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 climatological time scales has been addressed through a novel technique based on a rainfall zonation, by means of creating a regional precipitation database for a whole-region correlation approach. Pearson correlation was performed to evaluate the 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 Rainfall Zones (RZs) and seven teleconnection pattern indices relevant to the Mediterranean region, searching where results were significant (significance level < 0.05) and with an absolute correlation value higher than a prefixed threshold equal to 0.2. The Calabria region was chosen as it is located in the centre of the Mediterranean area, which constitutes a hot spot for climate change, and because it is equipped with a high-density, long-time series of precipitation gauge network, recently validated and homogenized. Correlation analysis between seasonal teleconnection indices and seasonal cumulated precipitation showed that the Western Mediterranean Oscillation and the East Atlantic/West Russian patterns were the most relevant teleconnections over all Calabria. Correlations of 3-month averaged teleconnection indices versus monthly precipitation showed that the Mediterranean and Western Mediterranean Oscillations produce most significant results with correlation values higher than 0.2, with East Atlantic pattern a third close. Comparison between monthly teleconnection indices and monthly cumulated precipitation indicate that all modes of variability taken into account share a similarly weak correlation. Comparing the rainfall zone-based study and the technique of averaging individual stations results post-correlation, it was shown that on average only 43% of the stations would produce useful correlations, while the novel technique used all the valid and available station data, resulting in statistically more robust findings.