Non-parametric multimodel Regional Frequency Analysis applied to climate change detection and attribution

TL;DR

This paper introduces a non-parametric clustering method for regional frequency analysis that improves climate change detection by accounting for dependence and biases in climate models, enhancing the attribution of anthropogenic effects on heavy precipitation patterns.

Contribution

It develops a covariate-free clustering algorithm based on a new dissimilarity measure that considers dependence and RFA constraints, with proven asymptotic properties, applied to climate model data.

Findings

Clustering improves spatial coherence of climate regions.

Method outperforms margin-based and dependence-based approaches.

Identifies anthropogenic impact on precipitation extremes.

Abstract

A recurrent question in climate risk analysis is determining how climate change will affect heavy precipitation patterns. Dividing the globe into homogeneous sub-regions should improve the modelling of heavy precipitation by inferring common regional distributional parameters. In addition, in the detection and attribution (D&A) field, biases due to model errors in global climate models (GCMs) should be considered to attribute the anthropogenic forcing effect. Within this D&A context, we propose an efficient clustering algorithm that, compared to classical regional frequency analysis (RFA) techniques, is covariate-free and accounts for dependence. It is based on a new non-parametric dissimilarity that combines both the RFA constraint and the pairwise dependence. We derive asymptotic properties of our dissimilarity estimator, and we interpret it for generalised extreme value distributed pairs. As a D&A application, we cluster annual daily precipitation maxima of 16 GCMs from the coupled model intercomparison project. We combine the climatologically consistent subregions identified for all GCMs. This improves the spatial clusters coherence and outperforms methods either based on margins or on dependence. Finally, by comparing the natural forcings partition with the one with all forcings, we assess the impact of anthropogenic forcing on precipitation extreme patterns.