SIMBOL: A method to co-produce impact-based seasonal outlooks

• Seasonal outlooks often fail to translate into probabilistic impact information. • Impact-Based Forecasting methods can be adapted to seasonal timescales. • Potential impacts can be characterized prior to the release of seasonal outlooks. • Co-production sessions at Regional/National Climate Outlook Forums play a vital role. • The SIMBOL method provides a scalable approach for impact-based seasonal outlooks. Communities across the world are sensitive to the impacts of seasonal climate variability, particularly in regions where distinct rainfall seasons support livelihoods and economic activities. Timely and actionable warnings of hazardous seasonal conditions and advisories tailored to different sectors can enable people to respond, reduce risks, and seize opportunities. Yet despite advances in seasonal forecasting methods and capabilities, there remains a lack of “impact-based” seasonal climate outlooks that more directly serve societal needs while preserving uncertainty information for risk-based decision making. Here we present a new method to address this gap, focusing on implementation in Regional and National Climate Outlook Forums and targeted at intermediary users who support the communication of seasonal outlooks across scales. The Seasonal IMpact-Based OutLook (SIMBOL) method provides a simple and scalable approach for use in regions across the world. We describe the conceptual basis for the method, embedded in the Impact-Based Forecasting (IBF) framework, and demonstrate its application through a case study of seasonal total rainfall impacts on groundwater in Somalia, trialled at the Greater Horn of Africa Climate Outlook Forum (GHACOF) in February 2024. We elaborate the critical role of co-production amongst different knowledge holders for characterizing impacts across all potential outlook outcomes, avoiding advisories that are biased towards the “most likely” outcome. We also discuss the importance of objective evidence from impact modelling and observations to consider antecedent conditions. Lessons learned and challenges encountered in developing the method are discussed to inform opportunities for future development and implementation in different contexts.