A spread-versus-error framework to reliably quantify the potential for subseasonal windows of forecast opportunity

Abstract. Mid-latitude forecast skill at subseasonal timescales often depends on 'windows of opportunity' that may be opened by slowly varying modes such as ENSO, the MJO or stratospheric variability. Most previous work has focused on the predictability of ensemble-mean states, with less attention paid to the reliability of such forecasts and how it relates to ensemble spread, which directly reflects intrinsic forecast uncertainty. Here, we introduce a spread-versus-error framework based on the Spread-Reliability Slope (SRS) to quantify whether fluctuations in ensemble spread provide reliable information about variations in forecast error. Using ECMWF S2S forecasts and ERA5 reanalysis data, aided by idealised toy-model experiments, we show that reliability is controlled by at least three intertwined factors: sampling error, the magnitude of physically driven spread variability and model fidelity in representing that variability. Regions such as northern Europe, the mid-east Pacific, and the tropical west Pacific exhibit robustly high SRS values (≈ 0.6 or greater for 50-member ensembles), consistent with robust modulation by slowly varying teleconnections. In contrast, areas like eastern Canada show little or no reliability, even for 100-member ensembles, reflecting limited low-frequency modulation of forecast uncertainty. We further demonstrate two practical implications: (i) a simple variance rescaling yields a post-processed 'corrected spread' that enforces reliability and may help to bridge ensemble output with user needs; and (ii) time averaging effectively boosts ensemble size, allowing even 10-member ensembles to achieve reliability of spread fluctuations comparable to larger ensembles. Finally, we discuss possible links to the signal-to-noise paradox and emphasize that adequate representation of ensemble spread variability is crucial for exploiting subseasonal windows of opportunity.