# Improved reliability and accuracy of CMIP5 global mean surface temperature projections

## TL;DR

This paper enhances climate projection reliability by applying weighted ensembles and past data to better estimate uncertainties in CMIP5 global temperature forecasts.

## Contribution

It introduces a method combining ensemble weighting and past data to improve the accuracy and reliability of climate projections.

## Key findings

- Reduced uncertainty ranges in GMST projections.
- Weighted ensembles outperform unweighted ones.
- Using past data improves projection reliability.

## Abstract

Climate predictions are only meaningful if the associated uncertainty is reliably estimated. A standard practice for providing climate projections is to use an ensemble of projections. The ensemble mean serves as the projection while the ensemble spread is used to estimate the associated uncertainty. The main drawbacks of this approach are the fact that there is no guarantee that the ensemble projections adequately sample the possible future climate conditions and that the quantification of the ensemble spread relies on assumptions that are not always justified. The relation between the true uncertainties associated with projections and ensemble spreads is not fully understood. Here, we suggest using simulations and measurements of past conditions in order to study both the performance of the ensemble members and the relation between the ensemble spread and the uncertainties associated with their predictions. Using an ensemble of CMIP5 long-term climate projections that was weighted according to a sequential learning algorithm and whose spread was linked to the range of past measurements, we found considerably reduced uncertainty ranges for the projected Global Mean Surface Temperature (GMST). The results suggest that by employing advanced ensemble methods and using past information, it is possible to provide more reliable and accurate climate projections.