Correlation between large-scale atmospheric fields and the olive pollen season in Central Italy

International Journal of Biometeorology - Tập 52 - Trang 787-796 - 2008
E. Avolio1,2, L. Pasqualoni2, S. Federico1,2, M. Fornaciari3, T. Bonofiglio3, F. Orlandi3, C. Bellecci4, B. Romano3
1ISAC-CNR, Lamezia Terme (CZ), Italy
2CRATI scrl, c/o University of Calabria, Rende (CS), Italy
3Department of Plant Biology and Agroenvironmental Biotechnology, University of Perugia, Perugia, Italy
4Department STFE, University of Rome “Tor Vergata”, Rome, Italy

Tóm tắt

Olives are one of the largest crops in the Mediterranean and in central and southern Italy. This work investigates the correlation of the Olea europaea L. pollen season in Perugia, the capital city of the region of Umbria in central Italy, with atmospheric parameters. The aim of the study is twofold. First, we study the correlation between the pollen season and the surface air temperature of the spring and late spring in Perugia. Second, the correlation between the pollen season and large-scale atmospheric patterns is investigated. The average surface temperature in the spring and late spring has a clear impact on the pollen season in Perugia. Years with higher average temperatures have an earlier onset of the pollen season. In particular, a 1°C higher (lower) average surface temperature corresponds to an earlier (later) start of the pollen season of about 1 week. The correlation between the pollen season and large-scale atmospheric patterns of sea level pressure and 500-hPa geopotential height shows that the cyclonic activity in the Mediterranean is unequivocally tied to the pollen season in Perugia. A larger than average cyclonic activity in the Mediterranean Basin corresponds to a later than average pollen season. Larger than average cyclonic activity in Northern Europe and Siberia corresponds to an earlier than average pollen season. A possible explanation of this correlation, that needs further investigation to be proven, is given. These results can have a practical application by using the seasonal forecast of atmospheric general circulation models.

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