[A-CG31] Climate Variability and Predictability on Subseasonal to Centennial Timescales

[ACG31-01] Windows of opportunity for prediciting near-term climate extremes

★Invited Papers

*Nick Dunstone¹ (1.Met Office, UK)

キーワード：Prediction, Seasonal, Extremes, Dynamics

Skilful predictions of near-term regional climate extremes are key to a resilient society. However, standard methods of analysing seasonal forecasts are not optimised to identify the rarer and most impactful extremes. For example, standard tercile probability maps, used in real-time regional climate outlooks, failed to convey the extreme magnitude of summer 2022 Pakistan rainfall that was, in fact, widely predicted by seasonal forecasts. Here I argue that, in this case, a strong summer La Niña provided a window of opportunity to issue a much more confident forecast for extreme rainfall than average skill estimates would suggest. I will explore ways of building forecast confidence via a physical understanding of dynamical mechanisms, perturbation experiments to isolate extreme drivers, and simple empirical relationships. I highlight the need for more detailed routine monitoring of forecasts, with improved tools, to identify regional climate extremes and hence utilise windows of opportunity to issue trustworthy and actionable early warnings.