Constraining near-term climate projections by combining observations with decadal predictions

Reducing the uncertainty associated with internal climate variability over the coming decades is crucial, as this time frame aligns with the strategic planning needs of stakeholders in climate-vulnerable sectors. Three sources of information are available: non-initialized ensembles of climate projections, initialized decadal predictions, and observations. Non-initialized ensembles of climate projections span seamlessly from the historical period to the end of the 21st century, encompassing the full range of uncertainty linked to internal climate variability. Initialized decadal predictions aim to reduce uncertainty from internal climate variability by initializing model simulations with observed oceanic states, phasing the simulated and observed climate variability modes. However, they are usually limited to 5 to 10 years, with small added value after a few years, and they are also subject to drift due to the shock from the initialization. Finally, we can also use observations that can provide information to constrain the climate evolution over the next decades. Providing the best climate information at regional scale over the next decades is therefore challenging. Previous methods addressed this challenge by using information from either the observations or the decadal predictions to constrain uninitialized projections. In this study, we propose a new method to make use of the different sources of information available to provide relevant information about near-term climate change with reduced uncertainty related to internal climate variability. First, we select a sub-ensemble of non-initialized climate simulations based on their similarity to observed predictors with multi-decadal signal potential over Europe, such as Atlantic multi-decadal variability (AMV). Then, we further refine this sub-ensemble of trajectories by selecting a subset based on its consistency with decadal predictions. We present a case study focused on predicting near-term future surface temperatures over Europe. To evaluate the effectiveness of this method in providing reliable climate information, we conduct a retrospective analysis over the historical period.