Forecasting across timescales by crossing “bridges ofopportunity”

Stakeholders in all socio-economic sectors require reliable forecasts at multiple timescales as part of their decision-making processes. Although basing decisions mostly on a particular timescale (e.g., weather, subseasonal, seasonal) is the present status quo, this approach tends to lead to missing opportunities for more comprehensive risk-management systems (Goddard et al. 2014).

Seamless operational climate prediction --ranging from hours to multiple decades-- is a sound theoretical possibility that in practice has not been fully realised  yet. While today a variety of forecasts are produced targeting distinct timescales in a routine way, these products are generally presented to the users in different websites and bulletins, often without an assessment of how consistent the predictions are across timescales.

Because different models and strategies are used at different timescales by both national and international decadal, seasonal and subseasonal forecasting centers (e.g. Kirtman et al. 2014, Kirtman et al. 2017, Vitart et al. 2017,Mahmood et al., 2021), and skill is different at those timescales, it is key to guarantee that a physically consistent "bridging" between the forecasts exists, and that the cross-timescale predictions are overall skilful and actionable, so decision makers can conduct their work. As recent research suggests, forecasts at different timescales can be merged to mimic the idea of seamless forecasts (e.g. Befort et al., 2022). 

Here, a method for merging forecasts across timescales (Muñoz et al., 2023) is discussed and illustrated. The approach is based on the identification of "bridges of opportunity" via a cross-wavelet spectral analysis of causal information flows between prediction systems. The new method enables to formally calculate the timing and length of the windows of opportunity, and the optimal temporal aggregation to conduct the bridging of the forecast systems. The approach is illustrated for operational (a) subseasonal-to-seasonal, and (b) seasonal-to-interannual forecast systems.