Relationships between southeast australian temperature anomalies and large-scale climate drivers

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...

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Bibliographic Details
Main Authors: Fierro, AO, Leslie, LM
Format: Article in Journal/Newspaper
Language:unknown
Published: 2014
Subjects:
Soi
Online Access:http://hdl.handle.net/10453/119031
Description
Summary: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 Niño-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 dipolemode index (DMI), andmarginal inverse relationships with the SouthernOscillation 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°E) clearly is the dominant climatemode affectingmaximumtemperature variability in SEAUS with negative correlations in the range from r520.30 to 20.65. These strong negative correlations arise from the usual definition ofBI,which is positivewhen blocking high pressure systems occur over the Tasman Sea (near 45°S, 160°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. © 2014 American Meteorological Society.