Beraki, Asmerom FLandman, WADeWitt, D2013-02-062013-02-062012-09Beraki, A.F., Landman, W.A. and DeWitt, D. 2012. Southern hemisphere climate variability as represented by an ocean-atmosphere coupled model. 28th Annual Conference of the South African Society of Atmospheric Sciences, Breakwater Protea Hotel, Cape Town, 26-27 September 2012http://hdl.handle.net/10204/653228th Annual Conference of the South African Society of Atmospheric Sciences, Breakwater Protea Hotel, Cape Town, 26-27 September 2012Southern Hemisphere (SH) climate variability has been the focus of several researchers (e.g., Wallace and Hsu, 1983). According to these early studies, the SH is characterized by quasistationary oscillations and zonally propagating waves in the atmospheric circulation. The ability of predicting these modes of climate variability on longer timescales is vital. Potential predictability is usually measured as a signal-to-noise contrast between the slowly evolving and chaotic components of the climate system. Such measures are certainly sensitive to how the variance decomposition is performed. One way of separating the variance is using a temporal filtering technique which assumes that weather noise dominates much shorter timescales (e.g., Basher and Thomosph, 1996). Notwithstanding, weather noise includes not only high-frequency, day to day fluctuations but also low-frequency intraseasonal fluctuations that give rise to chaotic, unpredictable variability through temporal fluctuation. The aim of this study is, therefore, to assess the ability of a coupled global climate model in reproducing observed SH climate variability using a variance decomposition procedure recently suggested by Zheng and Frederiksen (2004) and Zheng et al. (2009).enSouthern Hemisphere climateClimate researchClimate variabilityAtmospheric ScienceConference PresentationBeraki, A. F., Landman, W., & DeWitt, D. (2012). Southern hemisphere climate variability as represented by an ocean-atmosphere coupled model. http://hdl.handle.net/10204/6532Beraki, Asmerom F, WA Landman, and D DeWitt. "Southern hemisphere climate variability as represented by an ocean-atmosphere coupled model." (2012): http://hdl.handle.net/10204/6532Beraki AF, Landman W, DeWitt D, Southern hemisphere climate variability as represented by an ocean-atmosphere coupled model; 2012. http://hdl.handle.net/10204/6532 .TY - Conference Presentation AU - Beraki, Asmerom F AU - Landman, WA AU - DeWitt, D AB - Southern Hemisphere (SH) climate variability has been the focus of several researchers (e.g., Wallace and Hsu, 1983). According to these early studies, the SH is characterized by quasistationary oscillations and zonally propagating waves in the atmospheric circulation. The ability of predicting these modes of climate variability on longer timescales is vital. Potential predictability is usually measured as a signal-to-noise contrast between the slowly evolving and chaotic components of the climate system. Such measures are certainly sensitive to how the variance decomposition is performed. One way of separating the variance is using a temporal filtering technique which assumes that weather noise dominates much shorter timescales (e.g., Basher and Thomosph, 1996). Notwithstanding, weather noise includes not only high-frequency, day to day fluctuations but also low-frequency intraseasonal fluctuations that give rise to chaotic, unpredictable variability through temporal fluctuation. The aim of this study is, therefore, to assess the ability of a coupled global climate model in reproducing observed SH climate variability using a variance decomposition procedure recently suggested by Zheng and Frederiksen (2004) and Zheng et al. (2009). DA - 2012-09 DB - ResearchSpace DP - CSIR KW - Southern Hemisphere climate KW - Climate research KW - Climate variability KW - Atmospheric Science LK - https://researchspace.csir.co.za PY - 2012 T1 - Southern hemisphere climate variability as represented by an ocean-atmosphere coupled model TI - Southern hemisphere climate variability as represented by an ocean-atmosphere coupled model UR - http://hdl.handle.net/10204/6532 ER -