Ranking IPCC Models Using the Wasserstein Distance

TL;DR

This paper introduces a Wasserstein distance-based methodology for comparing climate models, enabling comprehensive ranking and identification of specific model weaknesses across multiple physical variables and regions.

Contribution

It presents a flexible, multidimensional approach for evaluating climate models using Wasserstein distance, allowing detailed performance assessment and targeted model improvement.

Findings

Models are effectively ranked based on their simulation accuracy.

The method identifies specific regional and variable deficiencies.

Wasserstein distance provides a comprehensive measure of model performance.

Abstract

We propose a methodology for intercomparing climate models and evaluating their performance against benchmarks based on the use of the Wasserstein distance (WD). This distance provides a rigorous way to measure quantitatively the difference between two probability distributions. The proposed approach is flexible and can be applied in any number of dimensions; it allows one to rank climate models taking into account all the moments of the distributions. By selecting the combination of climatic variables and the regions of interest, it is possible to highlight specific model deficiencies. The WD enables a comprehensive evaluation of climate model skill. We apply this approach to a selected number of physical fields, ranking the models in terms of their performance in simulating them, and pinpointing their weaknesses in the simulation of some of the selected physical fields in specific areas of the Earth.