Guest essay by Pat Frank
Today’s offering is a morality tale about the clash of honesty with self-interest, of integrity with income, and of arrogance with ignorance.
I’m bringing out the events below for general perusal only because they’re a perfect miniature of the sewer that is consensus climatology.
And also because corrupt practice battens in the dark. With Anthony’s help, we’ll let in some light.
On November third Anthony posted about a new statistical method of evaluating climate models, published in “Geoscientific Model Development” (GMD), a journal then unfamiliar to me.
WUWT readers will remember my recent post about unsuccessful attempts to publish on error propagation and climate model reliability. So I thought, “A new journal to try!”
Copernicus Publications publishes Geoscientific Model Development under the European Geosciences Union.
The Journal advertises itself as, “an international scientific journal dedicated to the publication and public discussion of the description, development, and evaluation of numerical models of the Earth system and its components.”
It welcomes papers that include, “new methods for assessment of models, including work on developing new metrics for assessing model performance and novel ways of comparing model results with observational data.”
GMD is the perfect Journal for the new method of model evaluation by propagation of calibration error.
So I gave it a try, and submitted my manuscript, “Propagation of Error and the Reliability of Global Air Temperature Projections“; samizdat manuscript here (13.5 mb pdf). Copernicus assigned a “topical editor” by reference to manuscript keywords.
My submission didn’t last 24 hours. It was rapidly rejected and deleted from the journal site.
The topical editor was Dr. James Annan, a climate modeler. Here’s what he wrote in full:
“Topical Editor Initial Decision: Reject (07 Nov 2017) by James Annan
“Comments to the Author:
“This manuscript is silly and I’d be embarrassed to waste the time of reputable scientists by sending it out for review. The trivial error of the author is the assumption that the ~4W/m^2 error in cloud forcing is compounded on an annual basis. Nowhere in the manuscript it is explained why the annual time scale is used as opposed to hourly, daily or centennially, which would make a huge difference to the results. The ~4W/m^2 error is in fact essentially time-invariant and thus if one is determined to pursue this approach, the correct time scale is actually infinite. Of course this is what underpins the use of anomalies for estimating change, versus using the absolute temperatures. I am confident that the author has already had this pointed out to them on numerous occasions (see refs below) and repeating this process in GMD will serve no useful purpose.”
Before I parse out the incompetent wonderfulness of Dr. Annan’s views, let’s take a very relevant excursion into GMD’s ethical guidelines about conflict of interest.
But if you’d like to anticipate the competence assessment, consult the 12 standard reviewer mistakes. Dr. Annan managed many ignorant gaffes in that one short paragraph.
But on to ethics: GMD’s ethical guidelines for editors include:
“An editor should give unbiased consideration to all manuscripts offered for publication…”
“Editors should avoid situations of real or perceived conflicts of interest in which the relationship could bias judgement of the manuscript.”
Copernicus Publications goes further and has a specific “Competing interests policy” for editors:
“A conflict of interest takes place when there is any interference with the objective decision making by an editor or objective peer review by the referee. Such secondary interests could be financial, personal, or in relation to any organization. If editors or referees encounter their own conflict of interest, they have to declare so and – if necessary – renounce their role in assessing the respective manuscript.”
In a lovely irony, my cover letter to chief editor Dr. Julia Hargreaves made this observation and request:
“Unfortunately, it is necessary to draw to your attention the very clear professional conflict of interest for any potential reviewer reliant on climate models for research. The same caution applies to a reviewer whose research is invested in the consensus position concerning the climatological impact of CO2 emissions.
“Therefore, it is requested that the choice of reviewers be among scientists who do not suffer such conflicts.
“I do understand that this study presents a severe test of professional integrity. Nevertheless I have confidence in your commitment to the full rigor of science.“
It turns out that Dr. Annan is co-principal of Blue Sky Research, Inc. Ltd., a for-profit company that offers climate modeling for hire, and that has at least one corporate contract.
Is it reasonable to surmise that Dr. Annan might have a financial conflict of interest with a critically negative appraisal of climate model reliability?
Is it another reasonable surmise that he may possibly have a strong negative, even reflexive, rejectionist response to a study that definitively finds climate models to have no predictive value?
In light of his very evident financial conflicts of interest, did editor Dr. Annan recuse himself knowing the actuality, not just the image, of a serious and impending impropriety? Nope.
It gets even better, though.
Dr. Julia Hargreaves is the GMD Chief Executive Editor. I cc’d her on the email correspondence with the Journal (see below). It is her responsibility to administer journal ethics.
Did she remove Dr. Annan? Nope.
I communicated Dr. Annan’s financial and professional conflicts of interest to Copernicus Publications (see the emails below). The Publisher is the ultimate administrator of Journal ethics.
Did the publisher step in to excuse Dr. Annan? Nope.
It also turns out that GMD Chief Executive Editor Dr. Julia Hargreaves is the other co-principal of Blue Sky Research, Inc. Ltd.
She shares the identical financial conflict of interest with Dr. Annan.
Julia Hargreaves and James Annan are also a co-live-in couple, perhaps even married.
One can’t help but wonder if there was a dinner-table conversation.
Is Julia capable of administering James’ obvious financial conflict of interest violation? Apparently no more than is James.
Is Julia capable of administering her own obvious financial conflict of interest? Does James have free rein at GMD, Julia’s Executive Editorship withal? Evidently, the answers are no and yes.
Should financially conflicted Julia and James have any editorial responsibilities at all, at a respectable Journal pretending critical appraisals of climate models?
Both Dr. Annan and Dr. Hargreaves also have a research focus on climate modeling. Any grant monies depend on the perceived efficacy of climate models.
They will have a separate professional conflict of interest with any critical study of climate models that comes to negative conclusions.
So much for conflict of interest.
Let’s proceed to Dr. Annan’s technical comments. This will be brief.
We can note his very unprofessional first sentence and bypass it in compassionate silence.
He wrote, “… ~4W/m^2 error in cloud forcing…” except it is ±4 W/m^2 not Dr. Annan’s positive sign +4 W/m^2. Apparently for Dr. Annan, ± = +.
And ±4 W/m^2 is a calibration error statistic, not an energetic forcing.
That one phrase alone engages mistakes 2, 4, and 6.
How does it happen that a PhD in mathematics does not understand rms (root-mean-square) and cannot distinguish a “±” from a “+”?
How comes a PhD mathematician unable to discern a physically real energy from a statistic?
Next, “the assumption that the [error] is compounded on an annual basis”
That “assumption” is instead a demonstration. Ten pages of the manuscript are dedicated to showing the error arises within the models, is a systematic calibration error, and necessarily propagates stepwise.
Dr. Annan here qualifies for the honor of mistakes 4 and 5.
Next, “Nowhere in the manuscript it is explained why the annual time scale is used as opposed to hourly, daily or centennially,…”
Exactly “why” was fully explained in manuscript Section 2.4.1 (pp. 28-30), and the full derivation was provided in Supporting Information Section 6.2.
Dr. Annan merits a specialty award for extraordinarily careless reading.
On to, “The ~4W/m^2 error is in fact essentially time-invariant…”
Like Mr. andthentheresphysics, Nick Stokes, and Dr. Patrick Brown, Dr. Annan apparently does not understand that a time average is a statistic conveying, ‘mean magnitude per time-unit.’ This concept is evidently not covered in the Ph.D.
And then, “the correct time scale is actually infinite.”
Except it’s not infinite, (see above), but here Dr. Annan has made a self-serving interpretative choice. Dr. Annan actually wrote that his +4 W/m^2 is “time-invariant,” which is also consistent with an infinitely short time. The propagated uncertainty is then also infinite; good job, Dr. Annan.
Penultimately, “this is what underpins the use of anomalies for estimating change…”
Dr. Annan again assumed ±4 W/m^2 statistic is a constant +4 W/m^2 physical offset error, reiterating mistakes 4, 6, 7, and 9.
And it’s always nice to finish up with an irony: “I am confident that the author has already had this pointed out to them on numerous occasions…”
In this, finally, Dr. Annan is correct (except grammatically; referencing a singular noun with a plural pronoun).
I have yet to encounter a single climate modeler who understands:
- that “±” is not “+,”
- that an error statistic is not a physical energy,
- that taking anomalies does not remove physical uncertainty,
- that models can be calibrated at all,
- or that systematic calibration error propagates through subsequent calculations.
Dr. Annan now joins that chorus.
The predominance of mathematicians among climate modelers, like Dr. Annan, explains why climate modeling is in such a shambles.
Dr. Annan’s publication list illustrates the problem. Not one paper concerns incorporating new physical theory into a model. Climate modeling is all about statistics.
It hardly bears mentioning that statistics is not physics. But that absolutely critical distinction is obviously lost on climate modelers, and even on consensus-supporting scientists.
None of these people are scientists. None of them know how to think scientifically.
They have made the whole modeling enterprise a warm little pool of Platonic idealism, untroubled by the cold relentless currents of science and its dreadfully impersonal tests of experiment, observation, and physical error.
In their hands, climate models have become more elaborate but not more accurate.
In fact, apart from Lindzen and Choi’s Iris theory, there doesn’t seem to have been any advance in the physical theory of climate since at least 1990.
Such is the baleful influence on science of unconstrained mathematical idealism.
The whole Journal response reeks of fake ethics and arrogant incompetence.
In my opinion, GMD ethics have proven to be window dressing on a house given over to corruption; a fraud.
Also in my opinion, this one episode is emblematic of all of consensus climate science.
Finally, the email traffic is reproduced below.
My responses to the Journal pointed out Dr. Annan’s conflict of interest and obvious errors. On those grounds, I asked that the manuscript be reinstated. I always cc’d GMD Chief Executive Editor Dr. Julia Hargreaves.
The Journal remained silent, no matter even the clear violations of its own ethical pronouncements; as did Dr. Hargreaves.
1. GMD’s notice of rejection:
From: editorial@xxx.xxx
Subject: gmd-2017-281 (author) – manuscript not accepted
Date: November 7, 2017 at 6:07 AM
To: pfrankxx@xxx.xxx
Dear Patrick Frank,
We regret that your following submission was not accepted for publication in GMD:
Title: Propagation of Error and the Reliability of Global Air Temperature Projections
Author(s): Patrick Frank
MS No.: gmd-2017-281
MS Type: Methods for assessment of models
Iteration: Initial Submission
You can view the reasons for this decision via your MS Overview: http://editor.copernicus.org/GMD/my_manuscript_overview
To log in, please use your Copernicus Office user ID xxxxx.
We thank you very much for your understanding and hope that you will consider GMD again for the publication of your future scientific papers.
In case any questions arise, please contact me.
Kind regards,
Natascha Töpfer
Copernicus Publications
Editorial Support
editorial@xxx.xxx
on behalf of the GMD Editorial Board
+++++++++++++++
2. My first response:
From: Patrick Frank pfrankxx@xxx.xxx
Subject: Re: gmd-2017-281 (author) – manuscript not accepted
Date: November 7, 2017 at 7:46 PM
To: editorial@xxx.xxx
Cc: jules@xxx.xxx.xxx
Dear Ms. Töpfer,
Dr. Annan has a vested economic interest in climate modeling. He does not qualify as editor under the ethical conflict of interest guidelines of the Journal.
Dr. Annan’s posted appraisal is factually, indeed fatally, incorrect.
Dr. Annan wrongly claimed the ±4 W/m^2 annual error is explained “nowhere in the manuscript.” It is explained on page 30, lines 571-584.
The full derivation is provided in Supporting Information Section 6.2.
There is no doubt that the ±4 W/m^2 is an annual calibration uncertainty.
One can only surmise that Dr. Annan did not read the manuscript before coming to his decision.
Dr. Annan also made the naïve error of supposing that the ±4 W/m^2 calibration uncertainty is a constant offset physical error.
Plus/minus cannot be constant positive (or negative). It cannot be subtracted away in an anomaly.
Dr. Annan’s rejection is not only scientifically unjustifiable. It is not even scientific.
I ask that Dr. Annan be excused on ethical grounds, and on the grounds of an obviously careless and truly incompetent initial appraisal.
I further respectfully ask that the manuscript be reinstated and re-assigned to an alternative editor who is capable of non-partisan stewardship.
Thank-you for your consideration,
Pat
Patrick Frank, Ph.D.
Palo Alto, CA 94301
email: pfrankxx@xxx.xxx
++++++++++++++++
3. Journal response #1: silence.
+++++++++++++
4. My second response:
From: Patrick Frank pfrankxx@xxx.xxx
Subject: Re: gmd-2017-281
Date: November 8, 2017 at 8:08 PM
To: editorial@xxx.xxx
Cc: jules@xxx.xxx.xxx
Dear Ms. Töpfer,
One suspects the present situation is difficult for you. So, let me make things plain.
I am a Ph.D. physical methods experimental chemist with emphasis in X-ray spectroscopy. I work at Stanford University.
My email address there is xxx@xxx.edu, if you would like to verify my standing.
I have 30+ years of experience, international collaborators, and an extensive publication record.
My most recent paper is Patrick Frank, et al., (2017) “Spin-Polarization-Induced Pre-edge Transitions in the Sulfur K‑Edge XAS Spectra of Open-Shell Transition-Metal Sulfates: Spectroscopic Validation of σ‑Bond Electron Transfer” Inorganic Chemistry 56, 1080-1093; doi: 10.1021/acs.inorgchem.6b00991.
Physical error analysis is routine for me. Manuscript gmd-2017-281 strictly focuses on physical error analysis.
Dr. Annan is a mathematician. He has no training in the physical sciences. He has no training or experience in assessing systematic physical error and its impacts.
He is unlikely to ever have made a measurement, or worked with an instrument, or to have propagated systematic physical error through a calculation.
A survey of Dr. Annan’s publication titles shows no indication of physical error analysis.
His comments on gmd-2017-281 reveal no understanding of the physical uncertainty deriving from model calibration error.
He evidently does not realize that physical knowledge statements are conditioned by physical uncertainty.
Dr. Annan has no training in physical error analysis. He has no experience with physical error analysis. He has never engaged the systematic error that is the focus of gmd-2017-281.
Dr. Annan is not qualified to evaluate the manuscript. He is not competent to be the manuscript editor. He is not competent to be a reviewer.
Dr. Annan’s comments on gmd-2017-281 are no more than ignorant.
This is all in addition to Dr. Annan’s very serious conflict of financial and professional interest with the content of gmd-2017-281.
Journal ethics demand that he should have immediately recused himself. However, he did not do so.
I ask you to reinstate gmd-2017-281 and assign a competent and ethical editor capable of knowledgeable and impartial review.
Geoscientific Model Development can be a Journal devoted to science.
Or it can play at nonsense.
The choice is yours.
I will not bother you further, of course. Silence will be evidence of your choice for nonsense.
Best wishes,
Pat
Patrick Frank, Ph.D.
Palo Alto, CA 94301
email: pfrankxx@xxx.xxx
++++++++++++++++++
5. Journal response #2: silence.
++++++++++++++++++
The journal has remained silent as of 11 November 2017.
They have chosen to play at nonsense. So chooses all of consensus climate so-called science.
Didier Roche addressed his evaluation only to me, by the way. Which I found a little peculiar.
Under the circumstances, wouldn’t the journal have wanted to be inclusively aware of his reasoning?
My reply included Executive Editor Julia Hargreaves and the journal office.
They deserve to know the full quality of editorial acuity they deploy.
From: Patrick Frank pfrankzzz@xxxx.xx
Subject: Re: manuscript gmd-2017-281
Date: November 14, 2017 at 9:42 PM
To: Didier M. Roche didier.roche@xxx.xxx.fr
Cc: jules@xxxxx.xxx.uk, editorial@xxxx.org
Dear Dr. Roche,
Thank-you for your email.
This will be short. Quote and response.
You wrote, “premises that the error arising from simulated cloud cover on an annual mean is a 4 W.m-2 error in long wave radiation calculations in CMIP models.”
This is not my premise. It is a result reported in Lauer and Hamilton, 2013.
The quantity ±4 W/m^2 is a rms uncertainty statistic. It is not a positive-sign physical error as you represented it.
“By thus doing an average over the year, you ignore completely their variations over the year. … you also ignore the fact that different types of clouds (low vs. high for example) have different radiation effects and that therefore their vertical distribution is also of major importance.”
Calculating annual GMST does not presume that every point on Earth is of uniform temperature every day, everywhere, all year.
Calculating global average irradiance does not presume that every point on Earth receives 340 W/m^2.
Calculating global average cloud forcing (Hartmann, 1992; Stephens, 2005) does not presume all clouds are the same everywhere.
Taking an average does not presume that a uniform magnitude reigns everywhere.
The complete ignorance reflected in your argument calls a judgment of incompetence.
“However, the valid point of Dr. Annan is that the *annual* timescale is explain nowhere in the manuscript.”
My source, Lauer and Hamilton, 2013, reported annual means; mentioned in ms line 575.
Manuscript lines 578-579 show exactly how and where the annual timescale arises. SI Section 6.2 derived the annual timescale
exactly.
Your statement is factually and demonstrably wrong; as was that of Dr. Annan.
You may have read the manuscript twice, but you did not understand it even once.
You are a climate modeler, Dr. Roche. Your publication list shows no relevant expertise; a condition obvious in the quality of your
commentary.
Like Dr. Annan you have profound professional and career conflicts of interest with a manuscript demonstrating that climate models
have no predictive value, which they do not.
You people are determinedly rejectionist. Protocol is your cover.
Yours sincerely,
Pat
Patrick Frank, Ph.D.
Palo Alto, CA 94301
email: pfrankzzz@xxxx.net
For those who have read this far (thanks), here’s what manuscript lines 578-579 say:
“Dimensional analysis of the derivation yields the units of the calibration error statistic: (cloud-cover unit)/grid-point × 1/year × 1/model × grid-points/globe = (cloud-cover unit) year-1 model-1 globe-1. This is a global annual average CMIP simulation error in cloud cover.”
Throughout their paper, Lauer and Hamilton, 2013 invariably refer to their results as annual means.
Somehow my reply to Dr. Roche has not appeared, after two attempts to post it.
Pat,
I think your paper is obviously flawed as I have explained on numerous occasions before. Patrick Brown gave a lengthy explanation as to the error. I believe Gavin Schmidt also explained the issue to you many years ago too. Now you have James Annan providing a similar explanation, plus an extra evaluation by the executive editor. It has now – IIRC – been rejected 7 times. Is there a stage at which you might take a step back and consider that you may actually be wrong?
Argument from authority, ATTP. Objectively wrong arguments multiply repeated do not become right arguments. Your arguments are wrong, and you’re wrong.
And here you are again, claiming ±4 W/m^-2 calibration error statistic is instead a positive-sign +4 W/m^2 forcing offset error.
How hard is it to realize that a statistic is not an energy?
Your self-anointed appellation of “and Then There’s Physics” is an utterly unintentional but perfect irony. Your entire criticism abuses physics.
Further, neither you, nor Patrick Brown, nor Gavin Schmidt ever realized that linear extrapolation of forcing entirely justifies linear propagation of error. This is the core issue, and it’s opaque to you.
That GCM output is linear in forcing is fully demonstrated. The rest follows. It’s obvious. And none of you get it. Or won’t get it.
Well said Pat.
http://www.populartechnology.net/2015/01/who-is-and-then-theres-physics.html
A non-physics, non-entity. !
Submitting the same paper over and over expecting different results (it being accepted) is well defined.
Robert Kernodle, editors and reviewers vary with the journal.
One can always rationally hope to finally find intellectual courage in the one and competence in the other at a different journal.
Thus far, unfortunately, I have found neither at any. Except for three reviews that did recommend publication, including one of two at Earth Science Reviews.
That event should have warranted publication given a compelling response to the negative review. But editor Timothy Li at ESR is a climate modeler. Guess what he decided about a paper definitively showing that models are unreliable.
“And here you are again, claiming ±4 W/m^-2 calibration error statistic is instead a positive-sign +4 W/m^2 forcing offset error.”
Despite my challenge, you have not found a single instance where someone writes a rmse as ±x. All the locations you found, and even your source, L&H, write it as a positive number.
I see in some recent posts, you still trot out cases where an interval is described as a±b, where b is an RMS. That does not imply a variable sign on b. You can write the interval 1 to 5 as 3±2. But 2 is still a positive number.
Pat Frank, there are plenty of of journals that will publish your work……all you have to do is pay the fee.
Have you tried the Chinese journal where His Majesty , the Viscount of Benchley got his paper published?
Nick, “Despite my challenge, you have not found a single instance where someone writes a rmse as ±x”
To the contrary: here (November 12, 2017 at 10:22 pm ), and here (November 12, 2017 at 11:48 pm Ineichen, et al.), and here (November 14, 2017 at 12:27 am, several examples).
We’ve established that reporting standard deviations (rmse) as positive roots is a mere convention.
Square roots are always “±.” RMSE is always “±.”
The “interval from 1 to 5” is not a rms. Non-sequitur.
Your argument also and nonsensically implies that error itself can only be of positive sign (November 12, 2017 at 8:22 pm ).
You know all that very well. Your challenge is your attempt to divert the debate into meaningless convention. So we all now know you’re just lying Nick.
Robert Kernodle, you mean Science Bulletin.” Yes, I did try them.
Their response was to say that my study didn’t meet their standards of “novelty and significance.”
In other words, the first ever propagation of error through a GCM projection and the first ever demonstration that GCMs have no predictive value wasn’t interesting enough to even consider.
Then they blocked my email account.
Science Bulletin cannot “block” your email account. Ignoring you is not “blocking.”
Anyone can block anyone’s email account, Robert. The domain name goes into the blocked sender list.
Pat,
I wasn’t really making an argument. I was asking a question, which you have still not answered. At what stage would you take a step back and consider that you may actually be wrong?
ATTP, I’ll step back when someone demonstrates an objective mistake.
You certainly have not done so. Neither has Patrick Brown, nor Nick Stokes.
And at what stage do you step back admit you’re wrong, ATTP?
My demonstration is intact, that climate models have no predictive value.
You must know that your arguments that annual means are not annual means, and that a ±rmse statistic is an energy are utterly and *obviously* wrong.
When do you step back and admit it, ATTP? You have no case.
Pat,
As far as I can see, many have already done this, so what you probably mean is “demonstrate an objective mistake to your satisfaction“. Given that this is probably not possible, is there some other point at which you would consider stepping back and considering that you have indeed made some kind of mistake?
Let’s have your mistake, ATTP. We can have it out right here.
Many years ago, Gavin was reduced to a manufactured log(0) error; one I never made. That was his entire remaining criticism by the end of our debate. He lost.
Gavin also apparently thinks that a ±K uncertainty is a physical temperature. He thinks that an uncertainty interval implies the model is wildly oscillating. There’s your source of critical authority, ATTP. A paragon of incompetent ideas.
You and Patrick Brown both assert what is obviously a ±4 W/m^2/year calibration error statistic is instead a +4 W/m^2 positive offset forcing error. You don’t recognize the difference between a statistic and a physical energy.
Years ago I’d have thought such a mistake incredible. Now it’s a banal universality.
Of course, the same people think a numerical construct is a physical temperature.
Further, you evidently cannot be convinced by the explicit derivation I provided and the extracted dimensional analysis that each demonstrates my point and your mistake.
You people are wrong, I’ve shown you are wrong, and your repeated insistence on wrong establishes nothing.
You’re apparently a trained physicist, ATTP, and you insistently argue that a rms calibration error statistic is an energetic forcing. You should feel ashamed of making such a naïve error.
I’m not surprised that Patrick Brown made that mistake, because I have yet to encounter a climate modeler who has any understanding whatever of calibration and physical error.
But you’re apparently a different case. You’ve evidently put aside all your training and taken up advocacy.
Pat,
I take it that the answer to my question is no?
Stop playing around ATTP. If you have a mistake to air out, let’s see it.
Otherwise you’re just hiding behind rhetorical poses.
You claim to know my mistakes. So here’s your challenge, ATTP. Put up or shut up.
Patrick Brown thinks that a ±K uncertainty is a physical temperature; quote below.
Do you think that, too, ATTP? Is one of your objections, like his, that a propagated uncertainty of ±15 K is unphysical?
Quote from minute 12:35: the ±15 C uncertainty envelope is, “a completely unphysical range of uncertainty, so it’s totally not plausible that temperature could decrease by 15 degrees as we’re increasing CO₂. And it’s implausible as well that temperature could increase by 17 decrees as we’re increasing CO₂ under the RCP 8.5 scenario. But as I understand it, this is the point Dr. Frank is trying to make.”
There it is. He thinks an uncertainty statistic is a physical temperature.
Is that one of your main objections, too, ATTP? Grounded in the idea that a statistic is a temperature?
Are you, apparently a trained physicist, truly that naïve?
And if you’re not, would you not worry that such a basic misunderstanding of uncertainty might indicate a profound lack of training in physical error analysis?
Pat,
I’m not really playing around. I was trying to establish if there was some realistic scenario under which you would take and step back and maybe consider that you were wrong. I think I have now established (to my satisfaction, admittedly) that there is not. I’m more than happy to leave it at that.
You made a disparaging reference to my satisfaction above, ATTP. And now you’ve credited yours. That’s not very consistent of you, is it.
Not one of your analytical criticisms has been valid, ATTP. Not one. That also is apparently to your satisfaction.
Let the record show you dodged the opportunity to make your case.
Pat,
What I’m finding remarkable is that you’ve had a paper rejected 7 times (I think) and had numerous others criticise what you’ve presented. Rather than trying to find some way to engage better with your critics, you seem pretty convinced that you’re completely right, and they’re completely wrong, and you’ve managed to insult most of them in the process. Even if by some chance you are correct, this seems a poor way in which to establish this, but my guess is that that is not actually your intent. I may be wrong, of course.
Pat ought to submit it to a mathematics journal,
Climatologist have proven they don’t have the wherewithal to comprehend.
They continue to be utterly blinkered because of their ideology and lower level of cognitive function
Like you, mr ZERO-physics.
Yes, this seem like a good suggestion.
Where is a correct criticism, ATTP? Yours are certainly not correct. Nor are Patrick Brown’s, nor Nick Stokes’.
My responses to the reviewers are available for examination. Some of them made mistakes similar to yours. Others made different mistakes.
Not even one rejectionist reviewer ever grasped the core point that linear extrapolation of forcing warrants linear propagation of error. You have never shown any awareness of that either.
Nevertheless, that warrant alone validates the uncertianty envelopes and the conclusion that climate models are predictively worthless.
How else should I engage my critics except to produce a valid study?
I’ve engaged you. I’ve engaged Patrick Brown. I engaged Nick Stokes and Gavin Schmidt. I engaged my critics. All with polite and detailed argument. I have demonstrated the intellectual poverty of your criticisms.
What good has it done?
I’m in the position of someone who might have tried to publish on the failings of Marxism in Soviet journals. Rejectionist reviewers and editorial malfeasance would rule the process.
Would the utter uniformity of their rejection indicate an invalid study?
Any hope for publication in a consensus climate journal has turned out to be a chimerical dream.
No ideologist is willing to entertain a disproof. That’s you people.
As to insults, I’ve not insulted anyone. Those I’ve called incompetent have merited that judgment; by supposing that ±K is a temperature for example. A statement of fact is not an insult.
James Annan is a mathematician. Gavin Schmidt is a mathematician. Neither of them know the first thing about physical error analysis.
Pat,
Since you’re the one with the ground-breaking idea, you should probably be the one defending/explaining it. In that light, can you briefly explain the basics of energy balance in the context of our climate, mentioning most of the major forcings/feedbacks?
Transparent attempt to divert from a losing argument, ATTP.
For the sake of readers ATTP’s objection is rendered here: href=“https://wattsupwiththat.com/2017/10/23/propagation-of-error-and-the-reliability-of-global-air-temperature-projections/#comment-2643716:
“The error that you’re trying to propagate is not an error at every timestep, but an offset.”
ATTP makes two objections:
1. The error does not arise in every time step.
2. The error is a constant offset, which can be subtracted away.
In response:
1. The manuscript shows CMIP5 simulated cloud error is ≥0.95 correlated among models. The error is thus not random but arises from model theory bias. It arises from the incorrect and/or incomplete physical theory deployed within the model itself.
This means long wave cloud forcing (LWCF) error also arises from within the model. LWCF error then necessarily makes a new appearance in every single step of a climate projection.
A multi-model, multi-year root-mean-square (rms) of LWCF error is a general model calibration error statistic. Model calibration error statistics express the uncertainty in a predicted magnitude.
When an error-ridden model is used to make sequential calculations, the model-inherent error is injected into every calculational step.
Each stepwise result is then erroneous. Each erroneous result is used as the initial value for the subsequent calculational step. Error must accumulate across the sequence of calculations.
But the magnitude of the error in each step is unknown, because no observations are available for reference.
As the error magnitude is unknowable, propagated uncertainty is the only available measure of reliability.
It is completely justified to propagate a calibration error uncertainty statistic to account for the increasing uncertainty due to an error of unknown magnitude reiterated in every sequential step of a stepwise calculation.
2. Lauer and Hamilton 2013 reported a multi-model 20-year annual mean root-mean-square (rms) error for simulated long wave cloud forcing. RMS error is a ± uncertainty statistic. For CMIP5 models the reported annual average LWCF rms error was ±4 W/m^2/year; applicable to the air temperature projection of any CMIP5 model.
A plus/minus uncertainty statistic is not a single-sign constant physical error. It cannot be subtracted away.
ATTP’s objections do not withstand critical examination.
ATTP wants to know what would convince me to agree that his obviously incorrect objections are *not* incorrect.
Pat,
I’m actually trying to establish if you even understand the basics well enough to make the claims that you’re making. There’s no reason why you wouldn’t want to at least try to illustrate that you do, is there?
Pat,
Actually, I didn’t say 2. The error is a constant offset, which can be subtracted away. In light of this, why don’t you just demonstrate that you do indeed understand the basics of energy balance in the context of our climate?
Pat Frank says: “Anyone can block anyone’s email account, Robert”
…
No Pat, the only place an email account can be blacklisted is on the server. Your email client cannot tell it’s server what to blacklist. I guess you are unable to differentiate between “client” and “server” in the world of email.
…
Your ignorance of such things is telling. Does it carry over to your knowledge of climate models?
If you’re not composing a diversion, ATTP, you’re just displaying ignorance.
My analysis has nothing to do with climate physics. Or energy balance.
It’s all about the observable behavior of climate models and physical error analysis.
Your challenge is an irrelevant non-sequitur through and through. Ignorance or diversion: there’s no third possibility.
You’ve continued to ignore that you agree ± = + and a statistic = an energy. How about ±K = K; do you agree with that, too?
Robert Kernodle, so you’ve decided that Science Bulletin doesn’t have access to their email server. Your decision to invent criticisms lets us know that you’re just so sincere.
ATTP, you wrote, “Actually, I didn’t say 2. The error is a constant offset, which can be subtracted away.”
To the contrary, that’s been your position all along. In the cross post on your own site, you say (January 26, 2017 at 6:10 pm), “However, this doesn’t mean that one should propagate those uncertainties through the model, because either this error is being compensated for elsewhere, or the model is not correctly representing the absolute state, but might still be useful for determine how the system responds to changes. (my bold)”
The bolded statement, specifically “changes, is standard usage in consensus work for taking differences to remove a supposedly constant-offset simulation error. Several of my reviewers emphatically make this claim.
Like you, they misconstrue the ±4 W/m^2 calibration statistic to be a +4 W/m^2 offset error in forcing. Like you, they claim a statistic is an energy.
The simulated change in climate is projected state minus base-state, right? And all your constant-offset errors just subtract away. Isn’t that convenient. And doesn’t that reveal why you all argue so vehemently that ± = +.
But ±rmse calibration uncertainty is not physical error, ATTP. And ± ≠ +.
And this, also your statement there, is relevant to our discussion (January 27, 2017 at 8:55 am): “However, Frank is essentially arguing that an individual model should have a large uncertainty because of the uncertainty in the cloud forcing, but the cloud forcing in an individual models does not vary wildly from step to step; it may differ – in an absolute sense – from observations, but that difference doesn’t mean that the cloud forcing in that model will wildly vary relative to observation.”
You there show a complete misunderstanding of uncertainty. You treat a ±rmse uncertainty statistic as though it were a physical energetic perturbation on the model.
A ±rmse calibration uncertainty has no impact whatever on model expectation values. You make a freshman mistake, ATTP. Is that competent?
Pat Frank writes: “The domain name goes into the blocked sender list.”
…..
So, should they want to block xyz@gmail.com, they block the domain? Do you realize how ignorant that is? If the domain is blocked, EVERYBODY with a gmail is blocked.
..
Next Pat Frank makes another dumb assertion, “you’ve decided that Science Bulletin doesn’t have access to their email server.” You misinterpreted what I said. I said, “Your email client cannot tell it’s server what to blacklist.”
..
For someone writing a paper about computer models, you display a lack of understanding about something as simple as email. The user of a mail server can access he server via POP, IMAP and often by web browser access. Such access does not include blacklisting a “domain” (your term.)
Robert Kernodle, “So, should they want to block xyz@gmail.com, they block the domain? Do you realize how ignorant that is? If the domain is blocked, EVERYBODY with a gmail is blocked.”
I guess you’ve got me Robert. I should have written the email address goes into a blocked sender list.
Whoop-de-do. That sure means I know nothing of physical error analysis, alright.
Your line of argument is over the border and into foolish-land, Robert. Try sticking to physical error analysis, the topic actually at hand.
Pat,
Yes, this may well be true, ironically. The problem, though, is that error analysis does require some understanding of the actual calculation/measurements. I’m trying to establish if you do indeed understand the underlying principles, because that is required if you are to do a proper error analysis.
Mr Empty of physics.
You have shown by your comments that your mathematical knowledge is not up to understanding even the basics of error propagation.
It is very obvious that your whole comprehension is deeply flawed.
Its is just part of being you.
http://www.populartechnology.net/2015/01/who-is-and-then-theres-physics.html
ATTP, climate model air temperature projections are fully demonstrated to be nothing but linear extrapolations of forcing. Therefore they are subject to linear propagation of error.
Refute that.
If you can’t refute that, you have no case.
Your excursion into energy balance is an attempt (most likely studied) to divert the conversation into an irrelevancy; a try to rescue a lost debate.
You have also signally ignored addressing your own mistakes of claiming a statistic is an energy, of claiming ± = +, and of claiming a ±rmse calibration uncertainty statistic is a single-sign positive offset physical error.
Given all that one is left bemused that you’d offer yourself as a judge of whether someone else is capable of physical error analysis. Your mistaken claims demonstrate you know nothing about it.
Your silence on the point also indicates you now admit that your adamant support of offset errors indeed includes that they subtract away.
You’ve yet to answer by the way whether you, like Patrick Brown and Gavin Schmidt, think that ±K = K.
Pat,
If we’re talking about GCMs then they’re three-dimensional, dynamical simulations. However, it is roughly correct that the globally averaged change in temperature depends approximately linearly on the change in forcing. To propagate the error, you would need to know the error in the change in forcing, not simply the error in one component of the forcings.
ATTP, thank-you for agreeing that GCM air temperature projections are linear extrapolations of forcing. Not your “roughly linear,” but demonstrated to be exactly linear.
Linear propagation of error obviously follows.
You wrote, “you would need to know the error in the change in forcing”
I need only know the uncertainty in the simulated tropospheric thermal energy flux. The calibration statistic, LWCF ±rmse gives me exactly that.
The average annual change in forcing since 1979 is about 0.035 W/m^2. The lower limit annual average uncertainty in simulated tropospheric thermal energy flux is ±4 W/m^2.
That annual 0.035 W/m^2 enters the tropospheric thermal energy flux and becomes part of it. The ±4 W/m^2 uncertainty tells us that we have no idea how clouds will respond to a 0.035 W/m^2 perturbation. That represents physical ignorance, ATTP, and is the source of the propagated uncertainty.
LWCF ±rmse represents a general model theory-bias error. It is injected into every single simulation step of a projection. Uncertainty in projected air temperature necessarily grows stepwise.
Pat.
No, they are not exactly linear.
No, you need to know the change in forcing and its uncertainty. The latter is not the same as the uncertainty in the tropospheric thermal energy flux.
ATTP, I used the IPCC SRES or Meinshausen forcings throughout. The emulations are excellent.
You wrote, “No, you need to know the change in forcing and its uncertainty. The latter is not the same as the uncertainty in the tropospheric thermal energy flux.”
Your second sentence is nearly correct. Your first is not.
The uncertainty I used is the ±rmse in simulated long wave cloud forcing. Cloud forcing and CO2 forcing jointly enter the tropospheric thermal energy flux.
Tropospheric thermal energy flux determines air temperature. Uncertainty in tropospheric thermal energy flux puts an uncertainty into the derived air temperature.
Simulated LWCF ±rmse is an uncertainty in tropospheric thermal energy flux. It puts an uncertainty into the derived air temperature.
I don’t need to know the uncertainty in CO2 forcing at all. I can derive an uncertainty in projected air temperature from simulated LWCF ±rmse.
If you can’t see that, you’re lost.
However, according to Entminan, et al. (2017) , Geophysical Research Letters, 43(24), 12,614-612,623, the uncertainty in CO2 long wave forcing is about ±5% (1 SD).
The simulated LWCF ±rmse provides a lower limit of uncertainty. If simulated LWCF ±rmse were combined with the uncertainty in CO2 forcing, the projection uncertainty envelopes would only become larger.
Pat,
I’ll repeat this one more time. To do what you’re trying to do, you need to know the uncertainty in the change in forcing (remember it’s linear in change in forcing). This is not what you are using.
ATTP, ”you need to know the uncertainty in the change in forcing.
No, I don’t ATTP. I need only to know the uncertainty in the simulated tropospheric forcing. And that I do know, in a lower limit.
A GCM simulates the cloud forcing as it varies across every projection year. That simulation includes the effect of increasing [CO2] on clouds and whatever else.
The GCM LWCF calibration error statistic tells us that the simulated annual average tropospheric thermal energy flux, including the simulated response to the annual change in CO2 forcing, is accurate only to ±4 W/m^2.
The simulated tropospheric thermal energy flux is never known to more accuracy than that ±rmse; a lower limit of resolution.
That means the change in air temperature due to increased CO2 is conditioned by our ignorance of the physically correct magnitude of the tropospheric thermal energy flux, within which CO2 has its effect.
That effect is worth only 0.035 W/m^2/year. That perturbation is completely lost within the annual average LWCF uncertainty of ±4 W/m^2/year.
If the GCMs cannot simulate the tropospheric thermal energy flux to better resolution than ±4 W/m^2, they cannot resolve a 0.035 W/m^2 perturbation and cannot accurately simulate the cloud response to that perturbation.
That means they cannot simulate the change in tropospheric thermal energy flux due to that perturbation or the corresponding response of air temperature.
At every time step, the simulated cloud cover is always freshly wrong but to some unknown amount.
Our ignorance of the relative phase-space positions of the simulated air temperature and the physically correct air temperature increases with every single projection simulation time-step.
Hence the increasing uncertainty bounds.
This analysis and conclusion should be obvious to any trained physicist (or chemist, or engineer).
Oh look, AGW troll “ZERO-physics” brings his arrant nonsense and base level ignorance……
….. in a vain attempt to try to help the drowning Nick.
Gavin Schmidt…… roflmao..
The guy wouldn’t even face up to Roy Spencer.
Cannot afford to have is mathematical malfeasance exposed.
ATTP, “Actually, I didn’t say 2. The error is a constant offset, which can be subtracted away.”
To the contrary, that’s been your position all along. In the cross post of Patrick Brown’s video on your own site, you say (January 26, 2017 at 6:10 pm), “However, this doesn’t mean that one should propagate those uncertainties through the model, because either this error is being compensated for elsewhere, or the model is not correctly representing the absolute state, but might still be useful for determine how the system responds to changes. (my bold)”
The bolded statement, specifically “changes,” is standard usage in consensus work for taking differences to remove a supposedly constant-offset simulation error. Several of my reviewers emphatically make this claim.
The simulated change in climate is projected state minus base-state, right? And all your constant-offset errors just subtract away, don’t they. Isn’t that convenient. And doesn’t that reveal why you all argue so vehemently that ± = +.
The accuracy wonderfulness of taking differences to remove error has been your position right from the start. But ±uncertainty is not physical error, ATTP. And ± ≠ +.
And this, also your statement on your site, is relevant to our discussion here (January 27, 2017 at 8:55 am): “However, Frank is essentially arguing that an individual model should have a large uncertainty because of the uncertainty in the cloud forcing, but the cloud forcing in an individual models does not vary wildly from step to step; it may differ – in an absolute sense – from observations, but that difference doesn’t mean that the cloud forcing in that model will wildly vary relative to observation.”
You there show a complete misunderstanding of uncertainty. You treat a ±rmse uncertainty statistic as though it were a physical energetic perturbation on the model.
A ±rmse calibration uncertainty has no impact whatever on model expectation values. You make a freshman mistake, ATTP. Is that competent?
Let’s try again: here’s my response to Dr. Roche.
+++++++++++++++
From: Patrick Frank pfrankzzz@xxxxx.net
Subject: Re: manuscript gmd-2017-281
Date: November 14, 2017 at 9:42 PM
To: Didier M. Roche didier.roche@xxx.xxx.fr
Cc: jules@xxxxx.xxx.uk, editorial@xxxxx.org
Dear Dr. Roche,
Thank-you for your email.
This will be short. Quote and response.
You wrote, “…premises that the error arising from simulated cloud cover on an annual mean is a 4 W.m-2 error in long wave radiation calculations in CMIP models.”
This is not my premise. It is a result reported in Lauer and Hamilton, 2013.
The quantity ±4 W/m^2 is a rms uncertainty statistic. It is not a positive-sign physical error as you represented it.
“By thus doing an average over the year, you ignore completely their variations over the year. … you also ignore the fact that different types of clouds (low vs. high for example) have different radiation effects and that therefore their vertical distribution is also of major importance.”
Calculating annual GMST does not presume that every point on Earth is of uniform temperature every day, everywhere, all year.
Calculating global average irradiance does not presume that every point on Earth receives 340 W/m^2.
Calculating global average cloud forcing (Hartmann, 1992; Stephens, 2005) does not presume all clouds are the same everywhere.
Taking an average does not presume that a uniform magnitude reigns everywhere.
The complete ignorance reflected in your argument calls a judgment of incompetence.
“However, the valid point of Dr. Annan is that the *annual* timescale is explain nowhere in the manuscript.”
My source, Lauer and Hamilton, 2013, reported annual means; mentioned in ms line 575. Manuscript lines 578-579 show exactly how and where the annual timescale arises. SI Section 6.2 derived the annual timescale exactly.
Your statement is factually and demonstrably wrong; as was that of Dr. Annan.
You may have read the manuscript twice, but you did not understand it even once.
You are a climate modeler, Dr. Roche. Your publication list shows no relevant expertise; a condition obvious in the quality of your commentary.
Like Dr. Annan you have profound professional and career conflicts of interest with a manuscript demonstrating that climate models
have no predictive value, which they do not.
You people are determinedly rejectionist. Protocol is your cover.
Yours sincerely,
Pat
Patrick Frank, Ph.D.
Palo Alto, CA 94301
email: pfrankzzz@xxxx.xx
++++++++++++++++++++++++++++++++++++
These things are, we conjecture, like the truth;
But as for certain truth, no one has known it.
Xenophanes, 570-500 BCE
++++++++++++++++++++++++++++++++++++
Let’s try again: my response to Dr. Roche.
From: Patrick Frank pfrankzzz@xxxxx.net
Subject: Re: manuscript gmd-2017-281
Date: November 14, 2017 at 9:42 PM
To: Didier M. Roche didier.roche@xxx.xxx.fr
Cc: jules@xxxxx.xxx.uk, editorial@xxxxx.org
Dear Dr. Roche,
Thank-you for your email.
This will be short. Quote and response.
You wrote, “…premises that the error arising from simulated cloud cover on an annual mean is a 4 W.m-2 error in long wave radiation calculations in CMIP models.”
This is not my premise. It is a result reported in Lauer and Hamilton, 2013.
The quantity ±4 W/m^2 is a rms uncertainty statistic. It is not a positive-sign physical error as you represented it.
“By thus doing an average over the year, you ignore completely their variations over the year. … you also ignore the fact that different types of clouds (low vs. high for example) have different radiation effects and that therefore their vertical distribution is also of major importance.”
Calculating annual GMST does not presume that every point on Earth is of uniform temperature every day, everywhere, all year.
Calculating global average irradiance does not presume that every point on Earth receives 340 W/m^2.
Calculating global average cloud forcing (Hartmann, 1992; Stephens, 2005) does not presume all clouds are the same everywhere.
Taking an average does not presume that a uniform magnitude reigns everywhere.
The complete ignorance reflected in your argument calls a judgment of incompetence.
“However, the valid point of Dr. Annan is that the *annual* timescale is explain nowhere in the manuscript.”
My source, Lauer and Hamilton, 2013, reported annual means; mentioned in ms line 575. Manuscript lines 578-579 show exactly how and where the annual timescale arises. SI Section 6.2 derived the annual timescale exactly.
Your statement is factually and demonstrably wrong; as was that of Dr. Annan.
You may have read the manuscript twice, but you did not understand it even once.
You are a climate modeler, Dr. Roche. Your publication list shows no relevant expertise; a condition obvious in the quality of your commentary.
Like Dr. Annan you have profound professional and career conflicts of interest with a manuscript demonstrating that climate models have no predictive value, which they do not.
You people are determinedly rejectionist. Protocol is your cover.
Yours sincerely,
Pat
Patrick Frank, Ph.D.
Palo Alto, CA 94301
email: pfrankzzz@xxxx.xx
++++++++++++++++++++++++++++++++++++
These things are, we conjecture, like the truth;
But as for certain truth, no one has known it.
Xenophanes, 570-500 BCE
++++++++++++++++++++++++++++++++++++
Dear Mod – attempts to post my reply to Dr. Roche seems to disappear into the ether. Can you check please if it’s in moderation?
[Done, two copies found. One deleted, one released. .mod]
Oops. Mod, please never mind. I see my reply above. Didn’t notice it on first go-round. 🙂
Please delete out my last comments about it.