The analysis uses the Self-Organizing Map (SOM) algorithm - an unsupervised learning process that groups large, multivariate datasets onto a 2-dimensional array or map. A strength of this algorithm is there are no assumptions made as to the structure of the clusters of the data. The SOM algorithm is used here to produce a synoptic climatology of sea level pressure patterns for 53 years for the Western Arctic. The same clustering is used to examine the decadal variability of the synoptic climatology, and relate this to the observed variability in temperature, circulation and other climatic parameters.
Using this methodology, we study the extreme events that affect Barrow by including local variables, such as Barrow winds and fetch to the ice edge, in the SOM analysis. The highest winds and greatest fetch events can be weighted in order to find the patterns that cause the most significant damage. Hence, a map can be constructed that is representative of the synoptic patterns that produce extreme events in Barrow. It is intended that such an analysis will allow an assessment of the likely temporal variability of extreme events in Barrow into the future.