Guest essay by Mike Jonas
There are dozens of climate models. They have been run many times. The great majority of model runs, from the high-profile UK Met Office’s Barbecue Summer to Roy Spencer’s Epic Fail analysis of the tropical troposphere, have produced global temperature forecasts that later turned out to be too high. Why?
The answer is, mathematically speaking, very simple.
The fourth IPCC report [para 9.1.3] says : “Results from forward calculations are used for formal detection and attribution analyses. In such studies, a climate model is used to calculate response patterns (‘fingerprints’) for individual forcings or sets of forcings, which are then combined linearly to provide the best fit to the observations.”
To a mathematician that is a massive warning bell. You simply cannot do that. [To be more precise, because obviously they did actually do it, you cannot do that and retain any credibility]. Let me explain :
The process was basically as follows
(1) All known (ie. well-understood) factors were built into the climate models, and estimates were included for the unknowns (The IPCC calls them parametrizations – in UK English : parameterisations).
(2) Model results were then compared with actual observations and were found to produce only about a third of the observed warming in the 20th century.
(3) Parameters controlling the unknowns in the models were then fiddled with (as in the above IPCC report quote) until they got a match.
(4) So necessarily, about two-thirds of the models’ predicted future warming comes from factors that are not understood.
Now you can see why I said “You simply cannot do that”: When you get a discrepancy between a model and reality, you obviously can’t change the model’s known factors – they are what they are known to be. If you want to fiddle the model to match reality then you have to fiddle the unknowns. If your model started off a long way from reality then inevitably the end result is that a large part of your model’s findings come from unknowns, ie, from factors that are not understood. To put it simply, you are guessing, and therefore your model is unreliable.
OK, that’s the general theory. Now let’s look at the climate models and see how it works in a bit more detail.
The Major Climate Factors
The climate models predict, on average, global warming of 0.2 deg C per decade for the indefinite future.
What are the components of climate that contribute to this predicted future warming, and how well do we understand them?
ENSO (El Nino Southern Oscillation) : We’ll start with El Nino, because it’s in the news with a major El Nino forecast for later this year. It is expected to take global temperature to a new high. The regrettable fact is that we do not understand El Nino at all well, or at least, not in the sense that we can predict it years ahead. Here we are, only a month or so before it is due to cut in, and we still aren’t absolutely sure that it will happen, we don’t know how strong it will be, and we don’t know how long it will last. Only a few months ago we had no idea at all whether there would be one this year. Last year an El Nino was predicted and didn’t happen. In summary : Do we understand ENSO (in the sense that we can predict El Ninos and La Ninas years ahead)? No. How much does ENSO contribute, on average, to the climate models’ predicted future warming? 0%.
El Nino and La Nina are relatively short-term phenomena, so a 0% contribution could well be correct but we just don’t actually know. There are suggestions that an El Nino has a step function component, ie. that when it is over it actually leaves the climate warmer than when it started. But we don’t know.
Ocean Oscillations : What about the larger and longer ocean effects like the AMO (Atlantic Multidecadal Oscillation), PDO (Pacific Decadal Oscillation), IOD (Indian Ocean Dipole), etc. Understood? No. Contribution in the models : 0%.
Ocean Currents : Are the major ocean currents, such as the THC (Thermohaline Circulation), understood? Well we do know a lot about them – we know where they go and how big they are, and what is in them (including heat), and we know much about how they affect climate – but we know very little about what changes them and by how much or over what time scale. In summary – Understood? No. Contribution in the models : 0%.
Volcanoes : Understood? No. Contribution in the models : 0%.
Wind : Understood? No. Contribution in the models : 0%.
Water cycle (ocean evaporation, precipitation) : Understood? Partly. Contribution in the models : the contribution in the climate models is actually slightly negative, but it is built into a larger total which I address later.
The Sun : Understood? No. Contribution in the models : 0%. Now this may come as a surprise to some people, because the Sun has been studied for centuries, we know that it is the source of virtually all the surface and atmospheric heat on Earth, and we do know quite a lot about it. Details of the 11(ish) year sunspot cycle, for example, have been recorded for centuries. But we don’t know what causes sunspots and we can’t predict even one sunspot cycle ahead. Various longer cycles in solar activity have been proposed, but we don’t even know for sure what those longer cycles are or have been, we don’t know what causes them, and we can’t predict them. On top of that, we don’t know what the sun’s effect on climate is – yes we can see big climate changes in the past and we are pretty sure that the sun played a major role (if it wasn’t the sun then what on Earth was it?) but we don’t know how the sun did it and in any case we don’t know what the sun will do next. So the assessment for the sun in climate models is : Understood? No. Contribution in the models : 0%. [Reminder : this is the contribution to predicted future warming]
Galactic Cosmic Rays (GCRs) : GCRs come mainly from supernovae remnants (SNRs). We know from laboratory experiment and real-world observation (eg. of Forbush decreases) that GCRs create aerosols that play a role in cloud formation. We know that solar activity affects the level of GCRs. But we can’t predict solar activity (and of course we can’t predict supernova activity either), so no matter how much more we learn about the effect of GCRs on climate, we can’t predict them and therefore we can’t predict their effect on climate. And by the way, we can’t predict aerosols from other causes either. In summary for GCRs : Understood? No. Contribution in the models : 0%.
Milankovich Cycles : Milankovich cycles are all to do with variations in Earth’s orbit around the sun, and can be quite accurately predicted. But we just don’t know how they affect climate. The most important-looking cycles don’t show up in the climate, and for the one that does seem to show up in the climate (orbital inclination) we just don’t know how or even whether it affects climate. In any case, its time-scale (tens of thousands of years) is too long for the climate models so it is ignored. In summary for Milankovich cycles : Understood? No. Contribution in the models : 0%. (Reminder : “Understood” is used in the context of predicting climate).
Carbon Dioxide (CO2) : At last we come to something which is quite well understood. The ability of CO2 to absorb and re-emit a specific part of the light spectrum is well understood and well quantified, supported by a multitude of laboratory experiments. [NB. I do not claim that we have perfect understanding, only that we have good understanding]. In summary – Understood? Yes. Contribution in the models : about 37%.
Water vapour : we know that water vapour is a powerful greenhouse gas, and that in total it has more effect than CO2 on global temperature. We know something about what causes it to change, for example the Clausius-Clapeyron equation is well accepted and states that water vapour increases by about 7% for each 1 deg C increase in atmospheric temperature. But we don’t know how it affects clouds (looked at next) and while we have reasonable evidence that the water cycle changes in line with water vapour, the climate models only allow for about a third to a quarter of that amount. Since the water cycle has a cooling effect, this gives the climate models a warming bias. In summary for water vapour – Understood? Partly. Contribution in the models : 22%, but suspect because of the missing water cycle.
Clouds : We don’t know what causes Earth’s cloud cover to change. Some kinds of cloud have a net warming effect and some have a net cooling effect, but we don’t know what the cloud mix will be in future years. Overall, we do know with some confidence that clouds at present have a net cooling effect, but because we don’t know what causes them to change we can’t know how they will affect climate in future. In particular, we don’t know whether clouds would cool or warm in reaction to an atmospheric temperature increase. In summary, for clouds : Understood? No. Contribution in the models : 41%, all of which is highly suspect.
Summary
The following table summarises all of the above:
| Factor | Understood? | Contribution to models’ predicted future warming |
| ENSO | No | 0% |
| Ocean Oscillations | No | 0% |
| Ocean Currents | No | 0% |
| Volcanoes | No | 0% |
| Wind | No | 0% |
| Water Cycle | Partly | (built into Water Vapour, below) |
| The Sun | No | 0% |
| Galactic Cosmic Rays (and aerosols) | No | 0% |
| Milankovich cycles | No | 0% |
| Carbon Dioxide | Yes | 37% |
| Water Vapour | Partly | 22% but suspect |
| Clouds | No | 41%, all highly suspect |
| Other (in case I have missed anything) | 0% |
The not-understood factors (water vapour, clouds) that were chosen to fiddle the models to match 20th-century temperatures were both portrayed as being in reaction to rising temperature – the IPCC calls them “feedbacks” – and the only known factor in the models that caused a future temperature increase was CO2. So those not-understood factors could be and were portrayed as being caused by CO2.
And that is how the models have come to predict a high level of future warming, and how they claim that it is all caused by CO2. The reality of course is that two-thirds of the predicted future warming is from guesswork and they don’t even know if the sign of the guesswork is correct. ie, they don’t even know whether the guessed factors actually warm the planet at all. They might even cool it (see Footnote 3).
One thing, though, is absolutely certain. The climate models’ predictions are very unreliable.
###
Mike Jonas
September 2015
Mike Jonas (MA Maths Oxford UK) retired some years ago after nearly 40 years in I.T.
Footnotes
1. If you still doubt that the climate models are unreliable, consider this : The models typically work on a grid system, where the planet’s surface and atmosphere are divided up into not-very-small chunks. The interactions between the chunks are then calculated over a small time period, and the whole process is then repeated a mammoth number of times in order to project forward over a long time period (that’s why they need such large computers). The process is similar to the process used for weather prediction but much less accurate. That’s because climate models run over much longer periods so they have to use larger chunks or they run out of computer power. The weather models become too inaccurate to predict local or regional weather in just a few days. The climate models are less accurate.
2. If you still doubt that the climate models are unreliable, then perhaps the IPCC themselves can convince you. Their Working Group 1 (WG1) assesses the physical scientific aspects of the climate system and climate change. In 2007, WG1.said “we should recognise that we are dealing with a coupled nonlinear chaotic system, and therefore that the long-term prediction of future climate states is not possible.”
3. The models correctly (as per the the Clausius-Clapeyron equation) show increased atmospheric water vapour from increased temperature. Water vapour is a greenhouse gas so there is some warming from that. In the real world, along with the increased water vapour there is more precipitation. Precipitation comes from clouds, so logically there will be more clouds. But this is where the models’ parameterisations go screwy. In the real world, the water cycle has a cooling effect, and clouds are net cooling overall, so both an increased water cycle and increased cloud cover will cool the planet. But, as it says in the IPCC report, they had to find a way to increase temperature in the models enough to match the observed 20th century temperature increase. To get the required result, the parameter setttings that were selected (ie, the ones that gave them the “best fit to the observations“), were the ones that minimised precipitation and sent clouds in the wrong direction. Particularly in the case of clouds, where there are no known ‘rules’, they can get away with it because, necessarily, they aren’t breaking any ‘rules’ (ie, no-one can prove absolutely that their settings are wrong). And that’s how, in the models, cloud “feedback” ends up making the largest contribution to predicted future warming, larger even than CO2 itself.
4. Some natural factors, such as ENSO, ocean oscillations, clouds (behaving naturally), etc, may well have caused most of the temperature increase of the 20th century. But the modellers chose not to use them to obtain the required “best fit“.If those natural factors did in fact cause most of the temperature increase of the 20th century then the models are barking up the wrong tree. Model results – consistent overestimation of temperature – suggest that this is the case.
5. To get their “best fit“, the chosen fiddle factors (that’s the correct mathematical term, aka fudge factors) were “combined linearly“. But as the IPCC themselves said, “we are dealing with a coupled nonlinear chaotic system”. Hmmmm ….
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I’ve said it before
So I’ll say it again,
Trying to model chaos
Borders on the insane;
Garbage in garbage out
Has never been more true,
Perhaps there’s an agenda
They want to pursue?
http://rhymeafterrhyme.net/computer-models/
As a long time coastal resident, I am still amazed by the lack of positive “feedbacks” for surface temperatures in places like Crescent City, CA where the daily high temp. matches the daily low temp. This past August one day I remember was 56F for the hi and lo temp. for the whole day. It’s utterly common to have a daily temperature swing of just couple degrees. You might want to bring a jacket or sweater while you are adjusting your models…just in case.
So you picked a random point at which the GCMs and observations peaked at roughly the same time, equalized them at that level, then misleadingly compare them on that basis to come to predetermined conclusions that they do not work? You also ignore that volcanoes are in fact included in the set of natural forcers used in historical reconstructions. Wow, the lengths you will go to just to support your delusion of having smashed the consensus…I am in awe. If you wish, I have a bridge I can sell to you…
Well the Climate modelers have their own bridge to sell. And they have been doing a fair job of it so far.
BTW. What is the value of the coupling coefficients for the various coupled processes?
Ross, that is quite enough Kool-Aid for you, young man.
There is a chart that was produced by the IPCC that is almost identical to the one above,
In your conspiratorial fantasies, is the IPCC also trying to discredit the models?
The divergence of models from actuality has been being mentioned a lot lately. There are usually two reasons:
1: Some of the graphs showing divergence either include (sometimes have only) RCP 8.5 of the CMIP5 models, which seem to have been based on an overprediction of greenhouse gases, especially methane. The RCPs lower than 8.5, such as 6.0 and 4.5, seem more realistic for total forcing through manmade greenhouse gases.
2: In general, these models seem to be tuned for success at hindcasting the past, but without consideration for multidecadal oscillations. I think about .2, maybe .22 degree C of the warming from the early 1970s to the 2004-2005 peak was from upward swing of one or a combination of multidecadal oscillations, and the pause period starting in the slowdown just before cresting the 2004-2005 peak seems to me as likely to last for a similar amount of time.
I suspect a possible third reason: The graph does not state what the temperature is of. However, balloon and satellite datasets are usually of the lower troposphere. The IPCC models are not even named as CMIP5 ones, let alone which RCP / RCPs. The model curve looks familiar to me as a composite of CMIP5 models that has often been compared not only to lower troposphere data, but also surface data. This has me thinking that the model forecast is of surface temperature. During the period starting with the beginning of 1979, balloon data indicate that the surface-adjacent 100-200 meters of the troposphere have warmed about .03 degree/decade more than the “satellite-measured lower troposphere” as a whole. See Figure 7 of http://www.drroyspencer.com/2015/04/version-6-0-of-the-uah-temperature-dataset-released-new-lt-trend-0-11-cdecade/#comments
This indicates surface temperature (after smoothing by a few years) being about .3 – .31 degree C warmer than in 1979, as opposed to ~.2 degree C. (The surface temperature dataset that agrees with this the best is (more like was but still is) HadCRUT3. This is still cooler than composites of lower-RCP CMIP5 models, but I think figuring for this instead of lower troposphere being what is being predicted (and hindcasted), and correcting models so that they are aware of multidecadal oscillation(s) accounting for some early-1970s to ~2004-2005 warming, will get composites of lower-RCP (4.5 and 6) CMIP5 models into doing an impressively good job.
re: “ I think about .2, maybe .22 degree C of the warming from the early 1970s to the 2004-2005 peak was from upward swing of one or a combination of multidecadal oscillations” – When I look at, say, the sunspot cycle, where the cycle amplitude varies a lot from cycle to cycle, I understand that just eyeballing (or even exhaustively analysing) the temperature graph will not tell you much at all about how much of the late 20thC warming was from those oscillations. You guess 0.2 -0.22 deg C. That’s a very tight range. I would have thought that we just don’t know to anything like that accuracy.
Real clouds function way below the huge grid cell sizes of the climate models. A tropical cumulus nimbus can short circuit the surface with the upper layers and transport a lot of energy up or down. They are too small and too numerous too be processed by even the fastest computers.
17 Sept: JoanneNova: Scandal Part 3: Bureau of Meteorology homogenized-the-heck out of rural sites too
The Australian Bureau of Meteorology have been struck by the most incredible bad luck. The fickle thermometers of Australia have been ruining climate records for 150 years, and the BOM have done a masterful job of recreating our “correct” climate trends, despite the data. Bob Fernley-Jones decided to help show the world how clever the BOM are. (Call them the Bureau of Magic)….
http://joannenova.com.au/2015/09/scandal-part-3-bureau-of-meteorology-homogenized-the-heck-out-of-rural-sites-too/
What are the coupling coefficients? (numeric value)
What surprises me is the steep climb of 1oC for the period 1995-2025, this is such a big incremental change. You would expect that if such result is possible. This increase is counteracted by the Boltzmann equation and contains the temperature T to the 4th power.
We don’t live in a greenhouse type atmosphere. We live in an atmosphere that more or less acts like a swamp cooler coupled with a chiller unit. I bet if some engineer could model that it would be closer to reality than computer models based on “CO2 drives the climate” theory.
I agree that an honest engineer could model the climate closer to reality than the present crop of computer games. But I also think that a drunken plowboy (or farmhand these days) could best the IPCC also.
Where I live, the atmosphere is more like a sauna bath with the window cracked a little, a fan in the corner with a randomly variable rheostat controlling it, and a bucket of ice thrown in every once in a while in Winter.
Climatologists are as accurate in predicting the climate as seismologists are in predicting earthquakes.
All climate models costing taxpayers billions of dollars, sterling and euros have gone wrong. All predictions about climate have gone wrong. Today we are supposed to be seeing an ice-free Arctic summer, a rise of 4 meters in ocean levels, the desertification of the northern Mediterranean shores, 50 million climate refugees, food shortages, a warming Antarctica which is actually getting colder, snowless northern countries which in fact are having more snow and many other predictions that have gone awry.
Presently, it is not known whether ENSO events cancel out and thus have no long term impact upon climate when viewed in the long run, or more particularly on a climatology timescale of say circa 30 to 50 or perhaps even 30 to 100 years.
However, there are reasons why ENSO events may not simply cancel each other out and why it may be the case that they do have an impact on short term climatology (ie., periods of 30 years, or at any rate less than 100 years).
Consider:
1. Due to the difference in latent energy contained within the atmosphere and the oceans, the atmosphere cannot heat the oceans. It is well known that it is extremely difficult to heat water in a container open only to the atmosphere above by warm air from above.
2. A warm ocean surface (El Nino) heats the atmosphere above and since hot air rises, it also alters convection rates.
3. The same is not so with a cool ocean surface.
4. Consider a chest freezer. Open the lid, and since cold air sinks, there will be very little impact upon the temperature in the room (at least over short periods). Contrast this with the same chest freezer but one that has been converted to a BBQ at the bottom. Open the lid and it will have an immediate impact on the temperature of the room. One warms the atmosphere, the other does not.
Thus in summary, if there is a short period (lets say 30 or so years) where there are more El Ninos than La Ninas (or where the El Ninos , or some of them,are particularly strong), on a short time scale (lets say 30 years or so) one would expect to see warming. But even if there was exactly the same number of El Ninos as La Ninas (or they were of equal strength), it does not automatically follow that on short time scales (say circa 30 years) the effect is neutral; that La Ninas will cancel out El Ninos. They may do, but since the energy flux is different and since one may have a greater impact upon convection, and thereby energy transport, it does not automatically follow that ENSO cancels out on short climatology time scales.
Further, it should not be overlooked that if one views the satellite data (from 1979 to date), there is no steady linear warming trend. There is simply a one off step change in and around the Super El Nino of 1998. Prior to that event temperatures were trending essentially flat. Following that event, temperature are trending essentially flat. In the satellite data, one can clearly see an ENSO signal and one that has left a marked signature following the extremely strong 1997/1998 Super El Nino.
The satellite data supports the view that ENSO may leave a signature, and that ENSO does not necessarily cancel out when viewed on short climatology time scales.
Just saying that the ENSO assumption is something requiring further consideration and one should remain sceptical as to the correctness of that assumption, at any rate as to its impact on short climatology time scales with which we are dealing with and during which we have some data.
“Thus in summary, if there is a short period (lets say 30 or so years) ”
Let’s say 1978 (low temp point in Hansen’s earliest charts) to the 1998 temp spike. This is the entire CAGW time frame, but only 20 years. Another 10 years for ENSO to cancel.
Carbon Dioxide: Contribution to percentage of general circulation model (GCM) warming: IPCC assertion 37%
Highest possible warming based on fundamental science rather than fudging of science to create an issue: 0.2C/3C = 6.7% 0.25 watts/m^2 without ‘feedbacks’. Actual best estimate warming for doubling of atmospheric is less than 0.1C.
If the assertion that the warming for a doubling of atmospheric CO2 without ‘feedbacks’ is 0.1C to 02C and likely less than 0.1C is correct (see below for support for that assertion is correct), there is no CAGW problem.
The majority of the warming in the last 150 years was due to solar cycle changes, not due to the increase in atmospheric CO2. There is no CAGW, there is in fact almost no AGW due to the increase in atmospheric CO2. If that assertion is correct global warming is reversible, if there is a sudden slow down or interruption to the solar cycle.
The GCM models have more than a hundred ‘variables’ and hence can be ‘tuned’ to produce 3C to 6C warming for a doubling of atmospheric CO2. The also could be tuned to produce 0.1C warming.
The one ring that rules the GCM is the initial so called 1-dimensional no ‘feedbacks’ study which determined the surface warming for a doubling of atmospheric CO2 is 1.2C, a forcing of 3.7 watts/m^2.
We had all assumed or at least I had assumed that the 1-dimensional no ‘feedbacks’ study, has scientifically accurate, on the correct page.
I had assumed the problem with why the planet has warmed less than the IPCC models predicted is due to the earth resisting forcing (negative feedback) rather than amplifying (positive feedback) forcing change, by an increase in cloud cover, increase in cloud albedo, and an increase in cloud duration in the tropics. That is the explanation for there being almost no warming in the tropics.
Negative feedback would for example explain why there has been also no warming in the tropical region.
Negative feedback does not however explain 18 years without warming and does not explain the fact that there has been 1/5 of the predicted warming of the tropical troposphere at 5km. Those observational facts support the assertion that the 1-dimensional no ‘feedbacks’ calculation the expected warming for a doubling of atmospheric CO2 is fundamentally incorrect.
The infamous without ‘feedbacks’ cult of CAGW’s calculation (this is the calculation that predicted 1.2C to 1.4C surface warming for a doubling of atmospheric CO2) incorrectly/illogical/irrationally/against the laws of physics held the lapse rate constant to determine (fudge) the estimated surface forcing for a doubling of atmospheric CO2. There is no scientific justification for fixing the lapse rate to calculate the no ‘feedback’ forcing of greenhouse gases.
Convection cooling is a physical fact not a theory and cannot be ignored in the without ‘feedbacks’ calculation. The change in forcing at the surface of the planet is less than the change in forcing higher in the atmosphere due to the increased convection cooling caused by greenhouse gases. We do not need to appeal to crank ‘science’ that there is no greenhouse gas forcing to destroy the cult of CAGW ‘scientific’ argument that there is a global warming crisis problem to solve.
There is a forcing change due to the increase in atmospheric CO2 however that forcing change is almost completely offset by the increase in convection. Due to the increased lapse rate (3% change) due to convection changes (the 3% change in the lapse rate, reduces the surface forcing by a factor of four, the forcing higher in the atmosphere remains the same) therefore warming at the surface of the planet is only 0.1C to 0.2C for a doubling of atmospheric CO2, while the warming at 5 km above the surface of the planet is 1C. As a warming of 0.1C to 0.2C is insufficient to cause any significant feedback change, the zero feedback change for a doubling of CO2 is ballpark the same as the with feedback response.
P.S. The cult of CAGW no ‘feedbacks’ 1-dimensional calculation also ignored the overlap of the absorption of water vapor and CO2. As the planet is 70% covered in water there is a great deal of water vapor in the atmosphere at lower levels, particularly in the tropics. Taking the amount of water vapor overlap into account (before warming) in the no ‘feedbacks’ 1 dimension calculation also reduces the surface warming due to a doubling of atmospheric to 0.1C to 0.2C. Double trump. If the both water vapor/CO2 absorption spectrum absorption overlap and the increased convection cooling of greenhouse gases is taken into account the forcing change due to a doubling of atmospheric CO2 is without feedbacks less than 0.1C.
http://hockeyschtick.blogspot.ca/2015/07/collapse-of-agw-theory-of-ipcc-most.html
https://drive.google.com/file/d/0B74u5vgGLaWoOEJhcUZBNzFBd3M/view?pli=1
Transcript of a portion of Weart’s interview of James Hansen.
Amen. Thanks for posting that. Proves even the canonical assumption that sensitivity to doubled CO2 in absence of feedbacks is ~1C. Wrong! And not only due to the false fixed-lapse rate false assumption, but also due to the false assumption of fixed atmospheric emissivity, a basic mathematical error in calculation of the Planck feedback parameter!
http://hockeyschtick.blogspot.com/search?q=kimoto
lf the UK Met Office weather models are any thing to go by its their forecasts of the tracking of the jet stream is where there going wrong. They tend to forecast that when high pressure builds it will come up from the south and so will push the track of the jet stream northwards. But recently there has been a trend of the jet stream taking a more southwards track then their models forecast at least during the summer months. Which has increased the amount of high pressure patterns forming to the north of the jet stream. Which helps to bring down cooler air from the north rather then pushing warmer air up from the south.
As the essay points out, there are so very many things we don’t understand about the weather machine. There are most likely factors that we don’t even know about, much less understand. Then there are things that we claim to know about but are very much wrong about. Take CO2 which the essay says is 37% of the models. (whatever that means) We claim to understand CO2 and its function concerning weather and climate but we are very much wrong on that. There are several credible theories of climate that do not have CO2 doing what the IPCC thinks CO2 does and I wager one of those theories will win out after we return to climate science and stop giving the paymasters the answers they want for political reasons.
Why don’t the models work? They are political constructs and not scientific ones. (my best answer)
~ Mark
From the article, I would guess that the 37% came from the fact that before fiddling, the models created only about 1/3rd of the observed warming when only CO2 was changed. I’m guessing that “about a third” from the text of the article, and 37% from the table, refer to the same thing.
Thanks. I bet that is it.
Yes, the “about a third” does relate to the 37%. The 37% is the proportion of the models’ predicted 0.2 deg C per decade that comes from CO2 itself.
“commieBob
September 17, 2015 at 8:25 pm
“One thing I haven’t seen in a discussion of chaotic system models, is the idea of attractors. After enough runs, valid models of chaotic systems should show where the attractors are”.
If you consider the Vostok data of temperature and submit them to a “phase plan” analysis, you ‘ll find two attractors: the glacial periods and the temperate ones. The system evolutes from one to the other along two tracks : a progressive cooloing and a fast heating one (which can also be seen directly from the time series.
All the rest is only made of chaotic fluctuations around the attractors or around the trajectories going from one attractor to the other(not to be confused with random fluctuations…in which case the phase plan will be filled completely and evenly).
A very well written summary – thank you. I have never seen the models explained in this way and it gives me a much clearer understanding of how the models have been constructed our of junk conjecture.
I broadly agree with the author’s assessment of the models and the ‘science’ that is engrossed into them. My suspicion however is that there is also an issue with the mesh size in that it is too coarse to possibly model much of the water cycle mechanisms, particularly in the tropics. As a consequence fiddle/fudge factors have to be introduces to guesstimate their quantitative contribution. Basically if the mechanism takes place at a smaller scale than the mesh size then the Navier Stokes equations are no longer governing the maths but some fudge factor is.
From my work using CFD I came across the mesh size phenomenon where a first cut mesh model, my very first attempt after reading the software manual, converged to a solution that was manifestly wrong ( since I had model test data to compare it with). In my case the software was always using the N-S model but it was the mesh size that was causing poor results. I had gone ‘coarse’ in order to reduce computation time and got burned for my lack of trouble. It was a salutary lesson and an sharp introduction to the ‘uncertainty principle’ of mesh models.
Accuracy is inversely related to computation time and thus cost and convenience.
On the ‘plus’ side (for ‘climate science’ that is), may also serve a useful marketing purpose in that it allows junk output to be marketed and its various financial and reputational rewards reaped with a viable ‘get out of gaol free’ excuse kept in the back pocket. And lets face it the press release does not need to contain such boring detail. ‘OH we never guessed that our models were too coarse and converging to false solutions. We had no way of verifying them against future field data’. That of course is true but micro models could be test run against others with different mesh size for solution comparison and iteration cycle behavior.
Just thinking out loud folks.
No mesh size will ever work over decadal+ time scales. See the IPCC quote in footnote 2. But the problems go much much further than just mesh size.
“henri Masson wrote, September 18, 2015 at 2:50 am
“If you consider the Vostok data of temperature and submit them to a “phase plan” analysis, you ‘ll find two attractors: the glacial periods and the temperate ones. The system evolutes from one to the other along two tracks : a progressive cooloing and a fast heating one (which can also be seen directly from the time series)”.
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For the one interested, please find hereunder the dropbox link presenting very briefly and graphically my story under the form of a few PowerPoint slides:
https://dl.dropboxusercontent.com/u/56918808/climate%20%26%20chaos.pptx
Bingo.
Excellent piece !!!
Anybody who has any experience of computer models of highly complex, dynamic, multivariate, non-linear systems knows full well that they are a largely a waste of time.
“Give me four parameters, and I can fit an elephant. Give me five, and I can wiggle its trunk.”
-John von Neumann
The IPCC says:
“we are dealing with a coupled nonlinear chaotic system”
Easy to “kill” on its own terms. Ask them “what are the coupling coefficients used in the models?” Electronics deals with this all the time in transformers. We call it the “coupling coefficient”. Climate has way more processes each with its own coupling. And we haven’t even touched “nonlinear”.
Defeating them on their own terms is the easiest way.
” Give me six, and I can put Babar to bed without his dinner for breaking his sister’s piano.”
LOL. I can give you 5 with one hand.
🙂
Very nice. I was keeping a list of “what was good” in their AGW world (to paraphrase Gene Kranz), but the blank sheet of paper was misplaced.
I usually evaluate models based on their component’s contribution to the stated error vice to the stated product. But, in this case since the product is so divergent from observed reality, maybe their skill is error. To the best of current computational ability we seem to have identified that CO2 is NOT a significant factor in short-term climate trends. This will have to verified by decades of observations.
You should consider adding a major source of unquantified error to your discussion; the spatial error introduced by the models gridding systems is generally not defined or accounted for. Spatial stats is specialized discipline and the value of gridded data versus data aggregated by major correlations (Land/Sea, Altitude, Latitude, Pop Density, etc.) needs some investment. At a minimum the grids need to be adjusted for the modern ellipsoidal earth models. We’re spinning thru space on an egg not a cue ball.
A fable about proxies and other indicators illustrating the importance of systemic thinking
“ The Blind Men and the Elephant ”
an Hindu fable of which the subject is originating from Jainism
retranscripted by the American poet John Godfrey SAXE ( 1816 – 1887 )
There were six men of Hindustan
To learning much inclined,
Who went to see the Elephant
( Though all of them were blind ),
That each by observation
Might satisfy his mind.
The First approached the Elephant,
And happening to fall
Against his broad and sturdy side,
At once began to bawl :
“ God bless me ! – but the Elephant
Is very like a wall ! ”
The Second, feeling of the tusk,
Cried : “ Ho ! – what have we here
So very round and smooth and sharp ?
To me ‘t is mighty clear
This wonder of an Elephant
Is very like a spear !
The Third approached the animal,
And happening to take
The squirming trunk within his hands,
Thus boldly up and spoke :
“ I see, ” quote he, “ the Elephant
Is very like a snake
”The Fourth reached out his eager hand,
And felt about the knee.
“ What most this wondrous beast is like
Is mighty plain, ” quote he ;
“ It is clear enough the Elephant
Is very like a tree ! ”
The Fifth, who chanced to touch the ear,
Said : “ I am the blindest man
Can tell what this resembles most ;
Deny the fact who can,
This marvel of an Elephant
Is very like a fan ! ”
The Sixth no sooner had begun
About the beast to grope,
Then, seizing on the swinging tail
That fell within his scope,
“ I see, ” quote he, “ the Elephant
Is very like a rope ! ”
And so these men of Hindustan
Disputed loud and long,
Each in his own opinion
Exceeding stiff and strong,
Though each was partly in the right,
And all were in the wrong !
So, often in theologic wars
The disputants, I weep,
Rail on in utter ignorance
Of what each other mean,
And prate about an Elephant
Not one of them has seen !”
And, of course, I personally think that IPCC suppo(r)ts are involved in a theological war (al what they do and say is finally just aimed to venerate Goddess Gaia to avoid being sinned for all the excess of our industrial economically developed world, in which they have huge difficulties to find their own place….without grants and subsidies)
In light of recent research, should ozone depletion be included in the list?
“Stratospheric Ozone Depletion: The Main Driver of Twentieth-Century Atmospheric Circulation Changes in the Southern Hemisphere” Polvani et al, 2011
http://journals.ametsoc.org/doi/abs/10.1175/2010JCLI3772.1
While it is true that the cyclic nature of the various oceanic cycles means that over time, they play no/little role in the climate. The problem comes from the tuning of the models. There was no attempt made to remove the affects of el nino/PDO/AMO and other cycles from the raw data prior to using that data to tune the models. As a result, short term warming that was caused by the oceanic cycles was assumed to have been due to CO2, as a result, they assumed that the warming from the mid-1970s to around 2000 would continue, even accelerate.
The fact that ocean currents and ENSO contribute 0% consideration to the climate models predicted future , makes them of 0% value in predicting global temperatures for the near term, decadal or the 60 year climate cycle period . We might as well stop comparing their predictions ,which are running 4 times higher than reality, because the models will be out in the left field constantly unless they change their methods . In my opinion they will not change because they are getting too much free money without any accountability for their clearly failed predictions . The models are paraded as PR material to the public and to exaggerate the non existing climate threat. It seems to me that . the reason they want to stop all future climate debate , is to prevent the public from really finding out how uncertain their models are and how uncertain their science really is which they have been pushing on the public, the politicians and the media as settled This science and their models are so uncertain that it should have never played a part in public policy. We will pay dearly for this lack of oversight and accountability to the public of these alarmist scientists.