English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

A recurrent question in climate risk analysis is determining how climatechange will affect heavy precipitation patterns. Dividing the globe intohomogeneous sub-regions should improve the modelling of heavy precipitation byinferring common regional distributional parameters. In addition, in thedetection and attribution (D&A) field, biases due to model errors in globalclimate models (GCMs) should be considered to attribute the anthropogenicforcing effect. Within this D&A context, we propose an efficient clusteringalgorithm that, compared to classical regional frequency analysis (RFA)techniques, is covariate-free and accounts for dependence. It is based on a newnon-parametric dissimilarity that combines both the RFA constraint and thepairwise dependence. We derive asymptotic properties of our dissimilarityestimator, and we interpret it for generalised extreme value distributed pairs.  As a D&A application, we cluster annual daily precipitation maxima of 16 GCMsfrom the coupled model intercomparison project. We combine the climatologicallyconsistent subregions identified for all GCMs. This improves the spatialclusters coherence and outperforms methods either based on margins or ondependence. Finally, by comparing the natural forcings partition with the onewith all forcings, we assess the impact of anthropogenic forcing onprecipitation extreme patterns.

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