journal article Mar 22, 2017

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

Abstract
ABSTRACTSeasonal 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.
Topics

No keywords indexed for this article. Browse by subject →

References
72
[3]
Influence of the Indian Ocean Dipole on the Australian winter rainfall

Karumuri ASHOK, Zhaoyong GUAN, Toshio Yamagata

Geophysical Research Letters 10.1029/2003gl017926
[4]
El Niño Modoki and its possible teleconnection

Karumuri ASHOK, Swadhin K. Behera, Suryachandra A. Rao et al.

Journal of Geophysical Research: Oceans 10.1029/2006jc003798
[5]
The Effective Number of Spatial Degrees of Freedom of a Time-Varying Field

Christopher S. Bretherton, Martin Widmann, Valentin P. Dymnikov et al.

Journal of Climate 10.1175/1520-0442(1999)012<1990:tenosd>2.0.co;2
[8]
ChambersLE.2003. South Australian rainfall variability and trends. BMRC Research Report No. 92.
[21]
HudsonD MarshallAG AlvesO ShiL YoungG.2015. Forecasting upcoming extreme heat on multi‐week to seasonal timescales: POAMA experimental forecast products. Bureau Research Report No. 001. 10.22499/4.0001
[28]
Comparison of different efficiency criteria for hydrological model assessment

P. Krause, D. P. Boyle, F. Bäse

Advances in Geosciences 10.5194/adgeo-5-89-2005
[29]
Lavery B "An extended high‐quality historical rainfall dataset for Australia" Aust. Meteorol. Mag. (1997)
[30]
Li F "Relationships between rainfall in the southwest of Western Australia and near‐global patterns of sea‐surface temperature and mean sea‐level pressure variability" Aust. Meteorol. Mag. (2005)
[31]
LimE HendonH AlvesO.2009. Intercomparison of seasonal forecast models for south‐eastern Australian climate. South Eastern Australian Climate Initiative Project 3.1.2.
[32]
LimE HendonH AlvesJOS YinY WangG HudsonD ZhaoM ShiL.2010a. Dynamical seasonal prediction of tropical Indo‐Pacific SST and Australian rainfall with improved ocean initial conditions. CAWCR Technical Report No. 032.
[34]
LimE HendonH LangfordS AlvesJOS.2012. Improvements in POAMA2 for the prediction of major climate drivers and south eastern Australian rainfall. CAWCR Technical Report No. 051.
[37]
Mason I "A model for assessment of weather forecasts" Aust. Meteorol. Mag. (1982)
[42]
River flow forecasting through conceptual models part I — A discussion of principles

J.E. Nash, J.V. Sutcliffe

Journal of Hydrology 10.1016/0022-1694(70)90255-6
[46]
A Caution Regarding Rules of Thumb for Variance Inflation Factors

Robert M. O’brien

Quality &amp; Quantity 10.1007/s11135-006-9018-6
[49]
Inter-decadal modulation of the impact of ENSO on Australia

S. Power, T. Casey, C. Folland et al.

Climate Dynamics 10.1007/s003820050284

Showing 50 of 72 references

Metrics
16
Citations
72
References
Details
Published
Mar 22, 2017
Vol/Issue
37(S1)
Pages
861-877
License
View
Funding
CSIRO Land & Water Flagship Scholarship
NCCARF Water Network Scholarship
Antarctic Climate and Ecosystems Cooperative Research Centre (ACE-CRC)
Centre for Water, Climate and Land (CWCL) at University of Newcastle (Australia)
Cite This Article
C. R. Tozer, A. S. Kiem, D. C. Verdon‐Kidd (2017). Large‐scale ocean‐atmospheric processes and seasonal rainfall variability in South Australia: potential for improving seasonal hydroclimatic forecasts. International Journal of Climatology, 37(S1), 861-877. https://doi.org/10.1002/joc.5043