3.6 Patterns of Atmospheric Circulation Variability

3.6.1 Teleconnections

The global atmospheric circulation has a number of preferred patterns of variability, all of which have expressions in surface climate. Box 3.4 discusses the main patterns and associated indices. Regional climates in different locations may vary out of phase, owing to the action of such ‘teleconnections’, which modulate the location and strength of the storm tracks (Section 3.5.3) and poleward fluxes of heat, moisture and momentum. A comprehensive review by Hurrell et al. (2003) has been updated by new analyses, notably from Quadrelli and Wallace (2004) and Trenberth et al. (2005b). Understanding the nature of teleconnections and changes in their behaviour is central to understanding regional climate variability and change. Such seasonal and longer time-scale anomalies have direct impacts on humans, as they are often associated with droughts, floods, heat waves and cold waves and other changes that can severely disrupt agriculture, water supply and fisheries, and can modulate air quality, fire risk, energy demand and supply and human health.

The analysis of teleconnections has typically employed a linear perspective, which assumes a basic spatial pattern with varying amplitude and mirror image positive and negative polarities (Hurrell et al., 2003; Quadrelli and Wallace, 2004). In contrast, nonlinear interpretations would identify preferred climate anomalies as recurrent states of a specific polarity (e.g., Corti et al., 1999; Cassou and Terray, 2001; Monahan et al., 2001). Climate change may result through changes from one quasi-stationary state to another, as a preference for one polarity of a pattern (Palmer, 1999), or through a change in the nature or number of states (Straus and Molteni, 2004).

In the NH, one-point correlation maps illustrate the Pacific-North American (PNA) pattern and the NAO (Figure 3.26), but in the SH, wave structures do not emerge as readily owing to the dominance of the SAM. Although teleconnections are best defined over a grid, simple indices based on a few key station locations remain attractive as the series can often be carried back in time long before complete gridded fields were available (see Section 3.6.4, Figure 3.31); the disadvantage is increased noise from the reduced spatial sampling. For instance, Hurrell et al. (2003) found that the residence time of the NAO in its positive phase in the early 20th century was not as great as indicated by the positive NAO index for that period.

Figure 3.26. The PNA (left) and NAO (right) teleconnection patterns, shown as one-point correlation maps of 500 hPa geopotential heights for boreal winter (DJF) over 1958 to 2005. In the left panel, the reference point is 45°N, 165°W, corresponding to the primary centre of action of the PNA pattern, given by the + sign. In the right panel, the NAO pattern is illustrated based on a reference point of 65°N, 30°W. Negative correlation coefficients are dashed, and the contour increment is 0.2. Adapted from Hurrell et al. (2003).

Many teleconnections have been identified, but combinations of only a small number of patterns can account for much of the interannual variability in the circulation and surface climate. Quadrelli and Wallace (2004) found that many patterns of NH interannual variability can be reconstructed as linear combinations of the first two Empirical Orthogonal Functions (EOFs) of sea level pressure (approximately the NAM and the PNA). Trenberth et al. (2005b) analysed global atmospheric mass and found four key rotated EOF patterns: the two annular modes (SAM and NAM), a global ENSO-related pattern and a fourth closely related to the North Pacific Index and the PDO, which in turn is closely related to ENSO and the PNA pattern.

Teleconnection patterns tend to be most prominent in the winter (especially in the NH), when the mean circulation is strongest. The strength of teleconnections and the way they influence surface climate also vary over long time scales. Both the NAO and ENSO exhibited marked changes in their surface climate expressions on multi-decadal time scales during the 20th century (e.g., Power et al., 1999b; Jones et al., 2003). Multi-decadal changes in influence are often real and not due just to poorer data quality in earlier decades.