A sub-regional approach to the influence analysis of teleconnection patterns on precipitation in Calabria (southern Italy)

In this study, the problem of determining, with an adequate statistical significance,the relationship between teleconnection pattern indices and precipitation at climatologicaltime scales has been addressed through a novel technique based on arainfall zonation, by means of creating a regional precipitation database for awhole-region correlation approach. Pearson correlation was performed to evaluatethe mesoscale influence on precipitation in the Calabria region over the 1951-2010 time period, by means of a database of 79 rain gauges, divided in five RainfallZones (RZs) and seven teleconnection pattern indices relevant to the Mediterraneanregion, searching where results were significant (significance level < 0.05)and with an absolute correlation value higher than a prefixed threshold equal to0.2. The Calabria region was chosen as it is located in the centre of the Mediterraneanarea, which constitutes a hot spot for climate change, and because it isequipped with a high-density, long-time series of precipitation gauge network,recently validated and homogenized. Correlation analysis between seasonal teleconnectionindices and seasonal cumulated precipitation showed that the WesternMediterranean Oscillation and the East Atlantic/West Russian patterns were themost relevant teleconnections over all Calabria. Correlations of 3-month averagedteleconnection indices versus monthly precipitation showed that the Mediterraneanand Western Mediterranean Oscillations produce most significant resultswith correlation values higher than 0.2, with East Atlantic pattern a third close.Comparison between monthly teleconnection indices and monthly cumulated precipitationindicate that all modes of variability taken into account share a similarlyweak correlation. Comparing the rainfall zone-based study and the technique ofaveraging individual stations results post-correlation, it was shown that on averageonly 43% of the stations would produce useful correlations, while the novel techniqueused all the valid and available station data, resulting in statistically morerobust findings.