Abstract
Abstract. The Southern Annular Mode (SAM) has been identified as a climate mechanism with potentially significant impacts on the Australian hydroclimate. However, despite the identification of relationships between SAM and Australia's hydroclimate using certain data sets, and focussed on certain time periods, the association has not been extensively explored and significant uncertainties remain. One reason for this is the existence of numerous indices, methods and data sets by which SAM has been approximated. In this paper, the various SAM definitions and indices are reviewed and the similarities and discrepancies are discussed, along with the strengths and weaknesses of each index development approach. Further, the sensitivity of the relationship between SAM and Australian rainfall to choice of SAM index is quantified and recommendations are given as to the most appropriate index to use when assessing the impacts of the SAM on Australia's hydroclimate. Importantly this study highlights the need to consider the impact that the choice of SAM index, and data set used to calculate the index, has on the outcomes of any SAM attribution study.
Highlights
It is well known that Australia’s hydroclimate exhibits significant spatial and temporal variability (e.g. Chiew et al, 1998; Franks and Kuczera, 2002; Nicholls, 2004; Verdon et al, 2004; Verdon-Kidd and Kiem, 2009a). Much of this variability has been linked to ocean-atmospheric processes occurring in the Pacific and Indian Ocean, the El Nino/Southern Oscillation (ENSO) (McBride and Nicholls, 1983; Drosdowsky, 1993; Kiem and Franks, 2001; Meyers et al, 2007), the Interdecadal Pacific Oscillation (IPO) (Power et al, 1999; Kiem et al, 2003; Kiem and Franks, 2004; Risbey et al, 2009) and the Indian Ocean Dipole (IOD) (Saji and Yamagata, 2003; Verdon and Franks, 2005; Meyers et al, 2007; Risbey et al, 2009)
Marshall (2003) has previously shown that the use of NCEP-NCAR data exaggerates trends in the Southern Annular Mode (SAM) signal by a factor of two or three, especially in winter, due to the nature of the NCEP-NCAR reanalysis, which has a positive pressure bias at higher latitudes compared with station data that decreases through time
The notable difference between the Visbeck index and both the Fogt and JW58 indices is that the Visbeck index uses a method of reconstructing sea level pressures in the mid-latitudes in order to gain insights into SAM behaviour and create a physically based extended SAM index record prior to the availability of continuous station records
Summary
It is well known that Australia’s hydroclimate exhibits significant spatial and temporal variability (e.g. Chiew et al, 1998; Franks and Kuczera, 2002; Nicholls, 2004; Verdon et al, 2004; Verdon-Kidd and Kiem, 2009a). The literature that does exist on SAM and its teleconnections reveals inconsistencies relating to (a) how SAM is defined, (b) the index used to represent SAM, (c) the data set used to calculate each SAM index and (d) how the various phases of SAM should be classified This confusion as to how to approximate (i.e. which SAM index to use) and classify SAM for hydroclimatic impact analysis, combined with the lack of research focusing on climate drivers originating from the Southern Ocean, has led to a significant knowledge gap in our understanding of how much of Australia’s hydroclimatic variability can be attributed to SAM. Preliminary investigations have been conducted (e.g. L’Heureux and Thompson, 2006), it is not yet clearly understood how SAM interacts with other large-scale climate drivers (e.g. ENSO, IOD, IPO) and local-scale synoptic weather patterns known to influence Australian hydroclimatology (Gallant et al, 2011; Kiem and Verdon-Kidd, 2011)
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