2017
DOI: 10.1175/jcli-d-16-0488.1
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Distinct Patterns of Tropical Pacific SST Anomaly and Their Impacts on North American Climate

Abstract: 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 Niña–like patterns, two near-normal patterns, and three El Niño–like patterns. These pat… Show more

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Cited by 35 publications
(21 citation statements)
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“…We additionally examine the effect of SST forcing on U.S. temperature trends using a 16-member ensemble of Global Ocean Global Atmosphere (GOGA) simulations (Guo et al 2016). The GOGA simulations were performed with the National Center for Atmospheric Research Community Atmosphere Model version 5 (NCAR CAM5), with an Eulerian spectral dy-core in T42 horizontal resolution and 30 vertical levels.…”
Section: Methodsmentioning
confidence: 99%
“…We additionally examine the effect of SST forcing on U.S. temperature trends using a 16-member ensemble of Global Ocean Global Atmosphere (GOGA) simulations (Guo et al 2016). The GOGA simulations were performed with the National Center for Atmospheric Research Community Atmosphere Model version 5 (NCAR CAM5), with an Eulerian spectral dy-core in T42 horizontal resolution and 30 vertical levels.…”
Section: Methodsmentioning
confidence: 99%
“…We use a field significance test (Johnson 2013;Guo et al 2017) that controls the ''false discovery rate'' (FDR) to determine how many distinct types of ARs can be classified on the basis of historical period as represented ERA-I. False discovery is an expected consequence of any statistical test since such tests operated by comparing values of a test statistic against values that would be considered, but not impossible, unusual under the null hypothesis.…”
Section: ) Determining the Number Of Nodes Kmentioning
confidence: 99%
“…There are several possible factors that cause variations in North American climate between El Niño events. These include random atmospheric internal variability and sensitivity to differences in the detailed structure and longitudinal location of the SST anomalies (SSTAs) (e.g., Hoerling and Kumar 1997;Guo et al 2017). The different longitudinal locations of the SSTAs can cause shifts in the forced stationary waves, leading to different teleconnection patterns and impacts on North American climate (e.g., Mo and Higgins 1998a,b;Barsugli and Sardeshmukh 2002;Hoerling and Kumar 2002).…”
Section: Introductionmentioning
confidence: 99%