Bishop Hill GCMs and public policy
Aug 24, 2014 Climate: Models Climate: Parliament In the thread beneath the posting about the Chen and Tung paper, Richard Betts left a comment that I thought was interesting and worthy of further thought.
Bish, as always I am slightly bemused over why you think GCMs are so central to climate policy.
Everyone* agrees that the greenhouse effect is real, and that CO2 is a greenhouse gas.
Everyone* agrees that CO2 rise is anthropogenic
Everyone** agrees that we can't predict the long-term response of the climate to ongoing CO2 rise with great accuracy. It could be large, it could be small. We don't know. The old-style energy balance models got us this far. We can't be certain of large changes in future, but can't rule them out either.
So climate mitigation policy is a political judgement based on what policymakers think carries the greater risk in the future - decarbonising or not decarbonising.A primary aim of developing GCMs these days is to improve forecasts of regional climate on nearer-term timescales (seasons, year and a couple of decades) in order to inform contingency planning and adaptation (and also simply to increase understanding of the climate system by seeing how well forecasts based on current understanding stack up against observations, and then futher refining the models). Clearly, contingency planning and adaptation need to be done in the face of large uncertainty.
*OK so not quite everyone, but everyone who has thought about it to any reasonable extent
**Apart from a few who think that observations of a decade or three of small forcing can be extrapolated to indicate the response to long-term larger forcing with confidence.
So, let me try to explain why I think GCMs are so important to the policy debate.
Let us start by considering climate sensitivity. As readers here know, the official IPCC position on climate sensitivity is largely based on the GCMs. This time round we have had some minor concessions to observational estimates, but a significant proportion of the probability density of the observational studies remains outwith the IPCC's likely range of 1.5-4.5°C. Proponents of GCMs might counter that the upper end of the GCMs are ignored too, but I would suggest that one should conclude that an ECS of 5-6°C in the light of temperature history.
Estimates of climate sensitivity - and therefore in practice GCM estimates of climate sensitivity - directly inform estimates of the social cost of carbon. So when people like Chris Hope are arguing for a carbon tax of $100/tCO2, this is a function of GCMs. I recall, I hope correctly, that Chris suggested a figure of $18/tCO2 if one used an ECS of 1.6, in line with observational estimates. This matters of course, because the policy response, if any, to an $18 problem is significantly different to that for a $100 problem.
Wherever we look in the interactions between scientists and politicians on climate questions, we see an emphasis on catastrophe. We see no confessions of ignorance, but only occasional reference to uncertainties. Here's some notes of Tim Palmer addressing the All-Party Climate Change Group:
With the amount of carbon dioxide already in the atmosphere, future emissions will need to be reduced by half to that of historical emissions to limit global average temperature rise to 2°C. However, if emissions are not curbed (under the business as usual scenario), the amount of carbon dioxide in the atmosphere will be three times the historical emissions and the temperatures might rise up to 4°C.
And on the other hand they might not. This idea does not, however, seem to have been put forward for consideration.
Readers might also wonder what explanations were given to our political masters on the credibility of the GCMs. Here's what Palmer said:
Climate models are only flawed only if the basic principles of physics are, but they can be improved. Many components of the climate system could be better quantified and therefore allow for greater parameterisation in the models to make the models more accurate. Additionally increasing the resolution of models to allow them to model processes at a finer scale, again increasing the accuracy of the results. However, advances in computing technologies would be needed to perform all the necessary calculations. However, although the accuracy of predictions could be improved, the underlying processes of the models are accurate.
Apart from the transport of heat to the deep ocean, if Friday's paper from Chen and Tung is to be believed.
You can see that policymakers are getting a thoroughly biased picture of what GCMs can do and whether they are reliable or not. They are also getting a thoroughly biased picture of the cost of climate change based on the output of those GCMs. They are simply not being asked to consider the possibility that warming might be negligible or non-existent or that the models could be complete and utter junk. They are not told about the aerosol fudging or the GCMs' ongoing failures.
And this is just scratching the surface.
[BTW: Could commenters who like to amuse themselves by baiting Richard please refrain from so doing!]
Reader Comments (306)
> Once again, you abuse the word "chaotically". It has a specific technical meaning.
http://plato.stanford.edu/entries/chaos/
Much as there is no good or safe way to critique a staunch feminist with bad ideas - you would be labeled a misogynist - there isn't one to question a physicist for his support of climate orthodoxy. You would be labeled 'anti-science'. 'Physics' is a crutch.
Spence_UK,
I thought we were discussing climate modelling. You appear to be the one claiming that chaos is so important as to dominate the evolution of our climate. My claim is that although many aspects are chaotic, that does not mean that we are incapable of use climate models to understand the evolution of climate trends.
Well, this came from your apparent suggestion that your - and others - views about the role (and significance of) chaos is being ignored by climate modellers. My point was that either you're right and have noticed something that hundreds/thousands of professional scientists have ignored, or they have considered this, have recognised the significance (or lack thereof), and you just haven't realised that they've done so. Also, all the work you mention is 20 years old or older. I have nothing against papers published 20 years ago or more, but I doubt that they're going to be able to tell me all that much about climate models. Also, they appear to be papers about chaos. This isn't about chaos specifically, this is about whether or not the chaotic aspects of our climate are so dominant as to make climate modelling virtually useless. I suggest that this isn't the case and that the boundary conditions constrain the chaotic evolution. You appear to disagree.
I think you're putting words in my mouth. Your claim appeared to be that it was farcical to suggest that we could say anything about the hydrological cycle. My suggestion is that we can. If it gets warmer, the hydrological cycle will intensify. I'm not suggesting that this means we know precisely what impact this will have on precipitation, but if the hydrological cycle intensifies, we might expect regions that currently get a lot of rain to get more, and regions that currently get very little will get less. Less well understood doesn't mean not understood at all.
Willard
That page is trying to explain there is no agreed formal mathematical definition.
It should be obvious that a term can have a clear technical meaning even without an agreed mathematical formalisation. Your quote only shows your lack of understanding of the subject. Although I guess it shows you are quicker to resort to google than aTTP.
Not had time to view this yet, leap of faith, but I would expect that this is more like the response that people would expect to this post
Doug McNeall a talk on climate modelling
Shub,
I have no idea why you think that label you'd be given for the way you choose to critique me is "anti-science". That's nothing like how I'd describe it.
Physics, please. I referred to arguments, not people.
Any system or proposal attempting to incorporate variability into deterministic frameworks such as the one the IPCC uses has been consistently dismissed as being 'unphysical'. It is not just you.
Another one of your favourites: internal variability is/has to be thermodynamically neutral so it can be disregarded. Rebuttal: In a system exhibiting variability at timescales which the average consensus supporter demonstrates no conception of, how do you rule it out with confidence? The upward slope of a multi-centennially variable trend will look like a 'forced' trend at the decadal scale.
Shub,
If you really think I'm going to engage in a serious discussion with you, you're sorely mistaken. Why do you think I'd bother. I've tried and failed too many times before. You may as well just carry on being rude and insulting. It's your forte, as far as I can tell.
Spence_UK,
I was looking back through some of the comments and read Carrick's from 6.31pm yesterday. I broadly agree with that comment and is essentially what I've been trying to say. Why is it that you seem to agree with what Carrick is saying, but not with what I'm saying? Maybe I'm not explaining myself clearly enough but this
essentially describes what I've been trying to say. Of course, if you're just disagreeing with me because that is what you're obliged to do, carry on.
Bishop Hill
Thanks for highlighting this. It's interesting to see your response (and how you contract your narrative against climate science).
I'd initially thought that you were claiming that the very need for any kind of climate policy was based on GCMs. Clearly it isn't, for the reasons I stated, but it seems this isn't your point here anyway. You seem to be moving a step further and talking about the importance of GCMs to the details of climate policy (eg. a carbon tax). Here I do partially agree with you - GCMs do of course play a role in the details, as they help with understanding the climate system, but they are by no means the only source of information. Moreover, I don't think the examples you give would be substantially affected if we didn't have GCMs.
You state:
No. The concept of Equilibrium Climate Sensitivity was basically developed as a simple metric of how climate models responded to increased GHGs, so the models could be compared. It is not something that we will ever be able to measure directly in the real world, as we won't ever see a neat doubling of CO2 with no other changes happening. But instrumental and palaeoclimate records have been used to try to constrain ECS, and the AR5 'likely range' of 1.5 to 4.5C is based on these constraints, not models - see AR5 WG1 Chapter 10 section 10.8.2.
So the simple models used by guys like Chris Hope wouldn't be any different if they used the IPCC data(not model)-constrained range of values for ECS.
I wouldn't say it was 'minor' - there's quite an extensive discussion - and in any case it's not 'this time round' either, as there's also quite an extensive discussion in AR4 WG1 Chapter 9". I'm surprised you've forgotten about that, since Nic Lewis commented on it some years ago, and it was discussed at Climate Audit and I'm sure we discussed it here too a while ago.
Also you are wrong in your claims that climate scientists keep policymakers in the dark about uncertainties. The IPCC SPMs are full of clear statements differentiating the more certain and less certain aspects of the science - that's why all those confidence statements are there. For example the IPCC AR5 WG2 SPM says:
and
Your narrative of "climate scientists only use models: climate scientists hide uncertainties from policymakers" is false. We use observations too, and are open about uncertainties. That's why policymakers have to make a judgement call in the face of these uncertainties, having been made aware of the full range of possible outcomes for their different policy choices.
>That page is trying to explain there is no agreed formal mathematical definition.
My quote does. The entry itself explains way more than that. For instance, it refutes two folklores:
Unless one is willing to assume an infinitely long climate, sensitive dependence, the main property one would like to model, is probelmatic.
Also, the entry shows that we still lack a chaos **theory**, that choosing chaotic models does not improve are chances to confirm them, and that embracing a chaotic framework has implications regarding determinism. The latter point is rather annoying, considering that Koutsoyannis use the good old falsification argument against mainstream models. One does not simply pray both the God of Chaos and the Pope of deductivism in Mordor.
The entry also mentions other interesting things.
***
> It should be obvious that a term can have a clear technical meaning even without an agreed mathematical formalisation.
Yes, and it should be obvious that the concept of chaos is not that clear. In any case, my point was to show that there was many concepts if chaos.This point matters because there's no definition here:
http://www.climatedialogue.org/long-term-persistence-and-trend-significance/
Nor can we find one there:
http://onlinelibrary.wiley.com/enhanced/doi/10.1029/2005GL024476/
Hurst coefficients and LPD do not suffice. Here's the best I could find:
> For such a long time we thought that most data must have a normal distribution and therefore that the mean is meaningful. With the perfect vision of hindsight, this is a bit odd. Much of the world around us is not normal. It consists of fractals, for example, mountain ranges, river basins, and broccoli. These fractals have an ever larger number of ever smaller pieces. Yet, we insisted on modeling many things as if all of their pieces had only one size. Similarly, it is also a bit odd that we thought for so long that if we knew the rules of the world that we could completely predict its future. The endless variety of chess games, the patterns of snowflakes, and the complexity of human relationships might have suggested something different to us.
http://www.ccs.fau.edu/~liebovitch/complexity-20.html
I humbly submit that this kind of argument runs the risk of conflating models and reality. Even if time is continuous, there' no reason for us to stop using atomic clocks. The same applies to random generators, which are not really random. Just as Koutsoyannis handwaves to Kolmogorov's work on chaos, we could point at his superposition theorem to argue that all this is much ado about very little.
The answer to the original question re the importance of GCMs could be much shorter:
"Because they are all you've got."
Richard,
Glad to see you respond.
Part of the problem with communicating uncertainty is that our policy makers by their very political nature rely on popularity which generally falls to the media to provide. As their sole role, the media, is to sell advertising then the stories that are portrayed are normally sensationalistic which gives the public and the policy makers the impression that "THIS" is what the scientists are saying without normally any reference whatsoever to uncertainty. Perhaps I am just reading the wrong stories.
There have been many reports of the intention to portray uncertainty more readily but it just doesn't seem to happen, and I am covering all distinctions in the debate, so why are we not getting more of a sober picture from blogs like the Met Office's own news blog or governmental departments? Or do you think that the uncertainty is well portrayed by them already?
Richard Betts,
Thank you for taking the time to respond.
One problem that seems clear in your defense is that in fact the climate science community has not at all been moderate in deiscussing the uncertainties. And as climategate and public actions as well demonstrate, many climate science opinion leaders work hard to suppress discussion about uncertainty.
Another is that the range you offer- 1.5 to 4.5 oC is so large as to be largely meaningless.
Richard Betts:
I can't agree with you fully here. The performances of the GCMs are much worse than is alluded to by the modelers and much more open to subjective decision making (e.g. "tuning") than is allowed by many of the modelers, or as described by the IPCC AR5. Gavin for example has famously claimed that the models are not tuned to temperature.
Possibly it wouldn't make a huge difference to the AR5 the GCMs weren't relied on directly by the IPCC, but we both know they are relied on in the other modeling. So there's a bit of circularity in your argument:
It's not just policy makers who put too much weight and use (IMO) improperly these complex models. Moreover climate model funding is heavily weighted towards these behemoths. Other approaches get starved because they aren't based on massive programs that require huge computational resources to run.
Perhaps this isn't a problem in England, but in the US, meteorological forecasting funding has been savaged in order to move more dollars into GCM simulations. It's hard to argue that an over-reliance on GCMs by policy makers hasn't occurred when you see a picture like this. It's hard to argue that this happened in spite of cautions by and against the advise of the researchers who received the extra funding.
Improved meteorological forecasting has huge immediate economic implications. But not only is the scientific effort getting denuded here, there are traceable economic impacts associated with the poor decision making by the policy makers.
Carrick
Thanks for your comment.
Meteorological forecasting funding and GCM funding are the same. GCM stands for General Circulation Model, i.e.: the models simulate the general circulation of the atmosphere (and oceans). GCMs are used for weather forecasting. In the Met Office, we have the Unified Model used for both operational weather forecasting and climate modelling (the climate model names like HadCM3, HadGEM2 etc are names of particular configurations of the model, defined according to the resolution of the grid and which set up updates is included).
Absolutely, I agree 100%. A large part of the Met Office GCM development activity goes into improving the models for use in seasonal forecasting at regional scales. As we all know, this is an area where the models are not always particularly useful (!) for everyday purposes, but advances are being made and there could be huge benefits if we can crack it….
Not in my experience! (see above)
But thanks for your comment.
Lord Beaverbrook
I share your despair of the media. They are all as bad as each other, whether it's the Guardian or the Daily Mail. They all have to shore up their end of the artificially-polarised debate.
Hunter
I don't think a large range is meaningless - it just shows that policy responses have to be resilient to large uncertainties (in either direction).
Cheers
Richard
Richard, you must realize that if model forecasts had small error bars (and temperatures landed within them by some miracle) the climate establishment claims credit for successful prediction, and if models threw up wide ranges in their forecasts the establishment resorts to the option of saying this shows 'policy responses have to be resilient to large uncertainties (in either direction).'
The former happened during AR3 and AR4 and the latter, in the present period.
Large error bars means lesser confidence in forecasts. But not in climate science, where small large error bars each have their own separate meaning.
This is timely:
http://www.rotman.uwo.ca/2014/call-for-abstracts-knowledge-and-models-in-climate-science-philosophical-historical-and-scientific-perspectives/
Call for Abstracts: Knowledge and Models in Climate Science: Philosophical, Historical, and Scientific Perspectives
We are delighted to announce that the Rotman Institute of Philosophy will host its second annual conference, Knowledge and Models in Climate Science, on Oct. 24-26, 2014. The conference will bring together researchers to discuss the use of models in understanding the climate from a variety of disciplinary perspectives. Models and computer simulations are essential not only for understanding the factors determining climate processes, but also for evaluating how changes in climate will affect ecosystems and human societies. Recent gains in modeling precision and realism have allowed climate researchers to address both questions more confidently, yet there are many remaining sources of uncertainty. Participants in the conference will explore different approaches to modeling in order to gain a better understanding of the nature, strengths and limitations of the knowledge it produces, and build a better understanding of the means by which these uncertainties can be managed.
Now that Richard is head of helixclimate (High-End cLimate Impacts and Extremes) – another EU taxpayer funded boondoggle – the only real uncertainty is whether warming will be 2, 4 or 6 degrees C. Isn't that right Richard? Nice trip to Delhi was it?
Richard Betts,
Of course range matters. att he low end of the range nothign much is happening and at the high things are possibhly pretty wild.
1.5 oC, (which I will grant you for the sake of this disscussion but is likely quite on the high side) is a trivial situation. 4.5 oC is on the other hand extreme and would maybe actually deliver some of the doom the climate catastrophe crowd has been braying on about for years.
As to the walk back from the GCM's as an integral part of the predictions climate scientists have been making, please at least admit that many of your colleagues were promoting the models and their ensembles as a way to show how terrible things were going to get. It is only since the models have failed that their seems to be a reconsideration of this. Something that would go far in this is for a person from the tribe to at least admit there is a likely chance that the current cliamte change is possibly going to actually do very little to Earth or its inhabitants.
Richard Betts, thanks for the comments. Regarding this:
From my understanding, I would say "technically no".
As you explain in your comments, GCMs attempt to include the full physics of atmospheric circulation. Meteorological models don't need to do this. They can use, e.g., ocean surface temperature data to forecast short-term atmospheric weather.
Mesoscale weather forecast models, such as the WRF model that we use for our own research, don't need to incorporate the physics of large scale flow, because the time scales for the large scale flow are much larger than the period that you are trying to make the forecast over.
As you go to extended forecasting and larger scale forecasting, it's obvious you will need to describe the evolution of large scale features in the atmosphere & oceans. But it's not obvious to me you need to use a full blown GCM to do this, for say Europe-scale forecasting and for periods of say three months.
I would guess simpler statistics-based models might be more reliable (in the metrological sense of the word), though were I working on this problem, I would likely still use GCMs to guide the development of the statistics-based model.
I'm not surprised. There are big differences in the way that US and European funding get handled.
I screwed up the link above. It was meant to point to Cliff Mass's blog. Repaired link
Climate change – your essential guide
It’s now clear that man-made greenhouse gases are causing climate change. The rate of change began as significant, has become alarming and is simply unsustainable in the long-term.
(...)
Road surfacing will melt unless replaced with different materials.
(...)
Are computer models reliable?
Yes. Computer models are an essential tool in understanding how the climate will
respond to changes in greenhouse gas concentrations, and other external effects,
such as solar output and volcanoes.
Computer models are the only reliable way to predict changes in climate. Their
reliability is tested by seeing if they are able to reproduce the past climate, which gives
scientists confidence that they can also predict the future.
(...)
Met Office Publication 2009
Purpose of models is clear, they enable warmists to argue from authority that dire consequences will occur unless CO2 is mitigated. It gives authority for 30 years or 50 years or by 2100, pegs to hang objectives on unless you want thermogeddon. Well the fox has been shot, the models are useless.
"Road surfacing will melt unless replaced with different materials."
Brilliant, road surfaces have been melting for the 60 odd years of my life, I didn't need a model to prove that, railway lines buckle, a solution was found by being smart, not because of a model.
Will we get more or less rain in the UK by 2070, will we need more or fewer reservoirs? It will not be answerd by a climate model but by looking at what happens vs demand etc etc etc.
I think you have made a mess of this post by assuming what you were trying to prove.
"the official IPCC position on climate sensitivity is largely based on the GCMs."
MikeN
Through AR4 the primary attribution argument has been based on two pillars: paleoclimate evidence and models.
Through AR4 and beyond, politicians and policy-makers have only referred to the support derived from models. None talk about problems with the whole framework of doing so.
The Bish is not making this up.
I just saw this:
http://wattsupwiththat.com/2014/08/26/a-lead-author-of-ipcc-ar5-downplays-importance-of-climate-models/#comment-1718374
Richard, you might want to add your comment above to the WUWT post.
Richard
It was very nice to have met you, albeit too briefly, at the Exeter climate conference. As you know I believe natural variability xplains most/ all of what we can observe today as the climate we can see today has all happened in the past as well.
In particular the evidence for extremes much greater than today can be seen in your own archives. There are huge instances of flooding, storms, droughs and heatwaves which make today's events look rather small fry.
I am collecting these together and would be pleased to share them with you, although Hubert lamb has been there before me.
I think we need to plan for the future by looking to the past. Our infrastructure was built for today's benign climate and wouldn't stand up to the extremes we can see from the past. The dawlish railway is a case in point. It was breached in its very first year of operation over 150 years ago and many times since . It is not resilient. If the storms of the 17th or 15th or 13th century were to return there would barely be a yard of track left along the coast.
Hope we can meet up again some time for a longer chat
Tonyb
We see once again the typical behavior of the cliamte catastrophe promtoers when confronted with the fialures of their own tools, methods or predictions: Denial. The plain truth is that the models have been used to justify every single climate treaty, law, agreement, associated peer review study showing some collateral damage resulting from the dangerous changes in climate, etc. etc. etc.
So now we have Richard Betts agreeing with skeptics.
Once again, skeptics are right: The models are not useful.
When will the climatocrats start admitting it?
From Martin A's post above attributed to the Met Office Chief Scientist
From Richard Betts quoted by BH
Seems 'everyone' does not include Julia?
Richard Betts - do you have any comment on the work of Bony and Stevens and whether the thrust of the critique they make of climate models is (or isn't) reflected in the discussions in ch9 of ar5? Thank you.
It has been obvious ab initio that GCM’s are inherently useless for forecasting future climate trends. Apart from the inherent impossibility of computing complex systems of multiple quasi independent variables, the IPCC models are in particular structurally flawed because they take no account of the natural periodicities in the climate especially as seen in the temperature data.
The quasi millennial ( 960- 80) periodicity shown in Figs 5 – 9 at
http://climatesense-norpag.blogspot.com
is the key to understanding and forecasting climate on time scales of human interest. Unless we know where we are with regard to the latest peak in this cycle we cannot even begin to make reasonable forecasts or approximate the effect of anthropogenic CO2.
The link above also provides estimates of the timing and amplitude of the possible coming cooling based on the reasonable and simple assumption that the recent peak in warming is a synchronous peak in both the 960 and 60 year natural solar driven cycles.
hmmmm . . .
Numerical climate simulators are scientific tools which provide key input to climate policy on mitigation and adaptation.
Palmer TN. 2014 More reliable forecasts with less precise computations: a fast-track route to cloud-resolved weather and climate simulators? Phil. Trans. R. Soc. A 372: 20130391. http://dx.doi.org/10.1098/rsta.2013.0391
Free open access paper.
More:
In addition to providing input into climate mitigation policy, global climate simulators have an important role to play in helping society worldwide adapt to climate change.
Yet even more:
Finally, reliable regional output from global climate simulators will be of paramount importance if society ever considers seriously the possibility of geoengineering climate, e.g. by spraying aerosols into the stratosphere.
That one is kind of scary, but he goes on to say:
As with the issues related to climate adaptation, however, our confidence in being able to estimate regional circulation changes arising from either human-induced climate change or geoengineering is rather poor [15].
It seems that some organization has really huge, ginormous plans for GCMs at the science-public policy interface.
And it seems that the purported degree of applications of GCMS to the public policy interface changes very, very rapidly with time. In the peer-reviewed literature, even.
"anti-science". That's nothing like how I'd describe it.
Aug 26, 2014 at 1:52 PM | Unregistered Commenter...and Then There's Physics
It's a phrase used by one or two AGW believers here relatively often.
If you are studying a chaotic system, you must follow the rules of studying chaotic systems, which may be different to other types of systems or classical approaches. If you can show that there are invariant properties of the attractor which allows you to use climate models to "understand the evolution of climate trends", then fine. As far as I am aware, nobody has ever done this (other than irrelevant analogies to low-dimensional chaotic systems).
Raising concerns that the complex dynamics of the system have not been properly addressed is not the same as claiming to be an "amazing polymath", and your insinuation is absurd. Also, I didn't claim it was ignored, I said I was unaware of a proper analysis - a rather different claim. And there is a simple solution to it, just point me to where it is answered. I won't hold my breath waiting.
No, I mentioned older papers to give you an idea of the origin of the ideas, then gave you a link which included many papers from the last decade, up to and including published this year.
Actually, that isn't what I claimed. I claimed there is much we can know and model about the hydrological cycle, and one of the links I gave you has a whole host of information on modelling the dynamics of the hydrological cycle from one of the world leading experts in the field.
Sorry, but that is just too funny. The cycle will "intensify", huh? Sounds intense. Not science, though.
I didn't agree with what Carrick originally said, but we did identify common ground in that we both agree the way in which the IPCC uses GCMs is flawed. Are you agreeing that the way the IPCC uses GCMs is flawed?
Willard,
Nothing you've written has any impact on the discussion with ATTP. ATTP was misusing the technical term chaos and nothing on your googled link changes that.
Yes, the link does address some common misconceptions. For example, numerical models of turbulence are continuous to the smallest scales, whereas the real world stops at the Kolmogorov microscale. This doesn't change anything - chaos theory still provides the best explanation for the limits of predictability and the fractal dynamics, and the predictability gets worse on longer timescales, not better, under these conditions. Fractals scale up as well as down and it is the longest scales that limit predictability. That is a level beyond what we are discussing here, but doesn't change a single thing about what I discussed with ATTP - it is all entirely irrelevant.
As for your swipe at Koutsoyiannis' work, of course it is easy to sit in the peanut gallery and heckle as you are doing here. But Koutsoyiannis' work is published in the peer-reviewed literature. ATTP demanded I should publish, apparently unaware that everything I have discussed here has already been published. So I'll just pull the reverse stunt; if you think it is wrong, then all you have to do is publish the refutation of this body of work.
Incidentally, in that climate dialogue, everything Dr K was saying was backed up by his papers. Obviously, it is not possible to recreate his papers in a blog debate like that, but nothing is stopping you from going and reading the papers. That said, apparently it was too much to expect the people engaging in the dialogue to read the papers, and I suspect this discussion will go the same way.
" Meteorological forecasting funding and GCM funding are the same. GCM
stands for General Circulation Model, i.e.: the models simulate the general
circulation of the atmosphere (and oceans). GCMs are used for weather
forecasting. In the Met Office"
Are virtual real time accurate observations not critical for a decent weather forecast? Putting half the GCM budget into better observational data would definitely improve the weather forecast. This is especially true for the UK where most of our weather, especially the hard to predict string of depressions tend to come in from the Atlantic where it is harder to get decent data.
Do you think it is time to stick with the models as they are and put all the money into getting better data?
Rob has just asked the question I was thinking.
we now have sat info/pics from space, even I can use this to predict/have a good estimate at what weather is coming our way +/- a bit.
from the above comments "GCMs are used for weather forecasting. In the Met Office" is the small 's' for satellite ?
ps- nice to to see the TV weather persons using "uncertain" & colder in non urban areas routinely while stressing the hottest temps ever at some place near London (airport maybe ?)
There have been past examples of Establishment science scares.
One that has uncanny similarities was documented in plausible, exquisite detail by Edith Efron <The Apocalyptics> 1964.
This scare said that the USA was to see a large epidemic of human cancers caused by man-made chemicals.
A particular and expensive bureaucracy was assembled, 1965 -1985. The EPA was active, a score of Federal Acts introduced or amended, regulations were there aplenty, the public were fed stories of impending doom by a compliant press that was soft on investigation.
The people driving this were dominantly from the green intellectual left.
There was model after model. None really advanced core human knowledge, except in the negative sense of "It is not this mechanism."
...............
There is no such epidemic. The scientists and their models were wrong. They had placed far too much emphasis on extrapolating animal models to humans.
The end came with some central scientists such as Bruce Ames 'defecting'. (Precedent, Richard?).
A major philosophic failure in hindsight is, "They investigated in the wrong order".
They investigated anthropogenic chemicals before they had investigated, in similar detail, natural chemicals (and models).
...........
Your understanding of global warming is incomplete if you have not studied this earlier example of poor science leading to ries that the end of the world is nigh.
Correction - Efron's book was issued in 1984, not 1964.
Geoff,
That is an amazing book. I am ordering it now.
If one was to consider where science left the rails, I think two intertwined events events besides the climate obsession would be notable:
1- The acceptance of Malthusian hype as personified in Paul Ehrlich's execrable work
2- The embrace of the failings Ms. Efron apparently outlines.
In both cases science has failed so far to self-correct.
The climate obsessed's ability to dominate the public square was enabled in no small part by the deleterious influences of the first two. I am really looking forward to this book.
" we now have sat info/pics from space, even I can use this to predict/have a
good estimate at what weather is coming our way +/- a bit."
hunter, it is tricky to see that much under the cloud which I believe can have quite a large effect especially on things like rainfall patterns. From an anecdotal point of view, MO forecasts that cross the Pennines seem pretty poor to me. Does anyone know how weather forecasting observations/data has changed over the last few decades?
Spence_UK
Really, I don't quite remember that. I know you clearly think that (because thinking anything else would probably be too much for you to consider) but I don't think I misused the term chaos. I may not have been able to answer all your posed questions about chaos, but I think I understand the concept of deterministic chaos. You should possible look up the concept of energy conservation. Even chaotic systems have to obey that.
No, not really. Probably not perfect, but I don't think it's entirely flawed either (not everything is black and white). Of course, I've just realised that this discussion wasn't actually about GCMs as such, it was about bashing the IPCC in any way possible. If only you'd just made it clearer that that was your goal in the beginning, I could have avoided wasting so much of my time.
Don't know if there is any more steam in this thread so big thanks to Spence and Carrick. Your patience with ATTP is commendable.
Water,
Haven't really interacted much with Carrick on this thread, but if this is Spence being patient, I'd hate to see him not being patient. Admittedly, in my experience the dictionaries used by pseudo-skeptics appears quite different to the dictionary I normally use, so maybe your meaning of the term "patience" is different to mine.
" Admittedly, in my experience the dictionaries used by pseudo-skeptics appears quite different to the dictionary I normally use, so maybe your meaning of the term "patience" is different to mine."
attp - with slyly derogatory sentences like that, you don't come across too well.
I think you have a simplistic understanding of what chaos is, which is founded in the basics, but not the correct technical usage of the terminology (which to be fair I occasionally get wrong as well) and not to a depth where, for example, the limits of predictability and fractal dynamics kick in, and how to objectively assess these things. Incidentally, willards link also discusses fractal dynamics extensively; the bits he quoted were irrelevant to the discussion (the presence of Kolmogorov microscales do not change the fundamental limits of predictability of the climate system), but the bits about fractal dynamics are directly pertinent to the current discussion.
Re: my discussion with Carrick and GCMs:
Then you do not find common ground where Carrick and I did find common ground. That might explain your surprise that I was more in agreement with Carrick than with you. And yes, the misuse of models in the way the IPCC (and to significant extent, the climate science community) is flawed, and it is a black-and-white issue. Prof Koutsoyiannis has some nice presentations on the topic of why Hurst-Kolmogorov (fractal) dynamics make the current application of GCMs simply wrong, for example:
http://www.itia.ntua.gr/getfile/1441/1/documents/2014EGU_Uncertainty.pdf
No, it's about GCMs; the IPCC is just a convenient and well known example to illustrate points. I can use other examples if you would prefer.
Ha ha. Apologies if I come across a bit snappy at times. Fractal dynamics and chaos is a difficult subject with many counter-intuitive results. It can be difficult to convey at the best of times. Since we probably disagree on the consequences of these things, it makes explanation all the more difficult, and sometimes it is easier to be blunt when misconceptions are presented.
Oh and H2O, thanks for your kind comment, which I do appreciate. I also think ATTP's conduct has been good (in the face of adversity) and deserves some credit.
Agreed. He/she is certainly game. And I very much hope he/she continues to contribute. Without questioning we learn nothing.
PS ATTP can you confirm your gender for the record. If you have done this before and I missed it please accept my apologies.
H20:
He.
Spence_UK,
Well, I think there is a difference between understanding the concept of deterministic chaos and being able to describe it in a fully rigorous, mathematical framework. The latter I can't do. The former I think I can. My issue with our discussion is that the global properties of our climate are bounded by external factors. It may well be possible that it could chaotically shift to some new kind of state, but there is little evidence to suggest that this is likely (as I understand it) - and here I mean globally, rather than simply a major change in some weather pattern.
Well, I don't. I don't think that any of this is strictly black-and-white. I get the impression that some (you?) assume that others have a a black-and-white views of things while, similarly, holding a black-and-white view of their own.
As far as the use of GCMs goes, I'll try to restate what I think Richard was getting at. There's much about our climate that we understand (have evidence for) that doesn't rely on GCMs . The influence of increased CO<sub>2</sub>, the hydrological cycle, sea level rise for example. As I understand it, we could estimate our future warming without GCMs. GCMs provide an additional tool and allow us to study processes on a smaller scale (regional effects). They're not perfect, but they're what we currently have. We could choose not to use them, but that just seems a little backwards. We don't start doing modelling only when they're absolutely perfect (if we can even define such a state).
I also suspect that much of this is acknowledged in the IPCC documents. It's my impression that many intepret what others say how they want to interpret it, rather than how it was intended. There may well be people who overstate the value of the results from GCMs, and that should certainly be avoided. Dismissing them out of hand, though, seems equally unfortunate.