Cooperative Institute for Research in Environmental Sciences

Methodology of using the self-organizing map algorithm to characterize and analyze widespread temperature extremes in Alaska and Canada

E.N. Cassano, J.M. Glisan, J.J. Cassano, W.J. Gutowski, and M.W. Seefeldt

2014, Climate Research, Accepted pending revisions

This paper presents an overview of how the method of self-organizing maps (SOMs) can be used to study the large-scale environment that leads to widespread temperature extremes. Much of the paper provides details on the mechanics of creating a SOM, how this methodology can be used to understand extreme events, and lessons learned in doing this research. The SOM is used to characterize the large-scale synoptic circulation that is associated with extreme events. Using a SOM can be helpful in understanding the underlying physical processes that control these events, if and how the extremes and the processes that control them may change in time or differ across space, and in improving response planning for local, regional, and national communities. Large-scale circulation is an aspect of the climate that is reasonably well represented by global climate models and reanalysis products. Therefore the methods presented here will allow analysis of both contemporary and future extreme events even in data sparse areas such as the polar regions or over oceans. While the methods described in this paper are applicable anywhere, examples of widespread temperature extremes in Alaska and northern Canada during winter (December, January, and February) are presented to illustrate the lessons learned.