The influence of the Southern Oscillation Index on the timing of breeding of a forest-bird community in south-eastern Australia

2021 ◽  
Author(s):  
R. Marchant ◽  
M. Guppy ◽  
S. Guppy
Keyword(s):  
Southern Oscillation Index ◽  
Southern Oscillation ◽  
Bird Community ◽  
Timing Of Breeding ◽  
Forest Bird ◽  
South Eastern ◽  
Eastern Australia ◽  
South Eastern Australia

The El Nino-Southern Oscillation in south-eastern Australian waters

1991 ◽  
Vol 42 (3) ◽  
pp. 263 ◽  
Author(s):  
WW Hsieh ◽  
BV Hamon
Keyword(s):  
Sea Level ◽  
Coastal Upwelling ◽  
Southern Oscillation ◽  
Geostrophic Wind ◽  
Air Pressure ◽  
Ekman Transport ◽  
Hydrographic Data ◽  
South Eastern ◽  
Eastern Australia ◽  
South Eastern Australia

Using four decades of hydrographic data collected off the coast near Sydney, New South Wales, and sea-level data at Sydney, we studied the interannual variability in south-eastern Australian shelf waters. The first two empirical orthogonal function (EOF) modes of the band-pass-filtered 50-m-depth hydrographic data (temperature, T; salinity, S; nitrate, N; inorganic phosphate, P; and oxygen, O) and the sea level (SL) and adjusted sea level (ASL) data accounted respectively for 51 and 27% of the total variance. Both modes were significantly correlated with the Southern Oscillation Index (SOI). The first mode, with T, S, O and ASL varying in opposition to N and P, represented the internal or baroclinic response, associated with vertical displacements of the isopycnals. The second mode, with large in-phase fluctuations in SL and ASL but small changes in the hydrographic variables, represented mainly the external or barotropic response during the El Niiio-Southern Oscillation (ENSO). Three-year composites centred around seven ENSO warm episodes revealed that T, S, O and ASL were generally low and N, P, SL and SO1 were high in the year before each ENSO warm episode, but the former group rose while the latter group dropped in the year of the warm episode. The changes in the hydrographic variables at 50 m depth were consistent with relatively shallow isopycnals in the year before the ENSO warm episode, followed by a deepening of the isopycnals during the warm episode. Estimates of this downward displacement of isopycnals, as determined from T, N, P and O, were in the range 7-10 m. The geostrophic wind arising from the pressure fluctuations during ENSO is proposed as a probable cause for the vertical displacement of the isopycnals. In the year before the warm episode, the low air pressure over Australia would produce a clockwise geostrophic wind around south-eastern Australia, generating offshore Ekman transport and coastal upwelling. During the warm episode, air pressure over Australia rises, the geostrophic wind reverses, and downward movement of the isopycnals would occur off south-eastern Australia.

Can outbreaks of house mice in south-eastern Australia be predicted by weather models?

2004 ◽  
Vol 31 (5) ◽  
pp. 465 ◽  
Author(s):  
Charles J. Krebs ◽  
Alice J. Kenney ◽  
Grant R. Singleton ◽  
Greg Mutze ◽  
Roger P. Pech ◽  
...  
Keyword(s):  
Large Scale ◽  
Southern Oscillation ◽  
Plant Production ◽  
House Mice ◽  
Weather Variables ◽  
Mouse Population ◽  
Enso Events ◽  
South Eastern ◽  
Eastern Australia ◽  
South Eastern Australia

Outbreaks of house mice (Mus domesticus) occur irregularly in the wheat-growing areas of south-eastern Australia, and are thought to be driven by weather variability, particularly rainfall. If rainfall drives grass and seed production, and vegetation production drives mouse dynamics, we should achieve better predictability of mouse outbreaks by the use of plant-production data. On a broader scale, if climatic variability is affected by El Niño–Southern Oscillation (ENSO) events, large-scale weather variables might be associated with mouse outbreaks. We could not find any association of mouse outbreaks over the last century with any ENSO measurements or other large-scale weather variables, indicating that the causal change linking mouse numbers with weather variation is more complex than is commonly assumed. For the 1960–2002 period we were only partly successful in using variation in cereal production to predict outbreaks of mice in nine areas of Victoria and South Australia, and we got better predictability of outbreaks from rainfall data alone. We achieved 70% correct predictions for a qualitative model using rainfall and 58% for a quantitative model using rainfall and spring mouse numbers. Without the detailed specific mechanisms underlying mouse population dynamics, we may not be able to improve on these simple models that link rainfall to mouse outbreaks.

Holocene ‘megadroughts’ in south-eastern Australia: deciphering regional patterns from lake sediment archives

2020 ◽  
Author(s):  
Jonathan Tyler ◽  
Cameron Barr ◽  
John Tibby ◽  
Asika Dhar ◽  
Chapman Andrew ◽  
...  
Keyword(s):  
Climate Model ◽  
Southern Oscillation ◽  
Global Climate ◽  
Climate Science ◽  
Natural Phenomenon ◽  
Geochemical Data ◽  
Regional Patterns ◽  
South Eastern ◽  
Eastern Australia ◽  
South Eastern Australia

<p>Documenting and understanding centennial scale hydroclimatic variability in Australia is significant both to global climate science and to regional efforts to predict and manage water resources. In particular, multidecadal to centennial periods of low rainfall – ‘megadroughts’ – have been observed in semi-arid climates worldwide, however they are poorly constrained in Australia. Here, we bring together multiple, sub-decadally resolved records of hydrological change inferred from lake sediments in western Victoria, Australia. Our analyses incorporate new elemental (ITRAX µXRF) and stable isotope (oxygen, carbon isotopes) geochemical data from West Basin and Lake Surprise, both augmented by high quality radiometric chronologies based on radiocarbon, <sup>210</sup>Pb and <sup>239/240</sup>Pu analyses. Collectively, the records document a transition towards a more arid and variable climate since the mid-late Holocene, which is comparable to reports of an intensification of the El Nino Southern Oscillation (ENSO) through this period. Furthermore, during the last 2000 years, the records exhibit marked periods of reduced effective moisture which contrast with records of Australian hydroclimate inferred from distal archives, as well those predicted by climate model hindcasts. Our analyses indicate that megadroughts are a natural phenomenon in south-eastern Australia, requiring greater attention in efforts to predict and mitigate future climatic change.</p>

An Automated Climatology of Cool Season Cutoff Lows over South-Eastern Australia, and Relationships with the Remote Climate Drivers

2021 ◽  
Keyword(s):  
Southern Oscillation ◽  
Low Frequency ◽  
Southern Annular Mode ◽  
Cool Season ◽  
South Eastern ◽  
Eastern Australia ◽  
The Indian Ocean ◽  
Cutoff Low ◽  
Upper Level ◽  
South Eastern Australia

Abstract Cutoff low pressure systems have been found to be the synoptic system responsible for the majority of rainfall in South-Eastern Australia during the cool season (April to October inclusive). Meanwhile, rainfall in South-Eastern Australia at the seasonal and interannual scale is known to be related to remote climate drivers, such as the El Niño Southern Oscillation, the Indian Ocean Dipole, and the Southern Annular Mode. In this study, a new automated tracking scheme to identify synoptic scale cutoff lows is developed, then applied to 500 hPa geopotential height data from the NCEP1 and ERA-Interim reanalyses, to create two databases of cool season cutoff lows for South Eastern Australia for the years 1979 to 2018 inclusive. Climatological characteristics of cutoff lows identified in both reanalyses are presented and compared, highlighting differences between the NCEP1 and ERA-Interim reanalyses over the Australian region. Finally, cool-season and monthly characteristics of cutoff low frequency, duration and location are plotted against cool-season and monthly values of climate driver indices (Oceanic Nino Index, Dipole Mean Index, and Antarctic Oscillation), to identify any evidence of linear correlation. Correlations between these aspects of cutoff low occurrence and the remote drivers were found to be statistically significant at the 95% level for only a single isolated month at a time, in contrast to results predicted by previous works. It is concluded that future studies of cutoff low variability over SEA should employ identification criteria that capture systems of only upper-level origin, and differentiate between cold-cored and cold-trough systems.

Quality and potential utility of ENSO-based forecasts of spring rainfall and wheat yield in south-eastern Australia

2008 ◽  
Vol 59 (2) ◽  
pp. 112 ◽  
Author(s):  
M. R. Anwar ◽  
D. Rodriguez ◽  
D. L. Liu ◽  
S. Power ◽  
G. J. O'Leary
Keyword(s):  
Grain Yield ◽  
Production Systems ◽  
Southern Oscillation ◽  
Wheat Yield ◽  
Good Reliability ◽  
South Eastern ◽  
South Wales ◽  
Eastern Australia ◽  
South Eastern Australia

Reliable seasonal climate forecasts are needed to aid tactical crop management decisions in south-eastern Australia (SEA). In this study we assessed the quality of two existing forecasting systems, i.e. the five phases of the Southern Oscillation Index (SOI) and a three phase Pacific Ocean sea-surface temperatures (SSTs), to predict spring rainfall (i.e. rainfall from 1 September to 31 November), and simulated wheat yield. The quality of the forecasts was evaluated by analysing four attributes of their performance: their reliability, the relative degree of shift and dispersion of the distributions, and measure of forecast consistency or skill. Available data included 117 years of spring rainfall and 104 years of grain yield simulated using the Agricultural Production Systems Simulator (APSIM) model, from four locations in SEA. Average values of spring rainfall were 102–174 mm with a coefficient of variation (CV) of 47%. Average simulated wheat yields were highest (5609 kg/ha) in Albury (New South Wales) and lowest (1668 kg/ha) in Birchip (Victoria). The average CV for simulated grain yields was 36%. Griffith (NSW) had the highest yield variability (CV = 50%). Some of this year-to-year variation was related to the El Niño Southern Oscillation (ENSO). Spring rainfall and simulated wheat yields showed a clear association with the SOI and SST phases at the end of July. Important variations in shift and dispersion in spring rainfall and simulated wheat yields were observed across the studied locations. The forecasts showed good reliability, indicating that both forecasting systems could be used with confidence to forecast spring rainfall or wheat yield as early as the end of July. The consistency of the forecast of spring rainfall and simulated wheat yield was 60–83%. We concluded that adequate forecasts of spring rainfall and grain yield could be produced at the end of July, using both the SOI and SST phase systems. These results are discussed in relation to the potential benefit of making tactical top-dress applications of nitrogen fertilisers during early August.

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