Factor analysis for El Nino signals in sea surface temperature and precipitation
Academic Article
Overview
Research
Identity
Additional Document Info
Other
View All
Overview
abstract
Maximum likelihood factor analysis (MLFA) is applied to investigate the variables of monthly Tropical Pacific sea surface temperatures (SST) from Nio 1+2, Nio 3, Nio 3.4, and Nio 4 and precipitation over New South Wales and Queensland of eastern Australia, Kalimantan Island of Indonesia, and California and Oregon of the west coast of the United States. The monthly data used were from 1950 to 1999. The November-February SST with time leads of 0, 1, 2, and 3 months to precipitation are considered for both El Nio warm phases and non El Nio seasons. Interpretations of the factor loadings are made to diagnose relationships between the SST and precipitation variables. For El Nio signals, the rotated FA loadings can efficiently group the SST and precipitation variables with interpretable physical meanings. When the time lag is 0 or 1 month, the November-February El Nio SST explains much of the drought signals over eastern Australia and Kalimantan. However, when the time lag is 2 or 3 months, the same SST cannot adequately explain the precipitation during January-May over the two regions. Communality results of five factors for precipitation indicate nearly 100% explanation of variances for Queensland and California, but the percentages are reduced to only about 30% for Oregon and Kalimantan. Factor scores clearly identify the strongest El Nio relevant to precipitation variations. Principal component factor analysis (PCFA) is also investigated, and its results are compared with MLFA. The comparison indicates that MLFA can better group SST data relevant to precipitation. The residuals of MLFA are always smaller than the PCFA. Thus, MLFA may become a useful tool for improving potential predictability of precipitation from SST predictors. Springer-Verlag 2008.
