Abstract In 2015, a new automatic weather station (AWS) was installed in a high elevation site in Gredos mountains (Central System, Spain). Since then, a surprisingly high number of heavy precipitation events have been recorded (55 days with precipitation over 50 mm, and a maximum daily precipitation of 446.9 mm), making this site a hotspot in Spain in terms of annual precipitation (2177 mm year) and extreme precipitation events. The neighboring stations available in the region with longer data series, including the closest ones, already informed of wet conditions in the area, but not comparable with such anomaly behavior detected in the new station (51% higher). In this study, we present the temporal variability of detected heavy precipitation events in this mountain area, and its narrow relation with atmospheric patterns over the Iberian Peninsula. Results revealed that 65% of the events occurred during advections from West, Southwest, South and cyclonic situations. A regression analysis showed that the precipitation anomaly is mostly explained by the location windward to the Atlantic wet air masses and the elevation. However, the variance explained by the models is rather low (average R 2 for all events > 50 mm is 0.21). The regression models underestimate on average a 60% intensity of rainfall events. Oppositely, the high-resolution weather forecast model AROME at 0.025° was able to point out the extraordinary character of precipitation at this site, and the underestimation of observed precipitation in the AWS was about 26%. This result strongly suggests the usefulness of weather models to improve the knowledge of climatic extremes over large areas, and to improve the design of currently available observational networks.

Abstract In 2015, a new automatic weather station (AWS) was installed in a high elevation site in Gredos mountains (Central System, Spain). Since then, a surprisingly high number of heavy precipitation events have been recorded (55 days with precipitation over 50 mm, and a maximum daily precipitation of 446.9 mm), making this site a hotspot in Spain in terms of annual precipitation (2177 mm year) and extreme precipitation events. The neighboring stations available in the region with longer data series, including the closest ones, already informed of wet conditions in the area, but not comparable with such anomaly behavior detected in the new station (51% higher). In this study, we present the temporal variability of detected heavy precipitation events in this mountain area, and its narrow relation with atmospheric patterns over the Iberian Peninsula. Results revealed that 65% of the events occurred during advections from West, Southwest, South and cyclonic situations. A regression analysis showed that the precipitation anomaly is mostly explained by the location windward to the Atlantic wet air masses and the elevation. However, the variance explained by the models is rather low (average R 2 for all events > 50 mm is 0.21). The regression models underestimate on average a 60% intensity of rainfall events. Oppositely, the high-resolution weather forecast model AROME at 0.025° was able to point out the extraordinary character of precipitation at this site, and the underestimation of observed precipitation in the AWS was about 26%. This result strongly suggests the usefulness of weather models to improve the knowledge of climatic extremes over large areas, and to improve the design of currently available observational networks.

Abstract In this study we investigated the sensitivity of the snowpack to increased temperature and short-wave radiation, and precipitation change along an elevation gradient (1500–2500 m a.s.l.) over the main mountain ranges of the Iberian Peninsula (Cantabrian Range, Central Range, Iberian Range, Pyrenees, and the Sierra Nevada). The output of a meso-atmospheric model (WRF) was used as forcing data in a physically-based energy and mass balance snowpack model (FSM2). A cluster analyses was applied to the input data of the FSM2 model to identify a total of 12 cells that summarized the climatic variability of the mountain ranges. The WRF output was then rescaled to various elevation bands using an array of psychrometric and radiative formulae and air temperature lapse rates. A factorial experiment was performed to generate synthetic meteorological series involving gradual alteration of the temperature (0–4 °C increases), short-wave radiation (0–40 Wm-2 increases), and precipitation (variations of ±20%) to force the FSM2. We found differing sensitivities across the various mountainous areas as a consequence of differences in their energy and mass balances. The results showed a generally negative impact of climate warming on the magnitude, duration, and melt rates of the snowpack over all elevation bands, even under scenarios of greater precipitation. The average effect of warming on the duration of the snowpack ranged from −23% per °C at 1500 m a.s.l. to −13% per °C at 2500 m a.s.l., on the peak snow water equivalent ranged from −20% per °C at 1500 m a.s.l. to −15% per °C at 2500 m a.s.l., and on melt rates ranged from −9% to −6% per °C. The effect of increasing short-wave radiation on the snowpack ranged from approximately −2% per 10 Wm−2 at 1500 m a.s.l. to −1% per 10 Wm−2 at 2500 m a.s.l. for both the snowpack duration and peak SWE indices. The effect on the snowpack caused by precipitation changes reduced gradually with increasing elevation, especially in the colder areas. The response of the melt rates to warming was negative in most of the areas at all elevations, suggesting less intense but longer melt seasons.