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The high-resolution global SST forecast set of the CSIR

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dc.contributor.author Landman, WA
dc.contributor.author DeWitt, DG
dc.contributor.author Lee, D-E
dc.date.accessioned 2012-03-27T13:51:33Z
dc.date.available 2012-03-27T13:51:33Z
dc.date.issued 2011-09
dc.identifier.citation Landman, WA, DeWitt, DG and Lee, D-E. The high-resolution global SST forecast set of the CSIR. 27th Annual Conference of the South African Society for Atmospheric Sciences, Hartbeespoort, North-West Province, 22-23 September 2011 en_US
dc.identifier.isbn 978-0-620-50849-0
dc.identifier.uri http://web.csag.uct.ac.za/sasas/images/stories/Peer_Reviewed_Conference_Proceeding.pdf
dc.identifier.uri http://hdl.handle.net/10204/5686
dc.description 27th Annual Conference of the South African Society for Atmospheric Sciences, Hartbeespoort, North-West Province, 22-23 September 2011 en_US
dc.description.abstract The evolution of global sea-surface temperature (SST) anomalies over several months ahead is often relatively predictable. Subsequently employing predicted SST in atmospheric general circulation models (AGCMs) can provide means of generating forecasts of seasonal-average weather. Coupled ocean-atmosphere general circulation models (CGCMs) have also been increasingly used worldwide for operational seasonal forecast production. Furthermore, coupled models can predict both the evolution of SSTs and atmospheric conditions at elevated levels of skill. However, when skilful SST forecasts are used AGCMs may perform equally well as the current CGCMs and so CGCMs can be used to supply SST forecasts to AGCMs. In this paper a high-resolution retro-active forecast set of global SST anomalies is presented, including verification statistics and the methodology used to generate this multi-decadal set for a number of forecast lead-times. en_US
dc.language.iso en en_US
dc.publisher SASAS en_US
dc.relation.ispartofseries Workflow;8587
dc.subject Sea-surface temperature predictions en_US
dc.subject Seasonal forecasting en_US
dc.subject Global oceans en_US
dc.subject El Nino en_US
dc.title The high-resolution global SST forecast set of the CSIR en_US
dc.type Conference Presentation en_US


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