Improving the forecast quality of near-term climate projections by constraining internal variability based on decadal predictions and observations

Abstract

Abstract Projections of near-term climate change in the next few decades are subject to substantial uncertainty from internal climate variability. Approaches to reduce this uncertainty by constraining the phasing of climate variability based on large ensembles of climate simulations have recently been developed. These approaches select those ensemble members that are in closer agreement with sea surface temperature patterns from either observations or initialized decadal predictions. Previous studies demonstrated the benefits of these constraints for projections up to 20 years into the future, but these studies applied the constraints to different ensembles of climate simulations, which prevents a consistent comparison of methods or identification of specific advantages of one method over another. Here we apply several methods to constrain internal variability phases, using either observations or decadal predictions as constraining reference, to an identical multi-model ensemble consisting of 311 simulations from 37 models from the Coupled Model Intercomparison Project phase 6 (CMIP6), and compare their forecast qualities. We show that constraining based on both observations and decadal predictions significantly enhances the skill of 10 and 20-year projections for near-surface temperatures in some regions, and that constraining based on decadal predictions leads to the largest added value in terms of probabilistic skill. We further explore the sensitivity to different implementations of the constraint that focus on the patterns of either internal variability alone or a combination of internal variability and long-term changes in response to forcing. Looking into the near-term future, all variations of the constraints suggest an accelerated warming of large parts of the Northern Hemisphere for the period 2020–2039, in comparison to the unconstrained CMIP6 ensemble.