Weather regime transitions and the interannual variability of the North Atlantic Oscillation. Part I: a likely connection
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4. Transitions between NAO  and NAO events and their link with interannual NAO variability
a) Examples of NAO  NAO and NAONAOtransition events
Before examining the impact of the NAO regime transitions on the interannual NAO variability, we first look at the frequency of occurrence of NAO Figure 4b shows the life cycle of a NAO NAO transition event. After anticyclonic synoptic-scale wave breaking is observed (15 Jan.), we see that the NAO event undergoes decay as an intensified blocking flow over Europe shifts westward. This is followed by the formation of a NAOevent (23 to 30 Jan.). The time scale of this NAONAO transition event is about 30 days, again within the range of intraseasonal time scale variability. Because the lifetime of the individual NAO (NAO) event within the NAO transition event is about 15 days (Feldstein 2003; Benedict et al. 2004), the total period of the NAO transition event is at the intraseasonal timescale.
b) NAO transition events and its occurrence frequency within P1 and P2. For the NAO NAOand NAONAOtransition events as shown in Fig.4, they satisfy the definition of a NAO transition event presented in section 2. According to this definition, the number of all NAO and NAO events, along with the number of transition events, is shown in Fig.6 for the two subperiods P1 and P2. It is found that NAO events are more frequent than NAO events within both P1 and P2. Within P1 there are 10 NAONAO transition events and 5 NAO NAO transition events. In contrast, within P2, there are 11 NAO NAO transition events and 6 NAONAOtransition events. Thus, NAO NAO transition events occur at about twice the frequency of NAO NAO transition events within P1. During P2, the frequency of the transition events is strikingly different, as the frequency of NAONAO transition events is twice that of the NAO NAO transition events. At the same time, we see that the NAONAO transition events comprise 42% of the total number of NAO events in P1, while the NAONAO transition events account for 55% of the total number of NAO events within P2. This contrasts the NAONAO transition events within P2 and the NAONAO transition events within P1, which occur at a much lower frequency, 15% and 26%, respectively. Correspondingly, the number of days within NAONAO (NAONAO) transition events comprise almost 41% (52%) of the total number of NAO(NAO) event days within P1 (P2). These results indicate that the interannual variability of the winter mean NAO index from P1 to P2 is probably affected by the different frequencies of NAONAO and NAONAO transition events within P1 and P2, although the role of in-situ NAO events is dominant (Johnson et al. 2008).
To evaluate how the frequency of NAO Thus, it is conjectured that the difference in the occurrence frequency of NAO transition events in both event and day numbers between P1 and P2 is likely to result in the interannual variation of the winter mean NAO index in the two time intervals even though there is a very large change in the number of in-situ positive NAO events between P1 and P2. The contribution of the NAO regime transitions to the interannual variability of the NAO pattern can be seen by subtracting NAO transition events. This is performed by removing NAO To investigate the impact of the NAO transition events on changes in multiple weather regimes over the North Atlantic, the cluster analyses is performed for two cases. Case 1 corresponds to a calculation in which all days are retained, and Case 2 to a calculation for which the NAO For Case 1, we show the four North Atlantic weather regimes for the two subperiods (Fig.8): P1 and P2, while Fig. 9 shows the corresponding regimes for Case 2. It is seen from Fig. 8 that the northern center of the NAO (NAO) anomaly within P1 (P2) is much stronger than that during P2 (P1). We also note that the SBL is stronger and located farther westward within P2 than within P1. By comparing Figs. 8a and 9a, it can be seen that the NAO to NAO transition events coincide with enhanced NAOand AR patterns within P1. Because the AR pattern exhibits a northeast-southwest tilted low-over-high dipole structure, the AR pattern can strengthen the NAO anomaly if it undergoes retrograde and southward movement. This is because an enhanced AR can strengthen the difference between the high pressure in lower latitudes and the low pressure in higher latitudes, thus, causing the intensification of the NAO anomaly. Thus, it appears that the NAONAOtransition within P1 is likely to be related to the enhancement of the AR pattern. This hints that the NAONAO transition takes place through the AR pattern, i.e., NAO to AR to NAO path. This is indirectly indicated by the observational study of Woollings et al. (2011), who found that the strengthening of the AR does coincide with the northward displacement of the Atlantic eddy-driven jet stream, a feature that corresponds to the positive phase of the NAO. Using a HMM approach, Franzke et al. (2011) identified three preferred transitions as corresponding to three jet states: southern to central jet, northern to southern jet, and central to northern jet. Here, we have noted that the NAO NAOtransition within P1 is, to some extent, dominated by the intensification of the AR pattern.
On the other hand, for the NAO More recently, Michel and Riviere (2011) found two distinct regime transitions: a Scandinavian blocking to Greenland anticyclone (or NAO Although the results from the cluster analyses cannot directly tell us how the transitions between NAO regimes are connected to the variations of the SBL and AR patterns, the above findings do at least suggest a likely link. Such a link can be better seen from composites of geopotential height anomalies at 300 hPa for the NAO d) Probability density function and the variation of the NAO index To evaluate the impact of the NAO regime transitions on the winter mean NAO index, it is helpful to examine the difference between two types of PDFs of the daily NAO index; one with all NAO and NAO events retained, and the other with the NAO (NAO) events within the NAONAO (NAONAO) transitions in P1 (P2) removed. Figure 10 shows the PDFs of the daily NAO index for these two cases and the corresponding Gaussian PDFs. In this figure, the thick solid (dashed) curve corresponds to the case with (without) the NAONAO (NAO NAO) transition events within P1 (P2), whereas the thin solid (dotted) curve represents the corresponding Gaussian PDF in P1 (P2). As revealed in this figure, the positive NAO index tends to have both a higher probability density and a shift to large positive values within P1 when the NAONAO transition events take place. This point is more evident, as can be seen from the Gaussian PDF distributions even though the probability density of the daily NAO index is far from being Gaussian. In contrast, the positive NAO index appears to have a lower probability density and a tendency toward relatively small positive values in P2 when NAO NAO transition events occur. Thus, as Fig.10 indicates, the NAO transition events can affect the NAO index by changing the frequencies of NAO and NAO events within P1 and P2. Furthermore, a comparison between Figs.8 and 9 shows that the strength and location of the NAO anomaly is influenced by the frequency of NAO transition events. Thus, it is likely that the interannual variability of the winter NAO index within P1 and P2 is related to the different number of intraseasonal NAONAO and NAONAOtransition events in the two time intervals.
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