AbstractSelf-organizing maps (SOMs) provide a powerful, non-linear technique to optimally summarize a complex geophysical dataset using a user-selected number of ‘icons’ or SOM states, allowing rapid identification of preferred patterns, predictability of transitions, rates of transitions, and hysteresis in cycles. The use of SOMs is demonstrated here through application to a 24 year dataset (1973–96) of monthly Antarctic sea-ice edge positions. Variability in sea-ice extent, concentration and other physical characteristics is an important component of the Earth’s dynamic climate system, particularly in the Southern Hemisphere where annual changes in sea-ice extent (temporarily) double the size of the Antarctic cryosphere. SOM-based patterns concisely capture the spatial and temporal variability in these data, including the annual progression of expansion and retreat, a general eastward propagation of anomalies during the winter, and sub-annual variability in the rate of change in extent at different times of the year (e.g. retreat in January is faster than in November). There is also often a general seasonal hysteresis, i.e. monthly anomalies during cooling follow a different spatial path than during warming.
Antarctic sea ice: a self-organizing map-based perspective
