Quantifying the uncertainties in an ensemble of decadal climate predictions

TL;DR

This paper introduces a new method for quantifying uncertainties in decadal climate predictions that does not assume any specific distribution and demonstrates improved performance over existing methods.

Contribution

A novel, assumption-free method for estimating uncertainties in climate ensemble predictions, validated with CMIP5 data, enhancing reliability of climate forecasts.

Findings

The proposed method outperforms two existing uncertainty estimation techniques.

It provides more accurate and reliable uncertainty ranges for climate predictions.

The method improves the assessment of significance in climate studies.

Abstract

Meaningful climate predictions must be accompanied by their corresponding range of uncertainty. Quantifying the uncertainties is non-trivial, and different methods have been suggested and used in the past. Here, we propose a method that does not rely on any assumptions regarding the distribution of the ensemble member predictions. The method is tested using the CMIP5 1981-2010 decadal predictions and is shown to perform better than two other methods considered here. The improved estimate of the uncertainties is of great importance for both practical use and for better assessing the significance of the effects seen in theoretical studies.