Development of a new process-based constraint technique to provide decadal climate prediction over Europe

While future emission pathways are the primary source of uncertainty in long-term global climate projections, internal climate variability dominates near-term uncertainty at the regional scale. Reducing this source of uncertainty is crucial, as it aligns with the planning horizons of stakeholders in climate-sensitive sectors. To address this challenge, decadal forecasts aim to reduce this uncertainty by initializing the climate model simulations from estimates of the observed state of the climate system, in order to phase the temporal evolution of the simulated and observed modes of climate variability. However, decadal forecasts are also subject to drift due to the initialisation shock arising from mismatch between biased models and assimilated observational estimates. In this study, we propose a novel decadal forecasting method based on a process-based constraint approach. This approach aims to align model internal variability with observations by selecting the member closest to the observed state—based on a given metric of interest—from a large ensemble of non-initialized simulations, generating a new ensemble from it, and repeating this process over time to produce decadal predictions. The added value of this approach is that it does not generate any drift. We demonstrate here its application in a case study predicting near-term surface temperatures over the North Atlantic, using observed subpolar gyre sea surface temperature as the basis for member selection.