Flexible forecast presentation overcomes longstanding obstacles to using probabilistic seasonal forecasts

We describe an innovative forecast presentation that aims to overcome obstacles to using seasonal climate forecasts for decision making, trace factors that influenced how seasonal forecast conventions have evolved, and describe a workshop process for training and supporting farmers in sub-Saharan Africa to use probabilistic seasonal forecasts. Mainstreaming seasonal climate forecasts through Regional Climate Outlook Forums (RCOFs) was an important milestone in the development of climate services. Most RCOFs and National Meteorological Services (NMS) adopted a subjective process to arrive at a consensus among different sources of prediction, and express the forecast as probabilities that rainfall in the upcoming season will fall in “below-normal,” “normal” or “above-normal” historical tercile categories. The Flexible Forecast is an online presentation that rectifies the main criticisms of the tercile convention by presenting downscaled forecasts as full probability distributions in probability-of-exceedance format along with the historical climate distribution. A map view provides seasonal forecast quantities, anomalies, or probabilities of experiencing above or below a user-selected threshold in amount or percentile, at the spatial resolution of the underlying gridded data (typically 4 to 5 km). We discuss factors that contributed to the persistence of the tercile convention, and milestones that paved the way to adopting seasonal forecast methods and formats that better align with user needs. The experience of adopting the new flexible forecast presentation regionally and at a national level in Eastern Africa illustrates the challenges and how they can be overcome. We also describe a seasonal forecast training and planning workshop process that has been piloted with smallholder farmers in several African countries. Beginning with participants' collective memory of past seasonal climate variations, the process leads them incrementally to understand the forecast presented in probability-of-exceedance format, and apply it to their seasonal planning decisions.