Guest Post by Willis Eschenbach
I’ve heard many times that whereas weather prediction is an “initial-value” problem, climate prediction is a “boundary problem”. I’ve often wondered about this, questions like “what is the boundary?”. I woke up today thinking that I didn’t have an adequately clear understanding of the difference between the two types of problems.
For these kinds of questions I find it’s hard to beat Wolfram Reference, which is a reference to the various functions in the computer program Mathematica. Wolfram is a total genius in my opinion, and the Wolfram site reflects that. Here’s what Wolfram Reference says (emphasis mine):
Introduction to Initial and Boundary Value Problems
DSolve [a Mathematica function] can be used for finding the general solution to a differential equation or system of differential equations. The general solution gives information about the structure of the complete solution space for the problem. However, in practice, one is often interested only in particular solutions that satisfy some conditions related to the area of application. These conditions are usually of two types.
• The solution x(t) and/or its derivatives are required to have specific values at a single point, for example, x(0)=1 and x’(0)=2. Such problems are traditionally called initial value problems (IVPs) because the system is assumed to start evolving from the fixed initial point (in this case, 0).
• The solution x(t) is required to have specific values at a pair of points, for example, x(0)=1 and x(1)=5. These problems are known as boundary value problems (BVPs) because the points 0 and 1 are regarded as boundary points (or edges) of the domain of interest in the application.
The symbolic solution of both IVPs and BVPs requires knowledge of the general solution for the problem. The final step, in which the particular solution is obtained using the initial or boundary values, involves mostly algebraic operations, and is similar for IVPs and for BVPs.
IVPs and BVPs for linear differential equations are solved rather easily since the final algebraic step involves the solution of linear equations. However, if the underlying equations are nonlinear, the solution could have several branches, or the arbitrary constants from the general solution could occur in different arguments of transcendental functions. As a result, it is not always possible to complete the final algebraic step for nonlinear problems. Finally, if the underlying equations have piecewise (that is, discontinuous) coefficients, an IVP naturally breaks up into simpler IVPs over the regions in which the coefficients are continuous.
Now, as I read that, it says that for an initial value problem (IVP) we need to know the initial conditions at the starting time, and for a boundary value problem (BVP) we need to know the future conditions at a particular boundary. For example, suppose we are interested in the future thermal behavior of an iron rod with one end in a ice-water bath. The boundary condition is that the end of the iron rod in the ice-water bath is at 0°C.
So my question is two-fold. IF predicting weather is an IVP and predicting climate is a BVP, then
1) What is the “boundary” in question?, and
2) Once we determine what the boundary is, how do we know the future value of the boundary?
Some investigation finds that for US$48 I can read the following:
Existence and regularity theorems for a free boundary problem governing a simple climate model
Xiangsheng Xua
Abstract
From a class of mean annual, zonally averaged energy–balance climate models of the Budyko‐Sellers type, we arrive at a free boundary problem with the free boundary being the interface between ice‐covered and ice-free areas. Existence and regularity properties are proved for weak solutions of the problem. In particular, the regularity of the free boundary is investigated.
Fortunately, I don’t need to read it to see that the boundary in question is the ice-water interface. Now, that actually seems like it might work, because we know that at any time in the future, the boundary is always at 0°C. Since we know the future temperature values at that boundary, we can treat it as a boundary problem.
But then I continue reading, and I find Dr. Pielke’s excellent work , which says (emphasis mine):
One set of commonly used definitions of weather and climate distinguishes these terms in the context of prediction: weather is considered an initial value problem, while climate is assumed to be a boundary value problem. Another perspective holds that climate and weather prediction are both initial value problems (Palmer 1998). If climate prediction were a boundary value problem, then the simulations of future climate will “forget” the initial values assumed in a model. The assumption that climate prediction is a boundary value problem is used, for example, to justify predicting future climate based on anthropogenic doubling of greenhouse gases. This correspondence proposes that weather prediction is a subset of climate predictions and that both are, therefore, initial value problems in the context of nonlinear geophysical flow. The consequence of climate prediction being an initial value problem is summarized in this correspondence.
The boundaries in the context of climate prediction are the ocean surface and the land surface. If these boundaries are fixed in time, evolve independently of the atmosphere such that their time evolution could be prescribed, or have response times that are much longer than the time period of interest in the climate prediction, than one may conclude that climate prediction is a boundary problem.
So Dr. Pielke says that there is an entirely different boundary in play, the boundary between the atmosphere and the surface.
But then my question is, how would we know the future conditions of that boundary? If it’s a BVP, we have to know future conditions.
Dr. Pielke takes an interesting turn. IF I understand his method in another paper, Seasonal weather prediction as an initial value problem, he shows that the chosen boundary (the atmosphere/surface interface) doesn’t “evolve independently of the atmosphere such that their time evolution could be prescribed” and thus seasonal weather prediction is shown to be an IVP rather than a BVP.
However … he’s using an entirely different boundary than that used by Xiangsheng Xua above. Which one is right? One, both, or neither?
And the underlying problem, of course, is that IF climate is an initial value problem just like weather, given the chaotic nature of both we have little hope of modeling or predicting the future evolution of the climate.
My conclusion from all of this, which I think is shared by Dr. Pielke, is that climate prediction is an initial value problem. I say this in part because I see no difference in “climate” and “weather” in that both seem to be self-similar, non-linear, and chaotic.
This view is also shared by Mandelbrot, as was discussed about a decade ago over at Steve McIntyre’s excellent blog … have we really been at it that long? Mandelbrot analyzed a number of long-term records and found no change in the fractal nature of the records with timespan. In other words, there’s no break between the chaotic nature of the short, medium, and long-term looks at weather.
Now, it’s often argued that weather prediction has gotten much better over the decades … and this is true. But remember, weather prediction is an initial value problem. That means that the more accurately and specifically and finely we can measure the initial conditions, the better our prediction will be. Much of the improvement in our weather predictions is a result of satellites which give us our initial conditions in exquisite detail. And despite all our advances in predictive ability, lots of weekend barbecues still get rained on.
And at the end of the day, I’m left with my initial questions:
• If modeling the future evolution of the climate a boundary problem, what exactly is the boundary?, and
• Having specified the boundary, how can we know the future conditions of the boundary?
Egads … a post without a single graphic … curious.
w.
My Usual Request: If you think something is incorrect, please have the courtesy to quote the exact words that you disagree with so that everyone can understand your objections.
Given that climate is weather’s strange attractor, consider using the Brouwer Fixed Thinking Theorem.
Willis,
You have looked at the terminology of ordinary (1D) differential equations. I do not think that is what is meant. Dr Spencer is on the right track.
The right analogy might be flow along a river or channel (PDE). Seen as an initial value problem, you calculate the velocity cross-section going in, solve for momentum etc. But that doesn’t get you very far.
As a BVP, you forget about initial conditions, and look at what happens along the way. There is loss of altitude, boundary friction, maybe cascades, and of course the constantly varying depth. These are what you use to determine the river flow in the longer term. You can’t usefully forecast the flow from the initial conditions, but that doesn’t mean you know nothing.
If you do a CFD model, you’ll need turbulence (weather). But it is random, and you are only interested in what it does to the mean flow.
I would add a third question: Does it matter if climate is an initial value problem or a boundary value problem?
Although it is likely possible to set up a simple climate model such that it is a boundary value problem, I think that the GCM climate models are numerically set up as initial value problems. So that seems to suggest that the modellers think that it does not really matter if the problem is initial value or boundary value.
It seems to me that the key issue is whether, on long enough times scales, the chaotic aspects of weather average out in the way that random noise averages out. That sort of averaging seems to be a central assumption of climate modelling for the simple reason that if it does not occur, then climate modelling is hopeless.
It is certainly possible for such averaging to occur, but has anyone demonstrated that it does occur for the climate system? If the problem can be cast as a boundary value problem, does that guarantee that such averaging occurs? I doubt that anyone can prove that. And I think it very likely that chaotic systems can be designed for which such averaging is invalid.
One possibility is that this is not meant to be a rigorous mathematical statement. So one might argue that since there is a boundary (TOA) for which a particular condition (energy balance) must apply in the long run, then the set of possible solutions must be bounded in the sense that only a certain range of solutions is allowed. Then sufficient sampling could give an estimate of most likely values. But that is only useful if the range of solutions is fairly tightly constrained and if a reasonable sampling occurs on a meaningful time scale. Given the ice ages, that is far from obvious.
Another possibility is that the “climate is a boundary value problem” meme is just mathematical bullying. “Do you have an answer to this profound statement? No? Then shut up and let those of us initiated into these mysteries get on with the job of telling you what should be done.”
BVP or IVP, any medium- or long-term prediction (‘projection,’ whatever) of climate necessarily assumes the following: “As long as nothing currently unknown, unexpected or not properly factored in happens along the way.” In other words, it assumes no more nor less than a glorified form of ceteris parabus.
Good luck with that.
1) What is the “boundary” in question?
The sun.
2) Once we determine what the boundary is, how do we know the future value of the boundary?
We can’t.
It is all piffle, artifact of the construction of a model. The initial condition is just the initial boundary, and eventual boundaries are just initial conditions for the next phase.
“As a result, it is not always possible to complete the final algebraic step for nonlinear problems.”
This is the root of the problem. The whedefugawe effect. Make your own model and you will see…
There is an interesting discussion of this topic at http://scienceofdoom.com/2014/11/29/natural-variability-and-chaos-four-the-thirty-year-myth/
Given that the algorithm “the weather in 4 days time will be the same as today’s” is almost as accurate as any other forecast, weather forecasts are clearly an initial value problem.
http://weather.slimyhorror.com/
I see climate BVP as the boundary of the range of output values for all initial conditions, where the pause is an excellent example of an IVP, the output is dependent on initial conditions. In this case BVD’S are used in worst-case and sensitivity analysis of electronic equipment.
And for this example most will find it obvious as to why this is even a topic of discussion.
When I was thinking of boundaries, I was thinking of the drought in California. Southern California is a desert and is definitely in drought. But the North West boundary of CA is as far as I can tell not in a drought. And since they get most of the rain in California there, you would think that they would have most of the dams to catch the plentiful water which they could pump to the dry regions. But if you look at the damn dam map of dams in California, it looks like there are not many dams in the part of CA that gets the most rain – the northwest corner:
WUWT?:
http://www.kqed.org/news/science/climatewatch/waterandpower/map.jsp
What was the boundary of the “Dust Bowl”??
“If you think something is incorrect, please have the courtesy to quote the exact words that you disagree with so that everyone can understand your objections.”
Just the part between “I’ve” and “curious”.
But seriously, if someone could tell me the difference between j x omega and s then I’d be happy to explain the difference between BVP’s and IVP’s.
” Mandelbrot analyzed a number of long-term records and found no change in the fractal nature of the records with timespan. In other words, there’s no break between the chaotic nature of the short, medium, and long-term looks at weather.”
A few weeks ago, you linked a video here; the title was something like ‘5 things to believe before breakfast’ (?) – Near the end of that presentation, the were a few examples of time-lapses that were supposed to illustrate that some things that seem to be chaotic, e.g. changing occupation of a car park, traffic on a street do not look chaotic at all in the long run, and the point to take away from that analogy was that we simply haven’t observed climate for long enough to make a call as to whether changes in climate that we can detect in our short lives are even relevant.
That seems to go contrary to what Mandlbrot has found, because he he could only observe the rather short climate record, isn’t it? Our records are not “long term” by any stretch of the meaning…
“…..Keep changing their minds, the best way to confuse.
Turning around the terms they like to use;
Climate’s not weather, well, more often than not,
But weather can be climate! Are you still with the plot?…..”
Read more: http://wp.me/p3KQlH-cq
Thanks Willis, an informative discussion in the comments also.
It’s a boundary problem. The boundaries are the solid land surface, including sea floor, and the top of the atmosphere. Inside the boundaries we have the oceans, atmosphere, and a can of worms.
Very high cosmic radiation. It is better not to sunbathe in Canada.
http://sol.spacenvironment.net/raps_ops/current_files/rtimg/dose.15km.png
Willis,
The “boundary value problem” here is the fact that for energy balance (stable temperatures over period t1) incoming absorbed solar radiation must balance the outgoing longwave radiation.
One of the problems is “what is t1?” as discussed in Natural Variability and Chaos – Four – The Thirty Year Myth.
Boundary value problems, as typically described in thermodynamics or heat transfer, usually mean “calculate the steady state solution given conditions A, B, C, etc”. Dynamic problems (initial value problems) usually mean “calculate the response as a function of time”.
Interesting. The “boundary” solution advocated by models of course has nothing to do with the freezing/melting point of water, but rather the theoretical fixed thermal enhancement properties of CO2. This is climatology’s version of a cosmological constant from which all other variables can be worked backwards from. Thus they argue that the non linear nature of the rest of the system is irrelevant because this fits inside the fixed calculation of greenhouse gasses hear trapping qualities which can be derived from x=y
This is a problem for all you “Luke Warmers” because deviation from x=y can be attributed to a fluctuation of the non linear “initial” state of weather/ natural variability, whilst the x=y “boundary” of GHG is adding a fixed input on top. 18 years no warming? No problem they say, we got the natural variability wrong. Sooner or later though that will swing the other way and x=y will pay you back with interest!
Happily for me, I am a proud “denier”. I refute the assertion that CO2 or any other gas has a magic heat trapping quality x=y (insert greenhouse equations instead) is a false premise to begin with. The boundary conditions are infact as follows:
1) Solar energy received
2) Albedo
3) Atmospheric Pressure.
Now you may add your initial state calculations for weather!
Hello Willis,
¿Why does the “boundary” have to be in the future? Climate model simulations used by IPCC do not start “today”. The simulated climate starts around 1900. The boundary can be set with the known conditions in 2 different times IN THE PAST, say, for instance, 1900 and 2000 (which would explain why they “more or less” replicate correctly the 20th century climate but fail miserably with the climate in the 21st).
Willis,
try to solve the energy balance model on your PC. Then you know the difference between IVP and BVP. Unfortunately, the energy balance model doesn’t describe the surface temperature profile of the earth very well. So you know nothing.
The link l see between weather and climate is the amount of variation there is within the weather over the longer term. As long as there is a high degree of variation within the weather patterns over the longer term, then this will block any extreme change in the climate from taking place. Because any extremes that do happen are too short lived to have any real impact over the longer term. Extreme climate change is much more likely to happen when the weather patterns become far less variable over the longer term. As this would allow weather patterns to turn up often enough to cause large changes in the climate. lts this lack of variation in the weather patterns is what l believe leads to ice ages.
The biggest problem with the CAGW hypothesis is the assumption that 3.7watts/M^2 of CO2 forcing per CO2 doubling will cause a runaway positive feedback loop involving ever increasing levels of atmospheric water vapor GHG forcing.
To keep this positive runaway feedback loop from going to infinity, the CAGW hypothesis assumes that airborne particulates from fossil fuel combustion (always man the center of the universe) will miraculously offset the sum of the feedbacks from exceeding 1, which is the point where global temps go all Buzz Lightyear “to infinity and beyond”…
Nature abhors runway positive feedback loops as our very existence proves… Nature seeks equilibrium– not its own destruction.
Earth doesn’t become Venus because Earth’s mass is about 23% greater than Venus, which generates enough additional gravity to keep ocean water from being blown out to space, which is apparently what happened to Venus. Venus is also closer to Sun so it gets more solar radiation, stronger solar winds, and its atmospheric CO2 concentration is roughly 965,000ppm compared to Earth’s 400ppm…
Earth’s climate is basically sinusoidal with various large-scale fluxes involving Milankovitch Cycles (Obliquity, Orbital Eccentricity, Axial precession, etc.) and solar cycles (Grand Solar Minima, 1,000-yr super cycles, etc.) that usually (not always) bring about significant climatic changes.
I’m sure I’m missing something important, but I don’t see the problem in thinking of climate as a BVP system that stays within 2 standard deviations of mean seasonal values, until some major variable shift (obliquity/orbital eccentricity/axial precession/solar cycle,etc., and their infinite permutations) bring about significant energy imbalances that cause significant climate changes.
CO2’s paltry logarithmic forcing effect of 3.7 watts/M^2 per doubling just doesn’t seem to provide enough energy to create any significant change other than perhaps 0.5C~1C per CO2 doubling, which seems to be what’s happening..
Willis’ excellent posts on ocean temps/ocean evaporation/cloud formation/albedo flux have been very helpful in understanding the natural bounds within which the climate works..
Thanks, Willis! Your posts are always thought provoking, informative and interesting.
As the things stand now, neither weather prediction nor climate prediction fit the paradigm assumed in the article. Full set of hydrodinamic equations known as Navier-Stokes system is so complicated that it is even not known if it has a solution in general case – no theorem of existence or uniquness exists for this system. And also there are more than one boundary for coupled ocean-atmosphere circulation: ocean bed is one boundary and land surface is another, and temperature should be set at ocean surface as well. We simply cannot pose these boundary conditions, so such problem is mathematically intractable. In reality models for weather prediction and climate prediction are not formulated from these first principles, but from greatly simplified description without real knowledge how this simplification affects the results.
What is more important issue is whether the non-linear dynamics of ocean-ocean interaction is regular or chaotic, both cases are possible. If it is chaotic, no adequate mathematical modelling and no prediction is possible in principle for sufficiently long period of time. This is certainly the case with weather prediction (that is how the first strange attractor, the Lorentz attractor was discovered by a meteorologist Edward Lorentz during a routine weather prognosis calculation). For climate prediction this question of regular or chaotic dynamics is still open.
Weather prediction — relates to wind and pressure patterns; cold wave, heat wave, precipitation, etc — at short and medium range predictions — long range prediction is a hypothetical statistical prediction, the can predict within the coefficient of variation range and rarely predict extremes [like excess rainfall or deficit rainfall ranges]
Climate prediction — relates to homogeneous zones; with the area of study the coefficient of variation reduces and thus the extremes changes. For example, All-India Southwest Monsoon rainfall presents 60-year cycle and within the 30-year above the average period the excess rainfall will be expected in 5 to 7 years and deficit in 2 to 3 years and in the 30-year below the average period the deficit rainfall will be expected in 5 to 7 years and deficit in 2 to 3 years; and in each thus the rainfall is within normal. Also, same way with El Nino, out of 126 years, 18 are El Nino years; of which 7 are deficit years and 10 are normal years and one year is excess rainfall year. In the case of 24 La Nina years, 10 are excess rainfall years, 14 are normal years. In the case of undivided Andhra Pradesh annual rainfall [with two monsoons and cyclonic activity] present 132 years cycle. In the 66-year below the average period 24 years are deficit years and 12 are surplus years with 30 normal years; and in the case of 66-year above the average rainfall period 24 years are surplus, 12 years are deficit and 30-years are normal. So, these clearly specify the need to homogenizaton of the regions for predicting climate. However, these will be modified with the time with changes in land use and land cover.
Dr. S. Jeevananda Reddy