Using a technique I have previously established, and used to isolate various signals in temperature data, I thought it would be interesting to identify the ENSO signal in global temperature data-using the “Invariant ENSO Index” described here. While I don’t think it generally wise to consider ENSO something to be “removed” from the temperature data (since ENSO is itself a part of the climate system and thus part of climate response) it is nevertheless interesting to examine the issue. because ENSO is clearly a major aspect of weather and climate variations, and it provides an additional opportunity to show how the technique I am using can identify signals in the temperature data that are not easily separated out otherwise. I identified events as any 12 month or longer excursion of the average of 13 and 11 month centered averages of the IEI (multiplied by -1 and divided by 10) above or below zero (continuously). That is, if the annually smoothed index changed sign for even a single month, month of the switch back was considered a new event for compositing. In compositing the time evolution of ENSO events, I used the unsmoothed and inverted and standardized index. This is what those look like:
Red is the composite evolution of El Niño events, green the composite evolution of La Niña events. Note that, the La Niña event of 2010 is not included as an event in the composite (except as a follow on of the previous event) because too few months have passed since then, the La Niña composite would be much shorter than the El Niño composite otherwise, instead of being of comparable length. Then I aligned the HadCRUT4 data similarly (with the low frequency signal removed as previously established in my post on volcanic signals in the data) The averages there look like this:
As one can clearly see, a typical El Niño event is indeed followed by an increase (red) in global average near surface air temperatures, and a typical La Niña by a decrease (blue). By smoothing both the temperature response profiles and the ENSO event profiles, and removing some trends in the first 28 and 24 months (when the smoothed profiles switch which is greater than the other) and rescaling the smoothed profiles, I identify the peak values of events and responses. Peak event values occur 12 to 11 months in (for El Niño and La Niña respectively) and peak responses occur 14 months into an event. I can then take those smoothed, early trend corrected, rescaled profiles’ values for their peak event magnitudes and responses, and use those to estimate the linear effect:
Encouragingly, the the responses to La Niña and El Niño seem to scale the same (that is, a straight line as opposed to one with an obvious bend indicating asymmetric response). Using the slope of the regression, and lagging one two or three months, I can then “remove” the ENSO signal thus detected from the global data. Here is what that looks like, annually smoothed:
It is evident that this did not remove the all the effects of every individual ENSO event-some may have a large impact than others-but it did, I think, remove the “average” ENSO response. The above graph has a number of interesting features-for example the effect of the large El Niño in the mid 1940’s was to in effect turn two isolated temperature spikes into a persistent “hump” in the temperature data.