Christopher Monckton of Brenchly posts a monthly article on the Watts Up With That website updating the extent of what he calls The Great
Pause
.
Each month he gives a very specific time frame, saying exactly when it started and how
long it has been going to the nearest month.
His most recent post (based on the January 2016 figures) is called
The Pause hangs on by its fingernails,
archived here.
In this he proclaims that the Great Pause is 18 years and 8 months old, having started in June
1997.
He also warns that the Pause will end next month.
I think he's being a little pessimistic here, as it will require a record breaking February for this to happen.
We will see in a few days time.
Here's what the Great Pause looks like:
How is Monckton able to get such a precise time for the start of the Great Pause?
He can do this because he has his own definition of a Pause, which makes little sense statistically.
What he says is:
The hiatus period of 18 years 8 months is the farthest back one can go in the RSS
satellite temperature record and still show a sub-zero trend.
The start date is not cherry-picked: it is calculated. And the graph does not mean there is
no such thing as global warming. Going back further shows a small warming rate. And yes, the
start-date for the Pause has been inching forward, though just a little more slowly than the
end-date, which is why the Pause has continued on average to lengthen.
As he says, his starting point is calculated each month - that calculation is to look at every month
from the start of the RSS data and check the trend, from there to the current month.
As soon as he finds a month that gives a negative trend, he declares that that is the start of the Great Pause.
There are many problems with this definition. For a start it assumes some magic property
relating to the difference between a negative and a positive trend, even if they differ by just
a few hundredths of a degree per century.
Then there's the fact that it is only defined up to the current date, which means its start
date can change each month, and as he says may disappear entirely.
Think about that. One month there is a Great Pause lasting exactly 18 years and 8 months- the
next month it has softly and suddenly vanished away.
This is not a robust definition.
What I find interesting about Monkton's Pause is that many of the problems with it are things
that Monckton himself
has warned about them in the past, often quite vociferously.
Cherry Picking the Start Date
The IPCC 4th assessment report contains this graph.
FAQ 3.1 How are
Temperatures on Earth Changing?
Monckton does not like the graph at all. He's still complaining about it in this current article - referring to it as
... one of the most mendacious graphs in the IPCC reports
.
I'm not particularly keen on it either - it's not so much the graph but the gloss the IPCC put
in the description that I find problematic (my emphasis):
Linear trend fits to the last 25 (yellow), 50 (orange), 100 (purple) and 150 years
(red)
are shown, and correspond to 1981 to 2005, 1956 to 2005, 1906 to 2005, and 1856 to 2005,
respectively. Note that for shorter recent periods, the slope is greater, indicating
accelerated warming.
The problem here is using different
length trends to infer acceleration. In general you would expect longer trends to show less variation than shorter ones.
You cannot necessarily conclude that a greater short trend proves acceleration, anymore than you can infer that a lesser trend proves a slowdown.
Monckton has much stronger objections to the graph, and focuses on what he calls the endpoint
fallacy
, which he says is a statistical lie
.
Dr. Pachauri's mistake, which was politely unchallenged by his learned audience, is an
elementary
instance of the insidious but well-known statistical fallacy known as the endpoint
fallacy
, by
which inappropriate conclusions are drawn through the careful selection of the start-point and
end-point of a graph.
SPPI
Monthly CO2 report - February 2009
It's clear from Monckton's description that by endpoint fallacy
he's talking about cherry
picking.
He is saying that by choosing the endpoints of a trend line carefully you can get any trend you
want.
But isn't Monckton's technique for finding the Pause specifically based on carefully selecting the start point that gives the longest negative trend?
As Monckton was answering questions in the comments section, I thought I'd ask him about this:
Monckton of Brenchley,
A few years back you made several references to the endpoint fallacy
, where you said:
This is a statistical lie known as the start point or endpoint fallacy. Where you take a
jiggly up and downy dataset like temperature, where you don't know which way it's going to
go next, a stochastic dataset. If you choose your start point and your endpoints carefully
enough you can make it look as though any trend you want is happening. Here they've tried to
show a rising trend.
Could you explain how your pause, defined as
The hiatus period of 18 years 8 months is the farthest back one can go in the RSS
satellite temperature record and still show a sub-zero trend.
is not an example of the endpoint fallacy?
Here is his response in full:
Bellman appears poorly schooled in logic. The very sentence he cites explains that the
start-point of my monthly Pause graphs is calculated: it is not plucked arbitrarily from the
ether.
The central point is very simple. One-third of all anthropogenic forcings since 1750 have
arisen during the period of the Pause. And that period is now of sufficient length to pass
the NOAA test: i.e., 15 years or more without any global warming indicates a discrepancy
between the predictions and the observations.
What is more, NOAA were talking of ENSO-adjusted data. After adjustment for ENSO, the
discrepancy between prediction and observation is actually wider than before making the
adjustment.
There is, therefore, a problem with the official global warming theory. The problem is that
the warming is either not happening at all, as the satellites suggest, or not happening at
anything like the predicted rate, as the terrestrial tamperature datasets suggest.
Given that the head posting considers two satellite and three terrestrial datasets over
periods commencing with three distinct starting dates, each chosen because it was the year
in which IPCC had made another set of predictions against which the observed trend could be
tested, the suggestion that the RSS graph in the head posting is an instance of the endpoint
fallacy is feeble-minded.
Only the first paragraph has any relevance to my question, and all it seems to say is that
it's
not an example of the endpoint fallacy because the start-point is
calculated
.
This is a very odd distinction, as calculating a start point to give you the desired result is,
self evidently,
more likely to give you the desired result than plucking a point arbitrarily from the
ether.
I did ask for clarification but Monckton didn't reply. Quite a few other commentators did try to
explain why this is not an example of the endpoint fallacy. Mostly this meant repeating what the
selection process was, and in some cases
demonstrating they didn't understand how Monckton was calculating it themselves. A few extracts
(all emphasis are from the original comments):
RACookPE1978:
We (the skeptical community of science observers and realists) do NOT pick
a start
date
for this trend. Granted, you
have been schooled (propagandandized) into
believing
the
start date is picked
but that is exactly opposite of the process, and your choice of
believing their lie does fit YOUR mindset and prejudices. (I will rant your prejudices
appear to control your thought processes, but do not (yet) accept your conjecture that you
are feeble minded. Firm-minded, prejudiced, incapable of independent thought based on the
facts presented? True. But feeble minded? Probably not.)
What actually happens each month is the following, and it was described in the original text
as well. The global average satellite temperature is read from those instruments, and a
flat line
is projected BACKWARDS as far as that trend line remains indistinguishable
from
zero. We DO NOT pick a point
and draw a line, we pick this month's global average
temperature (whatever it actually is) and go BACKWARDS until the trend line rises. Whatever
date that point is, we report. Same process every month, regardless of whether the results
confirm or contradict your assumptions. The process is neutral and breathe-tauntingly
honest. Indeed, the essence of this month's report is that February's report may actually
show a near-continuous rise!
richardscourtney:
The answer to your question is NO!
The 'endpoint fallacy' is when a researcher chooses the ends of an analysed period to
indicate a desired result.
The analysis of 'Pause' length does not involve choosing an analysed period. In this case,
the start point is now (i.e. the most recent month for which data is available). Each
previous
successive month is then assessed as being the other end of a time-series to determine the
longest period back from 'now' that shows a sub-zero trend
.
In other words, the endpoints
are determined by the data - they are NOT chosen by
the researcher - and, therefore, the 'endpoint fallacy' is not possible in this case.
Quite a few mention that the start point
is actually the most recent date,
but I failed to get an explanation as to what difference that made.
None of this really goes beyond claiming that because the start date of the Great Pause is
calculated, it cannot be a cherry pick.
I did also try asking whether it would have been
acceptable for the IPCC to have chosen their dates proving accelerated warming by calculation,
rather than choosing arbitrary dates such as 25 years? To which richardscourtney replied:
THE IPPC METHOD IS PLAIN WRONG. I REFUSE TO CONSIDER WHETHER OTHER END POINTS
WOULD MAKE IT MORE OR LESS WRONG.
Cherry Picking the Data
There are multiple data sets of global temperature, but for the current Pause calculation
Monckton only
uses the RSS satellite data. He does also mention the UAH satellite data, but only to say
the Pause is slightly shorter in that data.
Indeed this month he claims the Pause has gone completely in the UAH data,
but he's wrong - because of a minor update to the UAH data the pause reappeared, starting in November 1997.
The current non-beta version of UAH (5.6) is not mentioned by Monckton.
That version hasn't shown a long Pause since 2009.
Of the non-satellite data, Monckton only says After many unconvincing alterations to all of
the principal global surface tamperature datasets over the two years leading up to the Paris
climate conference, the Pause all the datasets once showed had been erased.
He makes no mention of the larger alterations to UAH, presumably because he finds them more
convincing.
For comparison, here are the different satellite trends over the length of the Great Pause, along with a sample surface set from NOAA.
(Anomalies based on the years 1981 - 2010.)
It's interesting to note that the older UAH data was very similar to the altered
surface data set,
whilst the adjusted UAH data set is in much better agreement with RSS.
So, what does Monckton think is the best data set to use for establishing a global warming trend?
Well quite a few times, Monckton has insisted in using averages of multiple data sets (both
satellite and land based).
In a long Letter
to Representatives Ed Markey & Joe Barton he says
(my emphasis):
The advantage of a composite global-temperature index, however, is that the satellite datasets
for the lower troposphere (not, as Mr. Karl implied, for the troposphere as a whole) are to some
extent less prone to heat-island distortions arising from progressive urbanization than the
terrestrial datasets on their own.
The composite index is accordingly more reliable than any individual dataset
,
particularly since there is evidence that at least one of the terrestrial datasets has been
tampered with
by its administrators to create a false impression that global temperature in the late 20th
century rose more sharply than it did in reality - a point to which I shall return infra.
Then there's the question of why use satellite sets which are measuring temperatures in the
troposphere, but ignore warming on the ground?
In 2012 Monckton wrote an open letter to Skeptical Science, which contained this interesting
point (my emphasis):
It is claimed that we were wrong to say there has been no statistically-significant global
warming because the oceans have warmed. However,
the standard definition of global
warming
is warming of the near-surface atmosphere
.
Also, measurements to date are inadequate to tell us reliably how much - if at all - the
oceans have warmed in recent years.
So why is is it now appropriate to refer to a pause in the lower troposphere as being a pause in
global warming?
Statistical Significance
Even if a trend line indicates some sort of pause, there is still the question - is the pause
statistically significant?
Lack of significance does not mean there is no pause, just that so far there is not enough
evidence to know if the pause
is a real phenomenon, or just something that has occurred by chance.
You could rewrite the previous paragraph and substitute the word warming
for pause
,
and the statement would be just as correct.
Which is where I have a problem with some pause advocates, they are quite happy to claim a
period of no statistically significant warming
as meaning no warming has occurred, but won't apply the same logic to any pause, let alone the
Great Pause.
As an aside, the main difference between two is that whilst you can easily find periods of no
significant warming
,
you can always resolve the question by adding more data, that is looking back a few more years
to find the warming has become significant.
By contrast if you do that to the claimed pauses you just find the no warming turns into some
warming.
Monckton himself has argued that statistical significance is required to confirm warming.
Here,
for example is a typically pretentious piece, where he tells a journalist how to work out
significance using Excel:
Step 4. Check whether the warming (which is the difference between the first and last
value on the trend-line) is greater or smaller than the measurement uncertainty. If it is
smaller, falling within the error-bars, the trend is
statistically indistinguishable from
zero. There has been no warming
- or, to be mathematically nerdy,
there has been no
statistically-significant warming
.
Aside from the fact that his description of how to calculate statistical significance is
completely wrong,
he does say that no statistically-significant warming means there has been no warming
.
By the same logic, if his Great Pause is not significantly different to the warming trend, then
it is statistically indistinguishable from the warming trend, and hence there has been no pause.
In order to get some idea of the problem here's a graph of the pause for RSS, using Dr Kevin Cowtan's
online trend calculator.
I'm using this rather than generating my own here, simply because it shows confidence intervals
that
take into account the all important corrections for autocorrelation.
As can be seen from this, the trend from June 1997 - Jan 2016 was effectively zero, but the
confidence interval was a whopping ±1.69 °C / Century.
In other words, we can only be about 95% confident that the RSS trend between those dates was
somewhere between -1.7 and + 1.68 °C / Century.
We cannot say the Pause was significantly different from the RSS trend before the Pause, or over
the whole of the satellite era.
Here's what the various trends look like for RSS data, along with their 2σ confidence
intervals.
In all cases the trends are within each other's confidence intervals, and so are not
significantly different from each other.
The Problem With Trends
When people apply linear trends to a time series, they usually only mention the slope of the line - that shows the rate of warming.
But there's also the intercept that determines how warm the trend was to start with.
Just referring to the slope of a trend over part of a series can give a very misleading impression.
In Monckton's narrative he mentions the slow rate of warming during the first 18 years of RSS data before the start of the Pause,
followed by the 18 plus years of no warming, but what does this look like if we show these two trends in context (anomalies
in °C):
What Monckton's narrative doesn't mention is the big break between the two trends.
If these trend lines are a correct interpretation of the data, we have to assume there was global warming
of around 0.2C all occurring in June 1997.
This shows how much warmer Monckton's Great Pause was compared to the pre-pause era.
You can give a very different impression by splitting the trends at different points.
Monckton himself mentions that the fastest warming period ended in 2006.
This alone seems to be a problem with the Pause narrative as it means the first nine years of the Great Pause were also the last nine years of the Great Warming.
The narrative of this warming scenario is that RSS data shows a pretty fast period of warming,
around 1.5 C° a century, lasting 27 years, followed by a 9 year period of only slightly
slower warming.
Here's the same data, but with trend lines before and after the end of 2006:
This narrative is also misleading.
What it misses is there's a bit of a drop at the end of 2006. Not as big as the rise in the
previous graph, but still misleading.
Finally, here's what the trend for the entire RSS data looks like.
To me the single linear trend seems as convincing as either of the split trends.
If a case can be made for a pause more likely to have started after 2000 and then only in the satellite data, but for now I think there is insufficient evidence.
