Kim,

I see UAH have released their November numbers. Seems to be the hottest November in that dataset. Not much sign of your beloved cooling.

You really think I'm paid for this? Really?

You missed the point, we're cooling from mid-holocene. And you are quite foolish to consider the Earth's temperature to be the atmospheric one. We don't know enough about the oceans to know whether or not they are cooling.

Nonetheless, the only looming climatic crisis is the onset of glaciation.

Also note Roy's point that warming has not accelerated, showing again my earlier point that Anthropogenic warming is not distinguishable, with certainty, from natural warming.

And you missed another point; I believe you to be a paid or deluded propagandist. Odds are you're paid, because you are bright enough to understand the skeptics' arguments about catastrophes and the rest. If paid, you are corrupt. If not paid, merely deluded.
=======================

Paid? Well there are a great many trained and paid Gorebots, and you sound a lot like them.

In 2008, Al Gore bragged that he had a $300,000,000 fund to promote climate alarm. That's Three Hundred Million Dollars. When asked what was the source of the money he replied that it was from internet and anonymous donors. Even Andy Revkin blanched at that.

So if the shoe fits wear it. It's clearly the right size. It is possible your foot is warped, just as is your science.
============================================

And the sun is up in the air, heh. Did you deliberately miss his point that we've not had long enough observation to understand the sun or its possible effects on climate, or was it just too easy, and lazy, to quote the IPCC.

I'm quite sure he is aware of the half percent difference from maximum to minimum. I, myself, have become more agnostic over time about the sun's effect on climate(once I was sure it was a slam dunk), but there is variation in some of the sun's phenomena that is a great deal more than a half percent variation, and a number of people are working on those potential mechanisms. That is what he was talking about, and your objection is irrelevant.

Did you not know that? Wow, what you are fed, heh.
=======================================

There is another possibility, and that is that you are, in fact, not bright enough to understand the skeptical position. You do seem only able to parrot the alarmist catechism. Maybe you are just highly trainable, with nothing of substance to add of your own, not even criticism.
=====================

Dec 5, 2019 at 10:35 PM Phil Clarke
You are relying on media sources with a track record of lying, deceit and dishonesty.

That last was actually a compliment. I would like to believe you are not corrupt, but that is a fault of mine, a triumph of hope over experience.
=======================

Hah. Not many cities around mid-Holocene, huh?

Tired old talking points, we don't know enough, too much uncertainty, blah blah.

"Hah. Not many cities around mid-Holocene, huh?

Tired old talking points, we don't know enough, too much uncertainty, blah blah."

Great post, Phil. Very illuminating!

Or you could read the paper being 'discussed' at WUWT.

Retrospectively comparing future model projections to observations provides a robust and independent test of model skill. Here we analyse the performance of climate models published between 1970 and 2007 in projecting future global mean surface temperature (GMST) changes. Models are compared to observations based on both the change in GMST over time and the change in GMST over the change in external forcing. The latter approach accounts for mismatches in model forcings, a potential source of error in model projections independent of the accuracy of model physics. We find that climate models published over the past five decades were skillful in predicting subsequent GMST changes, with most models examined showing warming consistent with observations, particularly when mismatches between model‐projected and observationally‐estimated forcings were taken into account.

Nick Stokes exposes Middleton's nonsense reality-check in the WUWT comments.

Dec 7, 2019 at 3:32 PM Phil Clarke
You keep demonstrating you are a liar, and now you refer to Nick Stokes who revealed his own lack of understanding in the comments on this thread

https://wattsupwiththat.com/2019/10/28/sour-milk-sourer-grapes-and-the-unnatural-greenhouse-effect/

Why Hockey Teamsters should be in prison

"Many here may already be familiar with this, but for those who haven't seen it, I'd say it's well worth a read:

"CLIMATEGATE
Untangling Myth and Reality Ten Years Later
By Stephen McIntyre and Ross McKitrick
December 5, 2019 "

http://www.climateaudit.info/pdf/climategate/Climategate.10YearsAfter.pdf

Dec 7, 2019 at 8:59 PM | Unregistered CommenterMark Hodgson"

You keep demonstrating you are a liar,

You could back that up with an example, you could retract it, or I could continue to regard you as a troll.

Your call.

We discovered, to our considerable surprise, that the verification r2 statistic for the AD1400 step was disastrously low (0.018). The verification r2 is a commonplace statistic, which ought to be easily passed by any reconstruction purporting to have statistical “skill”. It is not a guarantee of model validity, but failure is more or less a guarantee of model invalidity.

- McIntyre & McKitrick.

Specifically, r2 measures the strength of a linear relationship between two variables when the linear fit is determined by regression. For example, the correlation between the variables in Figure 9-1 is 0.88, which means that the regression line explains 100 × 0.882 = 77.4 percent of the variability in the temperature values. However, r2 measures how well some linear function of the predictions matches the data, not how well the predictions themselves perform. The coefficients in that linear function cannot be calculated without knowing the values being predicted, so it is not in itself a useful indication of merit

- NAS Panel on temperature reconstructions

M&M being as honest as usual. Really, you should read this stuff without your confirmation bias.

https://www.nap.edu/read/11676/chapter/12

"You could back that up with an example, you could retract it, or I could continue to regard you as a troll.

Your call.

Dec 7, 2019 at 10:56 PM Phil Clarke"

You are a liar, and Hockey Teamsters depend on liars.

Your call

http://bishophill.squarespace.com/discussion/post/2617002

Here

Actually, it is the Gergis Australia study, Joelle and her team have corrected the various issue and resubmitted the study and it has been reviewed and accepted, in the face of the usual denier unpleasantness.

Conclusion:"Overall, we are confident that observed temperatures in Australasia have been warmer in the past 30 years than every other 30-year period over the entire millennium (90% confidence based on 12,000 reconstructions, developed using four independent statistical methods and three different data subsets). Importantly, the climate modelling component of our study also shows that only human-caused greenhouse emissions can explain the recent warming recorded in our region."

Add it to the list.

Jul 11, 2016 at 10:46 PM Phil Clarke

Dirty laundry:

"Reconstruction Residuals
The “dirty laundry” data series are called residuals. They are the differences between the proxy
reconstruction estimates of past temperature and observed temperature records during the
model estimation (“calibration”) and testing (“verification”) periods. Since the residuals measure
the goodness-of-fit of the model, they are essential for computing verification test scores. In this
email Mann was supplying residuals for reconstructions (which he grandiosely calls
“experiments”) based on the post-1000, post-1400 and post-1600 intervals. The first two were
critical since they determine whether it is legitimate to do the reconstruction back that far.
Numerous statistical authorities, including those1 cited in Mann et al 1998, recommend testing
reconstruction validity using several different scores based on the residuals. Mann stated in his
1998 paper that he had computed two such scores, the Reduction of Error (RE) statistic and the
r2 score. But in his paper and in the accompanying archive he only listed the RE values. He had
not (and has never) released the r2 scores. Nor could they readily be computed from information
disclosed with the original publication because, contrary to widespread belief among climate
scientists, Mann’s archive omitted the complete reconstructions for each time step. For the
signature Northern Hemisphere (NH) reconstruction, Mann only archived the spliced
reconstruction segments in which, at each time step, the results of a later step were printed over
results from earlier steps. Without the residual series no one could compute the unreported r2
scores."

A good scientist always hides his data:

"What Was Being Hidden
In late 2003, only a few months after the “dirty laundry” email, we asked Mann to provide the
residual series for the AD1400 step of his reconstruction. He refused. We filed a Materials
Complaint to Nature, which had published the 1998 study, under their disclosure policies for
either the residual series or the reconstruction steps. To their shame and discredit, Nature
refused. We also requested the US National Science Foundation to require Mann to provide this
data. To their discredit, they also refused.
Despite disinformation to the contrary, the results of Mann’s individual steps remain unarchived
to this day.
We discovered the reason why Mann was so adamant about withholding his “dirty laundry” in
2004 – long before Climategate. By early 2004, despite many obstacles, we had been able to
replicate Mann’s peculiar and poorly documented methodology well enough to calculate the
residual series (and verification statistics) for the AD1400 step.
We discovered, to our considerable surprise, that the verification r2 statistic for the AD1400 step
was disastrously low (0.018). The verification r2 is a commonplace statistic, which ought to be
easily passed by any reconstruction purporting to have statistical “skill”. It is not a guarantee of
model validity, but failure is more or less a guarantee of model invalidity. We reported our
discoveries in two widely-discussed 2005 articles.2 At the time, we didn’t know for sure whether
Mann had overlooked calculation of verification r2 values (implausible but possible) or whether
he had calculated the values, discovered that they were disastrous and withheld them. Both
alternatives were disquieting.
The dispute was prominently reported on in 2005, including a frontpage article in the Wall St
Journal which attracted the attention of the US House Energy and Commerce Committee. They
sent a set of questions to Mann including ones about source code and verification r2 statistics.
These provoked vigorous protests from AAAS, AGU and other science institutions. Ralph
Cicerone, then chair of the National Academy of Sciences, wrote to the House Energy and
Commerce Committee offering their services, including, specifically, examination of the
verification r2. Two studies were commissioned by congressional committees: the 2006 National
Academy of Science and Wegman reports.
In partial response to the Committee questions, Mann archived some (but not all) source code
for Mann et al 1998. While incomplete, it confirmed our surmise that Mann had calculated
verification r2 statistics for each step of the signature NH reconstruction and had withheld them.
Subsequently, in 2006, Wahl and Ammann, both Mann allies and associates, did their own
replication of the various steps: we were quickly able to reconcile their calculations to ours. They
replicated the poor verification r2 for the AD1400 step and discovered that the score for the
AD1600 step was even worse – perhaps the worst verification r2 in any scientific study ever
published. Despite reconciling exactly to and confirming our results, their abstract misleadingly
asserted that they had verified Mann’s results, when, in fact, they replicated ours – a point made
at the time by Professor Wegman.
To this day, Mann has never archived the NH reconstructions for individual steps, the equivalent
residual series (the “dirty laundry”) or even the verification r2 results. "

Clarke, you have a reading comprehension problem.

So what Charly?

It is not a useful metric but anyone who wants Mann's r2 statistics has only to read Wahl and Amman 2007 where thay are published in black and white.

Still, McIntyre promotes the false narrative that the (useless) stats have been hidden.

Ever thought you may have been had?

Clarke, you should read and try to understand.

• PAGES2K and similar studies remain primarily dependent on problematic and
inconsistent tree ring data, many of which go down in the last half of the 20th century. In
order to extract a Hockey Stick shape from inconsistent tree ring data, climate academics,
including PAGES2K, have resorted to ad hoc methods (ex post screening, ex post
orientation) which are condemned by mainstream statisticians and in statistical
literature, but which enhance the hockey stickness of the resulting reconstruction. The
ex post screening and manipulation even extends to data used in seemingly technical
reports

• Use of tree ring widths as a temperature proxy is made even more problematic by the
impact of the extraordinary worldwide “greening” during past 30 years, primarily
attributed by specialists to carbon dioxide fertilization, on ring widths – an effect which
is not disentangled in PAGES2K.

• The controversial stripbark bristlecone series, relied upon by Mann et al 1998, continue
to be used in PAGES2K reports, even though the 2006 NAS panel recommended that such
data be “avoided” in temperature reconstructions.

• In their zeal to obtain a hockey stick, PAGES2K authors, like Mann et al 2008, have
introduced sediment series without taking care to ensure a physical link, leading to a
series of embarrassing gaffes arising from series contaminated by construction run-off
and even used upside down."

The ‘R2’ issue similarly – the NAS Chapter 9 deals with the issues there very clearly. The basic point is that when you get to the relatively sparse networks further back, the reconstructions don’t have fidelity at the year-to-year variability. If that is something you care about (i.e. whether 1237 was warmer or cooler than 1238), then you are out of luck. If instead you are interested in whether the 13th Century was cooler than the 12th C, it’s not the right metric to be using.

- Gavin Schmidt.

Educate yourself.

Charly, you can copy-paste as much McIntyre as you like, it doesn't make him any more reliable.

Example:

The controversial stripbark bristlecone series, relied upon by Mann et al 1998, continue
to be used in PAGES2K reports, even though the 2006 NAS panel recommended that such
data be “avoided” in temperature reconstructions.

The truth, as ever, is more nuanced. Not all bristlecones are stripbark and vice versa, and the science has moved on since 2006. Salzer et al (2009) found that bristlecones are a valid temperature proxy, not something you would ever discover by reading Climate Audit

You've been suckered. Sorry, but there it is.

More on McIntyre Mendacity.

In their zeal to obtain a hockey stick, PAGES2K authors, like Mann et al 2008, have introduced sediment series without taking care to ensure a physical link, leading to a
series of embarrassing gaffes arising from series contaminated by construction run-off
and even used upside down."

-McIntyre

Potential data quality problems. In addition to checking whether or not potential problems specific to tree-ring data have any significant impact on our reconstructions in earlier centuries (see Fig. S7), we also examined whether or not potential problems noted for several records (see Dataset S1 for details) might compromise the reconstructions. These records include the four Tiljander et al. (12) series used (see Fig. S9) for which the original authors note that human effects over the past few centuries unrelated to climate might impact records (the original paper states ‘‘Natural variability in the sediment record was disrupted by increased human impact in the catchment area at A.D. 1720.’’ and later, ‘‘In the case of Lake Korttajarvi it is a demanding task to calibrate the physical varve data we have collected against meteorological data, because human impacts have distorted the natural signal to varying extents’’). These issues are particularly significant because there are few proxy records, particularly in the temperature-screened dataset (see Fig. S9), available back through the 9th century. The Tijander et al. series constitute 4 of the 15 available Northern Hemisphere records before that point. In addition there are three other records in our database with potential data quality problems, as noted in the database notes: Benson et al. (13) (Mono Lake): ‘‘Data after 1940 no good— water exported to CA;’’ Isdale (14) (fluorescence): ‘‘anthropogenic influence after 1870;’’ and McCulloch (15) (Ba/Ca): ‘‘anthropogenic influence after 1870’’. We therefore performed additional analyses as in Fig. S7, but instead compared the reconstructions both with and without the above seven potentially problematic series, as shown in Fig. S8.

- Mann ey al 2008, Supplementary.

In plain English, Mann et al acknowledged the potential issues with the Tiljander data series and other potentially problematic proxies, and performed a sensitivity analysis. Bottom line: no impact on the conclusions of the study. Yet again, McIntyre is not acting in good faith.