Seasonal temperature trends on the Spanish mainland. A secular study (1916–2015)

Resum

Trends in seasonal mean values of maximum and minimum temperature are analysed in the Spanish mainland from the new MOTEDAS_century database. This new data set has been developed combining the digitalized archives from the Spanish Meteorological Agency (AEMET) with information retrieved from Annual Books published by the former Meteorological Agency dating back to 1916, and covers the period 1916–2015. In all four seasons, mean seasonal temperature of maximum (Tmax) and minimum (Tmin) increased. The raising occurred in two main pulses separated by a first pause around the middle of the 20th century, but differed among seasons and also between maximum and minimum temperature. Analysis of the percentage of land affected by significant trends in maximum temperature reveals two increasing phases in spring and summer for Tmax, and in spring, summer, and autumn for Tmin. However, winter Tmax only rose during the recent decades, and autumn Tmax in the first decades. Negative significant trends were found in extended areas in spring Tmax, and in spring, autumn, and summer Tmin, confirming the first pause around the 1940’s–1960’s. Trends of seasonal mean values of Tmax and Tmin are not significant for at least the last 25–35 years of the study period, depending on the season. The areas under significant positive trend are usually more extended for Tmin than Tmax at any season and period. Areas with significant trend expand and contract in time according to two spatial gradients. south-east to north-west (east-west) for Tmax, and west to east for Tmin. We hypothesize a relationship between atmospheric prevalent advection and relief as triggering factors to understand spatial and temporal differences in seasonal temperatures at regional scale during the 20th century in the Iberian Peninsula.

Publicació
International Journal of Climatology (41)
Marc Lemus-Canovas
Marc Lemus-Canovas
Investigador Postdoctoral

Sóc un apassionat del tractament de dades climàtiques, especialment utilitzant R!

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