IPCC Fourth Assessment Report: Climate Change 2007
Climate Change 2007: Working Group I: The Physical Science Basis

8.4.1 Northern and Southern Annular Modes

There is evidence (e.g., Fyfe et al., 1999; Shindell et al., 1999) that the simulated response to greenhouse gas forcing in AOGCMs has a pattern that resembles the models’ Northern Annular Mode (NAM), and thus it would appear important that the NAM (see Chapters 3 and 9) is realistically simulated. Analyses of individual AOGCMs (e.g., Fyfe et al., 1999; Shindell et al., 1999) have demonstrated that they are capable of simulating many aspects of the NAM and NAO patterns including linkages between circulation and temperature. Multi-model comparisons of winter atmospheric pressure (Osborn, 2004), winter temperature (Stephenson and Pavan, 2003) and atmospheric pressure across all months of the year (AchutaRao et al., 2004), including assessments of the MMD at PCMDI (Miller et al., 2006) confirm the overall skill of AOGCMs but also identify that teleconnections between the Atlantic and Pacific Oceans are stronger in many models than is observed (Osborn, 2004). In some models this is related to a bias towards a strong polar vortex in all winters so that their simulations nearly always reflect behaviour that is only observed at times with strong vortices (when a stronger Atlantic-Pacific correlation is observed; Castanheira and Graf, 2003).

Most AOGCMs organise too much sea level-pressure variability into the NAM and NAO (Miller et al., 2006). The year-to-year variance of the NAM or NAO is correctly simulated by some AOGCMs, while other simulations are significantly too variable (Osborn, 2004); for the models that simulate stronger variability, the persistence of anomalous states is greater than is observed (AchutaRao et al., 2004). The magnitude of multi-decadal variability (relative to sub-decadal variability) is lower in AOGCM control simulations than is observed, and cannot be reproduced in current model simulations with external forcings (Osborn, 2004; Gillett, 2005). However, Scaife et al. (2005) show that the observed multi-decadal trend in the surface NAM and NAO can be reproduced in an AOGCM if observed trends in the lower stratospheric circulation are prescribed in the model. Troposphere-stratosphere coupling processes may therefore need to be included in models to fully simulate NAM variability. The response of the NAM and NAO to volcanic aerosols (Stenchikov et al., 2002), sea surface temperature variability (Hurrell et al., 2004) and sea ice anomalies (Alexander et al., 2004) demonstrate some compatibility with observed variations, though the difficulties in determining cause and effect in the coupled system limit the conclusions that can be drawn with regards to the trustworthiness of model behaviour.

Like its NH counterpart, the NAM, the Southern Annular Mode (SAM; see Chapters 3 and 9) has signatures in the tropospheric circulation, the stratospheric polar vortex, mid-latitude storm tracks, ocean circulation and sea ice. AOGCMs generally simulate the SAM realistically (Fyfe et al., 1999; Cai et al., 2003; Miller et al., 2006). For example, Figure 8.12 compares the austral winter SAM simulated in the MMD at PCMDI to the observed SAM as represented in the National Centers for Environmental Prediction (NCEP) reanalysis. The main elements of the pattern, the low-pressure anomaly over Antarctica and the high-pressure anomalies equatorward of 60°S are captured well by the AOGCMs. In all but two AOGCMs, the spatial correlation between the observed and simulated SAM is greater than 0.95. Further analysis shows that the SAM signature in surface temperature, such as the surface warm anomaly over the Antarctic Peninsula associated with a positive SAM event, is also captured by some AOGCMs (e.g., Delworth et al., 2006; Otto-Bliesner et al., 2006). This follows from the realistic simulation of the SAM-related circulation shown in Figure 8.12, because the surface temperature signatures of the SAM typically reflect advection of the climatological temperature distribution by the SAM-related circulation (Thompson and Wallace, 2000).

Figure 8.12

Figure 8.12. Ensemble mean leading Empirical Orthogonal Function (EOF) of summer (November through February) Southern Hemisphere sea level pressure (hPa) for 1950 to 1999. The EOFs are scaled so that the associated principal component has unit variance over this period. The percentage of variance accounted for by the leading mode is listed at the upper left corner of each panel. The spatial correlation (r) with the observed pattern is given at the upper right corner. At the lower right is the ratio of the EOF spatial variance to the observed value. “Canadian CC” refers to CGCM3.1 (T47), and “Russell GISS” refers to the GISS AOM. Adapted from Miller et al. (2006).

Although the spatial structure of the SAM is well simulated by the AOGCMs in the MMD at PCMDI, other features of the SAM, such as the amplitude, the detailed zonal structure and the temporal spectra, do not always compare well with the NCEP reanalysis SAM (Miller et al., 2006; Raphael and Holland, 2006). For example, Figure 8.12 shows that the simulated SAM variance (the square of the SAM amplitude) ranges between 0.9 and 2.4 times the NCEP reanalysis SAM variance. However, such features vary considerably among different realisations of multiple-member ensembles (Raphael and Holland, 2006), and the temporal variability of the NCEP reanalysis SAM does not compare well to station data (Marshall, 2003). Thus, it is difficult to assess whether these discrepancies between the simulated SAM and the NCEP reanalysis SAM point to shortcomings in the models or to shortcomings in the observed analysis.

Resolving these issues may require a better understanding of SAM dynamics. Although the SAM exhibits clear signatures in the ocean and stratosphere, its tropospheric structure can be simulated, for example, in atmospheric GCMs with a poorly resolved stratosphere and driven by prescribed SSTs (e.g., Limpasuvan and Hartmann, 2000; Cai et al., 2003). Even much simpler atmospheric models with one or two vertical levels produce SAM-like variability (Vallis et al., 2004). These relatively simple models capture the dynamics that underlie SAM variability – namely, interactions between the tropospheric jet stream and extratropical weather systems (Limpasuvan and Hartmann, 2000; Lorenz and Hartmann, 2001). Nevertheless, the ocean and stratosphere might still influence SAM variability in important ways. For example, AOGCM simulations suggest strong SAM-related impacts on ocean temperature, ocean heat transport and sea ice distribution (Watterson, 2001; Hall and Visbeck, 2002), suggesting a potential for air-sea interactions to influence SAM dynamics. Furthermore, observational and modelling studies (e.g., Thompson and Solomon, 2002; Baldwin et al., 2003; Gillett and Thompson, 2003) suggest that the stratosphere might also influence the tropospheric SAM, at least in austral spring and summer. Thus, an accurate simulation of stratosphere-troposphere and ocean-atmosphere coupling may still be necessary to accurately simulate the SAM.