10:10: who are YOU going to kill to help save the planet?

http://blogs.telegraph.co.uk/news/jamesdelingpole/100058296/1010-who-are-you-going-to-kill-to-help-save-the-planet/

This one is pretty good
http://www.youtube.com/watch?v=nqT4gIZuaq8

Judith Currey’s approach may not be that honest but am willing to give her the benefit of doubt.

I get the impression that she is either:

A. Protecting the status quo by raising a strawman, or

B, Just has not investigated other avenues.

e.g. Her observation:

“So far, it seems that the biggest climate model uncertainty monsters are spawned by the complexity monster.”

I disagree entirely.

My thinking is that as models evolve and unknown functions addressed (clouds etc), certainty in the functions INCREASE but over the last 7 years say, certainty in the results have DECREASED (uncertainty increased).

As time progresses, certainty in functionality should continue to increase and therefore uncertainty in results SHOULD decrease but wont for the following reason which is the notion that I do not think she has entertained in B. above.

WE HAVE NOT TO DATE BEEN PRESENTED WITH ACTUAL SIMULATION COMPARISONS BETWEEN COMPETING CLIMATE DRIVER HYPOTHESES AND COMBINATIONS OF SUCH.

The PCMDI project that supposedly makes model inter-comparisons is a massive group-think exercise and somewhat incestuous.

The IPCC’s assertion that: well, we took out CO2 forcing and ran 15 simulations on 5 different models using natural forcing only (Lean solar) with OUR RF methodology and the simulations failed to mimic 90′s warming, JUST DOES NOT STAND UP TO SCRUTINY.

Both the IPCC’s ACO2 forced AND the naturally forced simulations, failed to mimic the 1930′s warming AND the ACO2 forced simulations are now diverging from the observed condition (points of inflexion across all metrics in the mid 2000′s).

I intend to make a comment on Judith’s “What can we learn from climate models?” thread (currently 188 comments) but have been distracted (Statement of Defence issues).

Given the rarified atmosphere of discussion, the key is to choose words carefully to get her attention and perhaps that of lurking heavyweights. Being an Antipodean, knuckle dragging, non-entity from downunda will not help my cause.

I posted this in comments at Hot Topic whereupon my very aggressive antagonist (RedLogix) immediately disengaged from scientific discussion and wandered off to an ideological vein:

Richard C2 October 5, 2010 at 8:37 am

Sorry, I’ll keep it simple.

CO2 fails dismally to account for the 1930′s warming but sunspot cycle length correlates with temperature over the entire warming period:

http://www.appinsys.com/GlobalWarming/GW_Part6_SolarEvidence_files/image013.jpg

CO2 fails dismally to correlate with Arctic-wide Surface Air Temperature anomalies:

http://www.appinsys.com/GlobalWarming/GW_Part6_SolarEvidence_files/image024.gif

But solar irradiance does:

http://www.appinsys.com/GlobalWarming/GW_Part6_SolarEvidence_files/image023.gif

Where are the models that mimic these natural phenomena?

If someone could point me to them, it would be greatly appreciated.

Reuters have now picked it up the same article, different headline:

“Climate Models: Get Ready for More Uncertainty”

http://www.reuters.com/article/idUS376390147920101006

Our worst nightmare is just around the corner.

That is the cobbling together of climate simulation models and economic simulation models.

Think Gareth Renowden – Gareth Morgan.

If dear reader, you are overwhelmed by the sheer volume of discussion, complexity and concept in regard to climate models, then let me alleviate your pain.

First, the significance of this development.

Think of how globally: politicians, policy-makers and the public have been hoodwinked by the results of the climate models.

Now consider that the next phase for IPCC AR5 will be policy built on the results of climate-economic coupled computer simulations with AGW hard-wired in.

If this is news to you then you are behind the 8 ball and without further education you will be blind-sided when you first encounter the executive summary.

Gareth Renowden is up to speed:

Gareth October 5, 2010 at 9:39 pm

Yes, the models are run to equilibrium state with a prescribed atmosphere and other forcings, before being fed the trajectory chosen for study. This does not mean that the “parameters are hard-wired”, it means that the intial climate forcings are chosen to allow a stable “climate” within the model. The response to forcing from that state is prescribed by the physics in the model. If you want to argue about the radiation transfer code, then you need a different debate.

AR5 modeling will use the latest versions of the earth systems models available, and also use new “policy relevant” scenarios. This means that they will (of course) produce projections that differ from AR4. That’s a good thing, not a sign of failure.

Are you?

Note Gareth’s obfuscation re AGW hard-wiring and the “good thing” spin (uncertainty has been increasing with each successive IPCC report).

An example of the “earth systems models” he is referring to is here:http://www.cim-earth.org/
which is an aforementioned climate-economic coupled model.

You can rapidly get up to speed in the progression of the climate model uncertainty discussion and where climate science stands in the context of scientific and engineering simulation generally (not good) in preparation for the next onslaught on your senses, by reading the following two threads (posts and comments):-

First from Climate Audit

Curry Reviews Jablonowski and Williamson
http://climateaudit.org/2008/02/03/curry-reviews-jablonski-and-williamson/

Second from Climate Etc

What can we learn from climate models?
http://judithcurry.com/2010/10/03/what-can-we-learn-from-climate-models/

Well, that’s your homework.

Don’t say I didn’t warn you.

More at a later date on the distinction between IPCC prescribed RF methodology (ACO2 forced and Naturally forced) and alternative simulations using NON-IPCC RF methodology and NON-IPCC Natural forcing (big difference).

I have already approached Dr David Wratt at NIWA in this regard (inserted in comments about 6 posts ago), he has acknowledged my approach and has said he will give a detailed reply but I have not heard to date. Given my non-entity status, I am not holding my breath.

Undaunted, I’m off to search the web for glimmers of hope in the NON-IPCC RF Method/Naturally forced sphere of climate model simulations. This shouldn’t be difficult, it must only be a very small sphere.

Climate science modeling now getting serious stick from other fields – nuclear, chemical etc.

Some very astute and knowledgeable comments in this (very long) thread at Climate Audit:

“Curry Reviews Jablonowski and Williamson”
http://climateaudit.org/2008/02/03/curry-reviews-jablonski-and-williamson/

Samples:-

#
Frank K.
Posted Feb 3, 2008 at 10:24 PM | Permalink

“There aren’t any standard test cases used by atmospheric modelers.”

I find this utterly astonishing! You mean no one has bothered to apply various GCMs to reference solutions until now? And we’re talking about just the dynamical cores here…

There is also another related issue that I believe plagues many of the numerical models. How do you prove that the algorithms expressed in the computer code are actually solving the equations they purport to be solving? That is, has anyone done a software audit on these codes? Many organizations, like NASA GISS, provide little to no documentation of the algorithms, even though the code itself is provided. For those who are interested, take a look at Model E for instance at the GISS website. What equations are used for the dynamical core? Are they implemented correctly? Has any stability analysis be performed on the discrete equations? What are the stability limits? How do changes in parametric models (e.g. ocean, ice, tracers, radiation, precipitation models) and initial conditions affect the stability? I could go on…

Yet, these are the numerical models that are being used to advise policy makers on future climate. I think it is high time we demand the same kind of software verification and validation for climate models in particular that we demand for codes used in, say, the nuclear industry.

[The formulations he is looking for are here http://aom.giss.nasa.gov/DOC4X3/ATMOC4X3.TXT but the symbols don't come across on the web]

[Also “There aren’t any standard test cases used by atmospheric modelers.” addressed (unconvincingly) by Curry down-thread]

#
Scott-in-WA
Posted Feb 3, 2008 at 10:44 PM | Permalink

#3 and #4:

Having spent fifteen years writing software in the nuclear industry, I have an appreciation for the issues associated with doing V&V on complex software. I’ve also been involved in several code re-engineering projects.

Referring to the discussion concerning GCM models underway in Steven Mosher’s link, it would be a most interesting (and expensive) experiment to take one of the older GCM models running in procedural language, construct a requirements document and a code design document for the GCM under current V&V practices, and then re-code it using a more modern object-oriented language.

Would the two incarnations of the same GCM model produce identical results starting out with identical initial parameters?

My guess is, not the first time around doing the re-coding, and probably not even the second time around.

#
Geoff Sherrington
Posted Feb 4, 2008 at 12:32 AM | Permalink

Prof Garth Partridge held several eminent positions in Australia, latterly CEO of the Antarctic CRC. So we can assume he is quotable, at least to provoke discussion. Quote -

One can throw a grid of measurement (as dense as you like) all over the plume (smoke in box example explained elsewhere) at some particular time, but won’t be able to forecast the eddy behaviour for very long thereafter. Basically this is because of the growth of sub-scale eddies which seem to come out of nowhere.

Is this just one of a set of limits to calculation that inherently restrict the utility of modelling? It takes my mind back to Mandelbrot’s fractal images, where you mined deeper and deeper to get different patterns, seemingly without limit.

Is there a worthwhile payback for the work Judith Curry proposes, or is it merely another demonstration that too many people drew inferences from models before they were ready? Will they ever be ready in the sense that they can be? Meanwhile, what of policy formulation……

#

#
William Newman
Posted Feb 4, 2008 at 8:14 AM | Permalink

#4: “There aren’t any standard test cases used by atmospheric modelers. I find this utterly astonishing!”

I don’t think this should be so astonishing, though you might want to be astonished by some other stuff. (And if my discussion of the other stuff is insufficiently close to the original topic, it’s OK with me if some moderator-type person deletes all but the next two paragraphs.)

I did some undergraduate research, and my Ph. D., in simulations of biomolecules. There are some standard sorts of checks that people tried to do — e.g., compare to the atomic positions found by experimentalists in really-well-studied macromolecules. But even with complete scientific integrity there can be vexing practical obstacles to making satisfactory “standard test case” choices. For example, the stuff which is known most clearly can be annoyingly far from the regime which is of the most practical interest. For the proteins-in-solution problem which motivated my work as an undergraduate programmer, the experimentalists had very good results for protein crystals, but they have to impose pretty weird harsh conditions (like extreme salt concentrations) to get them to crystallize, and what we care about much more in practice is how proteins behave in milder environments more like the insides of living organisms.

I am generally underimpressed by the climate science folk — e.g., yesterday I was idly wondering whether a page like http://www.realclimate.org/index.php?p=11 would look more like Darwinism or Lysenkoism to someone who doesn’t already have my cynical view. And I generally support some common criticisms of modelling, like Steve McIntyre’s remarks somewhere about how blurring the distinction between measured and modelled/extrapolated results is a well-recognized sin in mining-related advocacy and should be here too. But I don’t think you should necessarily be shocked at the lack of standard test cases.

One less-standard criticism you might want to be shocked at, or at least very seriously disturbed by, is lack of attention to other kinds of verification of predictive power. It is weird for me, coming from molecular modeling (and a general interest in the history of science), to see people paying so much attention to matching a very small number of observations given the large number of parameters in the model. It is particularly weird because I currently have _The Minimum Description Length Principle_ checked out from the local university library (for my own machine-learning reasons, not for policy advocacy reasons). In my experience, when scientists have a theory which they believe to have a lot of predictive power and only a few obvious numbers of practical importance to test it against, they look intensely for less obvious practically unimportant observations to test it against. These numbers may not come up in, e.g., testimony before Congress, since they may be honest-to-goodness very hard to present in a few pretty pictures. But they come up all the time in more technical discussions.

E.g., in molecular modelling, people got very interested in higher-dimensional nuclear magnetic resonance data. Such NMR doesn’t naturally give the same kind of pretty every-atom-in-its-fixed-place pictures as X-ray crystallography, but it gives a great volume of weird detailed little constraints and correlations which could be cross-checked against a model. And even if what eeveryone cared about in practice was some simple high-level summary like the function of a protein (e.g., something like the O2 affinity of hemoglobin), nobody would present a new model with hundreds of parameters and focus only on its fit to a few-parameter curve of bound O2 vs. partial pressure of O2. If you can’t find datasets with very large numbers of degrees of freedom to compare against (like higher dimensional NMR, or various other kinds of spectroscopy) caution is in order. And if the modeller isn’t terribly interested in finding such datasets, perhaps great caution is in order.

Without knowing much specific about climate models, coming from chemical modelling I’d expect two general things of them. First, they’d regularly refer to at least one huge family of checkable things about regional distributions and correlations and so forth, rather larger than “the number of adjustable parameters” (a vague concept, but one which can be made more precise, as e.g. in the book I mentioned) in their models. Second, if they’re confident their models are precise enough to pick out interesting nonobvious phenomena (e.g., famously, that the net temperature response to C02 concentration is considerably higher than the first-order effect) then even if the mechanism doesn’t leave clear footprints in the current experimental datasets, it should be leave footprints in some imaginable experimental datasets that the climate folk are now passionately longing to measure (I dunno: nocturnal concentration fluctuations of an ionized derivative of CO2 over the Arctic and Antarctic). I don’t absolutely know that these things don’t exist, but I’ve spent some hours surfing RealClimate without noticing any hints that they do. (And if molecular modelling had been subject to the same level of informed controversy as climate modelling, I’d’ve expected that a few hours surfing the RealBiomolecule site would have given many such hints.)

I’d be reassured to see climate modellers hammering on how detailed interesting regularities in experimental data (existing or wished-for) are explained or predicted by their model: something like the (honestly exasperated) way biologists refer to all the megabytes of detail revealed by DNA sequencing and other molecular biology which just keeps matching the constraints of Darwin’s model. So far, that honestly exasperated attitude hasn’t come through as strongly as I’d like.:-| I don’t expect the climate scientists to be angels: I’ve followed the creationism dispute for decades, and honest competent biologists are not immune to the temptation to be exasperated at distractions like their critics’ funding not coming from the holy NSF and their critics’ almighty credentials not being biology degrees. But biologists seldom get so fascinated by distractions that they forget to refer to fundamentals like the enormous volume of detailed regularities in nature that the consensus model matches.

MarkW
Posted Feb 4, 2008 at 2:07 PM | Permalink

lucia,

If climate models are to be used to learn what it is we don’t know about climte, then I agree with you vis a vis V&V.
If climate models are going to be used as the justification for restructuring the entire world’s economy, then nuclear/avionic levels of V&V are the absolute minimum that I am going to demand.

The climateers can’t have it both ways.

Judith Curry
Posted Feb 4, 2008 at 3:39 PM | Permalink

Re V&V, probably the model with the best documentation is ECMWF (which is the same dynamical core and mostly the same parameterizations of ECHAM climate model). Here is the link to the full archive of their technical notes. Read all this and let me know if you have confidence in the model.

http://www.ecmwf.int/publications/
specifically, the technical memos and technical reports

Judith Curry
Posted Feb 4, 2008 at 5:50 PM | Permalink

For those of you taking potshots at the models, going to the ECMWF site (#32) is a must, even if you just read the titles of the tech notes. This will give you an idea of what goes into these models and how they are evaluated. Rejecting the models because of the lack of a standardized set of tests is irrational.

Scott-in-WA has some sense of the reality of climate modelling. You will really need to crank up the activity in the tip jar to pay for the development and implementation of standardized tests for each element of a climate model. In the best of all worlds, would this be done? yes. But actually figuring out how to do this for such a complex numerical system is no easy task, and then getting people to agree on what tests to actually use is probably hopeless, and finding resources to fund such an activity is a fantasy. Unfortunate, but this is reality.

steven mosher
Posted Feb 4, 2008 at 6:43 PM | Permalink

re 37. Judy judy judy.

“For those of you taking potshots at the models, going to the ECMWF site (#32) is a must, even if you just read the titles of the tech notes. This will give you an idea of what goes into these models and how they are evaluated. Rejecting the models because of the lack of a standardized set of tests is irrational.”

1. I have slogged through almost the entire 100K lines of ModelE. Now I am I starting On the MIT GCM which is much easier. So, Some of us have earned our potshots. In walking through ModelE I found nothing to reccommend it. No test cases. No test suites. No test drivers. No unit test. No standardized test. At one point gavin directed me to a site of “test data”. I found errata exposing monumentaly stupid programing blunders that your worst GT undergraduate programing student wouldnt commit to a daily build after an all night bender
Worse, when I requested access to the IPCC data, I was denied. Private citizens cannot get access to this data
. You want to talk about irrational. Irrational is this: no spec. no coding standard. no test plan. No verification. No validation. No manual. No documentation. No public access. no accountability.

I’ll link some climate modelers in a bit saying essentially the same thing. you can potshot them

“Scott-in-WA has some sense of the reality of climate modelling. You will really need to crank up the activity in the tip jar to pay for the development and implementation of standardized tests for each element of a climate model.”

I ran cocomo which nasa uses to estimate a total rewrite of ModelE. With a full V&V its less than 10Million dollars. It is the responsibility of program manangers within nasa to make the appropriate budget requests. The problem is they dont value transparency and openness and testing and accountablity.
Ask the guys on challenger. opps they are dead. pity that. The explosion was pretty however.

Judith Curry
Posted Feb 4, 2008 at 7:19 PM | Permalink

I agree that anyone slogging through a GCM code is entitled to make potshots. But there is a WORLD of difference between the ECMWF model and the NASA GISS model. I encourage you to look at the documentation of what is regarded to be the best atmospheric model in the world. ECMWF puts NASA and NOAA to shame.

A model of the complexity of global models is never going to be perfect. What can we learn from an imperfect climate model? I refer you to a paper by Leonard Smith, who is somewhat of a guru in the field dynamical systems, their simulation, and applications to atmospheric models
http://www2.maths.ox.ac.uk/~lenny/PNAS.ps

Pat Frank
Posted Feb 4, 2008 at 8:40 PM | Permalink

#41 — “I agree that anyone slogging through a GCM code is entitled to make potshots.”

Sufficient, but not necessary. Anyone who can appreciate the model errors documented in the 4AR Chapter 8 and Chapter 8 Supplemental, which show that GCMs not only make large intrinsic errors when tested against observables but also that different high-resolution GCMs can make large-scale errors of the opposite sign when tested against the very same observable, is entitled to make potshots. These GCMs all presumably include the analogous physics and are parameterized with best-guess estimates. Nevertheless, the error residuals vary from GCM to GCM, often wildly. This is hardly cause for confidence in prediction.

Steve Hempell
Posted Feb 5, 2008 at 12:44 AM | Permalink

I just read the Dr. Syun-Ichi Akasofu paper that is up on ICECAP website. In it he states “we asked the IPCC arctic group (consisting of 14 sub-groups headedby V. Kattsov) to “hindcast” geographic distribution of the temperature change during the last half of the last century.” The result: “We were surprised at the difference between the two diagrams in Figure 11b. If both were reasonably accurate, they should look alike. Ideally, the pattern of change modeled by the GCMs should be identical or very similar to the pattern seen in the measured data. We assumed that the present GCMs would reproduce the observed pattern with at least reasonable fidelity. However, we found that there was no resemblance at all, even qualitatively.” I would have presumed the IPCC would have used their best GCM.

He then goes on to give two examples of how to use “GCM results to identify natural changes of unknown causes.” That was a nice twist.

Doesn’t fill you with a warm fuzzy feeling about GCMs.

Tom Vonk
Posted Feb 5, 2008 at 10:53 AM | Permalink

OK so I took my time and looked at that ECMWF that’s supposed to be the 8th marvel of the world . After having looked at the titles of all the documents accessible on line , I selected the Radiation Transfer that I know well . More precisely “The technical memorandum 539 . Recent Advances in Radiation Transfer Parameters .”

I took a stance of an independent expert charged to audit this piece of document .
I must say that the result was depressive – it has all the flaws that have already been mentioned in this thread . Specifically the part that should consist to compare the new McRad (new model) prediction to reality with a precise description of the experimental detail and data treatment is completely missing .

1)
They want to introduce a random cloud generator .
Besides the generic method consisting to compare the model vs model runs (which would consist to compare error to error if the models were inadequate) there is a weak attempt at comparing runs with reality .
So here with cloudiness and CERES is mentionned in that respect .
However the CERES equatorial satelite doesn’t work and the other 2 are at polar orbits what means that you get readings always at the same time of the day .
A question forces itself upon us – what kind of data did they use ? What kind of “cloudiness” did they extract from CERES ? How did they (re)treat it ?
Shouldn’t that be at least mentionned in a document that recommends nothing less than to redo the biggest part of a radiative model ?
Well it is not .
The argument for the “cloud generator” is weak and is supposed to be supported by one work mentionned as reference .
This question being central , something better than only a reference should be in the report .

2)
They also want to model aerosols .
Here is mentionned Modis Channel – same satellites as CERES , same remarks .
Yet Modis on top of the above caveat gives neither vertical distribution of aerosols nor their nature .
So what is it used for , what data treatment , what period ?
That is not mentioned either .
On the other hand valuable time and space is wasted on an anecdote showing a Modis picture of a sand plume coming from Sahara to Europe and a chart of a simulation .
There is a qualitative agreement between both .
Of course it doesn’t impress anybody because already the Romans knew 2000 years ago that when the wind was coming from the south and the weather was fine , sand from Sahara could come to Europe .
They didn’t need satellites and multimillion computers .
So also on this topic no adequate argument is made about why the skill of the new model should represent the reality better .
No attempt is made on justifying the statistics used .
For example what time averaging is relevant and what bias there may be ?

3)
Of course Hitran is used .
Therefore collisionnaly induced emissions/absorptions (and no it is neither 0 nor negligible) are ignored because they are not in Hitran . I already noticed that people use Hitran like a magical word – if you say Hitran , you access to the Nirvana of infinite accuracy and the world where “the radiative transfer is a settled science” .
Well it is not in the famous details where the devil is .
Also CFCs are mentionned .
We know about everything about their radiative properties but we know very little about their distribution and have practically no past data .

4)
The list of references and charts is almost as long as the text and that is never a good sign .

Conclusion after 2 hours of reading the document .
It is neither V nor V .
The way in which the document is presented doesn’t seek to present the reader with a logical argumentation structure , doesn’t separate the essential from secondary , doesn’t explain and structure experimental data and its treatment when applicable .
In short the reader is either supposed to be one of the 19 people (yes they wrote about 1 page per person) who wrote the report or to have unlimited faith in the 19 people .
The reader can neither validate the recommendation presented (the use of a new model) nor can he even in principle redo/verify any part or statement in the report .
If I was charged with a real audit of this document (or generally with other documents made by those 19 people) I would add :

- the above doesn’t mean that the 19 people don’t know what they do . They probably do .
- the above is not an exhausting analysis . Many more flaws , defaults and methodological errors would probably be appear after a thorough examination
- an advice on 1 document doesn’t represent a synthesis on all documents . There may be others of a much better quality
- but … between us , if you ask me , they are really sloppy

Judith Curry
Posted Feb 5, 2008 at 12:09 PM | Permalink

i just did an interesting exercise, google the the three words (no quotes)
ECMWF model validation (18,000 hits)
ECMWF model verification (10,400 hits)

reproduce this exercise, cruise the titles of the first few pages of hits, and you will get some sense of the complexity of this challenge and the huge amount of work that has been done on this issue.

lucia
Posted Feb 5, 2008 at 1:47 PM | Permalink

Judy:
Yes. Some people are arguing about whether or not the GCM’s are perfect. Other people really are discussing the V&V. These are entirely separate issues.

GCM’s can’t be perfect. The people asking for that will never be convinced by a GCM. But that’s probably only a very small fraction.

GCM’s are complex and so more difficult to validate than models describing simpler things.

However, neither complexity nor the impossibility of perfection interferes with the “do-ability” of a V&V! The goal of V&V is absolutely not to create a perfect model, and complexity is simply not a problem.

If you can write a code, you can do a V&V. Complexity doesn’t actually affect form Verification very much; it only means one requires longer document because there are more modules to test. If a model is approximate and has difficulties (like GCM’s), or the results are difficult to interpret, that is reflected in the narratives and figures contained in the Validation document. All the caveats you describe here would be included in the validation document in written from where third parties could read the caveats. That’s a purpose of the validation.

But with regard to this:

In the case of the ECMWF model, the V&V is very clear (read the technical notes, memos).

No. It’s nto at all clear in those notes and memos.

I suspect the V&V for ECMWF may well be very clearly stated somewhere. In so far as a model is used for weather prediction broadcast to the public, and it’s predictions have a direct impact on public safety, funding and regulatory agencies probably do require V&V for any model.

That said:, there are zillions of links at the site you point to. I’ve clicked on 20 or so links to reports and memos and skimmed. They look like good reports. They look like decent science. Unfortunately, with regard to the discussion going on in this thread, absolutely nothing I clicked remotely resembles a Verification. A small fraction of documents contain snippets that would belong in validations, but those would be stupendously incomplete as validations.

Could you point to an individual link that you think looks like a verification?

If you could point to a specific one, this get us all on the same page. Otherwise, right now , it looks like everyone is talking past each other.

Meanwhile– Dan, or Tom Vonk, could you supply Judy with examples of formal verification documents? (I know this is difficult since most are only available in the grey literature and they are generally a set of documents. At that, they represent such a small fraction of the grey literature that you need to know a named party did one. But if either of you was involved in one, then that might help Judy understand.)

(FWIW, in some ways, I’m agnostic on this isue. I frankly don’t give a hoot whether GCM’s are verified or validated because I don’t base any of my judgment about AGW on the results of GCM’s. I rely on simpler energy balance models supported by temperature trends, and some physical arguments. I think the balance of the evidence points toward warming caused by human activity.

Nevertheless, if documents describing formal validation and verification of GCM’s do exist (and I’m betting Quatloos they don’t,) it would be useful to the never ending discussion if someone could identify those specific documents.

Identifying alternative documents of the sort that appear in academic journals and incorrectly calling them V&V just won’t do for people who now what V&V is and want to see V&V. (It’s a bit like giving someone chocolate ice cream when they want chocolate fudge and saying “See, I gave you chocolate! And anyway, ice cream is better than fudge– you should want fudge!” Ice cream is tasty, but the customer wants fudge.)

Only bringing the V&V to table can bring this bitter arguments about whether or not V&V has been done.

[i.e. Don't take what someone tells you for granted]

[And now my personal favourite]

Craig Loehle
Posted Feb 4, 2008 at 8:14 AM | Permalink

For my Ph.D. thesis I developed a model of a grazing ecosystem in Pascal. It ran beautifully, but one curious bug was that when I simulated adding cows to the range, it stopped raining. Pretty realistic, a rancher would say, but really due to utilizing dynamic arrays of unequal length, so the added item stomped on some of the memory. I would say my code from 1981 was much better structured and documented than Model E etc. Sad but true.

30 Aug 2010: Analysis
“The Effect of Clouds on Climate: A Key Mystery for Researchers” by michael d. lemonick

http://e360.yale.edu/feature/the_effect_of_clouds_on_climate_a_key_mystery_for_researchers/2313/

Excerpt

A major problem facing climate modelers is extrapolating the behavior and impacts of clouds from an individual level to a regional scale. The resolution of climate models — the grid boxes researchers divide the atmosphere into for the purposes of simulations, analogous to the pixels that make up a digital image — is much bigger than any individual cloud. And, says Randall, what goes on inside those grid boxes in the real world varies widely depending on local conditions, including the type of particles around which water vapor condenses to form clouds.

And

Randall cited one example of a huge regional cloud phenomenon in the tropics whose behavior in a warming world is uncertain. Known as the Madden-Julian Oscillation, the phenomenon involves the formation of enormous systems of thunderstorms over the oceans, driving weather patterns affecting millions of people. “Most models do not even produce this phenomenon, even though it’s the largest feature in tropical atmosphere,” said Randall. “If you’re missing that, you’re missing an important thing. We’d like to be able to predict whether it will get stronger and more common, or less.”

Climate scientists would obviously be far more confident in the models if the simulations of cloud behavior matched the real world. But just as with the computer models, observations of clouds have been too spotty to get an accurate picture of what’s going on. Meteorologists have been taking reasonably consistent readings of temperatures around the world for more than a century, which is why the Intergovernmental Panel on Climate Change can talk so confidently about the fact of global warming. But there’s no comparable data set on clouds, which means that “there’s really nothing we can say about how clouds have changed globally over the 20th century,” says Amy Clement, a climatologist at the University of Miami.

Take a deep breath and actuate BS filter before reading the entire article – the author assumes a “warming world” but readable nonetheless.

05 Oct 2010: Analysis
“On Climate Models, the Case For Living with Uncertainties” by fred pearce

http://e360.yale.edu/feature/on_climate_models_the_case_for_living_with_uncertainties/2325/

Excerpt

Clearly, concerns about how climate scientists handle complex issues of scientific uncertainty are set to escalate. They were highlighted in a report about IPCC procedures published in late August in response to growing criticism about IPCC errors. The report highlighted distortions and exaggerations in IPCC reports, many of which involved not correctly representing uncertainty about specific predictions.

But efforts to rectify the problems in the next IPCC climate-science assessment (AR5) are likely to further shake public confidence in the reliability of IPCC climate forecasts.

Last January, Trenberth, head of climate analysis at the National Center for Atmospheric Research in Boulder, Colo., published a little-noticed commentary in Nature online. Headlined “More Knowledge, Less Certainty,” it warned that “the uncertainty in AR5’s predictions and projections will be much greater than in previous IPCC reports.” He added that “this could present a major problem for public understanding of climate change.” He can say that again.

Richard C2 October 6, 2010 at 9:01 am

“So you are happy to accept that you are wrong in the particular respects I have pointed out? Or are you just not prepared to continue the discussion”

Whether you or I are right or wrong at this juncture is immaterial in this 1:1 debate.

The appropriate international forum for all the issues we are covering has now opened up at Judith Curry’s blog.

I suggest that our respective points of view are presented on that forum where deficiencies of argument are immediately taken to task by experts. Given that the focus of the forum is uncertainty in the models, I think you will find that you are defending the indefensible.

So I will be continuing this discussion on that forum (but not here) and whether you contribute or not there is up to you.

Thank you for the opportunity to present my point of view on your Blog; this discussion has been much more stimulating than preaching to the converted at sceptic sites.

http://hot-topic.co.nz/no-pressure-1010-on-the-button/#comment-18535