Assessing gridded observations for daily precipitation extremes in the Alps with a focus on northwest Italy

Turco, M.; Zollo, A. L.; Ronchi, C.; De Luigi, C.; Mercogliano, P.

In this study we compare three gridded observed datasets of daily precipitation (EOBS, MAP and NWIOI) over the Great Alpine Region (GAR) and a subregion in northwest Italy (NWI) in order to better understand the past variability of daily climate extremes and to set up a basis for developing regional climate scenarios. The grids are first compared with respect to their temporal similarity by calculating the correlation and relative mean absolute error of the time series. They are then compared with respect to their spatial similarity to the climatology of the ETCCDI indices (characterizing total precipitation, dry and wet spells and extremes with short return periods). The results indicate first that most EOBS gridpoint series in northeastern Italy have to be shifted back by 1 day to have maximum overlap of the measurement period and, second, that both the temporal and spatial similarities of most indices are higher between the NWIOI and MAP than between MAP or the NWIOI and EOBS. These results suggest that, although there is generally good temporal agreement between the three datasets, EOBS should be treated with caution, especially for extreme indices over the GAR region, and it does not provide reliable climatology over the NWI region. The high agreement between MAP and NWIOI, on the other hand, builds confidence in using these datasets. Users should consider carefully the limitations of the gridded observations available: the uncertainties of the observed datasets cannot be neglected in the overall uncertainties cascade that characterizes climate change studies.



Turco, M. / Zollo, A. L. / Ronchi, C. / et al: Assessing gridded observations for daily precipitation extremes in the Alps with a focus on northwest Italy. 2013. Copernicus Publications.


12 Monate:

Grafik öffnen


Rechteinhaber: M. Turco et al.

Nutzung und Vervielfältigung: