Abstract: he majority of international climate modeling centers now produce seasonal rainfall forecasts from coupled general circulation models (GCMs). Seasonal rainfall forecasting is highly challenging, and GCM forecast accuracy ...

Abstract: agged oceanic and atmospheric climate indices are potentially useful predictors of seasonal rainfall totals. A rigorous Bayesian joint probability modeling approach is applied to find the cross-validation predictive densities ...

Abstract: here are a number of challenges that must be overcome if GCM forecasts are to be widely adopted in climate-sensitive industries such as agriculture and water management. GCM outputs are frequently biased relative to ...

Abstract: erging forecasts from multiple models has the potential to combine the strengths of individual models and to better represent forecast uncertainty than the use of a single model. This study develops a Bayesian model averaging ...

Abstract: oupled general circulation models (GCMs) are increasingly being used to forecast seasonal rainfall, but forecast skill is still low for many regions. GCM forecasts suffer from systematic biases, and forecast probabilities ...

Abstract: Recent research demonstrates that dynamical models sometimes fail to represent observed teleconnection patterns associated with predictable modes of climate variability. As a result, model forecast skill may be reduced. ...

Abstract: AbstractGCMs are used by many national weather services to produce seasonal outlooks of atmospheric and oceanic conditions and fluxes. Postprocessing is often a necessary step before GCM forecasts can be applied in practice. ...