Increase of extreme foolishness in a warming world

Guest Post by Willis Eschenbach

Stefan Rahmstorf and Dim Coumou have published a paper (paywalled, of course) in one of the best-known vanity presses of science, PNAS (Proceedings of the National Alarmists of Science). I think this is another PNAS study that appears to be peer-reviewed, but is actually only “edited”, whatever that means. It has been discussed at some length on the blogs, not always favorably.

Their paper is called “Increase of extreme events in a warming world” (R&C2011). They have developed a mathematical relationship to show that if there is a warming trend in a temperature record, the most recent years will likely be the warmest years. … …

… yeah, yeah, I know … no surprise, right. Seemed like that to me, too, the latest release from the Department of the Blindingly Obvious.

In any case, their test case is the July data for Moscow. Curiously, they use the unadjusted Moscow data, not the adjusted data usually used. Figure 1 shows a graph of the unadjusted and adjusted July temperature in Moscow for the last 130 years, along with the adjustment.

Figure 1. Adjusted and Unadjusted GISS temperatures for July in Moscow. Green line shows the amount of the adjustment (right scale). Adjustment shows the effect of the two-legged GISS method for removing UHI. 

Generally the GISS adjustment kinda makes sense, in that the effect of it is to adjust for a known heat island phenomenon in and around Moscow. The hook in the end is odd, but it’s the GISS computer algorithm and they’re sticking with it, and in this instance, it might be just by coincidence, for once the GISS adjustment is not unreasonable.

So … why did R&C2011 use the unadjusted GISS rather than the adjusted GISS data?

R&C discuss this question over at RealClimate. They put up a graph there that I agree with, showing a problem with the method GISS uses to adjust the temperature for UHI. The problem is that the UHI is larger in the winter, but the GISS adjustment is applied uniformly to every month. I was able to replicate their graph exactly from the GISS data. Here is their figure, and mine based on the same GISS data for Moscow. As you can see, my calculations match the R&C2011 results exactly.

Figure 2. Upper panel is Rahmstorf and Coumou’s Figure 2 from his discussion of his paper at RealClimate (RC). Lower panel shows my emulation, using GISS data downloaded from the web. I have given the figures in °/century, rather than per year in the Rahmstorf data, for comparison with the Rahmstorf quote below. End of data is 2010.

Here’s the odd part. At RC, Rahmstorf says of the graph:

But the graph shows some further interesting things. Winter warming in the unadjusted data is as large as 4.1ºC over the past 130 years, summer warming about 1.7ºC – both much larger than global mean warming. Now look at the difference between adjusted and unadjusted data (shown by the red line): it is exactly the same for every month! That means: the urban heat island adjustment is not computed for each month separately but just applied in annual average, and it is a whopping 1.8ºC downward adjustment.

It mystified me. Where in the graph was the 1.8°C adjustment, the red line shows 1.3°C adjustment? It took me a while to realize what they’d done. The graph shows trend per century. But R&C are talking about the trend per 130 years. That’s why the 1.8°C is “whopping”, because it’s not per century like the graphs. But that’s just the usual fast shuffle I’ve learned to expect from these guys, nothing substantial, just inflating their numbers for effect.

Also, he says that Moscow warming is “much warmer than the global mean warming,” as though that proved something. I cracked up when I read that. Dear R&C: about half of the individual station temperature trends worldwide are warmer than the global mean warming trend … duh …

Then I turned to their paper. Here, you do have to watch the pea under the shell very carefully, these guys will fool you. In the paper, R&C don’t use the trend measures discussed at RC. They don’t use the per-century trend of the entire dataset they show in the graph in Figure 2 of the discussion at RC. Instead, they use another measure of the trend entirely. Here’s their text from the paper:

Next we apply the analysis to the mean July temperatures at Moscow weather station (Fig. 1E), for which the linear trend over the past 100 y is 1.8 °C and the interannual variability is 1.7 °C.

I really don’t like that. That’s picking an arbitrary length of trend, a hundred years. There’s a tendency to think that over such a long period as a century, that the trend doesn’t change much. But that’s not the case. Figure 3 shows the century-long trailing trend for the Moscow July temperature.

Figure 3. Trailing 100-year temperature trend, July temperatures, Moscow. Trend varies greatly even year to year. Trend 1911-2010 = 1.83°C/century. Trend 1910-2009 1.40°C/century.

This makes the choice of the particular trend they used (1.8°/century 1911-2010) quite arbitrary. Why 100 years? Why not 80 years, or 120 years? In addition, even if we choose 100 years, why use that particular hundred years? Indeed, the 100 year trend ending the previous year is only 1.4°C/century, not 1.8.

I agree with R&C that the GISS adjustments distort the picture improperly for the monthly trends. This is actually the only novel part of the R&C paper. It is an interesting finding, one I had not considered. However, the proper way to resolve the problem with the temperature adjustment is not to throw out the adjustment and use unadjusted data, particularly with an arbitrary trend length. The way to resolve the issue is to figure out a way to adjust the data properly.

As a first cut, the obvious way to distribute it is proportionally, depending on the size of the warming. That should give an answer reasonably close to reality. Here is the same adjustment (1.3°/century) distributed proportionally across the months based on the size of each month’s warming trend.

Figure 4. Proportionally adjusted monthly trends for Moscow. Average adjustment to trend is the same as in Figure 2.

If you were going to use a trend for July, the trend shown in green in Figure 4 would be a more reasonable trend than the unadjusted value.

In any case, here’s the problem. They are using a July trend of 1.8°C/century, which is the 1911-2010 trend. The unadjusted July trend, calculated over the entire period of record as shown in their Figure 2, is 1.1°C/century. The proportionally adjusted July trend for the entire period of record is 0.4°C/century (green, Figure 4).

This illustrates the arbitrary nature of their entire process. Based on choices made with no ex-ante criteria, they’ve picked one of many possible linear trend intervals and ending points. I find it … mmm … coincidental that their mathematical procedure works so well with that particular trend (1911-2010, 1.8°C/century). Would it not give a totally different answer if they used the previous year’s trend? (1910-2009, 1.40°C/century) Surely the answer would be different if they used the proportionally adjusted values shown in Figure 4? I find their arbitrary choice indefensible.

Finally, although they tried to stay away from the “anthropogenetics made me do it” explanation, they couldn’t quite give it up entirely. To their credit, the abstract says nothing about humans. But they make three statements of attribution in the body, viz:

Our analysis of how the expected number of extremes is linked to climate trends does not say anything about the physical causes of the trend. However, the post-1980 warming in Moscow coincides with the bulk of the global-mean warming of the past 100 y, of which approximately 0.5 °C occurred over the past three decades (Fig. 1D), most of which the Intergovernmental Panel on Climate Change has attributed to anthropogenic greenhouse gas emissions [IPCC AR4].

Moscow warming “coincides” with warming which is attributed to humans.

The fact that observed warming in western Russia is over twice the global-mean warming is consistent with observations from other continental interior areas as well as with model predictions for western Russia under greenhouse gas scenarios [IPCC AR4]. Hence, we conclude that the warming trend that has multiplied the likelihood of a new heat record in Moscow is probably largely anthropogenic: a smaller part due to the Moscow urban heat island, a larger part due to greenhouse warming.

Here, the warming is fully partitioned. Part is from the UHI, and a “larger part” is due to greenhouse warming. Nothing is left over for natural variation.

Our statistical method does not consider the causes of climatic trends, but given the strong evidence that most of the warming of the past fifty years is anthropogenic [IPCC AR4], most of the recent extremes in monthly or annual temperature data would probably not have occurred without human influence on climate.

This last one is classic: “… most of the recent extremes … would probably not have occurred without human influence on climate”. I have to say I’m highly allergic to this kind of vague handwaving. It has no place in a scientific paper. “Most” of the extremes? How many, and which ones? “Probably would not have occurred” … what is the probability 55%? 95%? And “a human influence on climate”? What influence where? That is suitable for a children’s book, not a science paper.

In addition, I find these citations which simply refer the reader to the entire IPCC magnum opus to be totally lacking in scientific rigor. It reminds me of a fire-and-brimstone preacher of my youth in a tent revival, holding up the Bible and thumping it with his fist and saying “The answer’s in here”! Well, perhaps the answer is in there … but where? Waving the whole book means nothing. Anyone who does that kind of IPCC thumping without citing chapter and verse is a scientific poseur. R&C don’t even bother to specify Working Group 1, 2, or 3. We’re supposed to figure out where, in the several thousands of pages of the UN IPCC AR4, support for their claim is to be found. That is not a scientific citation in any sense of the word, and no reviewer should countenance such ludicrous lack of specificity. Oh, right … this is not peer-reviewed … well, no editor should allow it either.

This seems like the most modern of weapons, a stealth paper. It doesn’t say anything about humans in the abstract. In fact, R&C state quite correctly that their work does not “consider the causes of climatic trends”.

But gosh, despite that, the IPCC says Moscow is “consistent with model predictions”, so even though they don’t consider causes, R&C will consider causes … it’s humans’ fault, case closed.

Hey, here’s an idea for R&C. If your “statistical method does not consider the causes of climatic trends”, then don’t consider the causes of climatic trends. That’s stealth alarmism, not science.

In any case, following the trail of breadcrumbs, here’s a different look at the unadjusted Moscow July data:

Figure 5. Moscow temperature trends, split into pre- and post-1948 trends.

I bring this up, with the split in the trend in 1948 because the Moscow weather station has its own Wikipedia page. Wiki says that the station was established in 1948. Here’s what the station looks like:

Figure 6. Views looking across the Moscow weather station, showing views in all eight cardinal directions.

Of interest is the ring of trees which almost completely surrounds the weather station. This will have had a warming effect as the trees grew up. I can find no other metadata, I’m sure the readers can supply more. But the trees look like they could have been planted after the Great Patriotic War. Who knows?

I bring this last issue up, not to come to any conclusion about Moscow or the validity of the adjustments, but to emphasize the fragmented and complex nature of most long-term temperature records. The fact that we can take a 100-year trend of the Moscow data doesn’t mean that there is any meaning in that trend. The effects of a ring of slow-growing trees around the site, and a city behind the trees, plus a station move, make any measurements of the long-term Moscow trend speculative at best.

Regards to everyone,

w.