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A neural-network-based cluster technique, the so-called self-organizing map (SOM), was performed to extract distinct sea surface temperature (SST) anomaly patterns during boreal winter. The SOM technique has advantages in nonlinear feature extraction compared to the commonly used empirical orthogonal function analysis and is widely used in meteorology. The eight distinguishable SOM patterns so identified represent three La Nina–like patterns, two near-normal patterns, and three El Nino–like patterns. These patterns show the varied amplitude and location of the SST anomalies associated with El Nino and La Nina, such as the central Pacific (CP) and eastern Pacific (EP) El Nino. The impact of each distinctive SOM pattern on winter-mean surface temperature and precipitation changes over North America was examined. Based on composite maps with observational data, each SOM pattern corresponds to a distinguishable spatial structure of temperature and precipitation anomaly over North America, which seems ...

Distinct Patterns of Tropical Pacific SST Anomaly and Their Impacts on North American Climate