On the relationship between large-scale climate modes and regional synoptic patterns that drive Victorian rainfall

Abstract

Abstract. In this paper regional (synoptic) and large-scale climate drivers of rainfall are investigated for Victoria, Australia. A non-linear classification methodology known as self-organizing maps (SOM) is used to identify 20 key regional synoptic patterns, which are shown to capture a range of significant synoptic features known to influence the climate of the region. Rainfall distributions are assigned to each of the 20 patterns for nine rainfall stations located across Victoria, resulting in a clear distinction between wet and dry synoptic types at each station. The influence of large-scale climate modes on the frequency and timing of the regional synoptic patterns is also investigated. This analysis revealed that phase changes in the El Niño Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD) and/or the Southern Annular Mode (SAM) are associated with a shift in the relative frequency of wet and dry synoptic types on an annual to interannual timescale. In addition, the relative frequency of synoptic types is shown to vary on a multi-decadal timescale, associated with changes in the Inter-decadal Pacific Oscillation (IPO). Importantly, these results highlight the potential to utilise the link between the regional synoptic patterns derived in this study and large-scale climate modes to improve rainfall forecasting for Victoria, both in the short-(i.e. seasonal) and long-term (i.e. decadal/multi-decadal scale). In addition, the regional and large-scale climate drivers identified in this study provide a benchmark by which the performance of Global Climate Models (GCMs) may be assessed.

Extreme Rainfall Nonstationarity Investigation and Intensity–Frequency–Duration Relationship

“…The El Nino-Southern Oscillation (ENSO) with El Nino and La Lina phases (Verdon et al 2004), Indian Ocean Dipole (IOD) (Ashok et al 2003), and the Southern Annual Mode (SAM) (Meneghini et al 2007) were pointed as influential climate modes on the precipitation variability in Victoria (Australia), which includes the Melbourne region. However, Verdon-Kidd and Kiem (2009) Power et al (1999) and Kiem et al (2003) expressed that association between ENSO and Australian climate is modulated by IPO (Micevski et al 2006). In particular, the relationship was strong during the IPO negative phases (i.e associated with wetter conditions).…”

Links between the Big Dry in Australia and hemispheric multi-decadal climate variability – implications for water resource management

“…Changes in Indian Ocean SSTs also occurred during this time (Verdon and Franks, 2005;Samuel et al, 2006). It has also been established that these large-scale climate modes modulate the frequency and timing of synoptic systems in Australia, at least at a monthly timescale (Verdon-Kidd and Kiem, 2009b). The question remains, however, whether these modes either collectively or individually can explain the increase in rainfall observed during autumn and winter of the late 1980s/early 1990s.…”

“…associated with cut-off low pressure systems over this period (Pook et al, 2006) and an absence of persistent pre-frontal troughs that aid the penetration of rain-producing cold fronts into SEA (Verdon-Kidd and Kiem, 2009b;Alexander et al, 2010). There is considerable debate about the causes of the Big Dry.…”

“…The overall implication, nevertheless, is that the traditional filling season for water supply systems across most of SEA, which historically was considered to run from about May to November, may not be as reliable in the future. Rather, replenishment of storages may in the future be more dependent on warm season rainfall events which, in turn, will primarily depend on the status of the El Niño-Southern Oscillation (ENSO), the IOD and the Southern Annular Mode (SAM) (Verdon-Kidd and Kiem, 2009b;Gallant et al, 2012). However, future changes in these key climate influences (in particular ENSO) are also not certain, and it is unclear to what extent continuing reductions in cool season rainfall are likely to be offset by higher warm season rainfalls (which also suffer from higher evaporative losses).…”

“…The SOM methodology has been shown to be successful in identifying key regional synoptic patterns that drive local climate in other regions of the world (e.g. Cavazos, 2000;Cavazos et al, 2002;Hewitson and Crane, 2002;Hope et al, 2006;Reusch et al, 2007;Verdon-Kidd and Kiem, 2009b). Importantly, the SOM methodology is less subjective than other forms of pattern recognition, and the non-linear approach lends itself to regions where local climate is constantly changing due to large-scale climate variability.…”