Arctic sea ice extent and Greenland ice sheet surface climate co-variability investigated with self-organizing maps and singular value decomposition

Monday, 15 December 2014
John Mioduszewski1, Asa K Rennermalm1, Julienne Christine Stroeve2,3, Marco Tedesco4 and David A Robinson1, (1)Rutgers University New Brunswick, New Brunswick, NJ, United States, (2)University of Colorado at Boulder, Boulder, CO, United States, (3)University College London, Centre for Polar Observations and Modelling, London, United Kingdom, (4)CUNY City College, Earth and Atmospheric Sciences, New York, NY, United States
Rapid decline in Arctic sea ice cover in the 21st century may have wide-reaching effects on the Arctic climate system, including the Greenland ice sheet mass balance. Sea ice processes and ocean/atmosphere feedbacks that may affect ice sheet mass balance may be manifested in large-scale Arctic synoptic patterns and local effects arising from its influence on mixed ocean layer temperatures, overlying air temperatures, and water vapor. Here, we investigate potential co-variability between sea ice and ice sheet surface melting by applying a suite of statistical methods (e.g., Self Organizing Maps, SOM and Singular Value Decomposition, SVD) on a combination of datasets including sea ice extent and melt-onset timing from remote sensing observations, atmospheric reanalysis (Modern Era Retrospective-Analysis for Research and Applications, MERRA) variables, and outputs of a regional climate model (Modèle Atmosphérique Régional, MAR) over the Greenland ice sheet for the months of June through August for the period 1979 - 2013. Results obtained with SOM highlight the regime shift in sea ice extent and melt-onset timing during the summer months since 1979, and that observed increases in Greenland ice sheet melt during these months are highly correlated with this shift. Finally, we assess atmospheric linkages between these processes by mapping each onto SOM nodes of Arctic geopotential height data, as well as employing singular value decomposition (SVD) and partial correlation analysis on time series of these three fields to establish physical pathways. Our analysis considers synoptic patterns that may govern both sea ice decline and increased mass loss and provides insights into sea ice processes and feedbacks that may influence Greenland ice sheet mass balance.