Joint space-time modelling for upper daily maximum and minimum temperature record-breaking

Castillo-Mateo, Jorge (Universidad de Zaragoza) ; Gracia-Tabuenca, Zeus (Universidad de Zaragoza) ; Asín, Jesús (Universidad de Zaragoza) ; Cebrián, Ana C (Universidad de Zaragoza) ; Gelfand, Alan E

Resumen: Record-breaking temperature events are now frequently in the news, proffered as evidence of climate change, and often bring significant economic and human impacts. Our previous work undertook the first substantial spatial modelling investigation of temperature record-breaking across years for any given day within the year, employing a dataset consisting of over 60 years of daily maximum temperatures across peninsular Spain. That dataset also supplies daily minimum temperatures (which, in fact, are now available through 2023). Here, the dataset is converted into a daily pair of binary events, indicators, for that day, of whether a yearly record was broken for the daily maximum temperature and/or for the daily minimum temperature. Joint modelling addresses several inference issues: (i) defining/modelling record-breaking with bivariate time series of yearly indicators, (ii) strength of relationship between record-breaking events, (iii) prediction of joint, conditional and marginal record-breaking, (iv) persistence in record-breaking across days, and (v) spatial interpolation across peninsular Spain. We substantially expand our previous work to enable investigation of these issues. We observe strong correlation between both processes but a growing trend of climate change that is well differentiated between them both spatially and temporally as well as different strengths of persistence and spatial dependence.

Idioma: Inglés

DOI: 10.1093/jrsssc/qlaf046

Año: 2025

Publicado en: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS (2025), qlaf046 [28 pp.]
ISSN: 0035-9254

Financiación: info:eu-repo/grantAgreement/ES/AEI/TED2021-130702B-I00
Financiación: info:eu-repo/grantAgreement/ES/DGA/E46-23R
Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2020-116873GB-I00
Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2023-150234NB-I00

Tipo y forma: Article (PostPrint)

Área (Departamento): Área Estadís. Investig. Opera. (Dpto. Métodos Estadísticos)