Seamless climate information for the next months to multiple years: merging of seasonal and decadal predictions, and their comparison to multi-annual predictions

Abstract. Stakeholders across climate-sensitive sectors often require climate information that spans multiple timescales, e.g. from months to several years, to inform planning and decision-making. To satisfy this information request, climate services are typically developed by separately using seasonal predictions for the first few months, and decadal predictions for subsequent years. This shift in information source can introduce inconsistencies. To ensure the information is consistent across forecast time scales, some centres have produced initialised multi-annual predictions, run twice a year and covering 2–3 years ahead, with increased ensemble sizes. An alternative methodology to provide coherent climate information across timescales involves temporal merging, where seamless predictions are created by postprocessing seasonal and decadal forecasts in combination. One approach selects members from large ensembles of decadal predictions or climate projections that closely align with seasonal predictions or past observations, transferring short-term predictability into longer timescales.

This study evaluates the skill of seamless forecasts using different constraints (e.g. variables, regions, temporal aggregations), and compares them with initialised multi-annual predictions. The analysis focuses on predictions of the Niño3.4 index and spatial fields of surface temperature, precipitation, and sea level pressure for the first three forecast years. Results show that while initialised multi-annual predictions achieve the highest overall skill, temporally merged forecasts offer a computationally efficient alternative that still performs well and can be produced regularly as monthly updates of observations or seasonal predictions become available. Besides, both sets of predictions outperform the unconstrained ensembles of decadal predictions and climate projections over large regions. During the period where the seasonal predictions and seamless predictions overlap, their skill is comparable. These findings illustrate the potential of temporal merging as a cost-effective strategy for extending climate information across timescales and enhancing coherence for operational climate services provision.