000147884 001__ 147884
000147884 005__ 20250923084407.0
000147884 0247_ $$2doi$$a10.1080/01621459.2024.2427430
000147884 0248_ $$2sideral$$a141306
000147884 037__ $$aART-2024-141306
000147884 041__ $$aeng
000147884 100__ $$0(orcid)0000-0003-3859-0248$$aCastillo-Mateo, Jorge$$uUniversidad de Zaragoza
000147884 245__ $$aSpatio-temporal modeling for record-breaking temperature events in Spain
000147884 260__ $$c2024
000147884 5060_ $$aAccess copy available to the general public$$fUnrestricted
000147884 5203_ $$aRecord-breaking temperature events are now very frequently in the news, viewed as evidence of climate change. With this as motivation, we undertake the first substantial spatial modeling investigation of temperature record-breaking across years for any given day within the year. We work with a dataset consisting of over 60 years (1960–2021) of daily maximum temperatures across peninsular Spain. Formal statistical analysis of record-breaking events is an area that has received attention primarily within the probability community, dominated by results for the stationary record-breaking setting with some additional work addressing trends. Such effort is inadequate for analyzing actual record-breaking data. Resulting from novel and detailed exploratory data analysis, we propose rich hierarchical conditional modeling of the indicator events which define record-breaking sequences. After suitable model selection, we discover explicit trend behavior, necessary autoregression, significance of distance to the coast, useful interactions, helpful spatial random effects, and very strong daily random effects. Illustratively, the model estimates that global warming trends have increased the number of records expected in the past decade almost 2-fold, 1.93 (1.89,1.98), but also estimates highly differentiated climate warming rates in space and by season. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
000147884 536__ $$9info:eu-repo/grantAgreement/ES/DGA-CUS/1668-2022$$9info:eu-repo/grantAgreement/ES/DGA-CUS/581-2020$$9info:eu-repo/grantAgreement/ES/DGA/E46-23R$$9info:eu-repo/grantAgreement/ES/MICINN/PID2020-116873GB-I00$$9info:eu-repo/grantAgreement/ES/MICINN/PID2023-150234NB-I00$$9info:eu-repo/grantAgreement/EUR/MICINN/TED2021-130702B-I00$$9info:eu-repo/grantAgreement/ES/MIU/UNI 551-2021
000147884 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000147884 590__ $$a3.0$$b2024
000147884 592__ $$a4.103$$b2024
000147884 591__ $$aSTATISTICS & PROBABILITY$$b16 / 167 = 0.096$$c2024$$dQ1$$eT1
000147884 593__ $$aStatistics, Probability and Uncertainty$$c2024$$dQ1
000147884 593__ $$aStatistics and Probability$$c2024$$dQ1
000147884 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000147884 700__ $$aGelfand, Alan E.
000147884 700__ $$0(orcid)0000-0003-2461-8588$$aGracia-Tabuenca, Zeus$$uUniversidad de Zaragoza
000147884 700__ $$0(orcid)0000-0002-0174-789X$$aAsín, Jesús$$uUniversidad de Zaragoza
000147884 700__ $$0(orcid)0000-0002-9052-9674$$aCebrián, Ana C.$$uUniversidad de Zaragoza
000147884 7102_ $$12007$$2265$$aUniversidad de Zaragoza$$bDpto. Métodos Estadísticos$$cÁrea Estadís. Investig. Opera.
000147884 773__ $$g(2024), [13 pp.]$$pJ. Am. Stat. Assoc.$$tJOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION$$x0162-1459
000147884 8564_ $$s2734335$$uhttps://zaguan.unizar.es/record/147884/files/texto_completo.pdf$$yVersión publicada
000147884 8564_ $$s3597462$$uhttps://zaguan.unizar.es/record/147884/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000147884 909CO $$ooai:zaguan.unizar.es:147884$$particulos$$pdriver
000147884 951__ $$a2025-09-22-14:28:53
000147884 980__ $$aARTICLE