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The coastal geography,of Barrow makes the city vulnerable to weather events that cause flooding and erosion. This study uses the self-organizing map (SOM) algorithm, an unsupervised learning process that codifies large, multivariate datasets onto a 2- dimensional array, or map, to study large scale circulation patterns associated with temperature and high wind extremes at Barrow. The analysis first uses the SOM algorithm to produce an automated 55 year synoptic climatology of daily sea level pressure patterns for the Western Arctic for August-November when the area is potentially ice free. The results are in good agreement with previous Arctic climatologies, showing the Aleutian Low to be dominant in southern Alaska, and high pressure prevalent in the Beaufort/ChukchiSeas. The SOM algorithm is then used to study circulation patterns associated with temperature and high wind extremes at Barrow. These results show high winds associated with patterns containing a strong pressure gradient between the Aleutian Low and the Beaufort/ChukchiSeas and also patterns with a low pressure system to the north of Barrow. High temperature extreme anomalies are associated with patterns that produce strong, southerly flow at Barrow, while low temperature extreme anomalies are associated with patterns producing strong, northerly flow at Barrow. This paper demonstrates the utility of using SOMs to investigate the relationship between local weather conditions and large scale patterns. The approach described in this paper can be applied to GCM future climate simulations to investigate the impact of changes in large scale circulation patterns to local extreme events. 3

Classification of synoptic patterns in the western Arctic associated with extreme events at Barrow, Alaska, USA