The variable link between PNA and NAO in observations and in multi-century CGCM simulations
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The link between the Pacific/North American pattern (PNA) and the North Atlantic Oscillation (NAO) is investigated in reanalysis data (NCEP, ERA40) and multi-century CGCM runs for present day climate using three versions of the ECHAM model. PNA and NAO patterns and indices are determined via rotated principal component analysis on monthly mean 500 hPa geopotential height fields using the varimax criteria. On average, the multi-century CGCM simulations show a significant anti-correlation between PNA and NAO. Further, multi-decadal periods with significantly enhanced (high anti-correlation, active phase) or weakened (low correlations, inactive phase) coupling are found in all CGCMs. In the simulated active phases, the storm track activity near Newfoundland has a stronger link with the PNA variability than during the inactive phases. On average, the reanalysis datasets show no significant anti-correlation between PNA and NAO indices, but during the sub-period 1973–1994 a significant anti-correlation is detected, suggesting that the present climate could correspond to an inactive period as detected in the CGCMs. An analysis of possible physical mechanisms suggests that the link between the patterns is established by the baroclinic waves forming the North Atlantic storm track. The geopotential height anomalies associated with negative PNA phases induce an increased advection of warm and moist air from the Gulf of Mexico and cold air from Canada. Both types of advection contribute to increase baroclinicity over eastern North America and also to increase the low level latent heat content of the warm air masses. Thus, growth conditions for eddies at the entrance of the North Atlantic storm track are enhanced. Considering the average temporal development during winter for the CGCM, results show an enhanced Newfoundland storm track maximum in the early winter for negative PNA, followed by a downstream enhancement of the Atlantic storm track in the subsequent months. In active (passive) phases, this seasonal development is enhanced (suppressed). As the storm track over the central and eastern Atlantic is closely related to the NAO variability, this development can be explained by the shift of the NAO index to more positive values.
KeywordsPNA NAO Decadal variability Storm track CGCM
The large-scale variability over the mid- and high latitudes of the Northern Hemisphere features regions which are linked via teleconnections, with their cores often being called “centres of action”. Wallace and Gutzler (1981) and Barnston and Livezey (1987) identified the Pacific-North Atlantic Oscillation (PNA) and the North Atlantic Oscillation (NAO) as the two most prominent teleconnection patterns over the Northern Hemisphere (NH) extratropics. The PNA is a 3-center pattern extending over most of the North Pacific (NP) and North America (Wallace and Gutzler 1981), usually identified from 500 hPa geopotential heights. It is associated with changes of synoptic activity and subtropical jet stream over the NP (e.g. Franzke et al. 2009) and affects the climate conditions over large parts of the NP and the North American Continent (e.g. Rogers and Raphael 1992; Archambault et al. 2008). The NAO is a dipolar pattern of mean sea level pressure over the North Atlantic (NA) extending from the subtropical (Azores high) to sub-Artic latitudes (Icelandic low; e.g. Hurrell 1995). It is associated with westerly winds variations over the Eastern NA and Western Europe, thus being an important factor for winter climate in Europe (e.g. Wanner et al. 2001; Hurrell et al. 2003). The NAO is associated with intensity changes and north/south displacements of the mid-latitude jet and synoptic activity over the NA (e.g. Luo et al. 2007; Pinto et al. 2009).
The discussion of possible mechanisms steering the NAO has been an issue in recent decades. Several authors have suggested that the NA SSTs force the NAO on decadal and longer time scales (e.g. Rodwell et al. 1999; Manganello 2008; see Wanner et al. 2001 for a review). The respective SST anomalies could arise from modulations of the oceanic gyre circulation (e.g. Grötzner et al. 1998) or feedback processes related to the thermohaline overturning at high latitudes (e.g. Timmermann et al. 1998). On the other hand, atmospheric influences on the ocean are also active, and a realistic NAO is produced even in coupled global circulation models (CGCMs) which do not include a dynamical ocean (Christoph et al. 2000). Other authors suggest influences from Indo-Pacific SST anomalies (e.g. Hoerling et al. 2001) and from tropical Pacific variability (particularly the ENSO phenomenon, e.g. Trenberth et al. 1998; Lin et al. 2005; Sterl et al. 2008; Müller and Roeckner 2008; Müller et al. 2008). In addition, anomalies of snow cover over Eurasia have been linked to the variability of the NAO (e.g. Saito and Cohen 2003).
A connection between the NP and NA atmospheric circulation anomalies has been considered by a number of authors. Based on data from 1899 to 1975, Van Loon and Rogers (1978) identified a negative correlation between the monthly mean pressures of the Icelandic low and the Aleutian low in winter (December–February). Bongioannini Cerlini et al. (1999) computed indices of PNA and NAO on a monthly time scale and found a correlation of −0.42 for also for boreal winter (1949–1994). Honda et al. (2001) found a negative correlation between the intensities of the Aleutian and the Icelandic lows in NCEP re-analysis (1973–1994), reaching a value of −0.7 for the period February to Mid-March (cf. also Orsolini et al. 2008). Recently, Song et al. (2009) concluded that correlations between PNA and NAO on a daily scale are largest (and statistically significant) with zero time lag and in a range of lags of ±10 days. Thus, there is ample evidence for the existence of such a link between the PNA and the NAO.
Several studies found that the correlation between the Pacific and Atlantic regions undergo decadal changes. With respect to storm tracks, Chang (2004) found a significant positive link between the regions of main winter activity in the period 1975/1976 to 1998/1999, while correlations were low in an earlier period (1957/1958–1971/1972). He speculated that this effect might be an artefact of low aircraft observations prior to about 1970 and thus to an error in the reanalysis datasets, as model simulations driven with observed SSTs produce a weak link even for the later period. A variable link is, however, tentatively supported by the study of Knippertz et al. (2003), who looked at the changing correlations between ENSO and the NAO. Further, Raible et al. (2001) observed two decadal regimes for the NAO-variability in ECHAM4 model simulations: (a) phases with enhanced low-frequency variability (“active”) characterized by regional modes over the NA and NP (here with a barocline PNA mode) and (b) phases with reduced low-frequency variability (“inactive”), characterized by a global mode with a dominant PNA pattern and stronger links to SST anomalies over the tropical Pacific.
This statistical relation between PNA and NAO is also relevant when considering the Arctic Oscillation (AO) or Northern Annular Mode (e.g., Thompson and Wallace 1998). The AO has a similar structure to the NAO, but is zonally more symmetric (e.g. Thompson and Wallace 1998, 2000; Wallace 2000). There has been much discussion on the inter-relationships between PNA, NAO and AO and also on the influence of the stratospheric vortex variability in the troposphere (e.g. Baldwin and Dunkerton 1999; Ambaum and Hoskins 2002; Thompson et al. 2002; Scaife et al. 2005; Orsolini et al. 2008; Castanheira et al. 2009). For example, Baldwin and Dunkerton (2001) provided evidence that strength of the stratospheric vortex affects not only the phase of the AO/NAO indexes, but also the location of the mid-latitude storm tracks and the likelihood of storms. Further, Castanheira and Graf (2003) stated that the coupling between the atmospheric circulation over the NA and NP is larger in months with strong stratospheric polar vortex. Finally, Orsolini et al. (2008) have recently analyzed the formation and life cyclone of the Aleutian–Icelandic Low Seesaw in an atmospheric GCM with a well resolved stratosphere, and they identified a clear extension of this Seesaw into the stratosphere, where its presence modulates the polar night jet intensity.
Feldstein (2002, 2003) investigated the nature of both PNA and NAO modes and concluded that the most important difference is that while PNA life cycle is dominated by linear processes, the variability of the NAO is dominated by non-linear processes. Further, and unlike the PNA, the NAO may be interpreted as a forced phenomenon (Feldstein 2003). As a consequence, one may expect that the link between PNA and NAO is produced by mechanisms modulated by PNA. Benedict et al. (2004), Franzke et al. (2004) and Rivière and Orlanski (2007) suggested that the synoptic-scale waves propagating from the eastern Pacific and subsequent Rossby wave-breaking play a dominant role in the determination of the phase of the NAO. According to Benedict et al. (2004), this is even true for the maintenance of the NAO phase by a succession of breaking of upstream synoptic scale waves. If such disturbances are no longer present, the NAO phase decays. They also pointed out to the differences between the two NAO phases: while the positive phase is characterized by two breaking waves (one over the NA, one over the North American west coast), the negative phase is characterized by a single wave-breaking over the NA. Of course, the regional relationship between synoptic waves and the NAO is two-sided, as explored in a number of studies (e.g. Schneidereit et al. 2007; Pinto et al. 2009). Further, blocking also plays a role in the link between NAO and cyclones: Croci-Maspoli et al. (2007) and Woollings et al. (2008) found frequent NA blocking events in negative NAO phases, while positive phases are largely characterized by unblocked situations.
In the current paper, we consider a potential PNA influence on the NAO imposed by a modulation of growth conditions of tropospheric eddies over eastern North America and western NA. This is analysed using observational data (NCEP and ERA40 re-analysis) and multi-centennial simulations with coupled Ocean–Atmosphere CGCMs. Such multi-centenial CGCMs simulations are an optimal base to analyse these relationships, as they also permit to assess possible long term (decadal, centennial) regime variations of the intensity of the coupling. We will put the focus on considerations of the troposphere as two of the three CGCMs considered do not include a well resolved stratosphere.
This paper is structured as follows: Information on the Reanalysis datasets, the coupled CGCMs and on the considered methodologies are described in Sect. 2. In Sect. 3, the connection between the PNA, the NAO and the NA storm track are analyzed, considering possible long term variations of the intensity of the coupling. Further, a physical mechanism for the modulation of eddy activity in the core of the NA storm track is proposed. A short discussion concludes this paper.
2 Data and methods
As observational data we use National Center for Environmental Prediction re-analyses for 1950–1999 (Kistler et al. 2001; hereafter NCEP) and the European Center of Medium Range Weather Forecast reanalysis data for 1958–2001 (Uppala et al. 2005; hereafter ERA40). Unless otherwise stated, we consider boreal winter means in our study, covering the period November to March.
The multi century control runs have been conducted with three different versions of the ECHAM model. One is a 300-year simulation with the ECHAM4/OPYC3 CGCM (Bacher et al. 1998; hereafter ECHAM4) in T42L19 resolution for present-day conditions. The uppermost vertical model level is at a pressure of 30 hPa. The representation of the spatial structure, the variability of the NAO and of the mid tropospheric storm tracks have been found to be realistic in previous studies (Ulbrich and Christoph 1999; Christoph et al. 2000). Ocean–Atmosphere coupling involved an annual mean flux correction, restricted to heat and freshwater fluxes (in order to avoid climate drift). The model produces a generally realistic ENSO variability (Roeckner et al. 1996).
Secondly, a pre-industrial control simulation (505 years with fixed 1860 GHG concentrations) with the CGCM ECHAM5/MPI-OM1 is considered (hereafter ECHAM5). The atmospheric model has a spatial resolution of T63 and 31 vertical levels (Roeckner et al. 2003), reaching up to 10 hPa. The model does not employ flux adjustments. Further details can be found in Roeckner et al. (2003; 2006) and Jungclaus et al. (2006). This model also produces a generally realistic ENSO variability (Jungclaus et al. 2006). The pre-industrial run was preferred to the available present day control run as the latter was much shorter (only 100 years long). Storm track and NAO variability of this pre-run have been previously considered e.g. by Bengtsson et al. (2006) and Pinto et al. (2007).
Thirdly, a 300-year present climate control simulation performed with the ECHO-G with Middle Atmosphere Model (Hübener et al. 2007; hereafter EGMAM) CGCM is analysed. The basis is the ECHO-G coupled model (ECHAM4/HOPE-G, cf. Legutke and Voss 1999), now extended into the stratosphere (Manzini and McFarlane 1998). EGMAM has 39 layers (model top: 0.01 hPa, circa 80 km), and the horizontal atmospheric resolution is T30. Similarly to ECHO-G, EGMAM shows a 2-year peak on the ENSO variability (Min et al. 2005). A flux correction is applied for heat and freshwater exchange and is on global average zero. For more details see Hübener et al. (2007) and Spangehl et al. (2009).
From each dataset, 500 hPa geopotential height is used to analyse possible anomalies in the mid-tropospheric large scale circulation. As a measure of baroclinic wave activity, we consider the storm track intensities from daily 500 hPa data (cf. e.g. Blackmon 1976). The storm track is obtained by first applying a bandpass filter (half power cut-off periods: 2.5 and 8 days; see Christoph et al. 1995) to the daily 500 hPa geopotential height fields at each gridpoint and subsequently computing the standard deviation. Storm track intensities for the three CGCMs and re-analyses are found to be in close agreement with respect to structures and intensities (cf. Appendix, Fig. 13). As a measure of barocliniticy we consider the maximum Eady growth rate (cf. Hoskins and Valdes 1990), defined as BI = 0.31 (f/N) × |dv/dz|, where f is the Coriolis parameter, N is the static stability, z the vertical coordinate and v the horizontal wind vector. It quantifies the large-scale conditions for the potential growth of cyclones and is a good approximation of wave growth in observations even with longitudinally variable mean flow (Hoskins and Hodges 2002). We consider this quantity for the upper (300–500 hPa) troposphere. In order to investigate the possible role of latent heat, lower tropospheric humidity at 850 hPa is also analysed. The upper-air jet stream is considered in the upper troposphere, corresponding to the zonal wind speed at 250 hPa.
3.1 PNA, NAO and North Atlantic and North Pacific storm tracks
Characteristics of PNA and NAO indexes for the 3 CGCMs (ECHAM4, ECHAM5, EGMAM) and reanalysis (NCEP, ERA40) based on winter mean data: explained variance of the NAO pattern (NAO); explained variance of the PNA pattern (PNA); correlation between PNA and NAO indexes (Corr); percentage of mismatches (Mis; PNA and NAO of opposite sign); percentage of months with PNA− and NAO+ (PNA−/NAO+); average NAO index for active phase (Active); average NAO index for inactive phase (Inactive)
3.2 Decadal periods of weak and strong PNA–NAO relationships
3.3 PNA–NAO relationships in active versus inactive phases
3.4 Possible reasons for the variable link between PNA and NAO
From the assumption of a direct effect of the storm track on the NAO, the NAO index values should be typically larger during negative PNA phases than during positive PNA phases. This is indeed the case in all five datasets we have analysed (cf. last 2 columns in Table 1). Of course, the discussed link between PNA and NAO is only explaining part of NAO decadal variability. As mentioned before, the NAO itself produces conditions favourable (in the positive phase) to the intensification of the Newfoundland storm track core, contributing to the variability of the system.
4 Discussion and concluding remarks
Induced by its centre over the Gulf of Mexico, PNA leads to an increased (reduced) northward flux of humidity from the Caribbean sea along the American east coast in its negative (positive) phase. This northward moisture pathway from the subtropics to the extra-tropics corresponds to the third area with strong tropical moisture export recently identified by Knippertz and Wernli (2010). The increased (decreased) available humidity then enhances (reduces) the storm track activity near Newfoundland (red arrow in Fig. 9).
During negative (positive) PNA phases there is increased (reduced) baroclinicity in a southwest-northeast orientated band upstream of Newfoundland. The anomalies are generated by the modulation of cold air advection from the north and warm air advection from the south connected with the PNA centres over Canada and the Gulf of Mexico (red and blue arrow in Fig. 9). Increased (decreased) baroclinicity modulates the growth conditions for baroclinic waves on the NA storm track core.
During negative PNA and positive NAO phases, enhanced baroclinic wave activity is found in a band reaching from the NP to the NA. Eddies moving along this path may enter the (climatological) baroclinic zone upstream of Newfoundland with larger initial amplitude, so that their amplitudes are eventually increased over Newfoundland (pink arrow in Fig. 9). During positive PNA phases, eddies at this location are reduced.
Eddies in the NA storm track will impose a barotropic feedback on the NAO. This part of the chain has not been explicitly considered in this paper, as the basic process was confirmed in other studies (e.g., Orlanski 1998; Orlanski and Gross 2000). Basically, enhanced eddy activity would lead to reduced SLP to the north and rising SLP to the south. In addition to the PNA induced eddy variations and feedbacks, there is also an “internal” NAO-eddy feedback. However, the NAO’s influence on the storm track is largest over the eastern NA, while over Newfoundland, PNA influence is larger than NAO influence (cf. Sect. 3.1, Figs. 1, 2, 3). Based on the seasonal development of the storm track anomalies, we investigated how the PNA may influence the NAO phase according to CGCM data. Results show an enhanced Newfoundland storm track maximum in the early winter for PNA− and a subsequent enhancement of the storm track over the whole NA in the subsequent months. As the storm track over the central and eastern Atlantic is closely related to the NAO variability, this development can be explained by the shift of the NAO index to more positive values.
Beyond the rather modest (though significant) correlations of PNA and NAO in the CGCMs, periods of strong and weak coupling (“active” and “inactive phases”) can be identified by applying moving correlations. From this point of view, the whole reanalysis period could correspond on average to a weaker coupling phase in the CGCMs, as correlation- and composite patterns in the CGCMs and Reanalysis are similar. Nevertheless, phases with stronger/weaker coupling can also be identified in the reanalysis period (in accordance with Honda et al. 2001, 2005). Our results go well with conclusions from studies focussing Aleutian–Icelandic Low Seesaw, which modulates of the storm tracks over the Caribbean/Western Atlantic (Honda et al. 2001, 2005; Honda and Nakamura 2001). In particular, Honda and Nakamura (2001) identified a seasonal evolution of the Aleutian–Icelandic Low Seesaw, with a late winter peak, which is consistent with our findings about the seasonal evolution of the storm track modulation (Fig. 10). This suggests that the mechanisms found in the models are potentially also connecting PNA and NAO pattern (and the Aleutian and Icelandic lows) in the real world.
We have not tried to address differences between the three CGCMs in our study, in spite of different mean anti-correlations (Table 1) and the differences with respect to decadal variations of the coupling intensity. In particular, it would be interesting to explore the importance of the stratosphere in a next step, which is well represented in the EGMAM model (cf. Hübener et al. 2007; Spangehl et al. 2009). From our study, no first order effect is visible. Baldwin and Dunkerton (1999, 2001) suggested that large circulation anomalies in the observed lower stratosphere are related to substantial shifts in the AO/NAO phases, as large positive values of the AO/NAO indexes are much more probable for strong stratospheric vortex regimes. One may speculate that such a mechanism leads to the stronger anti-correlation of PNA and NAO in EGMAM compared to the other models.
Our study was restricted to the investigation of the link between PNA and NAO as two atmospheric modes. It is well known that PNA is closely related to ENSO variability (e.g. Straus and Shukla 2002), and the link between ENSO and NAO or European climate has been addressed in several studies (e.g. Pozo-Vázquez et al. 2005). Further work should address the nature of this link, taking into account the decadal variability of the PNA–NAO connection discussed here. Additionally, we will address the question of maintenance of the coupling from one winter to the next in the active phase, and the regime transition to an inactive period, which was not investigated in the present study.
We would like to thank Erich Roeckner and the MPI for Meteorology (Hamburg, Germany) for support and the ECHAM4 and ECHAM5 data, and Ulrich Cubasch for the EGMAM data. We thank DKRZ/WDCC (Hamburg, Germany) and ZAIK/RRZK (Cologne, Germany) for computer and storage capacity. We are indebted to Michael Christoph (Univ. Cologne, Germany), Thomas Spangehl and Falk Niehörster (both FU Berlin, Germany) for fruitful discussions. We thank two anonymous reviewers for their comments that helped to improve the manuscript.
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