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| Titel |
On the relationship between large-scale climate modes and regional synoptic patterns that drive Victorian rainfall |
| VerfasserIn |
D. C. Verdon-Kidd, A. S. Kiem |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 13, no. 4 ; Nr. 13, no. 4 (2009-04-07), S.467-479 |
| Datensatznummer |
250011829
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| Publikation (Nr.) |
 copernicus.org/hess-13-467-2009.pdf |
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| Zusammenfassung |
| In this paper regional (synoptic) and large-scale climate drivers of
rainfall are investigated for Victoria, Australia. A non-linear
classification methodology known as self-organizing maps (SOM) is used to
identify 20 key regional synoptic patterns, which are shown to capture a
range of significant synoptic features known to influence the climate of the
region. Rainfall distributions are assigned to each of the 20 patterns for
nine rainfall stations located across Victoria, resulting in a clear
distinction between wet and dry synoptic types at each station. The
influence of large-scale climate modes on the frequency and timing of the
regional synoptic patterns is also investigated. This analysis revealed that
phase changes in the El Niño Southern Oscillation (ENSO), the Indian
Ocean Dipole (IOD) and/or the Southern Annular Mode (SAM) are associated
with a shift in the relative frequency of wet and dry synoptic types on an
annual to inter-annual timescale. In addition, the relative frequency of
synoptic types is shown to vary on a multi-decadal timescale, associated
with changes in the Inter-decadal Pacific Oscillation (IPO). Importantly,
these results highlight the potential to utilise the link between the
regional synoptic patterns derived in this study and large-scale climate
modes to improve rainfall forecasting for Victoria, both in the short- (i.e.
seasonal) and long-term (i.e. decadal/multi-decadal scale). In addition, the
regional and large-scale climate drivers identified in this study provide a
benchmark by which the performance of Global Climate Models (GCMs) may be
assessed. |
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