The Southern Oscillation Index as a predictor of seasonal rainfall in the arable areas of the inland Australian subtropics

1993 ◽  
Vol 44 (6) ◽  
pp. 1337 ◽  
Author(s):  
JS Russell ◽  
IM McLeod ◽  
MB Dale ◽  
TR Valentine
Keyword(s):  
Southern Oscillation Index ◽  
Southern Oscillation ◽  
Linear Trend ◽  
Summer Rainfall ◽  
Seasonal Rainfall ◽  
Four Seasons ◽  
Eastern Australia ◽  
Australian Continent ◽  
Ace Algorithm ◽  
Linear Trends

A detailed study has been carried out in four regions in the subtropics of Eastern Australia to determine the relationship between the Southern Oscillation Index (SOI) and subsequent seasonal rainfall. The period studied was from 1915 to 1991 for 3-monthly periods of spring (SON), summer (DJF), autumn (MAM) and winter (JJA). The 3-monthly prior SOI values were plotted against seasonal rainfall of the four regions and four seasons. These data were widely scattered but with a linear trend showing increased seasonal rainfall as the SOI increased. Linear trends were plotted for each season and region. Comparisons were made between the use of the ACE algorithm, which transforms the SOI and rainfall data, and the use of linear trends. Polynomials were used to calculate equations for each region and season, but only spring and summer produced satisfactory ACE functions. Estimates were made of spring and summer rainfall relative to prior SOI values for each region. While the SOI as a predictor of rainfall broadly estimates spring and summer rainfall, this variable has limited usefulness on its own. One of the options available with the ACE program is that additional independent variables can be added as required. Current research suggests that sea surface temperature data from specific ocean areas surrounding the Australian continent is the most useful additional variable at present. However the complexity of such an analysis is greatly increased.

Relationship between the Southern Oscillation Index and Australian sugarcane yields

1994 ◽  
Vol 45 (7) ◽  
pp. 1557 ◽  
Author(s):  
I Kuhnel
Keyword(s):  
Southern Hemisphere ◽  
Southern Oscillation Index ◽  
Southern Oscillation ◽  
Significance Testing ◽  
The Past ◽  
Northern Areas ◽  
Eastern Australia ◽  
North Eastern ◽  
Sugarcane Yield ◽  
The Relationship

This study examines the relationship between the Southern Oscillation Index and the sugarcane yield anomalies at 27 mills in north-eastern Australia (Queensland) for the period 1950-1989. The major results of this work indicate that the SO1 alone seems to have only a limited value as predictor of total sugarcane yields over large areas (i.e. the whole of Queensland). However, on a smaller scale, the SO1 appears to be a useful indicator of yields for the northern sugarcane districts. In these northern areas, the highest correlations with the SO1 are reached during the southern hemisphere spring and summer months 6 to 11 months prior to the harvest. They are negative and explain about 40% of the total variance. They also suggest that a positive SO1 during the spring and summer months tends to be followed by lower-than-normal yields at the following harvest and vice versa. This signal is rather robust and withstands rigorous significance testing. Moreover, it appears that the relationship between the SO1 and the sugarcane yields has been relatively strong and stable for the past 40 years, but weakened substantially during the 1930-1940 period.

scholarly journals Forecasting rainfall based on the Southern Oscillation Index phases at longer lead-times in Australia

2013 ◽  
Vol 35 (4) ◽  
pp. 373 ◽  
Author(s):  
David H. Cobon ◽  
Nathan R. Toombs
Keyword(s):  
Key Management ◽  
Southern Oscillation Index ◽  
Southern Oscillation ◽  
Warm Season ◽  
Lead Times ◽  
Phase System ◽  
Management Decisions ◽  
Operational Forecasts ◽  
Eastern Australia ◽  
Forecast Quality

Under the extensive grazing conditions experienced in Australia, pastoralists would benefit from a long lead-time seasonal forecast issued for the austral warm season (November–March). Currently operational forecasts are issued publicly for rolling 3-month periods at lead-times of 0 or 1 month, usually without an indication of forecast quality. The short lag between the predictor and predictand limits use of forecasts because pastoralists operating large properties have insufficient time to implement key management decisions. The ability to forecast rainfall based on the Southern Oscillation Index (SOI) phase system was examined at 0–5-month lead-times for Australian rainfall. The SOI phase system provided a shift of adequate magnitude in the rainfall probabilities (–40 to +30%) and forecast quality for the 5-month austral warm season at lead-times >0 months. When data used to build the forecast system were used in verification, >20% of locations had a significant linear error in probability space (LEPS) and Kruskal–Wallis (KW) test for lead-times of 0–2 months. The majority of locations showing forecast quality were in northern Australia (north of 25°S), predominately in north-eastern Australia (north of 25°S, east of 140°E). Pastoralists in these areas can now apply key management decisions with more confidence up to 2 months before the November–March period. Useful lead-times of ≥3 months were not found.

scholarly journals Temporal rainfall trend analysis in different agro-ecological regions of southern Africa

Water SA ◽  
2021 ◽  
Vol 47 (4 October) ◽  
Author(s):  
W Mupangwa ◽  
R Makanza ◽  
L Chipindu ◽  
M Moeletsi ◽  
S Mkuhlani ◽  
...  
Keyword(s):  
Southern Africa ◽  
Linear Trend ◽  
Rainfall Variability ◽  
Daily Rainfall ◽  
Farming Systems ◽  
Seasonal Rainfall ◽  
Smallholder Farming ◽  
Significant Linear Trend ◽  
Semi Arid ◽  
Linear Trends

Rainfall is a major driver of food production in rainfed smallholder farming systems. This study was conducted to assess linear trends in (i) different daily rainfall amounts (<5, 5–10, 11–20, 21–40 and >40 mm∙day-1), and (ii) monthly and seasonal rainfall amounts. Drought was determined using the rainfall variability index. Daily rainfall data were derived from 18 meteorological stations in southern Africa. Daily rainfall was dominated by <5 mm∙day-1 followed by 5–10 mm∙day-1. Three locations experienced increasing linear trends of <5 mm∙day-1 amounts and two others in sub-humid region had increases in the >40 mm day-1 category. Semi-arid location experienced increasing trends in <5 and 5–10 mm∙day-1 events. A significant linear trend in seasonal rainfall occurred at two locations with decreasing rainfall (1.24 and 3 mm∙season-1). A 3 mm∙season-1 decrease in seasonal rainfall was experienced under semi-arid conditions. There were no apparent linear trends in monthly and seasonal rainfall at 15 of the 18 locations studied. Drought frequencies varied with location and were 50% or higher during the November–March growing season. Rainfall trends were location and agro-ecology specific, but most of the locations studied did not experience significant changes between the 1900s and 2000s.

The resolution and potential value of Australian seasonal rainfall forecasts based on the five phases of the Southern Oscillation Index

2009 ◽  
Vol 60 (3) ◽  
pp. 230 ◽  
Author(s):  
Andrew L. Vizard ◽  
Garry A. Anderson
Keyword(s):  
Southern Oscillation Index ◽  
Southern Oscillation ◽  
Mean Value ◽  
Expected Value ◽  
Variance Ratio ◽  
Large Shift ◽  
Potential Value ◽  
Eastern Australia ◽  
Forecasting System ◽  
Cape York

We assess the resolution of the Southern Oscillation Index (SOI) seasonal rainfall forecasting system and calculate the loss in potential value of the forecasting system using a cost/loss model. Forecasts of the probability of a ‘dry’ autumn, winter, spring, and summer were obtained for 226 towns across Australia, based on the 5 phases of the SOI. For every town the variance ratio, the observed forecast variance as a proportion of the variance of a perfect forecasting system, was calculated for each season. Value score curves, showing the expected value of the forecasts as a proportion of the expected value of perfect information, were calculated for every town for each season. Maps of variance ratio and maps of mean value scores across Australia were produced by ordinary kriging. In all seasons and regions the SOI forecasting system had a variance ratio of less than 0.20, indicating that resolution and skill were never high. Variance ratios greater than 0.10 only occurred in parts of south-eastern Australia and the Cape York region during spring and in the Townsville region during summer. The variance ratio was less than 0.05 for the majority of Australia during autumn, winter, and summer. The mean value scores for actions that are only triggered by a large shift in the forecast from climatology were uniformly close to zero in all seasons and regions, indicating that little or no value can be derived in such cases. Actions triggered by a moderate shift of the forecast were also generally associated with low value scores. Mean value scores above 0.20 were limited to actions with a decision threshold close to climatology and only occurred in parts of south-eastern Australia and the Cape York region during spring and in the Townsville region during summer. We conclude that the imperfect resolution of the SOI forecasting system has a substantial effect on potential value. The forecasting system can potentially deliver value to users with actions that are triggered by a small shift in the forecast from climatology, especially in eastern Australia during spring, but not to users with actions that are only triggered by a large shift of the forecasts from climatology.

scholarly journals Predictability of June–September Rainfall in Ethiopia

2007 ◽  
Vol 135 (2) ◽  
pp. 628-650 ◽  
Author(s):  
Diriba Korecha ◽  
Anthony G. Barnston
Keyword(s):  
Lead Time ◽  
Large Scale ◽  
Southern Oscillation ◽  
Summer Rainfall ◽  
Rainfall Anomaly ◽  
Seasonal Rainfall ◽  
Multivariate Statistical ◽  
Local Climate ◽  
Rainfall Prediction ◽  
Enso Predictability

Abstract In much of Ethiopia, similar to the Sahelian countries to its west, rainfall from June to September contributes the majority of the annual total, and is crucial to Ethiopia’s water resource and agriculture operations. Drought-related disasters could be mitigated by warnings if skillful summer rainfall predictions were possible with sufficient lead time. This study examines the predictive potential for June–September rainfall in Ethiopia using mainly statistical approaches. The skill of a dynamical approach to predicting the El Niño–Southern Oscillation (ENSO), which impacts Ethiopian rainfall, is assessed. The study attempts to identify global and more regional processes affecting the large-scale summer climate patterns that govern rainfall anomalies. Multivariate statistical techniques are applied to diagnose and predict seasonal rainfall patterns using historical monthly mean global sea surface temperatures and other physically relevant predictor data. Monthly rainfall data come from a newly assembled dense network of stations from the National Meteorological Agency of Ethiopia. Results show that Ethiopia’s June–September rainy season is governed primarily by ENSO, and secondarily reinforced by more local climate indicators near Africa and the Atlantic and Indian Oceans. Rainfall anomaly patterns can be predicted with some skill within a short lead time of the summer season, based on emerging ENSO developments. The ENSO predictability barrier in the Northern Hemisphere spring poses a major challenge to providing seasonal rainfall forecasts two or more months in advance. Prospects for future breakthroughs in ENSO prediction are thus critical to future improvements to Ethiopia’s summer rainfall prediction.

Long range forecasts of the numbers of Helicoverpa punctigera and H. armigera (Lepidoptera: Noctuidae) in Australia using the Southern Oscillation Index and the Sea Surface Temperature

2000 ◽  
Vol 90 (2) ◽  
pp. 133-146 ◽  
Author(s):  
D.A. Maelzer ◽  
M.P. Zalucki
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Pest Management ◽  
Southern Oscillation Index ◽  
Southern Oscillation ◽  
Sea Surface ◽  
Multiple Regressions ◽  
Eastern Australia ◽  
Trap Catches

The use of long-term forecasts of pest pressure is central to better pest management. We relate the Southern Oscillation Index (SOI) and the Sea Surface Temperature (SST) to long-term light-trap catches of the two key moth pests of Australian agriculture, Helicoverpa punctigera (Wallengren) and H. armigera (Hübner), at Narrabri, New South Wales over 11 years, and for H. punctigera only at Turretfield, South Australia over 22 years. At Narrabri, the size of the first spring generation of both species was significantly correlated with the SOI in certain months, sometimes up to 15 months before the date of trapping. Differences in the SOI and SST between significant months were used to build composite variables in multiple regressions which gave fitted values of the trap catches to less than 25% of the observed values. The regressions suggested that useful forecasts of both species could be made 6–15 months ahead. The influence of the two weather variables on trap catches of H. punctigera at Turretfield were not as strong as at Narrabri, probably because the SOI was not as strongly related to rainfall in southern Australia as it is in eastern Australia. The best fits were again given by multiple regressions with SOI plus SST variables, to within 40% of the observed values. The reliability of both variables as predictors of moth numbers may be limited by the lack of stability in the SOI-rainfall correlation over the historical record. As no other data set is available to test the regressions, they can only be tested by future use. The use of long-term forecasts in pest management is discussed, and preliminary analyses of other long sets of insect numbers suggest that the Southern Oscillation Index may be a useful predictor of insect numbers in other parts of the world.

scholarly journals Rainfall prediction for sustainable economic growth

2016 ◽  
Vol 7 (4) ◽  
pp. 120-129 ◽  
Author(s):  
Retius Chifurira ◽  
Delson Chikobvu ◽  
Dorah Dubihlela
Keyword(s):  
Economic Growth ◽  
Sea Level ◽  
Southern Oscillation Index ◽  
Southern Oscillation ◽  
Summer Rainfall ◽  
Sea Level Pressure ◽  
Level Pressure ◽  
Rainfall Prediction ◽  
Proposed Model ◽  
Sustainable Economic Growth

Agriculture is the backbone of Zimbabwe’s economy with the majority of Zimbabweans being rural people who derive their livelihood from agriculture and other agro-based economic activities. Zimbabwe’s agriculture depends on the erratic rainfall which threatens food, water and energy access, as well as vital livelihood systems which could severely undermine efforts to drive sustainable economic growth. For Zimbabwe, delivering a sustainable economic growth is intrinsically linked to improved climate modelling. Climate research plays a pivotal role in building Zimbabwe’s resilience to climate change and keeping the country on track, as it charts its path towards sustainable economic growth. This paper presents a simple tool to predict summer rainfall using standardized Darwin sea level pressure (SDSLP) anomalies and southern oscillation index (SOI) that are used as part of an early drought warning system. Results show that SDSLP anomalies and SOI for the month of April of the same year, i.e., seven months before onset of summer rainfall (December to February total rainfall) are a simple indicator of amount of summer rainfall in Zimbabwe. The low root mean square error (RMSE) and root mean absolute error (RMAE) values of the proposed model, make SDSLP anomalies for April and SOI for the same month an additional input candidates for regional rainfall prediction schemes. The results of the proposed model will benefit in the prediction of oncoming summer rainfall and will influence policy making in agriculture, environment planning, food redistribution and drought prediction for sustainable economic development. Keywords: sustainable economic growth, standardized Darwin sea level pressure anomalies, southern oscillation index, summer rainfall prediction, Zimbabwe. JEL Classification: Q16, Q25, Q54, Q55, Q58

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