Journal
JOURNAL OF CLIMATE
Volume 27, Issue 4, Pages 1395-1412Publisher
AMER METEOROLOGICAL SOC
DOI: 10.1175/JCLI-D-13-00229.1
Keywords
Anomalies; Climate variability; Decadal variability; Interannual variability; Seasonal variability; Trends
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Funding
- NOAA/Office of Oceanic and Atmospheric Research under NOAA-University of Oklahoma, U.S. Department of Commerce [NA11OAR4320072]
- NESDIS program under National Oceanic and Atmospheric Administration of the U.S. Department of Commerce [NOAA-NESDIS-OAR-NA08OAR4320904]
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Over the past century, particularly after the 1960s, observations of mean maximum temperatures reveal an increasing trend over the southeastern quadrant of the Australian continent. Correlation analysis of seasonally averaged mean maximum temperature anomaly data for the period 1958-2012 is carried out for a representative group of 10 stations in southeast Australia (SEAUS). For the warm season (November-April) there is a positive relationship with the El Nino-Southern Oscillation (ENSO) and the Pacific decadal oscillation (PDO) and an inverse relationship with the Antarctic Oscillation (AAO) for most stations. For the cool season (May-October), most stations exhibit similar relationships with the AAO, positive correlations with the dipole mode index (DMI), and marginal inverse relationships with the Southern Oscillation index (SOI) and the PDO. However, for both seasons, the blocking index (BI, as defined by M. Pook and T. Gibson) in the Tasman Sea (160 degrees E) clearly is the dominant climate mode affecting maximum temperature variability in SEAUS with negative correlations in the range from r = -0.30 to -0.65. These strong negative correlations arise from the usual definition of BI, which is positive when blocking high pressure systems occur over the Tasman Sea (near 45 degrees S, 160 degrees E), favoring the advection of modified cooler, higher-latitude maritime air over SEAUS.A point-by-point correlation with global sea surface temperatures (SSTs), principal component analysis, and wavelet power spectra support the relationships with ENSO and DMI. Notably, the analysis reveals that the maximum temperature variability of one group of stations is explained primarily by local factors (warmer near-coastal SSTs), rather than teleconnections with large-scale drivers.
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