Three putative types of El Nino revealed by spatial variability in impact on Australian wheat yield

Potgieter, A. B., Hammer, G. L., Meinke, H., Stone, R. C. and Goddard, L. (2005) Three putative types of El Nino revealed by spatial variability in impact on Australian wheat yield. Journal Of Climate, 18 10: 1566-1574. doi:10.1175/JCLI3349.1

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Author Potgieter, A. B.
Hammer, G. L.
Meinke, H.
Stone, R. C.
Goddard, L.
Title Three putative types of El Nino revealed by spatial variability in impact on Australian wheat yield
Journal name Journal Of Climate   Check publisher's open access policy
ISSN 0894-8755
1520-0442
Publication date 2005
Sub-type Article (original research)
DOI 10.1175/JCLI3349.1
Open Access Status File (Publisher version)
Volume 18
Issue 10
Start page 1566
End page 1574
Total pages 9
Place of publication Boston, MA, United States
Publisher American Meteorological Society
Collection year 2005
Language eng
Abstract The El Nino-Southern Oscillation (ENSO) phenomenon significantly impacts rainfall and ensuing crop yields in many parts of the world. In Australia, El Nino events are often associated with severe drought conditions. However, El Nino events differ spatially and temporally in their manifestations and impacts, reducing the relevance of ENSO-based seasonal forecasts. In this analysis, three putative types of El Nino are identified among the 24 occurrences since the beginning of the twentieth century. The three types are based on coherent spatial patterns (footprints) found in the El Nino impact on Australian wheat yield. This bioindicator reveals aligned spatial patterns in rainfall anomalies, indicating linkage to atmospheric drivers. Analysis of the associated ocean-atmosphere dynamics identifies three types of El Nino differing in the timing of onset and location of major ocean temperature and atmospheric pressure anomalies. Potential causal mechanisms associated with these differences in anomaly patterns need to be investigated further using the increasing capabilities of general circulation models. Any improved predictability would be extremely valuable in forecasting effects of individual El Nino events on agricultural systems.
Keyword Southern Oscillation Index
Cluster-analysis
Rainfall
Enso
Anomalies
Patterns
Climate
East
Q-Index Code C1

 
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Created: Wed, 15 Aug 2007, 06:09:07 EST