Estimating Concurrent Climate Extremes: A Conditional Approach

TL;DR

This paper introduces a statistical framework combining extreme value theory and quantile regression to estimate the likelihood and magnitude of concurrent climate extremes, such as wind speed conditioned on precipitation, using climate model data.

Contribution

It develops a novel conditional approach for modeling bivariate climate extremes, integrating univariate extreme value models with dependence analysis.

Findings

Framework effectively estimates concurrent extremes in climate data.

Performance validated through simulation and climate model analysis.

Provides insights into joint behavior of wind speed and precipitation.

Abstract

Simultaneous concurrence of extreme values across multiple climate variables can result in large societal and environmental impacts. Therefore, there is growing interest in understanding these concurrent extremes. In many applications, not only the frequency but also the magnitude of concurrent extremes are of interest. One way to approach this problem is to study the distribution of one climate variable given that another is extreme. In this work we develop a statistical framework for estimating bivariate concurrent extremes via a conditional approach, where univariate extreme value modeling is combined with dependence modeling of the conditional tail distribution using techniques from quantile regression and extreme value analysis to quantify concurrent extremes. We focus on the distribution of daily wind speed conditioned on daily precipitation taking its seasonal maximum. The Canadian Regional Climate Model large ensemble is used to assess the performance of the proposed framework both via a simulation study with specified dependence structure and via an analysis of the climate model-simulated dependence structure.