Guest post by David Middleton
From the BBC…
Climate: Cherries are not the only fruit
Just about the most predictable event of the week was the tempest of opinion created by the analysis of global temperature changes published in the Proceedings of the National Academy of Sciences (PNAS) on Monday.
As we (and a number of other mainstream news outlets) reported, Robert Kaufmann and colleagues analysed the impact of growing coal use, particularly in China, and the cooling effect of the sulphate aerosol particles emitted into the atmosphere.
They concluded that with a bit of help from changes in solar output and natural climatic cycles such as the El Nino Southern Oscillation (ENSO), the growth in the volume of aerosols being pumped up power station chimneys was probably enough to block the warming effect of rising greenhouse gas emissions over the period 1998-2008.
For some commentators, such as the UK Daily Mail’s Christopher Brooker, this was further proof that the “climate scaremongers” had got it wrong…
[…]
Cherry in the pie
One thing that everyone in the climate blogosphere seems to agree on is that the best fruit in the world is the cherry, judging by the number that are picked.
And the Kaufmann paper has brought a few more down from the tree.
The Global Warming Policy Foundation (GPWF), the UK-based pressure group, said researchers “tweak an out-of-date computer model and cherry-pick the outcome to get their desired result”.
To which the opponents’ rejoinder is, and long had been: “well, choosing 1998 as the baseline is cherry-picking, to start with”.
To illustrate the point, I’ve been through a quick exercise using the approach that groups such as GPWF favour – and that Kaufmann’s research group adopted – of using annual temperatures rather than any kind of smoothed average, and looking for the temperature change over a decade.
I took the record of global temperatures maintained by Nasa’s Goddard Institute for Space Studies (GISS) which is one of the three main global datasets, and calculated the rate of change over each of the most recent 10 decades – ie, 1991-2001, 1992-2002, and so on up to 2000-2010.
I’ve summarised the results in a table on this page. What it basically shows is two things:
- the numbers vary quite a bit from year to year; and
- all but one give a temperature rise – the only one that shows a small drop being 1998-2008.
Seeing as it’s logically impossible that the world warmed between 1997 and 2007, cooled between 1998 and 2008, and warmed again from 1999 to 2009, one conclusion you might reach is that using annual temperatures is not a sensible thing to do as it gives you a set of answers that does not make sense.
… which is why most scientists use the running mean approach.
[…]
Mr. Black seems to be suggesting that Figure 3 from Kaufmann is a cherry being picked by climate realists…
While he thinks that GISTEMP is the “tree”…
Well, I say that Mr. Black is Goldilocks-picking. Mr. Black asserts that it is cherry-picking to use 1998 as a starting point and that the starting point must be 1880. What’s so special about 1880 (apart from it being the start of the instrumental record)?
First off, let’s have a look at a few “cherries.”
Here is the HadCRUT3 global temperature anomaly (GTA) for 1977-2010 plotted with the GTA for 1911-1944…
HadCRUT3 Global Temperature Anomaly 1911-1944 & 1977-2010
Here’s the HadCRUT3 Northern Hemisphere temperature anomaly for 1976-2010 plotted with a non-carbonated interval from the Medieval Warm Period (Moberg et al., 2005)…
HadCRUT3 Northern Hemisphere 1976-2010 & Moberg 863-897
In both examples, the slopes are statistically indistinguishable.
The 66-yr period from 1944-2010 is pretty well indistinguishable from the first 66 years of three different century-scale cool-warm-cool cycles from Moberg’s Medieval Warm Period reconstruction…
HadCRUT3 global 1944-2010 & Moberg NH 831-930, 961-1050, 1038-1138 (Yes, I know I should have used HadCRUT3 NH… I just don’t have a display handy).
The peak of the Modern Warming is, at most, 0.1 to 0.2°C warmer than the peaks of three comparable, non-carbonated, intervals of the Medieval Warm Period, consistent with a net climate sensitivity of ~0.5°C. However, that difference is probably not statistically meaningful.
- The error bars of all of the data sets are greater than the differences between them.
- The proxy data show the MWP to be warmer than the late 20th century.
- The proxies invariably have a lower resolution than the instrumental data; thus the amplitude of the proxy time series is attenuated relative to the instrumental record.
This means that the late 20th century warming might have been slightly warmer than the peak of the MWP. Almost all of the potential error is in the direction of magnifying the warmth of the Modern Warming relative to the MWP, so the odds are that the modern warming is very comparable to the Medieval Warm Period.
Since Mr. Black would probably say that the Medieval Warm Period is another “cherry,” let’s go back another 1,000 years, or so.
What happens if I project the polynomial trend-line a few hundred years into the future?
It starts looking like a cyclical pattern doesn’t it?
One of the “problems” with the way climate data are handled is in the obsession with applying linear trend lines to non-linear data.
A Sine wave has no secular trend…
But… What happens if my data represent only a portion of a Sine wave pattern?
The r-squared of a linear trend line of this partial Sine wave is 0.88… 88% of the data fit the trend line. This implies a very strong secular trend; yet, we know that in reality Sine waves do not have secular trends.
If we take the entire HadCRUT3 series and apply a linear trend line, we get an apparent secular trend…
The r-squared is 0.55… 55% of the data fit the secular trend. This implies that there is a real long-term warming trend.
What happens to that secular trend if we expand our time series like we did with the Sine wave?
The apparent secular trend vanishes in a puff of mathematics…
How can such a clear secular trend vanish like that? The answer is easy. Each “up hill” and each “down hill” leg of a Sine wave has a very strong secular trend. Unless you have enough data to see several cycles, you don’t know if you are looking at a long-term trend or an incomplete cycle.
Using the GISP2 ice core data from central Greenland we can see that over the last 50,000 years, there have been statistically significant warming trends…
And there have been cooling trends of varying statistical significance…
What does all of this mean?
It means that the Earth’s climate is cyclical. It means that the climate changes we’ve experienced over the last 150 years are not anomalous in any way, shape, fashion or form. And it means that the Mr. Black and the other warmists must “Goldilocks-Pick” their data. Too short of a time series yields no warming trend and too long of a time series also yields no warming trend. The time series must be “just right” in order to show an anomalous warming trend.
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> What happens if I project the polynomial trend-line a few hundred years into the future?
You torture the data until it tells you what you want. Polynomial fits are generally completely useless for foreward/hindward projections. I tend to look at the end points of the range they cover with a lot of skepticism.
How many years forward does the fit have to go before the temperature goes below 0 K? What happens if you increase the order of the polynomial by one? One end will show an upward trend – it shouldn’t take much of a projection to get to boiling.
So giving the rampant cherry-public often seen here – are you formally saying that cherry picking is inherently wrong?
Climate change cycles seem to average about 1000years peak to trough, ie. 2000 year cycle length. Taking a small section 150 years long does not provide any idea of what is going on only that you have a trend, of either direction, which you naturally call CAGW.
Considering the great variation that atmospheric CO2 has had from 1000’s of ppmv to 300 ish ppmv, with no parallel change in climate one wonders where Black et al get their mad ideas.
Black’s mind clouded is from spinning for too long.
I’d really love to know where these magic Chinese aerosols appeared from. Not long before Kaufmann’s data ended, NASA was saying we were losing our aerosol “sunscreen” and it was worse than we thought.
http://www.nasa.gov/centers/goddard/news/topstory/2007/aerosol_dimming.html
Excellent piece. This illustrates why the hockey stick graph was so much propaganda. There is no interpreting current climate trends without a correct context.
The predictable observation from the article is that the warmists are scrambling to explain the flattening. Expect this to get much worse if we are indeed entering another little ice age. And help us all when we return to our most ‘normal’ temps which is true ice age conditions.
Even when a true ice age reappears, the faith of the religion of man made global warming will require convolutions stating the warming is still continuing, it’s just being masked. Then like any chaotic system the emphasis will switch and the cooling will be man’s fault again.
Or maybe the religious faithful will look at this little article and realize we are along for the ride for the most part.
Mr Black’s understanding of science is about the same as my understanding of Mandarin Chinese – nil.
I did a similar take back in Dec 2009 on how alarmist cherry pick their ranges to over-emphasize the secular trends in sinusoidal curves. It is the clearest example of twisting the full results and falsely imply a politically preferred result. That is not the scientific method, but the method of political PR.
They concluded that with a bit of help from changes in solar output and natural climatic cycles such as the El Nino Southern Oscillation (ENSO),..
Since the ENSO and its atmospheric component Southern Oscillation indices are highly correlated ( http://en.wikipedia.org/wiki/File:Enso-soi.png ) and having found what appears to be a driver modulating the SOI index all major indices (PDO, AMO and ENSO) appear to have common type of modulation caused by natural sources as displayed here:
http://www.vukcevic.talktalk.net/A&P.htm
I my opinion this paper is another example of how easy it is for climate modelers to adjust their models to apparently fit the data. These models have huge uncertainties in the parameters and by adjusting them any outcome is possible.
These climate computer modelers do not realize that a valid model should be able to reconstruct the patterns of the climate at multiple scales and forecast them to be credible.
As extensively proven in my paper
N. Scafetta, “Empirical evidence for a celestial origin of the climate oscillations and its implications”. Journal of Atmospheric and Solar-Terrestrial Physics 72, 951–970 (2010), doi:10.1016/j.jastp.2010.04.015 PDF
The climate system is clearly characterized by a 60-year cycle. We have seen statistically compatible periods of cooling during 1880-1910, 1940-1970, 2000-(2030 ?) and warming during 1850-1880, 1910-1940, 1970-2000.
This 60-year cycle goes back for centuries and millennia as also found by Knudsen et al (“Tracking the Atlantic Multidecadal Oscillation through the last 8,000 years”, Nature Communications, 2011, [http://www.nature.com/ncomms/journal/v2/n2/full/ncomms1186.html]):
It is overwhelming that the climate system is characterized by a 60-year cycle. The cooling observed since 2000 is perfectly compatible with this cycle.
So, the issue would be where were the Chinese sulphate aerosol particles during the period 1880-1910 and 1940-1970?
Note that the Kaufmann’s paper is very careful in keeping this 60-year cycle out of consideration by starting the analysis of their forcings in 1950 and running the climate model only since 1999 up to 2008 (?), and adding huge errors bars up to +/- 0.2 C.
Moreover, years ago China changed its policy because of the bad ambient air quality and from 2005 on all new installation have a state of the art desulfurization treatment. Sulfur is very likely going down since five years.
Finally, what happened to the data from 2008 to 2011 where the divergence data and model would be more drastic?
If aerosols pumped out by the Chinese are sufficient to pull down the global temperatures such that they counter-act the warming caused by CO2 then presumably the point at which they are most concentrated is a frozen wasteland?
One thing I can say for certain: Richard Black is an aerosol (just be careful how you say it….)
From the NASA Goddard site FergalR linked to above:
http://www.nasa.gov/centers/goddard/images/content/171624main_aerosol_dim_2sm.jpg
“Image above: Sun-blocking aerosols around the world steadily declined (red line) since the 1991 eruption of Mount Pinatubo, according to satellite estimates. The decline appears to have brought an end to the “global dimming” earlier in the century. Credit: Michael Mishchenko, NASA”
Increasing or decreasing aerosols? I wish they could make up their minds.
David & Anthony.
Selecting a start point is a fact of life. What matters is the basis and reason for selecting the start point and the consideration that you give to the results based on that choice.
Selecting 1998 as the start point because it’s the only possible way to to get the data to tell the story you want to tell when the greater mass of data paints an entirely different narrative and then declaring that the data does not support global warming (let alone asserting that there is statistically significant cooling) is at best incompetent, at worst deception.
Selecting a start date because of sound reason with a demonstrable basis in physics or because it is the totality of the data set is not – especially when the conclusions you draw give due consideration (which David fails to do throughout this piece, leading him to a conclusion that is arse about face) to the limits of the data set would represent good analysis.
Climate at the will, not of physics, but of sine waves? Only on WUWT. – Anthony, can I guest write a piece that demonstrates how the climate data in graph form looks like different mountain ranges?
In my opinion, anything that starts after 1700 is cherry picking…………..
The LIA taught everyone that we needed heat, a better way to produce food, better ways to cook, harvest……..everything we developed and invented.
And almost everything that was invented was a direct result of how to take care of ourselves in case it gets cold again………………….
I know that the polynomial function will go bonkers if I project it to far… I was just too lazy to figure out how to do a Gaussian filter in Excel.
Agree entirely with Ric Werme – never extrapolate from a polynomial fit!
Anyway, it is always too easy to see patterns in things, and you are better off trying to understand the underlying processes, rather than indulging in curve fitting.
If I’m looking for an analogy for my “cherry,” I kind of have to pick another cherry, or at least a cherry-like fruit.
My point is that “Goldilocks-picking” is “cherry-picking.” If you have a cyclical function, you have to have a sample large enough to see several full cycles – Otherwise, it’s all cherry-picking.
This is my problem with the use of “anomalies”. The definition of an anomaly is “Something that deviates from what is standard, normal, or expected” (http://dictionary.reference.com). Who is the authoritative voice to say what NORMAL is? NASA? Hardly! How about these outfits promote what the ACTUAL temperatures are / were / predicted? I know there are some benefits to calculating anomalies, but as soon as a statistician starts to calculate what is vs. what was expected (starting at point X), then the inevitable argument of cherry picking / Goldilocks picking is guaranteed to ensue. If they want to massage the cr*p out of the numbers to show their (expected) results, then that’s what Steve M and William B have the knowledge and experience to debunk. I just wish that Anomalies weren’t the go-to stat that journalists (hahahahaha I slay me!) like Black could so easily point to, as he screams “Ahhhhhh!!!” like a B-movie actress in a slasher film. Here’s a message for you Blackie: A shill is as a shill does.
FairPlay did you even read the article, didn’t you notice that your AGW belief have lets say some “minor” problems.
this might be nothing, im a computer scientist and did no statistics, but I was using excel…
I took the GISS data, calculated the mean anomoly for each year (average of each months anomoly), then subtracted each years mean from the previous to get a list of year on year change between each years mean value.
I didnt do an ABS at any point, I just wanted the change year on year between the means, interested in the magnitutde of change, not the direction.
I then did a min and a max on it to find the fastest changes between any two years in the data set. I got the same number for min and max 28.58333333 for 1976 to 1977 and -28.58333333.for 1963 to 1964.
What are the chances of this occuring in a real dataset?
http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts+dSST.txt
Jay says:
July 11, 2011 at 6:48 am
—————————————-
But, the only people who selected starting points are Kaufmann et al and Richard Black.
Ok then the AGW folks have their answer to controlling the climate and it is already pretty much in place. Crank up the coal-fired power plants with high-sulphur coal and turn the scrubbers on and off as needed to regulate the temperature. The bonus is that we get more CO2 which is needed to increase crop yields to feed the growing world population. Two problems solved at no cost to you. In addition you get lower cost electricity and less government involvement as a bonus. What’s wrong with that?
Ok, let’s try this running average thingee on the data from some hypothetical world.
1 1 1 1 1 1 1 1 1 1 3 2 2 2 2 2 2 2 2 2 2 2 2. Assuming these are the readings from some annual temperature series with the 3 reading being 1998 what do we get? In 1997 we have 1 as our running average. In 1998 it jumps to about 1.2 and then rises gradually to 2.1 by 2007 and then drops to 2.0 an then stays the same. Anyone seeing this series would immediately say the temperature has remained constant for the past decade. Arguing that actually the temperature has risen is just playing mind games. This is essentially what this article is claiming.
If Warmista Gaia Models are to be treated as scientific hypotheses then what we know about them is that since 1998 their predictions have been false. Rather than recognize that the models contain one or more false hypotheses, the Gaia Modelers introduced a new hypothesis, Chinese aerosols, to save their Gaia model from falsification. However, the tactic did not work because now the modelers must face the criticism that their Gaia models did not account for aerosols. The Gaia models were either false to experience or incomplete. In either case, the Gaia models are not up the standards of science.
If Gaia models are not to be treated as scientific hypotheses then they must be treated as statistical hypotheses about two sets of numbers, a temperature reading and a CO2 reading. Here we encounter all the problems of the famous Hockey Stick. As The Team learned when they decided to “hide the decline,” for the statistical comparison to be meaningful, you must know enough about the things compared to be able to explain why they are worthy of comparison. So, what do the Warmista do? They bring in aerosols to save their statistical hypotheses about CO2 and temperature. There can be no successful account of CO2, aerosols, and temperature until there is a scientific account of each, given in terms of physical hypotheses that describe natural processes, that enables the researchers to explain that the objects of their study, CO2 concentration, aerosol uptake, temperature records and such are not changing. They have no such accounts and are not seeking to create them. The Gaia Models are no more nor less than Hockey Sticks.
I think you give to much credit to Mr Black he’s more of a sycophant, “Yes emperor Gore your new kings cloths look amazing”.
While the sane people sit in the pub opposite pointing and laughing at a naked jester.
The reason Mr. Black picked 1880 starting point is that it is the beginning of the instrumental record. The entire instrumental record is within the warming leg of one cycle millennial-scale climate oscillation. It’s been warming on that millennial since ca.1600 AD.
If you look very closely at the GISP2 core, you’ll find that the millennial scale cycle had a period of ~1,470 (+/150 yrs) years during the Pleistocene. Many people call this the Dansgaard-Oeschger cycle. This cycle appears to have sped up in the Holocene (~800-yr period +/-300 yrs). If you look at Moberg et. al., 2005, Ljungqvist, 2009 or even Mann et al., 2008, you can see that the last 2-3 cycles of this oscillation have had periods of ~1,000 years… Alternating 400-500 year periods of warming and cooling.
Embedded on that millennial-scale cycle is a multi-decadal or century-scale oscillation with a period of ~60 years. The most recent warming leg of that ~60-yr oscillation ran from ca 1976 to ca 2003. 1998 just happened to be a monster El Nino near the peak of both the millennial and multi-decadal oscillations. This is why it is the warmest year “on record” everywhere outside of NASA & NOAA. The reason for picking 1998 as a starting point is the fact that it is the warmest year “on record.” Personally, I think 2003 would be a more relevant “cherry” from which to start, if the goal is to look at the current cooling leg of the multi-decadal cycle.
The use of a Sine wave was simply as an analogy .