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.