Wavelet coherence with the NAOI and the

observed for the mean seasonal snow depth and two global indexes:

the North Atlantic Oscillation Index (NAOI) and the Mediter-ranean Oscillation Index (MOI). The North Atlantic Oscillation Index (NAOI) is defined as the normalized pressure di↵erence between Stykkisholmur (Island) and Lisbon (Portugal) [Hurrell, 1995, 1996]. The NAOI has a strong influence on European precip-itation patterns. In the southern part of Europe, and in particular in the alpine region, the precipitations are negatively correlated with the NAOI. On the contrary, there is a positive correlation between the NAOI and the Alpine temperature [see e.g. Beniston, 2012b]. The Mediterranean Oscillation Index is defined as the

nor-4.3. Application to snow data 69 malized pressure di↵erence between the Gibiltrar’s Norther Fron-tier and Lod Airport in Israel [Palutikof, 2003]. The influence of the NAOI and of the MOI fluctuations on the snow dynamic is less obvious, since they do not have an instantaneous and direct e↵ect, but they influence both precipitation and temperature [Beniston, 2012b].

Figure 4.11: Wavelet coherence analysis between the average mean seasonal snow depth of the stations below 1350 m a.s.l. and the NAOI (plot above) and the MOI (plot below).

Figures 4.11-4.14 show the wavelet coherence analysis between the mean seasonal snow depth at di↵erent elevations and the two climatic indexes described above. The color indicates the coher-ence of the two signals for di↵erent times and periods (warm colors represent coherence close to 1, while cold colors represent coher-ence close to 0). The direction of the arrows indicate the relative phase between the signals [Grinsted et al., 2004]. For example, in case the signals are in phase the arrows point to the right, if the signals are in anti-phase the arrows point to the left. The white shaded area shows the cone of influence.

70 Chapter 4. Wavelet analysis

Figure 4.12: Wavelet coherence analysis between the average mean seasonal snow depth of the stations between 1350 m and 1650 m a.s.l. and the NAOI (plot above) and the MOI (plot below).

We observe that the two lowest classes have a similar behavior which di↵ers from the one of the two highest classes. For all ele-vation classes, the coherence with the NAOI is strong up to 2 year period, but for the stations below 1650 m a.s.l we observe a pe-riod at the end of the ’90s where there was no coherence between the NAOI and the mean seasonal snow depth at these scales (see Figures 4.11 and 4.12). Except for the 2 years period, no strong correlation with the NAOI is observed for the stations between 1650 m and 2000 m a.s.l., as shown in Figure 4.13. On the con-trary, Figure 4.14 shows a period with strong coherence at about 6 years period for about 10 years starting from the middle of the

’80s. Finally, Figures 4.11 and 4.12 show a strong coherence at 4 years period starting from 2000 for the lowest stations and starting from the ’90s for the stations between 1350 and 1650 m a.s.l..

The relationship between the NAOI and the mean seasonal snow depth in the Adige River Basin does not change in

accor-4.3. Application to snow data 71

Figure 4.13: Wavelet coherence analysis between the average mean seasonal snow depth of the stations between 1650 m and 2000 m a.s.l. and the NAOI (plot above) and the MOI (plot below).

dance with the changes observed in the behavior of the snow in the last decades, as observed in section 4.3.1. The coherence between snow depth and NAOI signals performed in this study shows that the two time series are generally poorly correlated. Therefore, the correlation between snow signals, such as snow cover dura-tion and snow depth, with the NAOI observed in the Swiss Alps [e.g., Beniston, 1997] is not necessarily representative for the en-tire Alpine region, as also evidenced for example by Durand et al.

[2009] and Schner et al. [2009]. Therefore, while the correlation between the snow signal and the NAOI is strongly heterogeneous throughout the Alps and in general in the Mediterranean Region [L´opez-Moreno et al., 2011, Luterbacher et al., 2006], the snow-scarce period observed in the ’90s is present both in the Northern as well as in the Southern European Alps. We conclude therefore that this behavior has to be mainly attributed to a forcing factor which is common to both sides of the mountain chain, such as

72 Chapter 4. Wavelet analysis

Figure 4.14: Wavelet coherence analysis between the average mean seasonal snow depth of the stations below 2000 m a.s.l. and the NAOI (plot above) and the MOI (plot below).

the observed increase in temperature, that has an a↵ect both on snow cover duration and on phase change in precipitation regime, particularly relevant for sites at lower altitudes [Serquet et al., 2011].

If we consider instead the coherence between the mean seasonal snow depth and the MOI, we find that the highest stations (above 1650 m a.s.l.) show a significant coherence between 6 and 4 year periods for all the time span we are observing, even if it is slightly less strong during the ’90s (see Figures 4.13 and 4.14). The lower stations (Figures 4.11 and 4.12) show instead a strong coherence before the ’90s at a 6 year period and after 2000 at 4 year period.

The arrows pointing to the left indicate that the 6-4 year scale component of the MOI signal is generally decreasing when the 6-4 year scale component of the HS signal is increasing. This anti-phase correlation is interrupted for low elevation sites and weakened for high elevation sites from the late ’80s to the end of

4.4. Application to discharge data 73