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.

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70 thoughts on “Increase of extreme foolishness in a warming world

  1. Something seems to have gotten lost in translation of the views. There are duplicates of the south and north west directions, but missing is the south and north east directions.

    Good article! I enjoyed the read.

  2. How can a UHI adjustment ever be positive ? I don’t understand the positive GISS adjustments … I see that their method flattens out the trend line but does nothing to “fix” the issue of UHI …

  3. Is Global warming projected to concentrate in one spot on the globe. Is that consistent with the models. Warming in the interior of continents is expected but why are the interior of the other continents so cold this month.

    Global land temperature anomalies (day-time) in July, 2010.

  4. Tell me it isn’t so. Tell me they were NOT knowingly excited and twitterpated over their sample station being [over, under, different] than the global average. I just TAUGHT that lesson with my 5th grade students.

  5. I’m one of those who thinks that urban temperatures are just that, urban temperatures. They only vaguely have anything to do with climate – they are the result of our land utilization. Rural temperatures show unchanged land – maybe, provided that a farmer hasn’t plowed recently, or changed the crop around the site, or…

    Having stable or invariant climate component measuring sites is a prerequisite to knowing what’s going on long term with the weather. Mostly, I suspect, having read a bunch on this site, that we should be instrumenting the oceans. They’re going to bring us our weather and our climate. Knowing what the Sun is doing is also a key part of the the equation.

    I suspect long term we need to get the politicians out of the climate business. Amateur science is what got us to where we once were, back before the “Pros” went on the dole. Somehow professional science is starting to sound like professional sports – entertaining, but nothing really earthshaking. It’s those in the trenches, doing the base building that let science progress, not those preening in the light of publicity.

  6. I love your title. “in a warming world” is classic psychological distancing. They want to give the impression that THE world is warming according to their theory, but can’t quite bring themselves to actually say so, to actually put their reputations on the line. So they move it one step away: they are talking about A warming world, not THE warming world or THIS warming world. Look around, they do it all over, article after article discusses its projections in the context of A warming world. You do well to satirise them for it. Now will they close the gap and actually make a scientific prediction about THIS planet?

  7. Something about problems with bushes and more:

    Parallel air temperature measurements atthe KNMI observatory in De Bilt (the Netherlands) May 2003 – June 2005

    http://www.knmi.nl/publications/fulltexts/wr2011_01.pdf
    (LONG LOADING TIME)

    The results indicate that changes in surroundings complicate or impede the use of present-day parallel measurements for correcting for sites changes in the past. In a few years time, the growth of bushes close to the thermometer screen may seriously disturb the temperature measurements. We quantied the possible effects of sheltering on temperature measurements at five sites. It appeared that, especially in summer, these effect on the the monthly mean temperatures may have the same order of magnitude as the long-term temperature
    trend (about 1.0 C/100yr in De Bilt). However, for most sites the inter-site temperature differences for maximum and minimum temperature have opposite signs. The net effect on the daily mean temperatures is, therefore, small (< 0.1 K).
    In practice, the largest inhomogeneities in mean temperature series may be anticipated in case of relocations from very enclosed sites to more open sites. The renovation of the wasteland area, close to the operational site DB260, had a signicant effect on the temperature of DB260. Both the location and shape of the distribution of daily temperatures differences are affected.
    The results indicate that the magnitude of the inter-site temperature differences strongly depends on wind speed and cloud cover. In the case of homogenization of daily temperature series, it is important to take this into account. A complication is that for wind speed the largest effects on inter-site night-time temperature differences occur in the range 0.0-1.0 m/s at screen level, thus strongly affecting the
    minimum temperature. In practice (a) wind speed is mostly not measured at screen level but at heights of 10-20 m (during stable nights, wind speeds at these heights are largely uncoupled from those at screen height), and (b) the measurement uncertainty for small wind speeds is large and often increases with the time during which the anemometer is in the field.
    Another complication for the modeling of daily temperature series, is the homogeneity of the time series of the explanatory variables wind speed and cloudiness. More research is needed in this area.
    Improvement of our understanding of inter-site temperature differences may enable the modeling of them. In the case of De Bilt there are certain aspects that are likely important and should be studied further. First, the non-uniformity of the KNMI-terrain may affect downstream sites by daytime advection and may cause temperature differences to be dependent on wind direction. It is recommended to study this further by measuring the sensible and latent heat uxes at several locations at the same time. Second, during night-time conditions, there are two main mechanisms that affect temperature differences between sites: (a) local stability differences, and (b) differences in sky-view-factor. Both mechanisms have an opposite effect on night-time temperature differences between sites and the net result may be a cooling or a warming. The interaction of those two mechanisms is not fully understood and needs to be investigated further to enable the modeling of them. Finally, local differences in soil type and groundwater levels between the sites may affect (apart from advection) the energy balance and may cause differences in observed temperatures.

  8. Seeing too much meaning in trends is itself a widespread trend.

    “Swathes of Australia’s seaweed are shifting south to escape warming oceans and many risk becoming extinct, a new study has found.”
    http://www.abc.net.au/news/2011-10-28/seaweed-advances-south/3605740

    All this does is prove that seaweed can spread faster than it can adapt. Once again the ability of life to adapt to climate change is downplayed or ignored in the article. To ABC’s credit they do also mention some other seaweed species moving towards warmer waters at the same time.

  9. “Stealth Paper”. I think that is very appropriate! Hide the agenda in the body….but it isn’t very well hidden! Pal review at its finest. That is the only way that could be overlooked. I made a hasty reference like that in my doctoral thesis years ago, and it got shredded by the committee. It spawned a rewrite, and basically taught me to apply more rigor to my utterances! In the process I developed an eye for the trite and spurious ‘smuggled’ stuff. CliSciFi is full of it. To the point of being comedic, if it wasn’t on my dime. How does one get under the edge of this bunker and tear the roof off, I wonder?

  10. I tried to read the RC article. I got about as far as the 10th comment, one of their usual suspects & closed the webpage.
    Nothing changes there.

  11. RC11 has this at the end: “We thank four anonymous reviewers for their constructive remarks on an earlier version of this manuscript.” So perhaps it has been reviewed. [Which of course does not guarantee correctness. But you were wondering.]

  12. “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. … …”

    Either you didn’t understand, or you’re deliberately misrepresenting. Which is it?

  13. “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.”

    Best summary of the problems I have seen. Great work as usual, Willis.

    Yep, Mother Nature recognizes no trends, suffers no fools with trends, and cannot be described via trends. And a wall of trees growing around a weather station will change temperatures recorded by that station. Ignoring the facts of weather stations is unscientific.

  14. @Pamela Gray

    Do your 5th Grade students exhibit more common sense than the average AGW proponent? I have a sneaky suspicion that they can spot inconsistent logic well, but might be baffled by the high-falutin’ BS and switcheroo’s as in the paper above. Does BS baffle brains or only simpletons?

    I feel children need only to be shown a couple of times how tricks work and they quickly spot new ones because of an awakened, inherent skepticism. As GW Bush said, “Fool me once…” etc.

    Thanks
    Crispin

  15. @Anthony and Willis

    Ibrahim points out that wind speed at the Screen height is not that measured and reported for that location. Is this not going to produce a very large error in temperature corrections? Or is it assumed that the Screen is mounted in an open space where the vertical speed profile is known? The Moscow station with growing trees will definitely affect the wind speed on the ground more than at 20m. Would that not that bias lead to warmer summer temps and colder winter ones?

    One could make a case for a static obstruction being a static influence, but trees have been observed to grow, often vertically.

  16. The future is determined by the law of large numbers. The further into the future we look, the bigger the numbers get. Happens for money, debt, prices, and for temperatures.

  17. Russia is not on my list yet,
    http://www.letterdash.com/HenryP/henrys-pool-table-on-global-warming
    but I am not doubting it got warmer in Russia over the years.

    They got better at removing snow, and, like you suspect, forests are growing faster due to more heat coming in (maxima rising) and more heat being trapped by the increase in forestry…
    Most sceptics do not doubt that it is warming. The question remains: what is causing it. Understand that it is alleged that due to increased green house gases in the atmosphere, heat is trapped that cannot escape from earth. So if an increase in green house gases were to be blamed for any warming, it should be minimum temperatures (that occur during the night) that must show the increase (of modern warming). In that case, the observed trend should be that minimum temperatures should be rising faster than maxima and mean temperatures, pushing up the average temperature.
    So don’t you think that any set of data displaying the increase in average temperatures is pretty useless unless it shown TOGETHER with the development of minima and maxima?
    So this paper and all the others, even all those from the US that we saw here recently are all useless. What a waste of time. Why don’t they concentrate on looking at the ratio of maxima, minima and means?
    http://www.letterdash.com/HenryP/more-carbon-dioxide-is-ok-ok

  18. Willis,

    Is figure #3 correct.? Text says 100 years but the graph is for 1980-2010. Or am I getting confused yet once again?

  19. Like so much we see today and not just in climatology, this paper appears to be little more then propaganda based on dubious methodology, (I mean the whole GISS foolishness) and unsound statistical treatments. This kind of thing deserves little more attention then to file it in bin 13.

  20. Why doesn’t the UHI adjustment start at 0?
    What does the trend look like with the UHI adjustment starding at 0?

  21. @wsbriggs
    I am not so worried about the rural stations as long as the farmer is planting the same crops at the same time of year, every year — wait, that isn’t good agricultural practice!…We would need metadata on the crop types year to year, dates of planting, irrigation methods used and timing. Letting a field be fallow for a couple of years could really put a zinger in the record. Then we would have to do controlled experiments to isolate the effects of each of those crop variables then apply a complete correction factor to the record on a day by day basis based on the metadata of the crop/irrigation records. Or we could be climate scientists and just pull it out of our nether orifices and call it climate. After all, it all averages out over time.

  22. Pamela Gray,

    I once heard a UK politician getting all discombobulated about the fact that “almost 50% of schoolchildren are below average reading ability”.

    Who’d a think it?

  23. Jeff D says:
    October 28, 2011 at 8:18 am

    Willis,

    Is figure #3 correct.? Text says 100 years but the graph is for 1980-2010. Or am I getting confused yet once again?
    _______________________________

    Ok, I think i get it. The graph is representative of the ” trailing part of the data set ” and the adjustments can effect greatly the the end results. I really hate being a newbie…

  24. Another excellent post Willis.Like you say these prople will do anything to persuade us that AGW is happening. A common trick used is to use a graph with a disproportionate scale to exaggerate small differences. I think that WUWT should convert all their graphs to a linear scale scale with the base of the y axis starting at 0° Kelvin. That would put it all in perspective!

  25. “I once heard a UK politician getting all discombobulated about the fact that “almost 50% of schoolchildren are below average reading ability”.”

    When and who was this?

  26. Willis, your work relates well to work I did using Russian data

    (1) Just before Climategate broke, Yamal was the cause du jour. I did three pages, the last of which was published here. Note how closely the Salehard record keeps in step with the 6 nearest GISS station records. No step changes, no latter-day divergence. Much more telling than the pea-soup area averaging all the official global records do.

    (2) Now look at Salehard seasonal anomalies, where Dec-Feb temperatures go through the roof after 2000, suggesting a clear correction needed for recent winter UHI.

    (3) Now look at the evidence for Russian UHI and rural Arctic no-UHI here

    (4) Now hear Dr Andrei Illarionov showing how delta T is highest in the most-rural subset today. Click both U-tube links just under the blue screenshots from his presentation to Heartland.

  27. “stevo says:
    October 28, 2011 at 7:35 am

    “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. … …”

    Either you didn’t understand, or you’re deliberately misrepresenting. Which is it?”

    I’m guessing the former – is it possible to mangle it this badly deliberately? I don’t think I could if I tried….

    “Andrew Harding says:
    October 28, 2011 at 9:47 am

    Another excellent post Willis.Like you say these prople will do anything to persuade us that AGW is happening. A common trick used…..”

    This “excellent post” bears almost no relationship to the contents of the paper under discussion. And this particular “trick” killed 56 000 people.

  28. stevo says:
    October 28, 2011 at 7:35 am

    “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. … …”

    Either you didn’t understand, or you’re deliberately misrepresenting. Which is it?

    I’m going with “you didn’t understand”, because I don’t think you deliberately misrepresented anything.

    w.

  29. Rattus Norvegicus says:
    October 28, 2011 at 7:46 am

    Uh, Willis, it was a direct submission so a standard peer-review process applies.

    Thanks, Rattus, that just makes it worse. Shame on the reviewers. I thought that when it said “edited by” or “editor” at the top it wasn’t peer-reviewed. What indicates that it was?

    w.

  30. Barry L. says:
    October 28, 2011 at 8:53 am

    Why doesn’t the UHI adjustment start at 0?
    What does the trend look like with the UHI adjustment starding at 0?

    The UHI adjustment is done from the present backwards, so it can start anywhere but it always ends at zero.

    w.

  31. caroza says:
    October 28, 2011 at 11:18 am

    … This “excellent post” bears almost no relationship to the contents of the paper under discussion. And this particular “trick” killed 56 000 people.

    OK, I’m clueless. What trick killed 56,000 people, and where?

    And why does my post bear “almost no relationship” to the paper? That’s a uselessly vague assertion, unfalsifiable, and without a scrap of explanation to let me know what you are all atwitter about.

    Truly, folks. If you want a coherent response, you need to write a coherent comment. Inter alia, this means

    1. QUOTE MY WORDS that you object to. I haven’t a clue, for example, what has caroza so excited.

    2. CITE YOUR POINTS with facts, observations, other authorities, and the like. Un-cited assertions don’t get much traction here.

    3. INCLUDE SUFFICIENT DETAIL. Don’t just dump 56,000 dead people in the middle of your thoughts. Let us know who died, when, and where, and most importantly, why the corpses are in the middle of your sentence.

    4. REMEMER “BCI”. Your post is much likely to receive an answer, from me or others, if it is Brief, Clear, and Interesting.

    w.

  32. Sure, Willis, no problem.

    Stevo quoted this:

    “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. … …”

    and asked whether you had misunderstood or were wilfully misrepresenting.

    Having read the paper, I had the same reaction. That is not what the paper was about. It set out to establish a probability distribution for extreme and record events (so nothing to do with either “recent years” or “warmest years”), using a Monte Carlo analysis, and to demonstrate that this depends on the ratio of size of trend (if there is one) to natural variability in the data. If there is no trend then intuitively the probability of a record event declines over time. This provides a mechanism for computing the expected number of record or extreme events in a given time period.

    What it then did was compute the probability that the 2010 heatwave (a record event) would have happened in the absence of a trend in the data – around 20% using 100 years, less if they use the 130 years for which data is available. They started out with 100 years because that was the period for which the theoretical work was done (the Monte Carlo) but then examined the longer period as well.

    The paper had nothing to do with UHI except to observe that annual rather than monthly correction for UHI had contaminated the Moscow station data, and it wasn’t about trends in the Moscow data per se, as is clear from the discussion at realclimate. However, those are the preoccupations of the balance of your post, hence my remark.

    It was probably a little unfair of me to pick on Andrew’s comment as opposed to others in the same vein – it happened to be near the bottom. But so far, you haven’t imo made a case against the paper, which actually looks at first reading as though it will provide an excellent attribution methodology. And in the case of the record heat event in Moscow, 56 000 more people died than the annual average. That’s quite a lot of people dead for something that isn’t supposed to be happening according to you (and Andrew, of course).

  33. Someone should check what is households warming energy kWh/m3 in Moscow compared much better isolated houses in Helsinki, Finland

  34. caroza says:
    October 28, 2011 at 12:08 pm

    Sure, Willis, no problem.

    Stevo quoted this:

    “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. … …”

    and asked whether you had misunderstood or were wilfully misrepresenting.

    Having read the paper, I had the same reaction. That is not what the paper was about. It set out to establish a probability distribution for extreme and record events (so nothing to do with either “recent years” or “warmest years”), using a Monte Carlo analysis, and to demonstrate that this depends on the ratio of size of trend (if there is one) to natural variability in the data. If there is no trend then intuitively the probability of a record event declines over time. This provides a mechanism for computing the expected number of record or extreme events in a given time period.

    Thanks, caroza.

    So is your claim that 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 NOT be the warmest years.”

    Or is it your claim they have developed ” a mathematical relationship to show that if there is a warming trend in a temperature record, their method says nothing about whether the most recent years will likely be the warmest years.”

    Only three possibilities here, caroza. Remember, we’re talking about the warmest years during a warming trend. So in a warming trend, either —

    1. Their math shows that ” recent years will likely be the warmest years”, or

    2. Their math shows that ” recent years will likely NOT be the warmest years”, or

    3. Their math says nothing about whether the ” recent years will likely be the warmest years”.

    I hold, as stated above in the head post, that the correct answer is #1. You and Stevo say that’s not the answer and imply that I’m an idiot for choosing that answer.

    So let’s start with clarifying your position on whether my statement is true or not, Caroza—#1, #2, or #3? Stevo, you can answer too.

    w.

  35. Caroza, you are right that I didn’t get into the specifics of the paper. This is because that has been discussed on a number of blogs, plus my stomach wouldn’t take it. My main problems with the paper are twofold. First, they didn’t (AFAIK) archive their data or their code.

    Second. their Monte Carlo analysis assumes that temperature is

    … random uncorrelated “noise” with various trends added.

    BWA-HA-HA-HA, anyone who makes that kind of ridiculous claim get no further analysis from me. The temperature records of the globe are many things, but uncorrelated Gaussian noise plus a trend? Get real, that’s a grade school assumption guaranteed to lead them wrong. The fact that the authors used it and the reviewers passed it is another measure of how far PNAS has fallen. I’ve actually heard that PNAs used to publish science, if you can believe that …

    w.

  36. Willis, the answer is #3. They are talking about the likelihood of extreme events, not “recent years being warmest”. Though surely, “most recent years will likely be the warmest years” must be true anyway, by definition, in a warming trend.

  37. Willis, the answer is #4: They searched and searched until they found a dataset and manipulation technique that gave them the excuse to say that “extreme events” increase in a “warming world” so that statement could be included in the next IPCC report.

    The biggest problem with the paper IMHO is the “extreme events” leap from looking at only one incident of one type of event. Do extreme cold events increase in a warming world? How about hurricanes, tornadoes, earthquakes, tsunamis, lightning, etc.? Or, as you put it in your post: “How many, and which ones?”

    Of course, the next biggest problem with the paper is the stretched logic to AGW, which you already covered as well. I have come to loathe “consistent with” in scientific papers. A half eaten cookie found on Christmas morning is “consistent with” a visit from Santa Claus.

  38. “This will have had a warming effect as the trees grew up.”

    My personal experiences suggest trees will have a cooling effect. Admittedly my experiences are mostly from the daytime but as I drive my motorcycle past fields and then forests, one can feel the cool air pouring out from under the trees when they cover upward slopes. Deciduous trees will not have much warming effect in the winter as the leafs are gone so their will be little to prevent radiative cooling. I expect a lot depends on the local topography and amount of water used by the trees. Growing trees are the only thing I can think of that could cause a cooling bias at a temperature station.

  39. So the problem was that you didn’t understand, not that you were deliberately misrepresenting. I don’t see how you could think what you did though. The title and the abstract seem crystal clear to me. “Increase of extreme events in a warming world” is the title. The first line of the abstract is “We develop a theoretical approach to quantify the effect of long-term trends on the expected number of extremes in generic time series”. Extreme events, not annual averages. The two may be related but are not the same.

    Do you feel qualified to understand climate science generally? Do you think that if you misunderstood this work, you might be capable of misunderstanding a lot of other work? Do you have confidence that your beliefs are grounded in good sense?

  40. John B says:
    October 28, 2011 at 3:00 pm

    Willis, the answer is #3. They are talking about the likelihood of extreme events, not “recent years being warmest”. Though surely, “most recent years will likely be the warmest years” must be true anyway, by definition, in a warming trend.

    My thanks for your answer, John.

    To refresh folks, number three was:

    3. Their math says nothing about whether the ” recent years will likely be the warmest years”.

    I had supported number one:

    1. Their math shows that ” recent years will likely be the warmest years”

    The paper actually says (emphasis mine):

    The case with warming trend (Fig. 1B) has more unprecedented heat extremes overall, in particular in the last decades of the series.

    So my claim was correct. They had shown that in warming times, there’s likely to be more records set in the recent past.

    OK, so that one is settled. Let’s move to the question of whether their model is adequate. For their Monte Carlo analysis they used

    … random uncorrelated “noise” with various trends added.

    My question is, do you think that temperature datasets can be well represented by that model, a trend plus white (uncorrelated) noise?

    Me, I’d never make that assumption. Generally, temperature datasets have a strange structure. They are at least passably represented by something like a one-lag ARMA model. The AR in “ARMA” means “auto-regressive”, a measure of how much today’s temperature influences tomorrow’s temperature. “MA” is “moving average”, a measure of how much tomorrow’s temperature is affected by the recent average temperature. Usually, these have coefficients on the order of [0.85,-0.31]. The high AR value means that tomorrow’s temperature, as common sense suggests, depends in part on today’s temperature. Un-intuitively, however, tomorrow’s temperature depends inversely on the moving average. It’s interesting, and I’m not sure what the difference in the sign does to the final output or what that means.

    The main problem with that ARMA representation is that nature likes wild cards, it has more outliers than a typical ARMA structure.

    But in any case, when I read that his monte carlo analysis used a trend plus white (uncorrelated Gaussian) noise model, I just laughed and put it down.

    It’s particularly inappropriate because the autocorrelation in the Moscow record differs greatly in the two parts (pre and post 1948, see Fig. 5). The first part has almost no autocorrelation (lag 1, 0.03). The autocorrelation in the second part is much larger (0.3). This is further support for the idea that we are looking at a spliced record. It also shows that while the first part of the Moscow record might be adequately represented by a white noise model, that is not true of the second part. So it cannot be represented by any single model.

    Warm regards,

    w.

  41. GaryP says:
    October 28, 2011 at 6:43 pm

    “This will have had a warming effect as the trees grew up.”

    My personal experiences suggest trees will have a cooling effect. Admittedly my experiences are mostly from the daytime but as I drive my motorcycle past fields and then forests, one can feel the cool air pouring out from under the trees when they cover upward slopes. Deciduous trees will not have much warming effect in the winter as the leafs are gone so their will be little to prevent radiative cooling. I expect a lot depends on the local topography and amount of water used by the trees. Growing trees are the only thing I can think of that could cause a cooling bias at a temperature station.

    Thanks, GaryP, and your experience is quite correct. Cool air under a forest rolls downhill.

    In this case, it seems more like a field with a band of trees around the perimeter. It also looks fairly level. I haven’t been able to pin it down on Google Earth yet though. If that is the case, what you get is a wind barrier growing up around the site. The trees appear to be far enough away so that shading won’t increase much. But the wind will be decreased.

    It’s very important because evaporation varies roughly linearly with wind speed. So if the trees cut the wind in half, they cut the evaporative cooling in half, and local temperatures rise accordingly.

    w.

  42. caroza says:
    October 28, 2011 at 12:08 pm

    The distribution should be skewing right if CAGW is true. How does Monte Carlo help with that? I would argue that CAGW actually requires the distribution to move intact to a new mean, but since some would like to have it both ways (the recent manatee-slaying cold snaps are consistent with warming), it would have to skew right, correct? So why Monte Carlo?

  43. Site location is on the North side of Moscow. Prevailing winds are S,SW. Large tree stand to the south of the site. Looks like a good candidate for UHI but that is for the pro’s to decide.

  44. Caroza, the “trick” mentioned should have been in a new paragraph as it obviously didn’t apply to this post. To repeat, anyone wishing to exaggerate claims of AGW (which is everyone with a grant at stake) uses graphs with disproportionate scales. If they used a graph in degrees Kelvin starting at 0, then the effect would hardly be noticeable. Before anyone else jumps down my throat, this comment is tongue in cheek.
    I am always suspicious of numbers of deaths ascribed to various agendas supported by their proponents. For instance we are now being told that 100,000 deaths pa in the UK are as a result of alcohol, the same number related to tobacco. Coincidence, I think not. Likewise 56,000 in a city where the maximum temperature in the summer is 26 Celsius.

  45. Willis, I think Stevo has answered you. The paper is not about averages, eg “warmest years”, but about probability of extreme events and records. So the answer to your list of options is “none of the above”.

    You wrote:
    “Caroza, you are right that I didn’t get into the specifics of the paper.”
    That much is clear.

    I see the goalpost has now moved to the Gaussian white noise. From the realclimate discussion:

    1) “we take the trend line and add random ‘noise’, i.e. random numbers with suitable statistical properties (Gaussian white noise with the same variance as the noise in the data).” i.e. the white noise is generated to have the same mathematical properties as the observed data, and

    2) “so we used a non-linear trend line (see Fig. 1 above) together with Monte Carlo simulations. What we found, as shown in Fig. 4 of our paper, is that up to the 1980s, the expected number of records does not deviate much from that of a stationary climate, except for the 1930s.” i.e. the simulated data is a good enough fit for the actual data to have predictive value.

  46. “The paper actually says (emphasis mine):

    The case with warming trend (Fig. 1B) has more unprecedented heat extremes overall, in particular in the last decades of the series.

    So my claim was correct. They had shown that in warming times, there’s likely to be more records set in the recent past.”

    Your claim was incorrect, and you are trying to show that it was correct by quoting something that shows it was incorrect. You bolded the wrong bit of that sentence. You claimed originally:

    “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.

    Your quote above, again, but with the relevant bit emphasised:

    The case with warming trend (Fig. 1B) has more unprecedented heat extremes overall, in particular in the last decades of the series.

    So your claim was incorrect. You have misunderstood the paper, and you don’t seem to be able to understand that you misunderstood it. It said nothing about warm years at all. It discussed extreme events. Do you understand the difference?

  47. I think this just shows how impossible it is to “adjust” the real data to take out specific effects. How do we know our adjustment is correct becuase we don’t have a comparison available of what the measurements would have been without the effect.

  48. I had said:

    “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.“

    stevo says:
    October 29, 2011 at 4:16 am

    … So your claim was incorrect. You have misunderstood the paper, and you don’t seem to be able to understand that you misunderstood it. It said nothing about warm years at all. It discussed extreme events. Do you understand the difference?

    Gosh, you’re right, stevo, the paper “said nothing about warm years at all”.

    But neither did my quote. I talked about “warmest years”. These are also called “record years” or “extreme events”.

    Do you understand the difference? My claim, that the “warmest years” (AKA extreme events) would tend to be found in the recent years, is exactly what the paper said.

    Call back in when you have understood the paper.

    w.

  49. caroza says:
    October 29, 2011 at 1:46 am

    Willis, I think Stevo has answered you. The paper is not about averages, eg “warmest years”, but about probability of extreme events and records. So the answer to your list of options is “none of the above”.

    Caroza, if you think that “averages” are also known as “warmest years”, you haven’t understood a word I said.

    “Warmest years” ARE extreme events, and that’s what the paper is about. That’s what “warmest” means, my slow-witted friend, warmer than the rest, AKA record years, extreme events. Warmest years are not “averages” as you seem to believe.

    Come back when you understand that. Until then …

    Bye,

    w.

  50. Willis

    I said ‘averages, for example “warmest years”‘, not ‘averages, aka “warmest years”‘. But are you seriously going to try to tell me that annual mean temperature isn’t an average?

    An extreme event is a very high (or low) point – i.e. a single data point. (Extreme is defined in terms of the number of standard deviations from the mean.) A record is an extreme event (high or low) which beats all previous events in the dataset studied.

    If the data points are monthly average temperatures as in the Moscow data under discussion, then no, the average temperature for a year is not an event, it is an average of the values of events. The only time a warmest year would also be an event would be the case where the data points examined were annual mean temperatures.

    Yes, they looked at global mean temperature as data points as well. But (to get you back on topic): you started your post with the remark Stevo and I both picked up on, went off on a tangent about the author’s observation of contamination of the data from correction for UHI, finished off with some pictures of trees and ended with this summing up: “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.”

    I still fail to see what any of this has to do with a paper which is about the distribution of extreme and record events (so yes, perhaps I am slow-witted, although I assure you I am not your friend). Either way, I’m unlikely to agree with you so yes, I think it’s time to draw this to a close.

  51. No, Willis, you did not understand the paper correctly, and you did not report its contents correctly. Perhaps this is because, as you have proudly said yourself, you have no scientific credentials whatsoever.

    You claimed

    “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”

    Myself and caroza have tried to explain to you that this is not what they did, but you lack the humility to accept that you are wrong. They did not need to develop a mathematical relationship for this, because it’s intuitively obvious. What they did was quantify the dependence of the probability of extreme events occurring on the underlying trend. It’s very different. Come back when you have understood that.

  52. stevo says:
    October 30, 2011 at 12:59 pm

    No, Willis, you did not understand the paper correctly, and you did not report its contents correctly. Perhaps this is because, as you have proudly said yourself, you have no scientific credentials whatsoever.

    You claimed

    “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”

    Myself and caroza have tried to explain to you that this is not what they did, but you lack the humility to accept that you are wrong. They did not need to develop a mathematical relationship for this, because it’s intuitively obvious. What they did was quantify the dependence of the probability of extreme events occurring on the underlying trend. It’s very different. Come back when you have understood that.

    They claimed to quantify the dependence. Unfortunately, unlike the actual temperature data, they used white noise instead of red noise for their monte carlo analysis.

    In addition, with every record in the world to choose from, they used a spliced, heteroskedastic record which is known to be affected by UHI. This invalidates any analysis they might have done, as their monte carlo analysis certainly didn’t include a spliced record with no autocorrelation in the first part and significant autocorrelation in the second part.

    As a result, the only supportable, verifiable outcome of their study is their finding that in a warming time, the most recent data will have an excess of records.

    Which is what I said.

    Come back when you have understood that, as an acquaintance of mine remarked …

    w.

  53. Incorrect again. You misrepresent the paper, and now you attempt to misrepresent what you said about it.

  54. Willis, how exactly did you obtain your Proportionally adjusted monthly trends for Moscow, as displayed in figure 4, green bars ?

  55. stevo says:
    October 30, 2011 at 8:11 pm

    Incorrect again. You misrepresent the paper, and now you attempt to misrepresent what you said about it.

    Sorry, but that’s content-free. Quote what you disagree with.

    w.

  56. Rob Dekker says:
    November 3, 2011 at 11:46 pm

    Willis, how exactly did you obtain your Proportionally adjusted monthly trends for Moscow, as displayed in figure 4, green bars ?

    Rob, I took the average adjustment (~1.3°/century). Rather than apply it evenly across the board, I allocated it based on the size of the trend.

    For a given month, this works out to 1.3 * 12 * (month’s trend / sum of all months trends).

    w.

  57. Willis says :

    Rob, I took the average adjustment (~1.3°/century). Rather than apply it evenly across the board, I allocated it based on the size of the trend.

    And how exactly did you “allocate” the global average UHI adjustment to each month ? The method that you used is important, since it tells us if your UHI adjustment has any basis in reality. So it would be nice if you would explain exactly what you did there for Figure 4.

    Also, regarding Figure 1, you plot a green line which supposedly is the “GISS adjusted minus unadjusted” graph, labeled “Adjustment shows the effect of the two-legged GISS method for removing UHI” in the figure caption. Do you have a reference to the publication that explains the “two-legged GISS method” and why it shows such a clean and surreal decadal step function ? And while you are at it, can you tell us why the “two-legged GISS method” seems to show a reduced UHI effect after 2000 ? Did the city of Moskow reduce energy use or so after 2000 ? Or is this “two-legged GISS method” more like a “two-arm-waving Eschenbach method” ?

  58. Rob Dekker says:
    November 4, 2011 at 11:48 pm

    Willis says :

    Rob, I took the average adjustment (~1.3°/century). Rather than apply it evenly across the board, I allocated it based on the size of the trend.

    And how exactly did you “allocate” the global average UHI adjustment to each month ? The method that you used is important, since it tells us if your UHI adjustment has any basis in reality. So it would be nice if you would explain exactly what you did there for Figure 4.

    I gave an example of the math. If you can’t figure it from there, not sure what else I can say to explain it. Here’s the math again.

    For a given month, this works out to 1.3 * 12 * (month’s trend / sum of all months trends).

    If you truly have a question about the math, ask it.

    Also, regarding Figure 1, you plot a green line which supposedly is the “GISS adjusted minus unadjusted” graph, labeled “Adjustment shows the effect of the two-legged GISS method for removing UHI” in the figure caption. Do you have a reference to the publication that explains the “two-legged GISS method” and why it shows such a clean and surreal decadal step function ? And while you are at it, can you tell us why the “two-legged GISS method” seems to show a reduced UHI effect after 2000 ? Did the city of Moskow reduce energy use or so after 2000 ? Or is this “two-legged GISS method” more like a “two-arm-waving Eschenbach method” ?

    Are you naturally a jerk, Rob, or do work on it special? What’s with the agro, did I trip over your ego or something? The GISS method is described somewhere in one of their pubs, and you know what? I’m not looking it up for you. You want to make nasty remarks, and also get me to answer your questions? Sorry, you only get one of those, not both.

    It’s a funny method, which assigns a pivot point and calculates a trend on either side of it. Do I know why it shows a reduced effect after 2000? Nope, that’s the mystery of the method. It’s based on nearby stations, and theoretically it adjusts urban stations to match the trend of the nearby rural stations.

    If you truly care about the method, I’m sure you can find it. I don’t care if you do, you are far too spiteful for my taste. Anyhow, when you find it, report back so we can know that you were serious about your question and not just being unpleasant for the sake of it. That’s how I found out about the GISS two-legged method, Rob, I went looking for it. If you’re actually interested, I’m sure you can do the same.

    w.

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