Would You Like Your Temperature Data Homogenized, or Pasteurized?

A Smoldering Gun From Nashville, TN

Guest post by Basil Copeland

The hits just keep on coming. About the same time that Willis Eschenbach revealed “The Smoking Gun at Darwin Zero,” The UK’s Met Office released a “subset” of the HadCRUT3 data set used to monitor global temperatures. I grabbed a copy of “the subset” and then began looking for a location near me (I live in central Arkansas) that had a long and generally complete station record that I could compare to a “homogenized” set of data for the same station from the GISTemp data set. I quickly, and more or less randomly, decided to take a closer look at the data for Nashville, TN. In the HadCRUT3 subset, this is “72730” in the folder “72.” A direct link to the homogenized GISTemp data used is here. After transforming the row data to column data (see the end of the post for a “bleg” about this), the first thing I did was plot the differences between the two series:

The GISTemp homogeneity adjustment looks a little hockey-stickish, and induces an upward trend by reducing older historical temperatures more than recent historical temperatures. This has the effect of turning what is a negative trend in the HadCRUT3 data into a positive trend in the GISTemp version:

So what would appear to be a general cooling trend over the past ~130 years at this location when using the unadjusted HadCRUT3 data, becomes a warming trend when the homogeneity adjustment is supplied.

“There is nothing to see here, move along.” I do not buy that. Whether or not the homogeneity adjustment is warranted, it has an effect that calls into question just how much the earth has in fact warmed over the past 120-150 years (the period covered, roughly, by GISTemp and HadCRUT3). There has to be a better, more “robust” way of measuring temperature trends, that is not so sensitive that it turns negative trends into positive trends (which we’ve seen it do twice how, first with Darwin Zero, and now here with Nashville). I believe there is.

Temperature Data: Pasteurized versus Homogenized

In a recent series of posts, here, here, and with Anthony here, I’ve been promoting a method of analyzing temperature data that reveals the full range of natural climate variability. Metaphorically, this strikes me as trying to make a case for “pasteurizing” the data, rather than “homogenizing” it. In homogenization, the object is to “mix things up” so that it is “the same throughout.” When milk is homogenized, this prevents the cream from rising to the top, thus preventing us from seeing the “natural variability” that is in milk. But with temperature data, I want very much to see the natural variability in the data. And I cannot see that with linear trends fitted through homogenized data. It may be a hokey analogy, but I want my data pasteurized – as clean as it can be – but not homogenized so that I cannot see the true and full range of natural climate variability.

I believe that the only way to truly do this is by analyzing, or studying, how differences in the temperature data vary over time. And they do not simply vary in a constant direction. As everybody knows, temperatures sometimes trend upwards, and at other times downward. The method of studying how differences in the temperature data allows us to see this far more clearly than simply fitting trend lines to undifferenced data. In fact, it can prevent us from reaching the wrong conclusion, as in fitting a positive trend when the real trend has been negative. To demonstrate this, here is a plot of monthly seasonal differences for the GISTemp version of the Nashville, TN data set:

Pay close attention as I describe what we’re seeing here. First, “sd” means “seasonal differences” (not “standard deviation”). That is, it is the year to year variation in each monthly observation, for example October 2009 compared to October 2008. Next, the “trend” is the result of smoothing with Hodrick-Prescott smoothing (lamnda = 14,400). The type of smoothing here is not as critical as is the decision to smooth the seasonal differences. If a reader prefers a different smoothing algorithm, have at at it. Just make sure you apply it to the seasonal differences, and that it not change the overall mean of the series. I.e., the mean of the seasonal differences, for GISTemp’s Nashville, TN data set, is -0.012647, whether smoothed or not. The smoothing simply helps us to see, a little more clearly, the regularity of warming and cooling trends over time. Now note clearly the sign of the mean seasonal difference: it is negative. Even in the GISTemp series, Nashville, TN has spent more time cooling (imagine here periods where the blue line in the chart above is below zero) than it has warming over the last ~130 years.

How can that be? Well, the method of analyzing differences is less sensitive – I.e. more “robust” — than fitting trend lines through the undifferenced data. “Step” type adjustments as we see with homogeneity adjustments only affect a single data point in the differenced series, but affect every data point (before or after it is applied) in the undifferenced series. We can see the effect of the GISTemp homogeneity adjustments here by comparing the previous figure with the following:

Here, in the HadCRUT3 series, the mean seasonal difference is more negative, -0.014863 versus -0.012647. The GISTemp adjustments increases the average seasonal difference by 0.002216, making it less negative, but not enough so that the result becomes positive. In both cases we still come to the conclusion that “on the average” monthly seasonal differences in temperatures in Nashville have been negative over the last ~130 years.

An Important Caveat

So have we actually shown that, at least for Nashville, TN, there has been no net warming over the past ~130 years? No, not necessarily. The average monthly seasonal difference has indeed been negative over the past 130 years. But it may have been becoming “less negative.” Since I have more confidence, at this point, in the integrity of the HadCRUT3 data, than the GISTemp data, I’ll discuss this solely in the context of the HadCRUT3 data. In both the “original data” and in the blue “trend” shown in the above figure, there is a slight upward trend over the past ~130 years:

Here, I’m only showing the fit relative to the smoothed (trend) data. (It is, however, exactly the same as the fit to the original, or unsmoothed, data.) Whereas the average seasonal difference for the HadCRUT3 data here was -0.014863, from the fit through the data it was only -0.007714 at the end of series (October 2009). Still cooling, but less so, and in that sense one could argue that there has been some “warming.” And overall – I.e. if a similar kind of analysis is applied to all of the stations in the HadCRUT3 data set (or “subset”) – I will not be surprised if there is not some evidence for warming. But that has never really be the issue. The issue has always been (a) how much warming, and (b) where has it come from?

I suggest that the above chart showing the fit through the smooth helps define the challenges we face in these issues. First, the light gray line depicts the range of natural climate variability on decadal time scales. This much – and it is very much of the data – is completely natural, and cannot be attributed to any kind of anthropogenic influence, whether UHI, land use/land cover changes, or, heaven forbid, greenhouse gases. If there is any anthropogenic impact here, it is in the blue line, what is in effect a trend in the trend. But even that is far from certain, for before we can conclude that, we have to rule out natural climate variability on centennial time scales. And we simply cannot do that with the instrumental temperature record, because it isn’t long enough. I hate to admit that, because it means either that we accept the depth of our ignorance here, or we look for answers in proxy data. And we’ve seen the mess that has been made of things in trying to rely on proxy data. I think we have to accept the depth of our ignorance, for now, and admit that we do not really have a clue about what might have caused the kind of upward drift we see in the blue trend line in the preceding figure. Of course, that means putting a hold on any radical socioeconomic transformations based on the notion that we know what in truth we do not know.