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

Distinct patterns of Tropical Pacific SST Anomaly and Their Impacts on North American Climate AbstractA 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 Niña-like patterns, two near normal patterns and three El Niño-like patterns. These patterns show the varied amplitude and location of the SST anomalies associated with El Niño and La Niña, such as the central Pacific (CP) and eastern Pacific (EP) El Niño. 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 to result from differing wave train patterns, extending from tropics to mid-high latitudes induced by longitudinally shifted tropical heating. The corresponding teleconnection as represented by the National Center for Atmospheric Research Community Atmospheric Model version 4 (CAM4) were compared with the observational results. It was found that the 16-member ensemble average of the CAM4 experiments with prescribed SST can reproduce the observed atmospheric circulation responses to the different SST SOM patterns, which suggests that the circulation differences are largely SST-driven rather than due to internal atmospheric variability. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Climate American Meteorological Society

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

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0442
eISSN
1520-0442
D.O.I.
10.1175/JCLI-D-16-0488.1
Publisher site
See Article on Publisher Site

Abstract

AbstractA 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 Niña-like patterns, two near normal patterns and three El Niño-like patterns. These patterns show the varied amplitude and location of the SST anomalies associated with El Niño and La Niña, such as the central Pacific (CP) and eastern Pacific (EP) El Niño. 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 to result from differing wave train patterns, extending from tropics to mid-high latitudes induced by longitudinally shifted tropical heating. The corresponding teleconnection as represented by the National Center for Atmospheric Research Community Atmospheric Model version 4 (CAM4) were compared with the observational results. It was found that the 16-member ensemble average of the CAM4 experiments with prescribed SST can reproduce the observed atmospheric circulation responses to the different SST SOM patterns, which suggests that the circulation differences are largely SST-driven rather than due to internal atmospheric variability.

Journal

Journal of ClimateAmerican Meteorological Society

Published: Mar 28, 2017

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