 and July 2014 are unfortunately contaminated, which means that there are less usable data and therefore of reduced accuracy. Data analysis conducted for a larger SAL and ToA dataset would be needed to provide a basis for studying the decadal and seasonal trend of the Arctic SAL and ToA albedo difference. The whole melting season beginning in March and ending in September is important to study to better understand seasonal variability and trends as well as decadal trends in the Arctic.