eCite Digital Repository

Large-scale ocean-atmospheric processes and seasonal rainfall variability in South Australia: potential for improving seasonal hydroclimatic forecasts

Citation

Tozer, CR and Kiem, AS and Verdon-Kidd, DC, Large-scale ocean-atmospheric processes and seasonal rainfall variability in South Australia: potential for improving seasonal hydroclimatic forecasts, International Journal of Climatology pp. 1-17. ISSN 0899-8418 (2017) [Refereed Article]

Copyright Statement

2017 Royal Meteorological Society

DOI: doi:10.1002/joc.5043

Abstract

Seasonal rainfall forecasts are an important tool for risk management across many sectors. However, significant challenges arise in the development of skilful and practically useful seasonal forecasts for regions where the temporal and spatial variability of rainfall is large and/or knowledge about what causes this variability is in its infancy. This is evident in the state of South Australia (SA), where seasonal rainfall currently has low predictive skill. The key climate processes have yet to be fully identified in SA and therefore may not be adequately represented in forecast models. The aim of this paper is to identify and quantify relationships between large-scale ocean-atmospheric processes and seasonal rainfall variability across SA. We identify two distinct climate zones: (1) the arid northern region, where rainfall is mostly influenced by climate processes stemming from the tropical Indian and/or Pacific Oceans and (2) southern SA, which is dominated by Southern Ocean processes. The average percent of variability of SA rainfall accounted for by any single large-scale climate process (i.e. linear regression using a single predictor) is 8% in summer, 19% in autumn, 33% in winter and 24% in spring. However, when two or more processes are considered in combination (through multiple linear regression), this rises to 13, 26, 46, and 33%, respectively, highlighting the importance of capturing the interaction among multiple climate processes. Importantly, the findings from this study provide a set of metrics against which existing statistical and dynamical forecasting schemes can be tested and highlight processes that should be focused on in order to improve (or develop new) forecasting schemes. The study also recommends the need for further investigations into non-linear relationships between rainfall and large-scale ocean-atmospheric processes and the development of more objective methods for determining which climate process, or combination of processes, are most important for a certain season or location.

Item Details

Item Type:Refereed Article
Keywords:rainfall variability, seasonal forecasting, ocean-atmospheric, ENSO, IOD, SAM, IPO, PDO, ENSO Modoki, STR, blocking, climate
Research Division:Earth Sciences
Research Group:Atmospheric Sciences
Research Field:Climatology (excl. Climate Change Processes)
Objective Division:Environment
Objective Group:Environmental and Natural Resource Evaluation
Objective Field:Environmental Management Systems
Author:Tozer, CR (Dr Carly Tozer)
ID Code:115779
Year Published:2017
Web of Science® Times Cited:1
Deposited By:CRC-Antarctic Climate & Ecosystems
Deposited On:2017-04-10
Last Modified:2017-05-23
Downloads:0

Repository Staff Only: item control page