A locally time-invariant metric for climate model ensemble predictions of extreme risk

TL;DR

This paper introduces a new locally time-invariant metric for evaluating climate model ensembles, specifically targeting the accurate prediction of extreme events like heatwaves in various cities.

Contribution

The paper presents a novel evaluation method that improves assessment of climate models' ability to simulate extreme risks, addressing limitations of existing performance-based weighting schemes.

Findings

Effective in predicting extreme heat days in Nairobi

Provides comparative evaluation for eight additional cities

Enhances assessment of climate model performance for extreme events

Abstract

Adaptation-relevant predictions of climate change are often derived by combining climate model simulations in a multi-model ensemble. Model evaluation methods used in performance-based ensemble weighting schemes have limitations in the context of high-impact extreme events. We introduce a locally time-invariant method for evaluating climate model simulations with a focus on assessing the simulation of extremes. We explore the behaviour of the proposed method in predicting extreme heat days in Nairobi and provide comparative results for eight additional cities.