Assessing space and time changes in daily maximum temperature in the Ebro basin (Spain) using model-based statistical tools
Resumen: There is continuing interest in the investigation of change in temperature over space and time. For this analysis, we offer statistical tools to illuminate changes temporally, at desired temporal resolution, and spatially, using data generated from suitable space–time models. The proposed tools can be used with the output from any suitable model fitted to any set of spatially referenced time series data. The tools to assess space and time changes include spatial surfaces of probabilities and spatial extents for events defined by exceeding a threshold. The spatial surfaces capture the spatial variation in the probability or risk of an exceedance event, while the spatial extents capture the expected proportion of incidence of an event for a region of interest. This approach is used analyse the changes in daily maximum temperature in an inland Mediterranean region (NE of Spain) in the period 1956–2015. The area is very heterogeneous in orography and climate, including the central Ebro valley and part of the Pyrenees. We use a collection of daily temperature series obtained from simulation under a Bayesian daily temperature model fitted to 18 stations in that area. The results for the summer period show that, although there is an increasing risk in all the events used to quantify the effects of climate change, it is not spatially homogeneous, with the largest increase arising in the centre of the Ebro valley and the Eastern Pyrenees area. The risk of an increase in the average daily maximum temperature from 1966–1975 to 2006–2015 higher than 1°C is higher than 0.5 over all of the region, and close to 1 in the previous areas. The extent of daily maximum temperature higher than the reference mean has increased 3.5% per decade. The mean of the extent indicates that 95% of the area under study has suffered a positive increment of the average temperature, and almost 70% an increment higher than 1°C.
Idioma: Inglés
DOI: 10.1002/joc.8305
Año: 2023
Publicado en: International Journal of Climatology 43, 16 (2023), 8036-8051
ISSN: 0899-8418

Factor impacto JCR: 3.5 (2023)
Categ. JCR: METEOROLOGY & ATMOSPHERIC SCIENCES rank: 41 / 110 = 0.373 (2023) - Q2 - T2
Factor impacto CITESCORE: 7.5 - Atmospheric Science (Q1)

Factor impacto SCIMAGO: 1.221 - Atmospheric Science (Q2)

Financiación: info:eu-repo/grantAgreement/ES/DGA/E46-20R
Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2020-116873GB-I00
Financiación: info:eu-repo/grantAgreement/EUR/MICINN/TED2021-130702B-I00
Tipo y forma: Artículo (Versión definitiva)
Área (Departamento): Área Estadís. Investig. Opera. (Dpto. Métodos Estadísticos)

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Artículos > Artículos por área > Estadística e Investigación Operativa



 Registro creado el 2023-12-15, última modificación el 2024-11-25


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