Using self-organizing maps to detail synoptic connections between climate indices and Alaska weather

Reynir C. Winnan
Wed, 22 Apr 2026 17:33:15 PDT

Seasonal forecasts for Alaska strongly depend on the phases of Pacific Decadal Oscillation (PDO), El Niño-Southern Oscillation (ENSO), and warm water in the North Pacific called the North Pacific Mode or more popularly the "Pacific blob." The canonical descriptions of these climate indices are based on seasonal averages, and anomalies that are based on a long-term mean. The patterns highlight general geographical placement and display a sharp contrast between opposing phases, but this may be misleading since seasonal averages hide much of the synoptic variability. Self-organizing maps (SOMs) are a way of grouping daily sea level pressure (SLP) patterns, over many time realizations into a specified set of maps (e.g. 35 maps) that describe commonly occurring patterns. This study uses the SOMs in the context of climate indices to describe the range of synoptic patterns that are relevant for Alaska. This study found that the patterns common during a given phase of the PDO include subtle differences that would result in Alaska weather that is very different from what is expected from the canonical PDO description, thus providing some explanation for recent studies that find the PDO link to Alaska climate is weakening. SOMs analysis is consistent with recent studies suggesting that the pattern responsible for the 2014 Pacific warm blob is linked to tropical sea-surface temperature (SST) forcing. An analysis of the summer SLP SOMs in the context of Alaska wildland fires was also conducted. This analysis identified several commonly occurring patterns during summers with large areas burned. These patterns are characterized by low pressure in the Bering Sea, which would be consistent with increased storm activity and thus an ignition source for the fires. Identifying synoptic patterns that occur during a particular phase of a teleconnection index contributes towards understanding the mechanisms of how these indices influence the weather and climate of Alaska.