5 The NAO regime transition and interannual variation of the NAO index, frequency and location
a) Regime transition and NAO frequency
To further investigate whether the result based upon the cluster analysis is robust, we present the time series of the number of the NAO event days for each phase by counting the number of days that comprise the NAO(NAO) events, according to the definition of NAO events presented in section 2. The number of NAO (NAO) event days per winter is shown in Fig.11a. A comparison between Figs.11a and 3a indicates that the result obtained from the cluster technique is valid. To facilitate a comparison with the results of the previous section, the NAO(NAO) events within the NAONAO (NAONAO) transitions are removed (Fig.11b). Such an approach can indicate the impact of in-situ events on interannual NAO variability. It is seen in Fig. 11a that the number of NAO days increases (decreases) within P1 (P2), whereas the number of NAO days decreases (slightly increases) within P1 (P2). The NAO and NAO event day time series tend to have a negative correlation of , which is statistically significant at the 99% confidence level for a two-sided student’s t-test. Amongst the various trends in Fig. 11, it is only the linear upward trend of the NAO days during P1 that is found to be statistically significant at the 85% confidence level for a student t-test. The other linear trends are less significant.
On the other hand, we can see in Fig. 11b that when there are no NAONAO (NAONAO) transition events, the linear trends of the NAO and NAO event days within P1 and P2 become weaker. The number of NAO days exhibits a slightly smaller linear downward trend during P1 and P2. As a result, during P2 the linear trend of the NAO days without NAO regime transition events becomes opposite to that with NAO regime transition events, indicating the important role of NAO regime transition events in the NAO interannual variability. The results can also be found by using cluster analysis (not shown). These results, especially when comparing the trends in Fig. 11b with those in Figs. 3a and 11a, suggest that the NAO regime transitions can have an important impact on the interannual variation of the winter mean NAO index.
In previous studies (Cassou et al. 2004, Johnson et al. 2008), the frequency of occurrence of NAO (NAO) events includes the frequency of the NAO transition events. In this work, we have found that the different frequency of NAO transition events between P1 and P2 can contribute to the interannual variability of the NAO pattern.
b) Impact of the regime transition on the variation of the winter monthly mean NAO index
In this subsection, to see the contribution of NAO transition events to the NAO index variability, we use the daily indices of NAO events to construct two new monthly mean NAO indices: one based on the removal of NAO transition events and the other on the retention of the transition events. Here, the removal of NAO transition events is represented by removing NAO(NAO) events or retaining NAO(NAO) events within the NAO NAO (NAONAO) transition events. The time series of the monthly mean NAO index obtained using daily NAO indices are shown in Fig.12 for two cases with and without NAO and NAO events within the NAO NAO (NAONAO) transition events. The solid (dotted) curve represents the monthly mean NAO index with and without transition NAO events, while the thick solid (cross) line indicates a linear trend for the case with (without) the transitions. It is found that during P1 (P2) the linear upward (downward) trend of this new monthly mean NAO index is statisically significant at the 95 % (90%) confidence level for a student’s t-test. However, such trends become statistically significant only at the 90% (80%) confidence level when the NAO(NAO) events within NAONAO (NAONAO) transition events within P1 (P2) are removed. A similar conclusion is obtained if the winter mean NAO index is used (not shown). Thus, it is suggested from the result obtained here that NAO transition events can significantly affect the linear trends of the NAO interannual variability in winter within P1 and P2.
c) Regime transition and NAO location
One can also see the impact of the NAO transition events on the location of the NAO anomalies by performing a separate cluster analysis for those times when the NAO and NAO events of the NAONAO (NAONAO) transition events are included and when they are removed. In contrast to the previous cluster analysis, all NAO transition events during 1978-2008 are excluded. The same four weather regimes as in previous calculations are obtained (see Fig. 13). A comparison with Fig. 2 shows that the northern center of the NAO pattern exhibits an eastward displacement when all NAO transition events during 1978-2008 are retained. Such an eastward shift appears more evident for the negative phase. Many studies have revealed that the NAO pattern has undergone a noticeable eastward displacement since the late 1970s (Hilmer and Jung 2000; Peterson et al. 2003). Several mechanisms have been proposed to account for this eastward shift (Ulbrich and Christoph 1999; Peterson et al. 2003; Cassou et al. 2004; Luo and Gong 2006; Johnson et al. 2008; Luo et al. 2010a). However, here we propose a new mechanism that the frequent occurrence of NAO transition events is also another important cause of the marked eastward shift of the NAO centers of action during 1978-2008.
6 Conclusion and discussions
In this study, we have examined interannual changes in NAONAO and NAONAO transition events within P1 and P2 and evaluated their likely link with the interannual variability of the winter mean NAO index. It was shown that in P1 the frequency of NAONAO transition events is about twice that for the NAONAOtransition events. During P2, the transition events showed very different characteristics, as the frequency of NAONAO transition events was double that of the NAONAO transition events.
A cluster analysis showed that the difference in the number of NAO regime transition events between P1 and P2 can account in part for changes in the NAO amplitude, frequency, and location for each phase between the two subperiods, thus contributing toward the upward trend of the NAO event day time series in P1 and its downward trend in P2. Moreover, the frequent occurrence of the NAO regime transition events is also found to play an important role in the interannual eastward shift of the NAO centers of action found by many investigators (Hilmer and Jung 2000, Peterson et al. 2003; Cassou et al. 2004; Johnson et al. 2008).
Although the present study describes a plausible link between the intraseasonal NAO regime transitions and interannual variability of the NAO pattern from P1 to P2, it must however be pointed out that the interannual or decadal variability of the NAO index can be interpreted as a combination of both internally and externally generated variations (Osborn 2004). The externally generated variations are generally forced by the increased greenhouse gases concentrations (Ulbrich and Christoph 1999;Osborn 2004), stratospheric processes (Scaife et al. 2005) and ocean warming (Selten et al. 2004; Li et al. 2010) etc., while the internally generated variations arise from climate noise (Feldstein 2000, 2002) or are induced by the air-sea coupling (Timmermann et al. 1998; Hoerling et al. 2001;Marshall et al 2008; Muller et al. 2008). In this work, we did not examine the different roles of internal variability and external forcing in the interannual variability of the NAO index. Instead we have evaluated the impact of the intraseasonal NAO regime transitions on the interannual variability of the NAO pattern, and found that the intraseasonal NAO regime transitions can contribute significantly to the interannual variability of the winter mean NAO index. The occurrence of NAO transition events may also be linked to changes in the strengths of the AR and SBL patterns. Nevertheless, it is unclear how the NAO transitions depend upon the occurrence of AR and SBL regime events. Moreover, the dynamical processes that drive the NAO to SBL to NAO (NAO to AR to NAO) transitions within P2 (P1) are not examined in this work. The investigation of these problems will be the main purpose of Part II.
The authors acknowledge the support from the “one Hundred Talent Plan” of the Chinese Academy of Sciences (Y163011) and National Science Foundation of China (41075042, 40921004) and National Science Foundation grants ATM- 0852379 and AGS-1036858. The authors would like to thank two anonymous reviewers for valuable comments that improved this paper.
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