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Please use this identifier to cite or link to this item: http://hdl.handle.net/10204/5792

Title: South African mid-summer seasonal rainfall prediction performance by a coupled ocean-atmosphere model
Authors: Landman, WA
DeWitt, D
Beraki, A
Keywords: Seasonal forecasting
Climate
Atmospheric general circulation models
AGCMs
Rainfall season
Rainfall predication
Ocean-atmosphere model
Seasonal prediction
Issue Date: Jan-2011
Publisher: International CLIVAR Project Office
Citation: Landman, WA, DeWitt, D and Beraki, A. 2011. South African mid-summer seasonal rainfall prediction performance by a coupled ocean-atmosphere model. Clivar exchanges, vol. 16(1), pp 3-6
Series/Report no.: Workflow;8574
Abstract: Estimation of the evolution of SST anomalies, which are often relatively predictable, and subsequently employing them in atmospheric general circulation models (AGCMs), provides means of generating forecasts of seasonal-average weather (Graham et al. 2000; Goddard and Mason, 2002). Such a so-called two-tiered procedure to predict the outcome of the rainfall season has been employed in South Africa for a number of years already (e.g., Landman et al., 2001). The advent of fully coupled ocean-atmosphere models (e.g. Stockdale et al, 1998), or one-tiered systems, promised improved seasonal forecasts since in theory coupled models should eventually outperform two-tiered systems because the former is able to describe the feedback between ocean and atmosphere while the latter assumes that the atmosphere responds to SST but does not in turn affect the oceans (Copsey et al., 2006). This notion will be tested here by comparing the seasonal rainfall forecast performance over the mid-summer season of December to February (DJF) of a two-tiered system with forecasts from a fully coupled system. For both the two-tiered and fully coupled systems the same AGCM will be used.
Description: Copyright: 2011 The authors.
URI: http://eprints.soton.ac.uk/177159/1/EXCH55_VAMOS.pdf
http://hdl.handle.net/10204/5792
ISSN: 1026-0471
Appears in Collections:High performance computing
Climate change
General science, engineering & technology

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