>Atmospheric CO2 Concentration versus Sea Surface Temperature

>Figure 1 is a curve most people investigating climate change have run across: the Keeling Curve, the Atmospheric CO2 concentration at the Mauna Loa Observatory in Hawaii since March 1958. There are variations of the graph that smooth out the seasonal variations, but they all illustrate that CO2 concentrations have risen from near 315ppm to 385ppm over the past 60+ years.

Figure 1

The monthly change in CO2 concentration (current month value minus previous month value), Figure 2, illustrates a strikingly familiar curve. I’m not the first person to report this, and I won’t be the last. When the data is smoothed, in this case with a 12-month filter to remove the seasonal variation, the graph of the monthly change in CO2 concentration illustrates the dependence of CO2 concentration on SST. (Note that the same effect results without smoothing if the seasonally adjusted data is used. The effect is not a result of the smoothing.)
Figure 2

In Figure 3, I’ve added NINO3.4 data for a comparison. The NINO3.4 data has been scaled with a multiplier of 0.06 for illustration purposes. The correlation in the variations is almost perfect. The exception is a 6-year period between 1989 and 1995 when the two run out of synch. I’ve added the trend lines to highlight the difference. Using those trend lines as reference, the monthly change in CO2 concentration is rising faster than the scaled NINO3.4 SST anomaly.
Figure 3

What about the SSTs of the waters adjacent to Hawaii? Refer to Figure 4. In it I replaced the NINO3.4 SST anomaly data with Pacific Ocean SST anomaly data for a small area surrounding Hawaii. The SST data is for a grid bordered by the coordinates of 18-21N, 154-157W. Mauna Loa is located at 19.5N, 155.6W. The SST data was scaled with a factor of 0.2. Eye-balling the two curves, the correlation has decreased and the SST trend has not improved.
Figure 4

The CO2 data from Mauna Loa is said to be well mixed, so the geographic area of the “Hawaiian” SST data is probably too small. In Figure 5, I changed the reference SST data set to the North Pacific. The North Pacific SST scaling factor is 0.5. The correlation between the two data sets is better than the “Hawaiian” SST comparison, and the trend of the North Pacific SST is greater than the monthly change in CO2 concentration.
Figure 5

Recall, however, that in Figures 3 through 5 I’ve illustrated SST anomaly data that’s been scaled. Figures 6 through 8 are those same data sets without the SST scaling.
Figure 6

Figure 7

Figure 8


Recall that the mid-latitude North Pacific is a Thermohaline Circulation/Meridional Overturning Circulation upwelling area. North Pacific SST anomalies vary significantly with longitude (Figure 9) as well as latitude. I could search for and probably find an SST data set for the North Pacific that best matches the monthly variations in CO2 and it could easily be in an area where the correlation would make sense.
Figure 9

To what end, though?

The solubility of CO2 in sea water is well established. Warmer waters dissolve less CO2. In fact, as the oceans warm they outgas CO2 to the atmosphere.

Not well established, though, are the long-term effects of individual ENSO events on SST. Most studies deal with atmospheric temperature and precipitation responses to ENSO. Yet, during an El Nino, a significant amount of heat is upwelled and distributed to sea surfaces as well, through atmospheric and oceanic Rossby waves and through surface winds and currents. I have also yet to see a study of sea surface temperatures that attempts to follow the heat of a significant El Nino, from the upwelling of the warmer water to its eventual subduction. This assumes there would still be heat left at the time of subduction. But since, in one study, Rossby waves from the 82/83 El Nino were still visible after a decade, I would imagine that some residual heat would remain for at least that period.

Also not well established are the cumulative effects of a greater frequency and magnitude of El Nino events versus La Nina events. El Nino frequency and their combined magnitude have been dominant during every period when global SST and atmospheric temperatures have risen over the past 150+ years. The reverse has occurred when the number and magnitudes of La Nina events outweigh El Nino events. Why? If the heat released by one El Nino is not countered by the heat absorbed of an equally weighted La Nina, there will be a net gain in atmospheric and oceanic heat. Temperatures rise as a consequence. And if that heat has not been fully dissipated before another El Nino adds more, the effect becomes additive. Each subsequent El Nino then adds to the prior oceanic and atmospheric heat, raising global temperature. The same holds true for periods of back-to-back El Nino events, or when the magnitude of a La Nina is not countered by an equally sized El Nino, except, of course, the result is a net heat loss and a decrease in temperature.

It would appear similar results can be expected for the growth rate in atmospheric CO2 concentration in response to ENSO events.


Mauna Loa Observatory CO2 Data is available at:

Sea Surface Temperature Data is Smith and Reynolds Extended Reconstructed SST (ERSST.v2) available through the NOAA National Operational Model Archive & Distribution System (NOMADS).

About Bob Tisdale

Research interest: the long-term aftereffects of El Niño and La Nina events on global sea surface temperature and ocean heat content. Author of the ebook Who Turned on the Heat? and regular contributor at WattsUpWithThat.
This entry was posted in Uncategorized. Bookmark the permalink.

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s