Forecasts on sub-seasonal to inter-decadal timescales have a diverse range of applications in climate services, including disaster preparedness, and short- mid- and long-term planning. However, the complexity of methods, uncertainty assessment and ways to merge forecasts across timescales presents a significant knowledge and skill gap. The WCRP School on Climate Prediction Across Timescales aims to address these gaps, and it is, designed for early-career researchers and advanced students interested in the science and application of climate predictions. The school will offer foundational and advanced lectures in the mornings and interactive, hands-on lab sessions in the afternoons.
Objectives and outcomes
– Foster understanding of key concepts including predictability, forecast skill, sources of predictability, and cross-timescale interactions
– Provide an overview of novel tools to determine the predictability and assess forecast skill.
– Introduce emerging tools in machine learning and AI for forecasting.
– Develop practical skills through interactive lab sessions focused on real data
Participants will:
– Gain new theoretical and technical skills
– Engage in group discussions and applied exercises with real (i.e. not synthetic) data.
