Inference for extreme spatial temperature events in a changing climate with application to Ireland
D\'aire Healy, Jonathan Tawn, Peter Thorne, Andrew Parnell

TL;DR
This paper develops a novel spatial-temporal extreme value model for Irish temperature data, revealing significant changes in extreme temperature behavior and spatial coverage over time, with implications for climate risk assessment.
Contribution
It introduces a new semi-parametric model capturing non-stationarity in extreme temperature events, integrating observational and climate model data for improved analysis.
Findings
Significant increase in extreme temperature events over time.
Expanded spatial extent of temperature extremes.
Model effectively combines observational and climate model data.
Abstract
We investigate the changing nature of the frequency, magnitude and spatial extent of extreme temperatures in Ireland from 1931 to 2022. We develop an extreme value model that captures spatial and temporal non-stationarity in extreme daily maximum temperature data. We model the tails of the marginal variables using the generalised Pareto distribution and the spatial dependence of extreme events by a semi-parametric Brown-Resnick r-generalised Pareto process, with parameters of each model allowed to change over time. We use weather station observations for modelling extreme events since data from climate models (not conditioned on observational data) can over-smooth these events and have trends determined by the specific climate model configuration. However, climate models do provide valuable information about the detailed physiography over Ireland and the associated climate response. We…
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Taxonomy
TopicsClimate variability and models · Meteorological Phenomena and Simulations · Hydrology and Drought Analysis
