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Relating changes in synoptic circulation to the surface rainfall response using self-organising maps

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Abstract

The climate of a particular region is governed by factors that may be remote, such as the El Nino Southern Oscillation or local, such as topography. However, the daily weather characteristics of a region are controlled by the synoptic-scale atmospheric state. Therefore changes in the type, frequency, duration or intensity of particular synoptic states over a region would result in changes to the local weather and long-term climatology of the region. The relationship between synoptic-scale circulation and the rainfall response is examined for a 31-year period at two stations in different rainfall regimes in South Africa. Dominant rain-bearing synoptic circulations are identified for austral winter and summer as mid-latitude cyclones and convective systems respectively whereas no circulations are dominantly associated with spring and autumn rainfall. Over the 31-year period a statistically significant increase in the frequency of characteristic summer circulation modes is observed during summer, winter and spring. During autumn a statistically significant shift towards characteristically winter circulation modes is evident. Seasonal rainfall trends computed at each station corroborate those of the circulation data. Extreme rainfall is associated with particular circulation modes and trends in both circulation and station data show an earlier occurrence of extreme rainfall during the rainy season.

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Notes

  1. Software and documentation available online at http://www.cis.hut.fi/research/som_lvq_pak.shtml.

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Acknowledgments

The authors would like to thank Alan Gelfand, Huiyan Sang, John Silander Jr and Bruce Hewitson for their support in this work. We are grateful to the National Science Foundation for financial support from the NSF DEB 0516198 grant. We also thank two anonymous reviewers for their useful suggestions.

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Lennard, C., Hegerl, G. Relating changes in synoptic circulation to the surface rainfall response using self-organising maps. Clim Dyn 44, 861–879 (2015). https://doi.org/10.1007/s00382-014-2169-6

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  • DOI: https://doi.org/10.1007/s00382-014-2169-6

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