tag:blogger.com,1999:blog-7729093380675162051.comments2017-11-25T07:17:50.713+11:00moyhuNick Stokeshttps://plus.google.com/103029875534779648576noreply@blogger.comBlogger8225125tag:blogger.com,1999:blog-7729093380675162051.post-59636980466749564932017-11-25T07:17:50.713+11:002017-11-25T07:17:50.713+11:00I wrote a response to this and published it here, ...I wrote a response to this and published it here, but apparently it got swallowed up by the computer. (Yours or mine, I don't know). <br /><br />Reading the paper a bit more thoroughly, I have to recant my original statement. If you actually look at the equations, there's more wrong with the paper than what I originally said. <br /><br />His "time-stepping" equation #6 doesn't actually deal with any kind of real time; it assumes equilibrium at each step. When you reach a new forcing, you automatically reach the new equilibrium temperature for that. While, yes, there is an "integral" in this step, it's just integrating over past forcing changes to get the current instantaneous forcing value. It's not integrating over past time to get the heat retained. <br /><br />Then, in equation #8, he adds the concept of an uncertainty in the forcing. Which is still fine, but then he tries to integrate over that uncertainty *as a function of time*, as if this was-always-at-equilibrium model suddenly has a real time aspect to it. <br /><br />Basically, he's mixing two different kinds of models; one that's at equilibrium at every step, and one that's not. From that, he gets mixed up about whether or not you can integrate over the uncertainty with respect to time. In a real time-sensitive model you can; in his linear always-at-equilibrium model, you can't. Windchasershttps://www.blogger.com/profile/11554275410734284781noreply@blogger.comtag:blogger.com,1999:blog-7729093380675162051.post-25812417518866840332017-11-25T02:35:30.554+11:002017-11-25T02:35:30.554+11:00Part of the problem that you have DY is that you a...Part of the problem that you have DY is that you are focusing your concern on limitations of the math instead of solving the geophysical problems. Case in point is the physics of QBO. If you look at the reams of math that Lindzen published on the topic and then step back and try to make any sense of it, you can't and it's likely all gibberish. But the basic physics behind the cause of the QBO monopole is pretty simple. It's just the draconic tidal forcing coupling to the seasonal cycle which produces the oscillations. Fitting a short data interval to a tidal model will reproduce the rest of the time series better than all the math that Lindzen has produced. <br /><br />There is no compounded numerical error here and any discrepancies from the model that are observed, such as the recent anomaly in QBO, will eventually get corrected. Just like ENSO, it's a forced response system.<br /><br />So to put it simply, you and Linden and the rest of you AGW deniers are essentially trying to solve the wrong problem! Your entire premise is invalid.@whuthttps://www.blogger.com/profile/18297101284358849575noreply@blogger.comtag:blogger.com,1999:blog-7729093380675162051.post-34074965041126987492017-11-24T14:46:05.227+11:002017-11-24T14:46:05.227+11:00It is going to be published. Am presenting at the ...It is going to be published. Am presenting at the AGU next month in NOLA, and the details will be in a book titled Mathematical GeoEnergy to be published by John Wiley late next year.<br /><br />I guess you have never done any geophysics models, as the topic I brought up is completely relevant to this thread. I am integrating Laplace's tidal equations with a lunisolar forcing to fit to the ENSO behavior. Numerical error of course would be a concern if I were using a brain-dead chaotic model such as a Lorenz formulation, but I'm not. The perturbation from linear systems are mainly in the applications of a slight Mathieu modulation and with seasonal delay differentials. Like I said, the numerical error does not accumulate since the strong forcing will constantly compensate any natural response that is completely dependent on initial conditions. A good example of this is in a conventional tidal prediction -- a tsunami could come along and wreak havoc via a natural response for a few days, but after that the forced response would continue as if the tsunami never happened. There is no numerical error accumulation here.<br /><br />So it's your own problem if you think "numerical error in time accurate chaotic systems" is an issue, because it is an issue in your own mind and not in the important class of problems in climate variability that are controlled by ENSO and other oscillating dipoles. <br /><br /><br />@whuthttps://www.blogger.com/profile/18297101284358849575noreply@blogger.comtag:blogger.com,1999:blog-7729093380675162051.post-45961723701122162602017-11-24T13:06:46.381+11:002017-11-24T13:06:46.381+11:00Well Paul, I would encourage you to publish it so...Well Paul, I would encourage you to publish it somewhere so we don't have to take just your word for it. It is odd you say that no-one needs the concept of an attractor, yet chaotic systems all have at least one strange attractor and its the basis for them not "blowing up." In any case, you are talking about something that is off topic in this thread. Nick was talking about numerical error in time accurate chaotic systems.David Younghttps://www.blogger.com/profile/17029429374522399227noreply@blogger.comtag:blogger.com,1999:blog-7729093380675162051.post-66797333673221212282017-11-24T12:55:05.547+11:002017-11-24T12:55:05.547+11:00Not at all a talking point, as it has everything t...Not at all a talking point, as it has everything to do with applying realistic geophysics to solving a problem. Over the centuries, no one ever needed to apply the concept of an "attractor", when it was straightforward to apply Newton's laws of gravitation to figure out the dynamics of a behavior such as the ocean tides. And this has turned out to be a stationary system. Same idea applies to ENSO, as the longer period tides impact the thermocline. There are no parameters to adjust as tidal periods are fixed, and as Lindzen pointed out periods that match to tidal periods must be due to tidal forcing <a rel="nofollow">http://imageshack.com/a/img922/3724/Du33JE.jpg</a><br /><br />Hundreds of years of applying the lunar gravitational forcing to tidal analysis, yet Lindzen never thought to apply it to ENSO. And of course, a turbulence modeler stuck in the trees isn't going to see the forest either.<br /> <br /><br /><br /><br /><br /><br />@whuthttps://www.blogger.com/profile/18297101284358849575noreply@blogger.comtag:blogger.com,1999:blog-7729093380675162051.post-71666253178698873992017-11-24T06:27:52.533+11:002017-11-24T06:27:52.533+11:00Paul, Your comment is just a talking point. It i...Paul, Your comment is just a talking point. It is a totally unsupported assertion about the strength of the attractor. In the case of non-stationary systems such as the climate, such assertions are not really scientific. They are instances of "everytime I run the code, I get something reasonable." Every time I've investigated such claims, they prove to be false or due to selection bias. This is true even for steady state turbulent flows.<br /><br />I'm not going to spend time on your ENSO models until you do the work to validate them including a careful sensitivity study of all parameters. You should have no trouble publishing it in a good journal if you have done the work correctly. There are thousands of such theories out there. It is preferable to spend time on real turbulence models where the scientific basis (while not rigorous) is at least grounded in a hundred years of careful experiments for boundary layers.David Younghttps://www.blogger.com/profile/17029429374522399227noreply@blogger.comtag:blogger.com,1999:blog-7729093380675162051.post-7833489729345748372017-11-24T01:07:27.278+11:002017-11-24T01:07:27.278+11:00Have you tried spatial averaging on sparse data in...Have you tried spatial averaging on sparse data in the land-ocean data set eg from 1850-1900? I think everyone agrees that local infilling is much more optimal on post-1950 data sets, but not much analysis has been done on the sparser data in the early record. <br /><br />This is important to constrain global T since pre-industrial baseline (say 1850-1879 as in Haustein et al). deepclimate.orghttps://deepclimate.org/noreply@blogger.comtag:blogger.com,1999:blog-7729093380675162051.post-37230207472466664152017-11-23T18:36:35.559+11:002017-11-23T18:36:35.559+11:00These guys are a bit nutty when it comes to unders...These guys are a bit nutty when it comes to understanding the physics. These are not initial condition problems but boundary value (i.e. forced response) problems. Any accumulation of error is compensated by an over-riding forcing or driving stimulus that will get the response back on track.<br /><br />For modeling, the real issue is not a butterfly effect but a hawkmoth effect, whereby mst of the effort involved is in determining the structural parameters and forcing that produces the observed result.<br /><br />Hawkmoth problems: ENSO, QBO<br /><br />@whuthttps://www.blogger.com/profile/18297101284358849575noreply@blogger.comtag:blogger.com,1999:blog-7729093380675162051.post-89910556984364418272017-11-23T15:32:51.246+11:002017-11-23T15:32:51.246+11:00Everett, I have already investigated the issue. ...Everett, I have already investigated the issue. There is the shadowing method of Wang from MIT, but it is computationally infeasible for at least a couple of decades. I do not portend the "death of numerical methods." That's an ignorant misrepresentation. There are very well developed numerical methods for well-posed problems that really work. Steady state CFD is very useful. However, for time accurate chaotic calculations, there is really nothing that gives much confidence. You are smart enough to do some work. Start with Wang's paper and then move on to the new LES paper on "alarming lack of grid convergence."David Younghttps://www.blogger.com/profile/17029429374522399227noreply@blogger.comtag:blogger.com,1999:blog-7729093380675162051.post-56415577013765237962017-11-23T14:14:57.750+11:002017-11-23T14:14:57.750+11:00Google ... control numerical error when the adjoin...Google ... control numerical error when the adjoint diverges ...<br />https://www.google.com/search?q=control+numerical+error+when+the+adjoint+diverges&oq=control+numerical+error+when+the+adjoint+diverges&aqs=chrome..69i57.1887j0j7&sourceid=chrome&ie=UTF-8<br /><br />David Young portends the death of numerical methods and modelling? I think not.<br /><br />Enough with the Debbie Downer and Negative Nancy (and Pat Frank) POV already.<br /><br />How do you propose to actually define error?<br /><br />The AOGCM's/ESM's only have to be good enough, they were never meant to be perfect, setting the bar too high, because you think so, only means that you are fated to fail.<br /><br />David, your reply here is good as far as it goes, but seems to me to focus on the trees rather than the forest.Everett F Sargenthttps://www.blogger.com/profile/00201577558036010680noreply@blogger.comtag:blogger.com,1999:blog-7729093380675162051.post-18721963068122758062017-11-23T13:03:26.018+11:002017-11-23T13:03:26.018+11:00Nick, I really think you are not being fully scie...Nick, I really think you are not being fully scientific in this post. Franks is surely wrong and unfortunately quite persistent. But the more fundamental question I asked that is raised by Franks work remains unanswered. I've spent a lot of time and talked to a lot of very smart people about it and I believe there is no convincing answer given the limitations of our current knowledge and numerical methods.<br /><br />David Younghttps://www.blogger.com/profile/17029429374522399227noreply@blogger.comtag:blogger.com,1999:blog-7729093380675162051.post-24120607888877934372017-11-23T09:25:37.650+11:002017-11-23T09:25:37.650+11:00Bill
"then dividing by the (dimensionless) nu...Bill<br /><i>"then dividing by the (dimensionless) number of years"</i><br />Yes. Pat insists that instead you divide by years (or Greeks) and the dimensions change accordingly. That leads to ridiculous results; for example, standard deviation would have units m/sqrt(Greek). <br />Nick Stokeshttps://www.blogger.com/profile/06377413236983002873noreply@blogger.comtag:blogger.com,1999:blog-7729093380675162051.post-39958656378373664182017-11-23T09:00:37.856+11:002017-11-23T09:00:37.856+11:00DY said:
"I'm more focused on how to rea...DY said:<br /><br /><i>"I'm more focused on how to really control numerical error when the adjoint diverges. Classical methods will fail and results can be very inconsistent. You can of course apply gross approximations like error per unit step for time step size control and use implicit methods that are LINEARLY stable, but linearly is the key word there.<br /><br />How do you propose to actually control error?"</i><br /><br />Why is this AGW denier so interested in working weather simulations, when the climate simulations are much more important? <br /><br />ENSO is one of the primary behaviors that controls natural variation in climate, yet can be straightforwardly modeled without having to resort to overtly complex models. All that is required is a near equivalent tidal forcing model, which is akin to working a conventional tidal analysis.<br /><br /><a href="http://contextearth.com/2017/11/22/the-enso-forcing-potential-cheaper-faster-and-better/" rel="nofollow">http://contextearth.com/2017/11/22/the-enso-forcing-potential-cheaper-faster-and-better/</a><br /><br /><br />@whuthttps://www.blogger.com/profile/18297101284358849575noreply@blogger.comtag:blogger.com,1999:blog-7729093380675162051.post-49955735853432168602017-11-23T06:54:29.032+11:002017-11-23T06:54:29.032+11:00(Reading Frank's paper)
It looks like Frank u...(Reading Frank's paper)<br /><br />It looks like Frank uses a linearized connection between temperature and forcing. E.g., this is equation #6, on page 15. <br /><br />T_i = T0 + SUM( alpha*F_i )<br /><br />where alpha is some constant I'm using to simplify his equation. <br /><br />Certainly if you have a linear iterative equation, and the input forcing at a timestep, F_i, is subject to a Gaussian variation at each timestep, then the end result will be an ever-expanding range of possibilities for the temperature. This is his equation #8, on page 31:<br /><br />T_i +/- (temp uncertainty) = T0 + alpha*(F_i +/- (forcing uncertainty) )<br /><br /><br />Note that he dropped the integration here (seriously, wtf), but let's pretend he didn't. <br /><br />I don't think the point about changing units is a fundamental issue. You can resolve that. If integrating properly, a forcing over a given period of time becomes heat retained, then that heat retained becomes temperature via the heat capacity. All of this ends up being encapsulated in that constant. The forcing is already time-sensitive (in units of energy/area/second), so the size of the timestep is generally irrelevant for the ultimate result. You get an ever-expanding uncertainty of the temperature. <br /><br />*If* you use a linear-integrative model for the forcing-temperature relationship, then yes, an uncertainty in the forcing propagates through as he claims. <br /><br />...So... the problem is really that the models don't use iterative linear relationships between the forcing and temperature. Windchasershttps://www.blogger.com/profile/11554275410734284781noreply@blogger.comtag:blogger.com,1999:blog-7729093380675162051.post-31193361851736155582017-11-23T02:12:47.945+11:002017-11-23T02:12:47.945+11:00Olof R - thanks. I'm printing out your graph s...Olof R - thanks. I'm printing out your graph so I can mark the months and compare the ONI periods and the weekly SST maps: just for fun. Looks like the JIASO PDO may finally sag into negative numbers. Nino 1.2, on the other hand, has suddenly warmed. The La NiĆ±a is off to a rocky beginning. - JCHAnonymousnoreply@blogger.comtag:blogger.com,1999:blog-7729093380675162051.post-12070953401510032652017-11-23T00:57:43.160+11:002017-11-23T00:57:43.160+11:00JCH, Argo Marine Atlas has updated through Oct now...JCH, Argo Marine Atlas has updated through Oct now, so just use the links above and updated chart will appear.<br /><br />The large OHC increase continued in September and October.<br />Only SST and top 100 m are cooling.<br /><br />Regarding the seasonal OHC change in recent years, I would describe it such as the SH oceans accumulate relatively more heat now, and have become more dominating in the global OHC.<br />The solar insolation to Earth is largest in the SH summer (january) so a change in albedo (ice and clouds) could explain the altered seasonal pattern in OHC..<br />Olof Rhttps://www.blogger.com/profile/18244733455655978307noreply@blogger.comtag:blogger.com,1999:blog-7729093380675162051.post-66323391029648882212017-11-22T23:56:06.524+11:002017-11-22T23:56:06.524+11:00oops, the last word in my previous post should be ...oops, the last word in my previous post should be "celsius", not "years"bill hnoreply@blogger.comtag:blogger.com,1999:blog-7729093380675162051.post-1647126477420637062017-11-22T23:34:28.243+11:002017-11-22T23:34:28.243+11:00Nick, Thanks for providing an account of this stra...Nick, Thanks for providing an account of this strange business with a far higher signal to noise ratio than can be found on WUWT. I did come across the post attacking Annan on one of my rare visits to WUWT, but lost interest over some weird argument as to why Annan should not be taken seriously as a scientist because express he expressed an uncertainty as a magnitude without a preceding "+/-". Having worked in metrology myself I know it is standard to drop the "+/-" term when talking about uncertainty. <br /><br />As for the weird temperature/year units for averaging annual temperatures isn't the obvious refutation that taking an average involves summing a series of temperatures, units celsius, giving a result also with units of celsius, then dividing by the (dimensionless) number of years to give an average also with units of years?bill hnoreply@blogger.comtag:blogger.com,1999:blog-7729093380675162051.post-87710504657470211592017-11-22T14:52:21.960+11:002017-11-22T14:52:21.960+11:00Nick, I can't seem to get the reply button to...Nick, I can't seem to get the reply button to work so I'll put this at the end. I'm not sure that gravity waves are an important issue here. I'm assuming they can be filtered out just as acoustic waves are.<br /><br />I'm more focused on how to really control numerical error when the adjoint diverges. Classical methods will fail and results can be very inconsistent. You can of course apply gross approximations like error per unit step for time step size control and use implicit methods that are LINEARLY stable, but linearly is the key word there.<br /><br />How do you propose to actually control error?David Younghttps://www.blogger.com/profile/17029429374522399227noreply@blogger.comtag:blogger.com,1999:blog-7729093380675162051.post-39831303567305828462017-11-21T17:34:30.830+11:002017-11-21T17:34:30.830+11:00For a behavior like ENSO, there is absolutely no d...For a behavior like ENSO, there is absolutely no dependence on initial conditions. The behavior is completely forced by lunisolar cycles applied to Laplace's tidal equations. <br /><br /><i>"this is a classical ill-posed problem."</i><br /><br /><br />No it's not. So much for David Young's continual harping on the difficulty of modeling climate because the math is too difficult. I have often noticed over the years that people will use the "ill-posed problem" canard when they assume since they themselves can't solve a particular problem, then nobody can.<br /><br /><br />@whuthttps://www.blogger.com/profile/18297101284358849575noreply@blogger.comtag:blogger.com,1999:blog-7729093380675162051.post-50676640393071836002017-11-21T08:12:51.724+11:002017-11-21T08:12:51.724+11:00David,
"You mention Rossby waves which involv...David,<br /><i>"You mention Rossby waves which involve small pressure gradients."</i><br />I'm not talking about Rossby waves. Just ordinary atmospheric gravity waves, which normally don't get much attention because they don't do much, unless there is a breaking phenomenon or some such. In an acoustic wave, energy oscillates between elastic, when a converging flow increases pressure, and kinetic, when the resulting pressure gradient accelerates fluid. For long wavelengths (order horiz grid length) in the atmosphere, converging flow horizontal also creates potential energy as the air rises. The waves may not have much climate effect but they are important computationally, because they have to be resolved adequately in time, else they will not converge energy and may grow. The need to resolve them bounds the maximum timestep (Courant). Because the air can respond to converging flow by rising as well as compressing, it isn't as stiff so the wavespeed is less than acoustic.<br />Nick Stokeshttps://www.blogger.com/profile/06377413236983002873noreply@blogger.comtag:blogger.com,1999:blog-7729093380675162051.post-51789192763655464072017-11-21T07:52:27.065+11:002017-11-21T07:52:27.065+11:00The argument I like the best is your "but the...The argument I like the best is your "but the answer would be different if you used units other than years" as it doesn't require examining anything about the system other than the problem as formulated by Frank, but here is how I thought about this:<br /><br />The intuition is whether a baseline error compounds or stays constant. Here are 2 very simple physical examples of each:<br />1) I want to estimate the height of a lego tower that is being constructed on a table. I know the height of a single brick, but I don't know how high the table is. It is obvious that the error of the table height stays constant regardless of how many bricks I add. <br />2) I want to estimate the location of a car. I know how much the car speeds up or slows down, but I don't know its initial velocity. Here, the initial uncertainty (in m/s) is multiplied by the number of seconds at which I make my calculation in order to provide uncertainty in car location at that time. <br /><br />Pat Frank thinks he is living in the 2nd example. To me, it seems fairly clear that we are living in a world more like the 1st example. And the proof in the pudding is that we can construct different models, with different assumptions about table height, and yet they calculate very similar lego tower heights. If we were in world 2, then different models with different assumptions about car velocity would, by definition, get different answers about car location. <br /><br />Now, an initial error in W/m2 in long wave cloud forcing is a bit more complicated because for the model to approximate an earth-like climate while getting cloud forcing wrong, it must also be getting other parameters wrong to produce compensating errors, and some of these errors likely have an influence on climate sensitivity to forcing. So, unlike the table example, uncertainty in long-wave cloud forcing does not produce zero uncertainty in lego tower height... but, again, an obvious way to measure the impact of that uncertainty is to examine a group of models with forcings that span the uncertainty range. I'd argue that the uncertainty propagated as a result of long-wave cloud error and its accompanying compensating errors is almost certainly smaller than the range of model results - I have trouble imagining a scenario where it could be larger. It certainly doesn't blow up to a 40 degree error after 100 years. <br /><br />-MMM<br />Anonymousnoreply@blogger.comtag:blogger.com,1999:blog-7729093380675162051.post-23959196335352769072017-11-21T06:06:20.871+11:002017-11-21T06:06:20.871+11:00Well, Thats one issue. Isaac Held has a nice pos...Well, Thats one issue. Isaac Held has a nice post on this from a couple years back showing some restricted domain conviction modeling. There is a strong dependency on the size of the domain. So there is computation evidence for what is really settled science, viz., this is a classical ill-posed problem. It is a testament to the denial in the "colorful fluid dynamics" ether that is is necessary to rehash such classical results.David Younghttps://www.blogger.com/profile/17029429374522399227noreply@blogger.comtag:blogger.com,1999:blog-7729093380675162051.post-53290153331993838342017-11-21T05:15:14.103+11:002017-11-21T05:15:14.103+11:00"Tropical convection is much less well modele..."Tropical convection is much less well modeled"<br />Does that refer to the missing (weak) tropospheric hotspot?<br /><br />(I believe that there are other explanations, i e the Pacific trade winds have been stronger and tropical SST cooler than in the models)Olof Rhttps://www.blogger.com/profile/18244733455655978307noreply@blogger.comtag:blogger.com,1999:blog-7729093380675162051.post-91590496217073092422017-11-21T03:30:54.667+11:002017-11-21T03:30:54.667+11:00Nick, This is just hand waving though, isn't ...Nick, This is just hand waving though, isn't it? You mention Rossby waves which involve small pressure gradients. But they are only a small part of climate. Tropical convection is much less well modeled. One would think that if Rossby waves are "right" regional climate would be better modeled.<br /><br />And that's the heart of my point, we have here a lot of qualititative "colorful fluid dynamics" and heuristic arguments about eigenvalues. We don't really have much to back it up. What would you say if the simulations didn't converge as the grid is refined?David Younghttps://www.blogger.com/profile/17029429374522399227noreply@blogger.com