El Nino’s effect on CO2 causes confusion about CO2’s role for climate change

Fig. 2: Reproduction of the lower panel of Fig 2 in Humlum et al (2012). Also shown is a 'DIFF12' for the Keeling curve (light green). Some of the strongest El Ninos are shown with grey hatching.

Their main argument about causality between temperature and CO2, however, was based on a lagged correlation analysis between ‘DIFF12’ series from temperature and CO2. Fig. 3 corresponds to figure 4b in Humlum et al. (2012):

Fig. 3: Lag correlation where the lines mark the peak value obtained in Humlum et al. Grey curve is from the longer Keeling record. Here the HadCRUT3, which was one of the data sets they used.

The correlation that I get is similar but not identical to theirs. Using a longer record did affect the lag correlation analysis as seen in Fig. 3. Nevertheless, the analysis still indicated that CO2 lagged the temperature. Big surprise?

No! Applying correlation to the results from the ‘DIFF12’ quantities cannot detect any trends – it’s just a simple mathematical fact. These results merely confirm already well-known facts, which ironically, they themselves hinted to in their paper (but they obviously did not make the connection):

changes in atmospheric CO2 appears to be initiated near or a short distance south of the Equator, and from there spreads towards the two Poles within a year or so.

The answer is of course: El Nino! A google scholar search with ‘”El Nino” AND CO2’ gives more than 20,000 hits, and Humlum et al. have rediscovered well-known facts which Keeling and Revelle discussed already in 1985.

El Ninos affect the CO2 concentrations for a brief time interval, through their effect on temperature and marine biology. But unlike Keeling and Revelle, this discovery caused quite some confusion, as evident in the following citation:

…showing that changes in the emission of anthropogene CO2 are not causing changes in atmospheric CO2.

So how did they get to this conclusion? The answer is in their analytical set-up, and for this they have quite an unusual record (here and here).

It’s well-known that taking differences also picks up short-term rather than long-term variations where mean trends are represented by a constant value. Hence, a correlation analysis is bound to give mean trends zero weight. This is demonstrated in Fig. 4:

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  1. O. Humlum, K. Stordahl, and J. Solheim, "The phase relation between atmospheric carbon dioxide and global temperature", Global and Planetary Change, vol. 100, pp. 51-69, 2013. http://dx.doi.org/10.1016/j.gloplacha.2012.08.008