https://doi.org/10.1007/s00704-021-03791-x

Morán-Tejeda, E., Llorente-Pinto, J. M., Ceballos-Barbancho, A., Tomás-Burguera, M., Azorín-Molina, C., Alonso-González, E., ... & López-Moreno, J. I. (2021). The significance of monitoring high mountain environments to detect heavy precipitation hotspots: a case study in Gredos, Central Spain. Theoretical and Applied Climatology, 1-14.

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 R2 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.

INARCH

https://doi.org/10.1007/s00704-021-03791-x