The term teleconnection has long been defined as interactions between behaviors separated by geographical distances. Using Google Scholar, the first consistent use in a climate context was by De Geer in the 1920’s [1]. He astutely contrasted the term teleconnection with telecorrelation, with the implication being that the latter describes a situation where two behaviors are simply correlated through some common-mode mechanism — in the case that De Geer describes, the self-registration of the annual solar signal with respect to two geographically displaced sedimentation features.

As an alternate analogy, the hibernation of groundhogs and black bears isn’t due to some teleconnection between the two species but simply a correlation due to the onset of winter. The timing of cold weather is the common-mode mechanism that connects the two behaviors. This may seem obvious enough that the annual cycle should and often does serve as the null hypothesis for ascertaining correlations of climate data against behavioral models.

Yet, this distinction seems to have been lost over the years, as one will often find papers hypothesizing that one climate behavior is influencing another geographically distant behavior via a physical teleconnection (see e.g. [2]). This has become an increasingly trendy viewpoint since the GWPF advisor A.A. Tsonis added the term network to indicate that behaviors may contain linkages between multiple nodes, and that the seeming complexity of individual behavior is only discovered by decoding the individual teleconnections [3].

That’s acceptable as a theory, but in practice, it’s still important to consider the possible common-mode mechanisms that may be involved. In this post we will look at a possible common-mode mechanisms between the atmospheric behavior of QBO (see Chapter 11 in the book) and the oceanic behavior of ENSO (see Chapter 12). As reference [3] suggests, this may be a physical teleconnection, but the following analysis shows how a common-mode forcing may be much more likely.

From the outset, the forcing to the modeled behaviors of ENSO and QBO were split into just a few categories. There is (1) the declination of the lunar cycle, (2) the ellipticity of the lunar cycle, and (3) the earth’s orbit around the sun while it is rotating itself, as potential null hypothesis forcings. Each of these categories has similar complexity with cross-terms similar to those found with conventional tidal analysis.

As it turns out, the declination forcing for ENSO, when isolated on its own and retaining the 2^nd-order detail in the Draconic month variation, provides an almost exact match as a QBO forcing, as shown in Figure 1 below.

It makes sense that the QBO model only uses a single forcing factor considering how regular the cycle appears. Yet the regularity is obscured on a closer look by a variation in the 2.38 year average period. The common-mode mechanism is that the model from ENSO assumes a specific variation in the Draconic month [4], see Figure 2 for the alignment of the QBO forcing applied — they are essentially the same.

Without the Draconic variation, the correlation coefficient of the QBO data with a model applying a purely sinusoidal forcing is only ~0.5, instead of reaching 0.75.

In contrast, the Draconic forcing alone is not enough to provide the variation required to model ENSO, and so the orthogonal Anomalistic and Synodic factors provide the additional detailed forcing. The common-mode Draconic forcing is shown in green in Figure 4.

The utility of this common-mode mechanism is that it may enable an additional constraint to fitting ENSO and reducing the number of degrees of freedom.

References

[1] G. De Geer, “Teleconnections contra so-called telecorrelations,” Geologiska Föreningen i Stockholm Förhandlingar, vol. 57, no. 2, pp. 341–346, 1935.

[2] D. I. V. Domeisen, C. I. Garfinkel, and A. H. Butler, “The Teleconnection of El Niño Southern Oscillation to the Stratosphere,” Reviews of Geophysics, vol. 57, no. 1, pp. 5–47, Mar. 2019.

[3] A. A. Tsonis and K. L. Swanson, “Topology and predictability of El Nino and La Nina networks,” Physical Review Letters, vol. 100, no. 22, p. 228502, 2008.

[4] P. R. Pukite, D. Coyne, and D. Challou, “Ephemeris calibration of Laplace’s tidal equation model for ENSO,” presented at the AGU Fall Meeting Abstracts, 2018.