From dynamical model predictions to seasonal climate forecasts

Producing a seasonal climate forecast from a dynamical model involves a great deal more than simply running the model and viewing the results. The first problem is to decide which dynamical model(s) should be run given the practical constraints of computing resources. In this chapter the pros and cons of using the more computationally intensive fully coupled models compared to atmosphere-only models are discussed. After running a dynamical model, regardless of its complexity, corrections need to be made for systematic errors because the model's climatology and that of the observed climate are invariably different. Some simple procedures for correcting these systematic errors are assessed, but more sophisticated methods are advisable to adjust for spatial displacements of the model climate. Since the model predictions represent large spatial averages, and generally are presented as seasonal averages, downscaling may be required to make the forecast relevant for specific locations, and to provide more detailed information about the statistics of weather within the season. Commonly used spatial and temporal downscaling procedures are described. Some procedures for describing the uncertainty in the forecast are discussed (further details are provided in Chapter 9). Evidence is presented that forecasts can be improved by combining outputs from different models. Finally, the reliability of the forecast needs to be determined by verification of a historical set of forecasts. Verification procedures are discussed in Chapter 10.