AI Insight
This study develops methods to estimate how extreme temperature events in desert regions are changing over time using climate model data from CMIP6. The researchers used generalized extreme value regression with Bayesian inference and tested different model selection criteria through simulation studies, finding that the Bayesian Information Criterion performed best for predicting changes in 100-year return values of temperature between 2015 and 2125. Results show significant increases in extreme maximum temperatures across all desert regions under stronger climate forcing scenarios, with Antarctic regions showing more rapid warming of extreme minima compared to extreme maxima, while hot desert regions exhibit the opposite pattern.
Why it matters
Understanding how the frequency and intensity of extreme temperature events will change in desert regions is critical for planning infrastructure, water resources, and ecosystem management in these vulnerable environments. The methodological advances in model selection for extreme value statistics from small samples can improve climate risk assessment across various environmental applications.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Abstract: Estimating changes in extremes quantiles from environmental processes non-stationary in time, from small samples is challenging since it is difficult to characterise tail non-stationarity adequately. Using annual maxima and minima of near-surface temperature (tas) from CMIP6 output for some Earth desert regions, we use generalised extreme value (GEV) regression to model changes in extreme quantiles in time. We consider candidate models with different parametric forms for the variation of GEV parameters with time, estimating parameters using Bayesian inference. We select optimal candidate models using model selection criteria, including the Akaike, Bayesian, divergence and widely-applicable information criteria. In an extreme value setting, the performance of different criteria is unreliable. We therefore undertake a simulation study using ground truth models generating data similar to our extrema, to assess performance of the criteria to minimise error in prediction of change DeltaQ in the 100-year return value of tas over (2015,2125). The Bayesian information criterion (BIC) provides best performance, out-performing the divergence and widely-applicable Information criteria. We compare BIC model selection with a stacked Bayesian model average. Using BIC-selected GEV regression, we estimate joint posterior distributions of DeltaQ, coupled over three climate scenarios, for different combinations of desert region, global climate model and climate ensemble. We find significant increases in DeltaQ for regional annual maxima under stronger forcing scenarios for all desert regions. Similar but weaker trends are observed for regional annual minima. There is evidence that extreme minima are warming more rapidly than extreme maxima in Antarctica, whereas most hot desert regions exhibit the opposite effect.
An ancillary file of supplementary material is provided.