Seasonal climate forecasting

Abstract

ABSTRACT:The fascination of seasonal climate forecasting, of which El Niño forecasting is the prime example, comes from its multi-faceted character. Not only does it pose interesting new challenges for the climate scientific community but also it is naturally linked to a great variety of socio-economic applications. Seasonal climate forecasts are indeed becoming a most important element in some policy/decision making systems, especially within the context of climate change adaptation. Thus, seriously considering the management of risks posed by the variability of climate on the seasonal to interannual time scale is key to achieving the longer terms goals of climate change adaptation strategy. This review paper explores the main components needed to construct a seasonal forecasting system, from the physical basis of climate seasonal predictions, to the tools used for producing them, to the importance of assessing their skill, to their use in risk management decision-making. Future challenges are also examined.

Seasonal precipitation forecast skill over Iran

“…In this case the forecast has high resolution but poor reliability (Troccoli et al, 2008). In Figures 8a and 8e, the frequencies histogram indicate some sharpness since the peak of histogram for the wet and dry OND season are in the intervals 25-35% and 35-45%, which are near to the climatology probability of 25%.…”

“…Prefiltering and orthogonalisation of predictors and predictands prior to CCA have been recommended by researchers such as Barnett and Preisendorfer (1987) and Barnston and Ropelewski (1992). The optimal number of modes to be retained in the CCA equations is determined using retroactive validation (Troccoli et al, 2008;. To estimate true forecast skill, a set of data independent of the training period is predicted.…”

“…The ROC (Mason, 1982;Marzban, 2004;Wilks, 2011) and reliability diagrams (Hamill, 1997;Wilks, 2011) are used for verification of the probabilistic hindcasts in order to determine skill. The ROC indicates whether or not the forecast probability was higher when an event occurred compared to when it did not (Troccoli et al, 2008). …”

CFSv2-based sub-seasonal precipitation and temperature forecast skill over the contiguous United States

“…The medium-range weather forecast is strongly influenced by atmospheric initial conditions (Vitart et al, 2008), while the seasonal climate forecast depends on slowly evolving components of the climate system (e.g., sea surface temperature and soil moisture) (Troccoli, 2010). However, since the subseasonal timescale is usually too long to be favored by the atmospheric initial conditions (Vitart, 2004) and too short to be strongly influenced by the variability of the ocean, making skillful sub-seasonal forecasts is particularly difficult and thus far has less progress than the medium-range weather forecasts and seasonal climate forecasts.…”

“…Previous studies have evaluated the potential of sub-seasonal to seasonal forecasts for heat wave forecasting (e.g., Hudson et al, 2011a;White et al, 2014), hydrological forecasting (e.g., Orth and Seneviratne, 2013;Yuan et al, 2014), water resources management (e.g., Sankarasubramanian et al, 2009), hydropower production management (e.g., Garcia-Morales and Dubus, 2007), and crop yield predic-tion (e.g., Hansen et al, 2006;Zinyengere et al, 2011). Due to the improvement of numerical models, prediction techniques, and computing resources, there is an increasing focus on sub-seasonal forecasts (e.g., Toth et al, 2007;Vitart et al, 2008;Brunet et al, 2010;Hudson et al, 2011bHudson et al, , 2013Robertson et al, 2014).…”