Utilising insights into rainfall patterns and climate drivers to inform seasonal rainfall forecasting in South Australia

• Main Author: Tozer, Carly Renee
• Other Authors: University of Newcastle. Faculty of Science & Information Technology, School of Environmental and Life Sciences
• Format: Thesis
• Language: English
• Published: 2015
• Subjects: rainfall; climate; variability; seasonal forecasting; subtropical ridge; partial mutual information; Indian; Pacific; North Atlantic
• Online Access: http://hdl.handle.net/1959.13/1059990

Research Doctorate - Doctor of Philosophy (PhD) The national seasonal forecasting system utilised by Australia’s Bureau of Meteorology is specifically focused on capturing El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) induced atmospheric and oceanic variability in the Indian and Pacific Ocean regions. Seasonal forecast skill is reasonable in some parts of the country but not in South Australia (SA). It is hypothesised that ENSO and IOD are not the major climate drivers of seasonal rainfall variability in SA which results in poor seasonal forecast skill in SA and that identification of the key climate drivers can help guide seasonal forecasting in this region. An investigation is therefore undertaken with the aim of identifying the key climate drivers of rainfall variability in SA. Initially, to determine the appropriate rainfall data to be used in the investigation, an in depth analysis of rainfall data quality is carried out which provides key insights into issues with gridded rainfall data products and provides recommendations for the use of gridded rainfall data in climate studies. A suite of climate drivers (and their associated indices) from the Indian, Pacific, Southern and North Atlantic Ocean regions are then identified and their relationships between seasonal rainfall recorded at various gauges in SA are assessed using a simple linear correlation analysis and nonlinear stratification approach. Relationships between individual climate drivers and rainfall are initially investigated but the importance of taking the combined impacts of climate drivers into account is also highlighted. The results of this analysis indeed confirm that ENSO and IOD are not the key drivers of seasonal rainfall variability in SA. The work is then extended by using a novel method for climate predictor selection to both identify the key combination of drivers that explain the most seasonal rainfall variability in different regions of SA and to determine the hierarchy of importance of the key drivers. The Subtropical Ridge (STR) is confirmed as the most important driver of rainfall variability in southern SA in autumn, winter and spring with SST variability in the Indian and Pacific Oceans acting as secondary drivers. The importance of the Southern Annular Mode in combination with the STR for spring rainfall in southern SA is also identified. In northern SA, rainfall variability is found to be dominated by a combination of Indian and Pacific Ocean SST variability but not specifically ENSO and IOD. It is found from this analysis that a maximum of 55% of rainfall variability can be explained by any of the selected climate driver combinations for any season and gauge in SA, which is an improvement on existing seasonal forecast skill in SA. Finally, the practical implications of the climate driver-rainfall relationships identified are investigated through the assessment of the variability of the SA’s cropping boundary, known as Goyder’s Line. It is found that the cropping boundary shifts according to different phases of large-scale climate drivers, in particular the STR intensity, such that when (for example) the STR is more intense than average, areas that are normally deemed as suitable for cropping have an increased chance of not receiving adequate growing season rainfall. This risk is found to be further enhanced when the STR is considered in combination with other drivers. Ultimately, the insights from this thesis and the future work proposed provide a key focus and direction for improving seasonal rainfall forecasting in SA.