Prospects of Constraining Internal Variability for Multi‐Annual to Multi‐Decadal Predictions in a Perfect‐Model Framework

Abstract

Initialized climate prediction systems provide forecasts on seasonal‐to‐decadal timescales, buttheir computational cost has motivated the development of cheaper alternatives such as analog‐basedapproaches. Recent studies suggest that constraining large climate model ensembles using analogs of past seasurface temperature (SST) can yield skillful forecasts on multi‐annual to multi‐decadal timescales. Here, weadapt an analog‐based method to a perfect‐model framework, using large ensembles to assess maximumpotential skill arising from internal variability. Results show that constraining internal variability producesskillful forecasts for global and regional SSTs up to 2–3 years, with declining predictability on longertimescales. Beyond 5 years, skill largely vanishes, indicating that multi‐decadal skill in previous studies mightbe driven by forced climate response rather than internal variability. These findings benchmark the limits of acollection of analog‐based prediction approaches, based on SST pattern correlations, and provide a frameworkfor disentangling sources of skill in near‐term climate predictions.