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A spread-versus-error framework to reliably quantify the potential for subseasonal windows of forecast opportunity
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DOI：10.5194/wcd-7-767-2026.png)
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Abstract
En 中文
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 spread reliability is controlled by at least three intertwined factors: (1) sampling error; (2) the magnitude of physically driven spread variability and (3) 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 (i.e. reliable spread fluctuations) for 50-member ensembles; consistent with robust spread modulation by slowly varying teleconnections. In contrast; areas like eastern Canada show very low SRS (little or no spread 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.
Keywords:
Subseasonal forecasting
Ensemble spread reliability
Spread-Reliability Slope (SRS)
Forecast uncertainty
Teleconnections