Deciphering Prediction Windows of Opportunity: A Cross Time-Scale Causality Framework &#160;
Authors: Materia Stefano , Ardilouze Constantin , Muñoz Ángel G. 
Published: March 2024

While subseasonal forecasts often exhibit limited skill across mid-latitudes, occasional improvements are observed in specific locations during certain periods, known as "windows of opportunity." Understanding the causal factors behind these windows is complex due to the diverse and interdependent nature of predictors, their spatial and temporal variability, and the challenges in establishing causality relationships.&#160;Traditional lagged-correlations methods provide only a partial view, lacking insights into causality. Based on previous work on the role of land surface processes, multi-model subseasonal model skill assessment and the use of causality metrics in predictions across timescales (e.g. Ardilouze et al., 2020, 2021; Materia et al 2020, 2022; Mu&#241;oz et al., 2023), here we propose an approach based on the Liang-Kleeman information flow, allowing the assessment of statistically significant causal links across various lead times.Applied to reforecast and reanalysis data, our framework successfully identifies significant predictability drivers -involving sea-surface temperatures, atmospheric circulation and remote and local land-surface processes-, revealing their interference (interplay), evolving patterns and prevalence from seasonal to subseasonal scales.&#160;Furthermore, the comparison between reanalysis and reforecast results aids in assessing the capability of models to capture these causality features, suggesting additional ways to conduct model diagnostics. We illustrate here the theoretical background by showcasing the causal factors influencing a window of opportunity identified from a multimodel subseasonal reforecast.&#160;ReferencesArdilouze, C., Materia, S., Batt&#233;, L., Benassi, M., &amp; Prodhomme, C. (2020). Precipitation response to extreme soil moisture conditions over the Mediterranean. Climate Dynamics, 1, 1&#8211;16. https://doi.org/10.1007/S00382-020-05519-5/TABLES/2Ardilouze, C., Specq, D., Batt&#233;, L., &amp; Cassou, C. (2021). Flow dependence of wintertime subseasonal prediction skill over Europe. Weather and Climate Dynamics, 2(4), 1033-1049. https://doi.org/10.5194/wcd-2-1033-2021&#160;Materia, S., Mu&#241;oz, &#193;. G., &#193;lvarez-Castro, M. C., Mason, S. J., Vitart, F., &amp; Gualdi, S. (2020). Multi-model subseasonal forecasts of spring cold spells: potential value for the hazelnut agribusiness. Weather and Forecasting. https://doi.org/10.1175/waf-d-19-0086.1&#160;Materia, S., Ardilouze, C., Prodhomme, C., &amp; et al. (2022). Summer temperature response to extreme soil water conditions in the Mediterranean transitional climate regime. Climate Dynamics, 58, 1943&#8211;1963. https://doi.org/10.1007/s00382-021-05815-8Mu&#241;oz, &#193;. G., Doblas-Reyes, F., DiSera, L., Donat, M., Gonz&#225;lez-Reviriego, N., Soret, A., Terrado, M., &amp; Torralba, V. (2023). Hunting for &#8220;Windows of Opportunity&#8221; in Forecasts Across Timescales? Cross it. EGUGA, EGU-15594. https://doi.org/10.5194/EGUSPHERE-EGU23-15594&#160;