资源环境科技发展态势分析平台

Regional-scale seasonal climate outlooks are typically produced using forecast information either local to the region or from another area with teleconnections to the region. Dynamical global long-range forecast (LRF) systems can provide both types of information, and these two approaches are compared in the context of seasonal rainfall forecasts for two adjoining areas in the Greater Horn of Africa region in tropical East Africa. The direct method utilizes the unprocessed LRF outputs for the region. For the "indirect" method, canonical correlation analysis is used and works by identifying patterns in historical LRF predictions over large tropical domains that relate to observed variability in the East Africa regions. For a given year, projections of the LRF forecasts onto those patterns can then be employed to construct the regional forecasts. This approach takes advantage of the tendency for LRF systems to have greater skill for large-scale variability than for smaller regional-scale features. Using case studies, it is found the two approaches contain complementary information: the indirect approach can provide notable skill benefits in years with strong large-scale forcing while in some years, particularly when large-scale signals are weak, the "direct" forecast are superior. Skill comparisons over many years found that, although results are region/season dependent, in general the indirect approach has higher skill overall-with improvements equal to or greater than those afforded by a 1-month reduction in lead time with the direct approach. Results for both methods used separately and in combination are provided for the March-April-May and September-to-December seasons in the two regions, using data from currently operational dynamical LRF systems. Skill was best for the September-to-December season in the southern region, using "indirect" forecasts. The "direct" approach was better than "indirect" for the March-to-May season in the northern region. In general, combination did not produce a substantial benefit.