Analysis of global bias-corrected seasonal Forecasts: Where do the strengths and challenges lie?

Droughts, prolonged heatwaves, heavy rainfall and multiple large-scale floods - recent years have shown that global climate change requires more sustainable and timely water management at all levels. In particular, for the optimized use of water resources for irrigation or hydropower generation, it is essential to know their likely availability in the coming months anywhere in the world. This sub-seasonal to seasonal time range is covered by seasonal forecasting systems such as SEAS5 developed by the European Center for Medium-Range Weather Forecasts (ECMWF). These systems have the potential to provide important data for improving water management practices. However, without a bias correction, the data deviate strongly from the actual data. We have shown for several regions of the world that the Bias Correction and Spatial Disaggregation (BCSD) method can significantly improve predictive capability. By storing fixed cumulative distribution functions (CDFs) and parallelization, we were able to extend the system from the regional to the global level, i.e. to produce BCSD predictions for the entire globe and present this version at EGU24.

Our next step is to evaluate the resulting seasonal forecasts in terms of their predictive quality. This evaluation is carried out using several measures of quality, including the Continuous Ranked Probability Skill Score (CRPSS) and the Brier Skill Score (BSS). To illustrate this, the strengths and weaknesses of the bias-corrected seasonal forecasts are highlighted using two regions. The focus is on the Sahel region, which has a lower forecast quality despite its high social relevance, and on the Lake Victoria catchment area, for which a high forecast quality is achieved. The aim is to achieve as precise an assessment as possible of the global forecast quality, which allows a realistic assessment of the forecasts, particularly in regions with strong fluctuations in water availability. By providing this bias-corrected forecast data in near real time together with an analysis of its quality, better estimates will be available for direct use by water managers or as input for subsequent modeling processes.