**5.8. The multi-model approach**

Another approach to the quantification of model error is to combine forecasts from a number of different yet plausible dynamical models. Multi-model combination aims to benefit from a better representation of uncertainty in model physics, model configuration and initialisation strategy. The multi-model approach is widely used in operational weather prediction (out to 7 to 10 days ahead). Model combination is complicated by varying grid resolutions, ensemble sizes, different model skill and mean biases between models, as well as unresolved questions about model weighting. The multi-model approach has been criticized on the grounds that combining a forecast from a bad model with a forecast from a good model may result in a less skillful forecast if one does not weight models to reflect their level of skill.

### **5.9. Downscaling**

Another family of model adjustments is motivated by the mismatch between the resolved scale of GCMs and the scale at which most decisions are made. GCMs can provide useful forecasts of atmospheric fields at seasonal timescales but are typically run at coarse spatial resolution such that the direct model output represents spatial averages over thousands of square kilometres (typically grid cells some 100 km in size). This coarse resolution poses a problem for applications that require forecasts at a finer spatial scale, especially in regions where the real topography causes local rainfall to diverge significantly from model grid averages. Where the errors to be corrected are primarily a result of the spatial scale of the GCM, the correction is called 'downscaling'. Downscaling is desired for those Pacific islands where the interaction between the prevailing winds and local topography is a significant driver of variability, but the GCM does not resolve local topography.

The primary goal of downscaling is to replace the large-scale grid box climate variable, in this case rainfall, with rainfall that is better representative of the local situation. One method of downscaling is that of meteorological analogues. In this approach, large-scale synoptic meteorological fields are used as predictors for small scale variables. The output of a seasonal timescale GCM is used to generate forecasts of the large-scale fields. The analogue methods has been shown has been shown to produce good results for Twentieth Century South Eastern Australian rainfall in the context of downscaling for climate change projections[33]. As with most statistical downscaling techniques, analogue downscaling is computationally cheap, in contrast to resource-intensive dynamical downscaling using nested atmospheric models.

Figure 6 shows the topography resolved by a high-resolution numerical weather prediction model, and the topography resolved by a coarse resolution seasonal prediction model.

**Figure 8.** Left: topography resolved in a high resolution weather model. Right: topography resolved in a coarse resolution seasonal prediction GCM.
