Or, “could the multiple regression approach detect a recent pause in warming, part 4”. For those following the series, you know what I mean by “underlying temperatures” is the temperature evolution if we attempted to remove the influence of solar, volcanic, and ENSO variations.

It has been a while since I posted the first three parts of a series on whether using multiple linear regressions to remove the solar, volcanic, and ENSO effects from temperature was an accurate way to "reconstruct" the underlying trend. Generally, these did not perform too well, and tended to overestimate the solar influence and underestimate the volcanic influence, particularly if there was indeed a "slowdown" in the underlying temperature data. One of the problems with that method is that it includes an assumption about the form of the underlying trend when doing the regressions.

So, I’d thought I’d put a temperature series (actually, a couple of options) out there that have been adjusted for these factors, using a method that is not particularly sensitive to the form of the underlying trend. Essentially, I take the multi-model mean of the models I used in the last post in this series to adjust for the volcanic and solar components, and then remove ENSO based on a regression against that adjusted series. Fortunately, the ENSO variations are high enough frequency that the regression is not particularly sensitive to form of the the underlying trend (whether it be linear or quadratic) as we have limited the number of variables.

It should be noted that this method might *over-adjust* for volcanic and solar if the CMIP5 models are too sensitive, which my recent paper (Masters 2013, Climate Dynamics) seems to indicate. I have therefore included an adjusted series that adjusts by only 50% of the MMM as well. Since the difference between the sensitivies in the transient state are likely to be less than after equilibration, let’s say the "true" adjustment should lie somewhere in-between those two adjustments.

Anyhow, here is the reconstructed series of NCDC (NOAA) temperatures. (On a side note, I have become a little annoyed with trying to grab data from HadCRUT4 and GISS. The former seems to return a "Not Found" error quite frequently, and the latter doesn’t let the R default user-agent grab data at all. Hence the usage of NOAA temperatures).

If I were to go strictly by the eyeball test, the blue line (adjusted by 50% of MMM) seems to get it “most right” in terms of compensating for the volcanic eruptions without over-adjusting. Below are the trends for the various start years ending in 2012 in these series:

Here you’ll note that the “adjusted” series actually results in a lower trend for all start years up until about 2001, when the influence of ENSO seems to really take over. The blue line never dips below 0 for these adjusted trends of 10 years or longer, so one could argue that the underlying warming (if the blue line indeed captures this correctly) never really “stopped”. On the other hand, the trends are substantially lower towards the end than they are at the beginning (and indeed smaller than in most model runs), so saying that the recent “slowdown” is simply the result of known natural factors rings a bit hollow to me. It would be interesting to run a similar experiment on the CMIP5 model runs and see how much “natural” variation remains in those runs, of if this is something unique to the real world.

Code and data for this post available here.