Decadal climate predictions using sequential learning algorithms
This work addresses the challenge of enhancing climate prediction accuracy for climate scientists and policymakers, though it appears incremental as it applies existing SLAs to a known ensemble weighting problem.
The paper tackled the problem of improving decadal climate predictions by using sequential learning algorithms (SLAs) to weight ensemble models, resulting in reduced forecast errors and uncertainties compared to methods like equally weighted ensembles, linear regression, and climatology.
Ensembles of climate models are commonly used to improve climate predictions and assess the uncertainties associated with them. Weighting the models according to their performances holds the promise of further improving their predictions. Here, we use an ensemble of decadal climate predictions to demonstrate the ability of sequential learning algorithms (SLAs) to reduce the forecast errors and reduce the uncertainties. Three different SLAs are considered, and their performances are compared with those of an equally weighted ensemble, a linear regression and the climatology. Predictions of four different variables--the surface temperature, the zonal and meridional wind, and pressure--are considered. The spatial distributions of the performances are presented, and the statistical significance of the improvements achieved by the SLAs is tested. Based on the performances of the SLAs, we propose one to be highly suitable for the improvement of decadal climate predictions.