Landman, WAMason, SJ2013-05-272013-05-272012-11Landman, WA, Mason, SJ. 2012. Improving the reliability of seasonal climate forecasts through empirical downscaling and multi-model considerations. In: National Conference on Global Change, Birchwood Hotel Boksburg, 26-28 November 2012, 15pphttp://hdl.handle.net/10204/6762National Conference on Global Change, Birchwood Hotel Boksburg, 26-28 November 2012Investigation into the predictability of seasonal climate extremes such as droughts and flood seasons provide insight into the limits of predictability of the ocean-land-atmosphere system. However, expressions on what the future may hold always embody degrees of uncertainty, often expressed as a probabilistic outcome. Since seasonal prediction is inherently probabilistic in nature they are judged (i.e. verified) probabilistically through attributes including reliability, resolution, discrimination and sharpness. We present seasonal prediction verification for the equatorial Pacific Ocean (where El Niño and La Niña events occur) sea-surface temperatures. The verification is done over a recent multi-decadal period for which hindcasts (re-forecasts) have been generated by a statistical model and by state-of-the-art fully coupled ocean-atmosphere general circulation models. Since forecast users generally require well-calibrated probability forecasts we employ a model output statistics approach to improve on raw coupled model forecasts, and further enhance the forecasts by considering a range of possible methods for combining the coupled models' output in order to provide the most informative forecasts of future observables.enSeasonal climate forecastsClimate extremesOcean-land-atmosphere systemLa NiñaEl NiñoSeasonal-to-interannual variabilityOcean-atmosphere coupled modelsRetrospective forecastingModel output statisticsMulti-modelsForecast skill and predictabilityConference PresentationLandman, W., & Mason, S. (2012). Improving the reliability of seasonal climate forecasts through empirical downscaling and multi-model considerations; presentation. http://hdl.handle.net/10204/6762Landman, WA, and SJ Mason. "Improving the reliability of seasonal climate forecasts through empirical downscaling and multi-model considerations; presentation." (2012): http://hdl.handle.net/10204/6762Landman W, Mason S, Improving the reliability of seasonal climate forecasts through empirical downscaling and multi-model considerations; presentation; 2012. http://hdl.handle.net/10204/6762 .TY - Conference Presentation AU - Landman, WA AU - Mason, SJ AB - Investigation into the predictability of seasonal climate extremes such as droughts and flood seasons provide insight into the limits of predictability of the ocean-land-atmosphere system. However, expressions on what the future may hold always embody degrees of uncertainty, often expressed as a probabilistic outcome. Since seasonal prediction is inherently probabilistic in nature they are judged (i.e. verified) probabilistically through attributes including reliability, resolution, discrimination and sharpness. We present seasonal prediction verification for the equatorial Pacific Ocean (where El Niño and La Niña events occur) sea-surface temperatures. The verification is done over a recent multi-decadal period for which hindcasts (re-forecasts) have been generated by a statistical model and by state-of-the-art fully coupled ocean-atmosphere general circulation models. Since forecast users generally require well-calibrated probability forecasts we employ a model output statistics approach to improve on raw coupled model forecasts, and further enhance the forecasts by considering a range of possible methods for combining the coupled models' output in order to provide the most informative forecasts of future observables. DA - 2012-11 DB - ResearchSpace DP - CSIR KW - Seasonal climate forecasts KW - Climate extremes KW - Ocean-land-atmosphere system KW - La Niña KW - El Niño KW - Seasonal-to-interannual variability KW - Ocean-atmosphere coupled models KW - Retrospective forecasting KW - Model output statistics KW - Multi-models KW - Forecast skill and predictability LK - https://researchspace.csir.co.za PY - 2012 T1 - Improving the reliability of seasonal climate forecasts through empirical downscaling and multi-model considerations; presentation TI - Improving the reliability of seasonal climate forecasts through empirical downscaling and multi-model considerations; presentation UR - http://hdl.handle.net/10204/6762 ER -