Technique could help climate models sweat the small stuff
PROVIDENCE, R.I. [Brown University] — A team of physicists and mathematicians has come up with a statistical technique that puts the fine details back into computer simulations of large-scale phenomena like air circulation in the atmosphere and currents in the ocean.
Computer models are generally good at capturing the big picture, but they are often forced to ignore things that happen at small scales. For example, models of a planet’s atmosphere capture the large-scale dynamics of jets and airflows, but they don’t include small-scale dynamics created by things like clouds and localized turbulence, despite the fact that those dynamics can often influence the larger scales.
“There are simply too many numbers for the computer to simulate it at a reasonable speed,” said Brad Marston, a Brown University physicist. “It might take years to simulate a day of the atmosphere, which wouldn’t be good.”
The traditional approach to dealing with the problem is to simply lop the small scales off of the simulation. A few ad hoc ways of putting some of that information back in exist, but they tend not to be mathematically rigorous.
“These schemes have always suffered from the criticism that they lack predictive power,” Marston said. “You have to make a lot of decisions that you really shouldn’t have to make but you’re forced to make.”
In a paper published in the journal Physical Review Letters, Marston and his colleagues show a method of averaging out those small-scale dynamics in a way that is computationally tractable, which allows those dynamics to be simulated and their effects to be captured in a rigorous way.
“We’re retaining the degrees of freedom at the small scale, but treating them in a different way,” Marston said. “We don’t have to simulate all the little swirls, so to speak. We treat them by using their averages and the sizes of their fluctuations. It allows us to capture the contributions of these small-scale dynamics that would normally not be included.”
In their paper, the researchers used the technique to model air jets forming on a round surface. They showed that the method produces results similar to brute-force numerical simulations of the same jets.
There have been prior attempts to treat small-scale disturbances statistically, Marston said, but those haven’t fared very well. Prior attempts have treated disturbances as being homogeneous and assumed they were not traveling in any one particular direction.
“But that almost never happens in nature,” Marston said. “Turbulence almost always has some directionality to it. That directionality is what makes these kinds of approximations work. It makes these approximations tenable.”
The researchers hope that the method might make for more accurate simulations of a wide variety of natural phenomena, from how the churning interiors of planets create magnetic fields to how air flows across the surfaces of cars or airplanes.
The method could be particularly useful in modeling Earth’s changing climate because the technique can more rigorously capture the influence of cloud formation.
“Cloud formation is seen as the largest source of uncertainty in climate models right now,” Marston said. “There are famous examples where different climate models that have different ways of dealing with the clouds give you qualitatively different results. In a warming world, one model might produce more clouds and another might produce fewer.”
By averaging those cloud dynamics and then simulating them in the models, it might be possible to reduce some of that uncertainty, Marston said.
The team has already started working to incorporate the method in climate simulations, as well as simulations of ocean currents and problems in astrophysics dealing with the behavior of plasmas.
“There are a whole bunch of problems out there where we feel this could be helpful,” Marston said.
###
Generalized Quasilinear Approximation: Application to Zonal Jets
J. B. Marston, G. P. Chini, and S. M. Tobias
Phys. Rev. Lett. 116, 214501 – Published 27 May 2016
Paper: http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.116.214501
ABSTRACT
Quasilinear theory is often utilized to approximate the dynamics of fluids exhibiting significant interactions between mean flows and eddies. We present a generalization of quasilinear theory to include dynamic mode interactions on the large scales. This generalized quasilinear (GQL) approximation is achieved by separating the state variables into large and small zonal scales via a spectral filter rather than by a decomposition into a formal mean and fluctuations. Nonlinear interactions involving only small zonal scales are then removed. The approximation is conservative and allows for scattering of energy between small-scale modes via the large scale (through nonlocal spectral interactions). We evaluate GQL for the paradigmatic problems of the driving of large-scale jets on a spherical surface and on the beta plane and show that it is accurate even for a small number of large-scale modes. As GQL is formally linear in the small zonal scales, it allows for the closure of the system and can be utilized in direct statistical simulation schemes that have proved an attractive alternative to direct numerical simulation for many geophysical and astrophysical problems
Yep, sounds “settled” to me.
Well the thing about clouds, is that they affect what goes on underneath them, so you have to be making measurements underneath the clouds, not on top of them.
And I’m sorry to inform Brown University, but so far we have not seen any models correctly report on the large scale facts of what we know already happened. All 57 models give results different from the recorded history.
G
So …. in fact they DONT make a generally good job of capturing the big picture, because they get it wrong.
Duh.
george e. smith
June 3, 2016 at 12:26 pm
Well the thing about clouds, is that they affect what goes on underneath them, so you have to be making measurements underneath the clouds, not on top of them.
I’ve seen the reverse. A cold mountain sticking up into a moist, warmer airflow causing cloud to form which persisted downwind of the mountain.
Everything is possible in cloud formation.
It is precisely small scale non linear interactions which define thunderstorms which determine tropical climate, which is the fundamental weakness of current models.
General circulation models have no predictive value.
This new step will not change that.
Instead of wasting their time “revising” those FUBAR Climate Modeling computer programs …… those brilliant programmers should direct their talents and energy too performing a much simpler task of creating a Birth Baby computer modeling program whereby the pregnant female provides a “sample” of both hers and her baby’s biological father’s DNA ……. so that the BB program can create a visual “model” of all the physical attributes that her birthed child will possess, including a summary “list” of INHERITED mental attributes and health issues.
I guess my English is a lot worse than I thought. Let me try saying it again ; this time in another language.
a) The sun; being the source of SOLAR ENERGY, is above the clouds. Excuse me, that’s WAY above the clouds.
b) The clouds block SOME of that solar energy, when it finally gets to those clouds.
c) Some of that cloud blocked solar energy gets scatter reflected (actually refracted) back to space; and some of it gets absorbed by the clouds; actually by the water in the clouds.
d) The solar energy that is not blocked by the clouds or scattered as blue sky, ends up underneath the clouds which did not block that energy.
e) The solar energy that ends up underneath the clouds which failed to block it, lands on the ground, most of which is wet.
f) The solar energy that lands on the wet ground, called ocean, goes deep down and eventually is largely wasted as “heat” energy (noun), but some does biological things instead.
g) Since the solar energy that gets by the clouds and lands on the ground; wet or dry, is what “heats” (verb) the earth, there is some evidence that this is what we should measure to find out if it is getting hotter or colder.
h) Such measurements of the solar energy that lands on the wet or dry ground, are best made with sensors on the ground, UNDERNEATH the clouds.
i) Measurements made of solar energy above the clouds, such as by satellites have consistently given higher values that clearly are wrong, and exaggerate the amount of “heating” (verb) that solar energy under the clouds, that lands on earth can cause.
j) Ergo, the solar energy that gets underneath the clouds and lands on wet or dry ground, is best measured on the ground, underneath the clouds, where the real values can be obtained.
G
Otherwise, what I said before, pretty much says the same thing, but in that other hard to understand dialect.
[Inelegant but very effective phrasing. .mod]
George
You might like this, these are graphs of the daily rate of temp change at the surface stations divided by the solar forcing in watts for that station (calculated for each location). These are are then averaged into 10 degree latitude bands. showing temperature change in F/WattM^2
https://micro6500blog.wordpress.com/2016/05/18/measuring-surface-climate-sensitivity/
I’m sorry, but if a minuscule amount of CO2 is a big picture…
…then clouds are an even bigger picture
Hallelujah! When the history of this asinine scare mongering fiasco is written it will be shown that water vapour is the WHOLE story! It is the great “balancer”, with surface warming causing more evaporation and the resultant vapour carrying massive quantities of heat aloft to be rejected closer to it’s ultimate destination and condensing into high albedo clouds before returning to earth to pick up another load. If the planet tries to get hotter, this ” engine ” speeds up and produces more cloud.
Way to go, John. Now they will have to open more unemployment offices.
Sorry to be off topic, but the readership should know of an excellent takedown of climate models, using the poor performance of economic models to predict the future. Here’s the link–http://www.bloomberg.com/view/articles/2016-06-01/global-warming-alarmists-you-re-doing-it-wrong
To get a hot link to work, put it on a line all by itself and WordPress automagically takes care of the rest.
http://www.bloomberg.com/view/articles/2016-06-01/global-warming-alarmists-you-re-doing-it-wrong
“automagically” ….my new favorite word of the month !! Thanks…
That works this week. Next week, they may improve it so it won’t work.
jorgekafkazar: Such an optimist!
In the meantime, there’s always the “copy the link and paste it” move.
Thanks, great article! R.
If these techniques are truly able to make the climate models more accurate, then more power to them.
I fear though that they will add these techniques in such a way as to make sure that the results remain the same.
Then the modelers will proclaim that since they now “include” clouds, that nobody has any reason to criticize the outputs.
The climatologists should be very afraid that physicists are sniffing around their turf. That is the start of making this great stinking pile into a real science. Much more rigorous minds are coming to bear. Like the great eye of Sauron that sees the weakness in things. Just wait til Dad gets home!
@MarkW:
“Any technology, sufficiently advanced, is indistinguishable from bullshit”
Well you have to make the climate models accurate, before you can make them MORE accurate.
So far, they are not even approximately accurate.
G
Water vapor has so much more effect than CO2 this lack of accounting for clouds makes it entirely obvious why the climate models do not work.
It has never ceased to amaze me how eloquently these “researchers” can describe something they can’t do, and make you believe they did it.
“By averaging those cloud dynamics and then simulating them in the models, it might be possible to reduce some of that uncertainty, Marston said.”
Please include the error bars. And, also explain how one can average a dynamic.
“In a warming world, one model might produce more clouds and another might produce fewer.
By averaging those cloud dynamics and then simulating them in the models, it might be possible to reduce some of that uncertainty, Marston said.”
So they are averaging the right and the wrong to get a wrongish but more certain answer? Have I understood correctly?
They propose averaging two wrong answers in the belief that the two wrong answers are normally distributed about the true mean. That is of course only true if the model is fundamentally correct in the first place. Prove it…
If it is fundamentally biased to one side of reality or the other then there isn’t much to gain by averaging the biased answers unless you have nothing else to do with your free time. It is as dumb as producing a ‘model mean’ using a bunch of models you know perfectly well are wildly wrong, all to the ‘high’ side. It shows true disingenuousness.
“””””…… That is of course only true if the model is fundamentally correct in the first place. …..”””””
There’s no evidence to support that. The symmetrical Gaussian distribution is the exception rather than the norm.
Most real distributions in real physical situations are not symmetrical. The Maxwell Boltzmann distribution that crops up all over thermo-dynamics for example is quite asymmetrical.
Our Temperature scale is bounded on one end by zero kelvin, but completely unbounded at the other end.
So it is highly unlikely that any physical process involving Temperature would ever yield results that would be symmetrically disposed about some ” proper ” value. Gaussian distributions are more fictional than real.
G
Well if you average the cloud dynamics, it is absolutely certain to be not accurate.
The MEASURED values are what is accurate. Whereas the AVERAGE can even give you a values that was NEVER measured by anybody, anywhere at any time; so it is completely fake and fraudulent.
G
“In their paper, the researchers used the technique to model air jets forming on a round surface. They showed that the method produces results similar to brute-force numerical simulations of the same jets.”
On the scale of the real world, convection cells start small, only a few meters across (dust devils) to a few tens of meters across (F1 tornadoes) to several kilometers across (thunderstorms and F5 tornadoes). Even at the scale of an F5, the ‘surface’ is flat. Their model may apply to large cyclonic storms, but most are not so large.
Until they can model the small scale instabilities that produce updrafts and downdrafts from which the Coriolis effect creates turbulent circulation and vortices, they are ‘not ready-for-prime-time.’
On the scale that we are talking about, the coriolis effect has no impact.
Coriolis seems to work on my kitchen sink – just how small a scale are we talking about here?
What do *you* think induces cyclonic circulation in hurricanes, typhoons, and tornadoes, if not the Coriolis effect?
What is the boundary line between Coriolis scales, and non-Coriolis scales, and what sort of thing happens at that boundary ??
G
One should not be giving all the credit to the “Coriolis effect” until such time that it has been proven that the action of “fluid logics” [gas or liquid fluidicis] is not the primary driver of in-line directional flow and/or “rotational and vortices” movement in minor to major volumes of gasses or liquids.
Me thinks a prime example of the action of “fluid logics” is the ….. Thermohaline circulation (THC) or the “great ocean conveyor belt”, to wit:
http://stempreacademy.hawaii.edu/sites/default/files/c-more/ocean-conveyor-belt/ocean-conveyor-belt-light_375px.jpg
Read more about fluid logics @ https://en.wikipedia.org/wiki/Fluidics
Why exactly would any “Thermo-haline” gradient prefer one direction of circulation over the opposite direction ??
The rotation of the earth has had the same direction of rotation for as long as we have been observing it, and nothing more than that, and the arrangement of the land and ocean masses is required to explain the gulf stream.
So the surface waters along the East coast of South America are not flowing anywhere, in either direction. There’s no Japan Current taking warm water North into the Arctic, and sending cold surface waters south along the west coast of North America. WE have cold surface water coming down from Alaska, NOT warm surface water.
The “thermo-haline” theory comes somewhat later in the credibility index, than either string theory or parallel multiverses. (strictly MY opinion of course; not for general dissemination, or PhD Oral Presentations).
PS I do NOT dispute published ocean current observed directions of motion; just what drives them.
G
george e. smith – June 6, 2016 at 9:02 am
george e.,
Given my limited experience with “fluid logics” I believe you have unknowingly asked the wrong question, …… with the correct question being: just what pulls them?
In other words, me thinks that when “molecular segment Y” of a gas/liquid current is forced into motion ….. then that “segment Y” does NOT force, push or drive “segment Z” in front of it forward, ….. but on the contrary, ……. “segment Y” pulls or sucks “segment X” along behind it.
Given the above sucking (cohesion???) motion then a “Thermo-haline” gradient would have no preference for one direction of circulation over the opposite direction.
I think the really important question is: Just how in ell did the THC “current” initially get started?
Did the THC “current” form as a very small “loop” many eons ago when Pangaea was the only continent …… and then grew larger and convoluted as tectonic processes created the current continental configuration?
So many questions, so few answers.
Well a famous Nobel Prize winning Physicist told me, over a beer, sitting out in a colleague’s cool garden, that no matter HOW SMALL a section of a sphere you select, the curvature of that piece remains exactly the same. It NEVER EVER goes flat.
G
I wanted to ask him about blackbody radiation (fictional), and also quarks (he knows quarks), but I was too embarrassed, so we talked about dinosaurs instead. (He had been out dinosaur hunting) He works(ed) at SLAC
Now, about model validation……
That sounds like Step 1.
Actually, step one is the observation of the phenomenon. That comes long before one tries to model it.
Give me the top ten Physical theories that were simply dreamed up in somebody’s head before anybody had observed any phenomenon.
Yes there are breakthrough theories, of unexpected phenomena, that subsequently are observed experimentally.
But those theories are ALWAYS developed as a result of the FAILURE of some already well established models and theories, of well known phenomena, to correctly explain some observations.
But I’m eager to learn about those top ten “gee whizzes”
G
“…puts the fine details back into computer simulations of large-scale phenomena like air circulation…” back into? No, they were/are not in the simulations.
“but they don’t include small-scale dynamics created by things like clouds and localized turbulence, despite the fact that those dynamics can often influence the larger scales…” Yep, clouds are small scale but “influence” the larger scales. So they aren’t important for an accurate model but they are important for an accurate model I guess.
“A few ad hoc ways of putting some of that information back in exist, but they tend not to be mathematically rigorous.” ad hoc you say? So somebody is just pulling numbers out of the air to come up with an answer that someone desires? And if the models contain “ad hoc” information, what does that say about the results of the models?
“Cloud formation is seen as the largest source of uncertainty in climate models right now,” Marston said. “There are famous examples where different climate models that have different ways of dealing with the clouds give you qualitatively different results. ” So there’s uncertainty in the models? From more than one source? Is this what you mean by settled?
Brown has got to stop letting those unemployable English majors write the press releases without vetting them with the professors.
So “might” might reduce margin of “might”?
😀
Might makes Right !
g
Even with additional complexity, chaos always wins in the end.
Buzzkill.
Let’s see what happens when you tweak garbage. Maybe you come up with perfume. Dunno.
or enhanced garbage.
Or Twerked Garbage
..Twittered Cow Droppings ?
enhanced garbage is valuable for some folks.
Paywalled, of course. I wonder if Brown is attempting to patent the approach, or maybe the code.
Just typical academic journal behavior. It looks very mathematical so unless you have a PhD in applied mathematics you probably wouldn’t get much out of it.
The abstract reminds me of the lyrics from the chorus of Both Sides Now by Judy Collins:
“I’ve looked at clouds from both sides now
From up and down and still somehow
It’s cloud’s illusions I recall
I really don’t know clouds at all”
They don’t know clouds at all.
Joni Mitchell. Comes up every time clouds are mentioned on WUWT.
“Cloud formation is seen as the largest source of uncertainty in climate models right now,” Marston said.
It’s a good thing Brad Marston doesn’t live near California. He might be tried as a blasphemer for even suggesting that climate science isn’t 100 percent and irrevocably settled. There is no uncertainty in climate science according to California politicians. And they should know because they are the world’s leading scientists and the smartest people around. Just ask them.
Clouds are not ‘small details’. They are generated by small scale mechanics, but they are large scale phenomenon. I am highly skeptical of a statistical system purporting to predict cloud behavior.
Statistics is manipulation of what we already know (exactly). It is not predictive of anything we do not know already. The act of prediction is a leap of faith.
G
More fudge to go around.
As usual they offer no verifiable tests to judge whether or not this tweak provides realistic results.
But I am sure there will be numerous publications using this technique and confirming that the pet theory was not influenced by clouds, water or other inconvenient non-linear factors.
It is my understanding that the GCMs are limited by our current computing power to cell sizes of about 100 km. Until the resolution can be improved by 3 to 4 orders of magnitude (to 10 – 100 m), they fail to capture the relevant detail and interactions of these complex self-organizing dissipative systems (atmosphere, cryosphere, hydrosphere, lithosphere, heliosphere). These quasilinear approximations may help cheat that a bit, but you still need correct parameterization of your forcing factors. There is still a long way to go before we can build a decent model of something as vast and complex as earth’s climate. That doesn’t mean we should stop trying, but don’t bet the farm on the results of our current crude attempts. Hell, I wouldn’t even bet on one of Toneb’s 3-day forecasts.
Design is the problem, not resolution.
Truth!
Actually you are talking about a sampled data system. So compliance with the Nyquist sampling theorem, is the very first thing you MUST achieve or your “measurements” are not even valid data.
And (b) your model, if correct, should agree with what is observed in the real universe.
So that means your calculated model values should all agree with the already known measured values at those points in the real system where you have actually made real observational measurements.
For example, if you are designing the Electron Optics of say a Cathode Ray tube; the “gun” and the deflection optics, using an electrolytic tank (one method), you can probe around in a two dimensional section of the three dimensional system, with a voltmeter probe, and plot iso-potential lines giving a map of the electric fields.
Then you can use Finite Element Analysis (FEA) to create expected values at points not yet measured, to look for parasitic effects that might be in need of corrective design alterations.
It is quite pointless to calculate a map of supposed real physical values of variables, and not be able to check them against real observed values at known locations.
G
Cmon people, why be so negative about a subject you clearly know little about? Treating the relations between mean flow and fluctuations goes back to Osborne Reynolds and after a century of development those methods have about run their course. These guys are advertising a novel approach based upon a spectral decomposition (perhaps capturing something like “large eddy simulation” method results at lower cost?). If the small scale interactions are locally statistically related to computable scales in a reproducible way (a testable assumption) this could produce worthwhile improvements in GCM fidelity with only modest additional computational cost. Will it work in climate simulation context? Time will tell, off the cuff criticism won’t.
CarlH June 3, 2016 at 3:34 pm
Thanks, Carl, but c’mon, why be so positive about a subject you clearly know little about? At present, global temperature results from the current crop of climate models can be very closely approximated (R^2 over 0.90) by merely lagging and scaling the input forcings. They are mere mechanical automatons, varying only in their inputs.
Not only that, but nobody has ever shown that the fluid equations used in the models actually converge. It’s one of the reasons that they don’t use finer gridcells—it gets unstable quite rapidly.
Add to that the fact that they cannot replicate the historical drops in global temperatures (e.g. 1940’s to 1970’s) without bogus specially-chosen values for their aerosol forcing … and often can’t replicate the drops at all. Take a look at the individual CMIP5 runs, many of them are a joke.
And this doesn’t even start to mention that the models have not been subjected to V&V (Verification and Validation) even as strong as the V&V applied to elevator software …
Nor have I ever seen an analysis of a “failed” run, or even a report of a “failed” run, so you can bet that all of their runs in the literature and in the archives are specially selected. We never get to see what gets left on the cutting room floor.
So yes, it’s possible that this new technique may assist as you say … but until some other major problems are solved, I fear it may well be lipstick on a pig.
Finally, I note that the majority of the negative comments are not about the new method itself. They are about the foolish, and in some cases even incoherent, claims made by the press release.
Best regards,
w.
PS—For more info on how mechanistic climate models actually are, and how their output is just lagged and scaled input, see:
You should enter this in the Bullwer Lytton prize competition. You’ve got more three dollar words in less space, than was previously thought possible.
Some of us make no bones about not knowing a whole lot about the climate in places we have never been. What we are concerned about is the claims made by others about what they think they know, about stuff they have never even measured.
But thanks a whole bunch CarlH; all my life I have wondered who the hell it was that came up with the Reynolds Number.
G
Pick a cell . . . any cell. Where do its initial values come from?
I pick 200 miles west of Easter Island. Got data?
“All previous models were junk. Our new, improved one isn’t so good. It will be better in the future.”
This will simply mean a rewrite of the cloud mechanics followed by tweaking of the model’s parameters to (allow the modelers to) get a better historical fit and we’ll have models that still can’t accurately project but we’ll not know it for another decade or two.
Did some research before commenting. Their new method, while interesting, does nothing to solve the fundamental climate model problem of attribution in parameterization. They still parameterize via ‘spectral averaging’. Clouds are still parameterized, only differently. So how much of the ‘new’ cloud parametization is anthropogenic, and how much is natural variation?
Their ‘proof’ of their method’s validity on atmospheric jets from a spinning sphere does NOT address or resolve the climate attribution problem in any way, shape, or form. Logic fail.
Quasilinear: Now there’s a word that screams pseudo science.
But will it impress the girls?
Democratic girls.
Actually I have just about enough basic math to understand what they are saying a little bit, and I think you may be misjudging these people.
If I understand correctly, this could actually be a fundamental breakthrough in non linear system analysis.
And if it is, and it is accurate enough, and it gets put into all those GCMs then they WILL start to predict at least more accurately, and guess what. That could be the end of AGW as we know it.
And its the perfect let out for climate scientists ‘well the models weren’t good enough, and that’s why we raised the alarm’ .
I think we should watch this one. It has a slight tremor of authenticity about it.
Rem,ember these are not climate scientists here. These are mathematicians. And physicists.
If nothing else, it might lead to a CFD package worth billions.
It’s a five sillabel word for nonlinear which is only four sillabels, just in case you are trying to write a Haiku.
g
ps. Nowhere in my entire thought process, about the role of clouds in earth climate, does it EVER occur to me that some of those clouds might be moving around microscopically and macroscopically, locally and globally, so that one needs to check on Reynolds numbers to determine the relevant fluid flow processes that might be going on.
To me, clouds are 4H-club dirt simple.
Solar energy runs into clouds; whatever clouds there are. Some of the solar energy is refractively scattered back out into space. Some additional components of the solar energy gets absorbed in those clouds; whatever clouds there are.
None of that solar energy ever makes it down to the surface of the earth as solar spectrum wavelength EM radiant energy, and get mostly absorbed in the ocean, with minor reflection from the ocean, or absorbed in the solid surface, with somewhat larger reflection from the solid surface.
The only thing that is of any consequence in those interactions, is HOW MUCH incoming solar energy doesn’t make it to the surface absorption and reflection interface, and that depends on HOW MUCH cloud there is and where that cloud is relative to where the solar radiant energy is arriving.
The solar energy is traveling at the velocity of light; about a one millisecond transit time through the atmosphere, to the surface.
The most violent atmospheric particle motions ever observed or conjured up by play station, don’t even show up as having moved at all, and affecting the resultant photon trajectory to the surface.
G
[When misspelling sylables incorrectly, ’tis best to have many sillybells as possible .. .mod]
In climate, it matters how cloud behaviour changes over decades. Working out a better method of dealing with “averaging out those small-scale dynamics” adds little or nothing to actual knowledge of cloud behaviour. Even if there is improvement, when fed back into one of today’s “climate” models the improvement will show up for a very short time only. I doubt they could get a single day out of it. The reason is that the “climate” models are really only low-grade weather models that are incapable of predicting anything. They can never have any value for climate prediction while they retain the same basic structure and methodology. https://wattsupwiththat.com/2015/11/08/inside-the-climate-computer-models/
If these researchers do find out more about how clouds work, then good luck to them. But until they feed results into a climate model (not a weather model) they won’t get a climate forecast.
Aye, there’s the rub. When I found I had to substitute a statistical routine in a model where I’d intended to use a determinate one, I knew I had lost. The model was doomed to fail eventually once it left the bounds of the statistical validation period. Often sooner.
When the average happens it is too late to do anything about it.
Nothing in the entire universe that is NOT human homo sapiens sapiens, is even aware of or responds to an average value of anything, or even the average value of everything.
Real systems operate in real time, and do everything they can do immediately. They do not wait for anything before doing anything, and nothing happens before it can happen, nor does it wait until it feels like happening. When it is possible to happen, it happens, not before nor later.
G
Well sure. It might improve the performance of the GCMs.
You actually need to HAVE a performance, before you can improve it.
GCMs don’t even take notice that the whole earth is circulating every 24 hours. KT has it stationary, no circulation at all.
G
Must have missed the seminal paper explaining how deeply non-linear dynamical systems are now readily computationally accessible. The usual total junk “science” which has become so tedious it’s barely worth commenting on.
Here is my main comment on this new method’s applicability to climate science.
The problem is not that we are unable to model the clouds properly on a computer.
The problem is that we don’t understand the clouds properly in the real world.
As a result, I suspect that this new method of modeling the clouds may indeed be of use … but only once we actually understand the clouds.
w.
Willis Eschenbach June 3, 2016 at 5:17 pm
————————————————–
Touché Willis.
Again, how clouds work is basic physics at the partial differential level. The problem has always been integrating that to a macro description of behaviour.
In the context of climate we aren’t for example interested in the exact shape of every cloud: we want to know how BIG they are and maybe what temperature, and that’s about it.
The problem is that numerical integration leaves a huge amount of detail as to the exact shape that is ultimately ‘too much detail’ You need to know it to predict how the cloud will evolve, but its not part of what you are doing the analysis for.
So this is typical of a branch of maths that gets a good approximation to the value you want to examine, and ignores the data that is present in the real world, that you dont want to know. As all linear systems are. You dont need to know the speed and location of every molecule to know how much pressure is in your car tyres.
Hence the term ‘quasi linear’ which to me means ‘linear enough so we can use linear systems analysis without ending up in the doggy doo’.
So a word of caution: Just because 97% of what ends up reported here as science is grant grabbing gobbledeygook, doesn’t mean it all is.
I have to query what exactly is meant by ‘dont understand’ here. If it means ‘don’t have a usable model for’; then OK, but then this methodology may allow the development of such.
And as I understand it, the problem is exactly the other way around. We do understand how clouds work – at the partial differential level – but we can’t model that on a computer less than the size of the whole planet on any sort of detail in any sort of sane time.
And the various techniques for putting in a fudged value for clouds in GCMs are simply not good enough.
The proof of the pudding is in the eating however, and I can assure you as an engineer, that if this technique works and gets popped into the top level CFD packages and if it works, money will flood into the coffers of these scientists if they play their cards right.
At the moment whole car or whole boat or whole aircraft analysis for turbulent flow – which is a the main component of drag at speed – is impossible to simulate numerically, which is why we still have wind tunnels and boat pools full of water.
If this technique works then its the equivalent maybe of a couple of orders of magnitude increase in computer power.
The jury is still out, but these guys may have just opened the door to a step change in accuracy of fluid dynamic numerical modelling. And that’s a small goldmine and a major breakthrough.
I dont understand the cynicism and negativity. AGW models don’t work, and yet computer models in general often do. This ought to make them better. Better AGW models might actually start to track reality. Surely we aren’t afraid of that?
We aren’t all here because we believe and want to believe that AGW isn’t happening surely. I thought we we here to understand the real situation, whatever it is.
Leo Smith June 4, 2016 at 1:22 am
We aren’t all here because we believe and want to believe that AGW isn’t happening surely. I thought we we here to understand the real situation, whatever it is.
———————————————–
“””whatever it is.”””
Combination of a lot of different factors in the realm of natural variability. Separating the different factors out from the human contribution has become quite challenging.
Do I think that mankind has raped, pillaged, burned and destroyed areas of our planet.
For the sake of, taking care of an ever perpetually growing population base.
Sometimes we just need to think about what mankind has really done.
Sewers, tunnels, ‘electrical wiring, roads paved, structures, cities, depopulation of animal kingdom, over fishing, deforestation, ‘mining iron ore, gold, copper lead, water use, land use, just to name a few.
There is a big growing problem…but don’t forget about the greenhouse gases and pollution, which are only a symptom of the bigger growing problem.
AGW theory seems to revolve around LWIR and CO2 and “mean global lower tropospheric temperature anomaly” ,whatever the hell that is.
Absolutely none of that has anything to do with clouds. (meaning it doesn’t link to clouds).
But solar TSI, clouds and “mean global lower tropospheric Temperature” do seem to be related.
But nobody is making measurements of the latter feedback system; only the first one; which isn’t even a feedback system.
No atmospheric dynamic differential process is anywhere near fast enough to have any effect on the transit of a photon through that atmosphere. As far as solar energy absorption by the earth, the atmosphere is a completely static system. The most violent of tornadoes, or hurricanes does not change the direction of a TSI photon, as a consequence of the differential motions of molecules in that atmosphere. The effect would be the same if every single molecule in the atmosphere stood absolutely still long enough for the photon to pass through to the surface (or not).
G
I would have thought you can reduce a system to statistical output simplicity once you’ve captured all of the data and understand the mechanism/s in their entirety. Then you can show a mean trend.
How they plan to predict the future with most of my above not even being true, is beyond me.
“As a result, I suspect that this new method of modeling the clouds may indeed be of use … but only once we actually understand the clouds.”
It’s clouds’ illusions after all?
We really don’t know clouds at all.
What’s to understand Willis ?? Clouds reduce the solar energy that earth absorbs. How much clouds, affects how much solar energy we don’t get. We don’t have any monitoring system UNDERNEATH the clouds to measure how much solar energy we get, so we don’t know how much we get.
Nothing to understand. More clouds less solar energy; less clouds more solar energy.
G
But you are correct. X-box clouds are of no use to us.
The difference between clouds and no clouds make as much as 100W/m^2 in surface forcing from space (to space), and water vapor carried in the air can make a +20F change in daily high temp, and we are comparing these 2 natural regulating mechanisms to the added forcing of 3.7W/m^2
Sorry for the pass first read and comment well done will I really have no idea how they can publish this stuff and sleep at night .
Cloud formation is too complex, localized and random to model on a global scale without way more observation and analysis of the cloud nucleation mechanisms with present (and future) technology.
You have to be able to predict cloud formation before you can predict it’s impact on climate.
Since the atmosphere is a smallish secondary effect of the ocean, start with that.
Wonderful Climate….
Just as long as they realise that every one of those small scale phenomena is a negative system response to any forcing element that might be trying to move system temperature away from that set by insolation acting on mass suspended off the surface within a gravity field and able to convect freely.
If the new model bumbles along the current trend and looks really boring I will be pleasantly amazed.
If it rockets up towards Thermageddon I will do a Gallic shrug.
If it plummets towards a new glaciation I will move my money in to coal.
Who knows, but the quasilinearity (whatever that means) of the method at small scales seems to ignore a crucial (mathematical) issue in this type of dynamics (whether physically relevant is another matter, it seems). To quote Tao of NS and other fame,
“behaviour of three-dimensional Navier-Stokes equations at fine scales is much more nonlinear (and hence unstable) than at coarse scales”.
On the topic of clouds and cloud formation, I recently found this article.
Focus on high energy particles and atmospheric processes
RGiles Harrison1, Keri Nicoll1, Yukihiro Takahashi2 and Yoav Yair
6 October 2015
Abstract
The letters published in the ‘Focus issue on high energy particles and atmospheric processes’ serve to
broaden the discussion about the influence of high energy particles on the atmosphere beyond their
possible effects on clouds and climate. These letters link climate and meteorological processes with
atmospheric electricity, atmospheric chemistry, high energy physics and aerosol science from the
smallest molecular cluster ions through to liquid droplets. Progress in such a disparate and complex
topic is very likely to benefit from continued interdisciplinary interactions between traditionally
distinct science areas.
3.2. Layer cloud global circuit mechanism
Vertical current flow occurs throughout the atmosphere in fair weather regions through the global atmospheric electric circuit—a conceptual legacy of CTR Wilson—and cosmic ray ionisation. This provides a coupling mechanism between electrically-induced changes, for example from ionisation changes, and low level layer clouds. The ion current’s passage through the cloud-clear air boundary, which also represents a change in electrical conductivity, leads to local charge separation in the droplet formation and evaporation region, in proportion to the current.
Voiculescu et al (2013) found a positive relationship between mid-latitude cloud cover and the interplanetary electric field, which they considered could be occurring through the global circuit mechanism. A further suggestion of a global circuit effect was made by Lam et al (2013), as part of the atmospheric response to the By component of the Interplanetary Magnetic Field. Lam et al (2013) showed differences in the surface pressure patterns between large and small circumstances of By. A defining characteristic of the global circuit is its single maximum diurnal variation, known as the Carnegie curve (Harrison 2013). Harrison and Ambaum (2013) reported an averaged diurnal variation in cloud base properties similar to that of the Carnegie curve, in separate series of data obtained during the polar night in the northern and summer hemisphere. Harrison et al (2015) have shown a sensitivity of cloud droplet distributions to charging of small droplets, such as that typical of layer cloud electrification induced by the global circuit….
http://iopscience.iop.org/article/10.1088/1748-9326/10/10/100201/meta
One of the things I like to tease the modelling community with is how much energy is dissipated by a wee little tropical cyclone – not a Katrina or anything big enough to earn a name, just a little one with an eye perhaps 1km across and a surface peak wind speed of 100km/h. The calculation of the mass flows and energy involved in accelerating the air in that volume to those speeds is actually quite simple. The power is in the 100TW range – and is omitted in every model because the scale is just too small for them to capture. So perhaps they can start to take these little things into account – it might just improve their ability to predict something, which right now they can’t do.
” The power is in the 100TW range”
I estimated Hurricane David carried about 1/4-1/3 the volume of Lake Erie dropping it all the way into Canada.
That’s a lot of power.
Your storm, which is a category 2 hurricane, is put in perspective by the 173,000 terawatts of incoming solar radiation. The power of your storm represents .06% of incoming radiation. This is probably not going to throw off the conclusions by very much. Particularly as we only get tens of these each year, and most of the time there is not one of these going on. Perhaps the modelling community needs to tease you a little more.
No, I described a tropical storm, not a hurricane. National Hurricane Center reports “the 1981-2010 averages are 12 tropical storms, 6 hurricanes, and 3 major hurricanes”The energy of a hurricane is of the order of 10 greater and a major hurricane 10 greater again, so the average annual energy dissipated by all classes of storm is some 370 times higher, i.e. about 37 000TW, or some 20% of the incoming radiation.
I wish that a true expert in computational fluid dynamics would weigh in on this. The global climate models all use the Navier Stokes equations as a starting point. Why they do that is anyone’s guess. The analysis of the flowfield around a subsonic aircraft (whose surfaces are closely modeled, and have few features) with the Navier Stokes solvers available still requires Reynolds averaging, because no computer can handle the number of grid points needed to resolve the finest scale of turbulence. And steady-state solutions are accurate to only a few percent. Still, aircraft CFD models can get down to the millimeter range if necessary, and that is fairly impressive resolution. But an aircraft may be 180 feet long. The earth is 20,900,000 feet radius. And the atmosphere is 80,000 feet deep (for CFD purposes). An aircraft model might have up to 1 billion nodes, and achieve a turbulence resolution of a few millimeters. AN earth model using the same number of nodes, achieves a resolution no better than 100 km horizontal, and a few 10s of meters vertically. But the scale of significant turbulence on earth is on the order of a few feet (a tornado).
No one knows whether Reynolds averaging is valid over such a tremendous scale range. But the fact that aircraft CFD is no more accurate than a few percent for steady state, combined with the fact that there is no such thing as a steady state solution for the earth’s atmosphere, should raise some questions about the validity of models. How can a high-resolution model no better than a few percent accurate for a steady state solution be expanded to a low-resolution model run for a hundred years with no steady-state solution possible even in principal, and be expected to yield meaningful results? I just don’t get it.
None of that fluid dynamics, whether at the aeroplane scale or the global scale, can have ANY possible influence on the trajectory of a solar photon transiting the atmosphere in less than one millisecond. ( I took the liberty of setting the atmosphere at 300 km thick. I have no idea how many Navier Stokes or Reynolds feet that is)
G
Life, the Universe, and Climate Change… As a glider pilot, the core challenge is to locate “lift”, which could be columns, rivers, or pockets of rising air and fly in them to gain altitude. Different terrain produces different types of lift; deserts, forests, lakes, oceans, fields of corn; it’s all different. Different latitudes produce different lift at different times of the year; it’s all seasonal too. Golly, I wish I had thought to check with the experts at Brown University to take some of the guess work out what’s happening with the winds aloft. That’s why I always laugh when I see cartoon-like drawings that are said to depict the function of a multi-variable dynamic system. Watts per meter squared = 42 lol If only I had known THE answer!
mairon62 – June 5, 2016 at 1:06 am
HA, that reminded me of many years ago (late 1960’s) when a good friend and work associate who was an Electrical Engineer ….. and an avid glider pilot …. told me that he was going to design himself a “thermal detector” to attach to the wing tips of his glider.
“Original thinkers” are far n’ few between.
I’ve looked at clouds from both sides now…
So in the end the folks at Brown have created a way to incorporate something huge into models that hasn’t ( by the admission of the experts) been adequately taken into account. Of course, even without this, the models were somehow accepted as being something we could rely on. Now with the inclusion of the Brown methodology, they will include the effects of clouds…or at least they will be advertised as including the effects of clouds.
Maybe in 500 years we will know if this actually works.
YES The past 400 years Humans are causing SOME Global Warming fossil fuels and Deforesting releasing carbon , Something Bigger Is Happening.
Need to think outside the box, the solar system is not alone and not stationary it is on the move , the solar system is reacting to things humans do not understand in the galaxy the Milky Way.
The system and “”all of its planets”” are showing signs of change, even the Oort cloud showing signs of change more deep space Comet diving into the sun, Earth’s north and south auroras have been massive in the past years, this strange activity has to be caused by something.
Here’s my out-of-the-box thinking , Scientists throughout the World know about Dark Energy and Dark Matter Scientists have measured it but still do not know what it is , my thinking is our solar system and all of its planets and the Oort is Passing through a Swirl or an Arm of Dark Energy or Dark Matter which is not affected by the Heliosphere Causing Unusual Friction, Causing These Changes on the Earth and Solar System and its planets.
“”Sun Is Reacting to This Strange Galaxy Matter Energy more solar flares “” causing planet atmospheric change. Warming up planet Earth releasing methane deposits in the permafrost regions.
A nether thought ,, The Earth’s surface is a Natural Heat Disperser or Heat Sink of Solar Energy, also the earth reflects Solar Energy back into space , Solar Panels and Solar Collectors for heating sodium or oils capture the sun’s energy and releases it straight into the atmosphere.
(( Have you touched a Solar Panel in Full Sunlight , you can not do it too hot , it will Severely Burn You )). There are Billions of Black Solar Panels scattered over the Entire Globe.
Hot Black Panels “Releasing Heat Directly into the Atmosphere” heating the atmosphere.