Wednesday, October 16, 2019

GISS September global unchanged from August.

The GISS V4 land/ocean temperature anomaly stayed at 0.90°C in September, same as August. It compared with a 0.043deg;C fall in TempLS V4 mesh

The overall pattern was similar to that in TempLS. Warm in Africa, N of China, Eastern US, NE Pacific, Alaska/Arctic. Cool over Urals, in West Coast USA and Atlantic Canada. Mostly cool in Antarctica.

As usual here, I will compare the GISS and earlier TempLS plots below the jump.

Monday, October 14, 2019

New FEM/LOESS method of integrating temperature anomalies on the globe

Update Correction to sensitivity to rotation below

Yet another post on this topic, which I have written a lot about. It is the the basis of calculation of global temperature anomaly, which I do every month with TempLS. I have developed three methods that I consider advanced, and I post averages using each here (click TempLS tab). They agree fairly well, and I think they are all satisfactory (and better than the alternatives in common use). The point of developing three was partly that they are based on different principles, and yet give concordant results. Since we don't have an exact solution to check against, that is the next best thing.

So why another one? Despite their good results, the methods have strong points and some imperfections. I would like a method that combines the virtues of the three, and sheds some faults. A bonus would be that it runs faster. I think I have that here. I'll call it the FEM method, since it makes more elaborate use of finite element ideas. I'll first briefly describe the existing methods and their pros and cons.

Mesh method

This has been my mainstay for about eight years. For each month an irregular triangular mesh (convex hull) is drawn connecting the stations that reported. The function formed by linear interpolation within each triangle is integrated. The good thing about the method is that it adapts well to varying coverage, giving the (near) best even where sparse. One bad thing is the different mesh for each month, which takes time to calculate (if needed) and is bulky to put online for graphics. The time isn't much; about an hour in full for GHCN V4, but I usually store geometry data for past months, which can reduce process time to a minute or so. But GHCN V4 is apt to introduce new stations, which messes up my storage.

A significant point is that the mesh method isn't stochastic, even though the data is best thought of as having a random component. By that I mean that it doesn't explicitly try to integrate an estimated average, but relies o exact integration to average out. It does, generally, very well. But a stochastic method gives more control, and is alos more efficient.

Grid method with infill

Like most people, I started with a lat/lon grid, averaging station values within each cell, and then an area-weighted average of cells. This has a big problem with empty cells (no data) and so I developed infill schemes to estimate those from local dat. Here is an early description of a rather ad hoc method. Later I got more systematic about it, eventually solving a Laplace equation for empty cell regions, using data cells as boundary conditions.

The method is good for averaging, and reasonably fast. It is stochastic, in the cell averaging step. But I see it now in finite element terms, and it uses a zero order representation within the cell (constant), with discontinuity at the boundary. In FEM, such an element would be scorned. We can do better. It is also not good for graphics.,

LOESS method

My third, and most recent method, is described here. It starts with a regular icosahedral grid of near uniform density. LOESS (local weighted linear regression) is used to assign values to the nodes of that grid, and an interpolation function (linear mesh) is created on that grid which is either integrated or used for graphics. It is accurate and gives the best graphics.

Being LOESS, it is explicitly stochastic. I use an exponential weighting function derived from Hansen's spatial correlation decay, but a more relevant cut-off is that for each node I choose the nearest 20 points to average. There are some practical reasons for this. An odd side effect is that about a third of stations do not contribute; they are in dense regions where they don't make the nearest 20 of any node. This is in a situation of surfeit of information, but it seems a pity to not use their data in some way.



The new FEM method.

I take again a regular triangle grid based on subdividing the icosahedron (projected onto the sphere). Then I form polynomial basis functions of some order (called P1, P2 etc in the trade. These have generally a node for each function, of which there may be several per triangle element - the node arrangement within triangles are shown in a diagram below. The functions are "tent-like", and go to zero on the boundaries, unless the node is common to several elements, in which case it is zero on the boundary of that set of elements and beyond. They have the property of being one at the centre and zero at all other nodes, so if you have a function with values at the node, multiplying those by the basis functions and adding forms a polynomial interpolation of appropriate order, which can be integrated or graphed. Naturally, there is a WebGL visualisation of these shape functions; see at the end.

The next step is somewhat nonstandard FEM. I use the basis functions as in LOESS. That is, I require that the interpolating will be a best least squares fit to the data at its scattered stations. This is again local regression. But the advantage over the above LOESS is that the basis functions have compact support. That is, you only have to regress, for each node, data offer the elements of which the node is part.

Once that is done, the regression expressions are aggregated as in finite element assembly, to produce the equivalent of a mass matrix which has to be inverted. The matrix can be large but it is sparse (most element zero). It is also positive definite and well conditioned, so I can use a preconditioned conjugate gradient method to solve. This converges quickly.

Advantages of the method

  • Speed - binning of nodes is fast compared to finding pairwise distances as in LOESS, and furthermore it can be done just once for the whole inventory. Solution is very fast.
  • Graphics - the method explicitly creates an interpolation method.
  • Convergence - you can look at varying subdivision (h) and polynomial order of basis (p). There is a powerful method in FEM of hp convergence, which says that if you improve h and p jointly on some way, you get much faster convergence than improving one with the other fixed.

Failure modes

The method eventually fails when elements don't have enough data to constrain the parameters (node value) that are being sought. This can happen either because the subdivision is too fine (near empty cells) or the order of fitting is too high for the available data. This is a similar problem to the empty cells in simple gridding, and there is a simple solution, which limits bad consequences, so missing data in one area won't mess up the whole integral. The specific fault is that the global matrix to be inverted becomes ill-conditioned (near-singular) so there are spurious modes from its null space that can grow. The answer is to add a matrix corresponding to a Laplacian, with a small multiplier. The effect of this is to say that where a region is unconstrained, a smoothness constraint is added. A light penalty is put on rapid change at the boundaries. This has little effect on the non-null space of the mass matrix, but means that the smoothness requirement becomes dominant where other constraints fail. This is analogous to the Laplace infilling I now do with the grid method.

Comparisons and some results

I have posted comparisons of the various methods used with global time series above and others, most recently here. Soon I will do the same for these methods, but for now I just want to show how the hp-system converges. Here is the listing of global averages of anomalies calculated by the mesh method for February to July, 2019. I'll use the FEM hp notation, where h1 is the original icosahedron, and higher orders have each triangle divided into 4, so h4 has 1280 triangles. p represents polynomial order, so p1 is linear, p2 quadratic.

July 2019 Mesh Anomaly 0.829
p1p2p3p4
h10.70.8380.8210.836
h20.8160.8210.820.828
h30.8090.8190.8210.822
h40.8220.8250.8210.819
June 2019 Mesh Anomaly 0.779
p1p2p3p4
h10.8130.7710.8010.824
h20.7920.8110.7830.773
h30.8170.7890.7830.78
h40.8090.7760.7810.778
May 2019 Mesh Anomaly 0.713
p1p2p3p4
h10.5140.7420.7660.729
h20.7150.6890.7210.714
h30.7630.7120.7070.707
h40.7090.710.710.709
April 2019 Mesh Anomaly 0.88
p1p2p3p4
h10.8950.8860.9250.902
h20.890.8850.8940.88
h30.890.8880.8790.88
h40.890.8810.8790.877
March 2019 Mesh Anomaly 0.982
p1p2p3p4
h10.8261.0720.9861.003
h20.9880.9990.9950.999
h30.9690.990.9930.994
h41.0140.9920.9880.989
February 2019 Mesh Anomaly 0.765
p1p2p3p4
h10.580.8160.7940.779
h20.7420.7270.7840.784
h30.7570.7610.7760.772
h40.7460.7860.7690.772


Note that h1p1 is the main outlier. But the best convergence is toward bottom right.

Sensitivity analysis

Update Correction - I made a programming error with the numbers that appeared here earlier. The new numbers are larger, making significant uncertainty in the third decimal place at best, and more for h1.

I did a test of how sensitive the result was to placement of the icosahedral mesh. For the three months May-July, I took the original placement of the icosahedron, with vertex at the N pole, and rotated about each axis successively by random angles. I did this 50 times, and computed the standard deviations of the results. Here they are, multiplied by a million:

July 2019
p1 p2 p3 p4
h127568244121688910918
h2167681015058826052
h310741673070974977
h49335632243674286
June 2019
p1 p2 p3 p4
h121515310101853014455
h220484947981425397
h315790864066055480
h413283785365225085
May 2019
p1 p2 p3 p4
h126422334622915418505
h2187931436176084356
h39908819548915406
h411188759254923925

The error affects the third decimal place sometimes. I think this understates the error for higher resolution, since the Laplacian interpolation that then comes into play creates an error that isn't likely to be sensitive to orientation. The sd results do not seem to conform to the distribution one might expect. I think that is because the variability is greatly influenced by highly weighted nodes in sparse regions, and the variability in sd seen here depends on how different those points were from each other and their neighbors.

Convergent plots

Here is a collection of plots for the various hp pairs in the table, for the month of July. The resolution goes from poor to quite good. But you don't need very high resolution for a global integral. Click the arrow buttons below to cycle through.

Visualisation of shape functions

A shape functions is, within a triangle, the unique polynomial of appropriate order which take value 1 at their node, and zero at all other nodes. The arrangement of these nodes is shown below:





Here is a visualisation of some shape functions. Each is nonzero over just a few triangles in the icosahedron mesh. Vertex functions have a hexagon base, being the six triangles to which the vertex is common. Functions centered on a side have just the two adjacent triangles as base. Higher order elements also have internal functions over just the one triangle, which I haven't shown. The functions are zero in the mesh beyond their base, as shown with grey. The colors represent height above zero, so violet etc is usually negative.

It is the usual Moyhu WebGL plot, so you can drag with the mouse to move it about. The radio buttons allow you to vary the shape function in view, using the hnpn notation from above.

Next step

For use in TempLS, the integration method needs to be converted to return weights for integrating the station values. This has been done and in the next post I will compare time series plots for the three existing advanced methods and the new FEM method with the range of hp values.









Tuesday, October 8, 2019

September global surface TempLS down 0.043°C from August.

The TempLS mesh anomaly (1961-90 base) was 0.758deg;C in September vs 0.801°C in August. This contrasts with the 0.03°C rise in the NCEP/NCAR reanalysis base index. This makes it the second warmest September in the record, just behind 2016.

SST was down somewhat, mainly due to far Southern Ocean. There was also a cool area north of Australia, and in Russia around the Urals. Most of US was warm, except the Pacific coast; E Canada was cool. There were warm areas N of China, in S America E of Bolivia, and Alaska/E Siberia. Africa was warm.

There was a sharp rise of about 0.2°C in satellite indices, which Roy Spencer attributes to stratospheric warming over Antarctica. TempLS found that Antarctica was net cool at surface, although it shows as rather warm on the lat/lon map. As always, the 3D globe map gives better detail.

Here is the temperature map, using the LOESS-based map of anomalies.





Thursday, October 3, 2019

September NCEP/NCAR global surface anomaly up 0.03°C from August

The Moyhu NCEP/NCAR index rose from 0.389°C in August to 0.419°C in September, on a 1994-2013 anomaly base. It continued the pattern of the last three months of small rises with only small excursions during the month. In fact there hasn't really been a cold spell globally since February, which is unusual. It is the warmest September since 2016 in this record.

N America was warm E of Rockies, colder W, but warm toward Alaska, extending across N of Siberia.. A large cool atch ar in N Australia and further N. Mostly cool in and around Antarctica. A warm patch N of China.



Tuesday, September 17, 2019

GISS August global down 0.04°C from July.

The GISS V4 land/ocean temperature anomaly fell 0.04°C in August. The anomaly average was 0.90°C, down from July 0.94°C. It compared with a 0.029°C fall in TempLS V4 mesh

The overall pattern was similar to that in TempLS. Warm in Africa, N central Siberia, NE Canada, NE Pacific. Cool in a band from US Great Lakes to NW Canada and in NW Russia. Mostly warm in Antarctica.

As usual here, I will compare the GISS and earlier TempLS plots below the jump.

Wednesday, September 11, 2019

How errors really propagate in differential equations (and GCMs).

There has been more activity on Pat Frank's paper since my last post. A long thread at WUWT, with many comments from me. And two good posts and threads at ATTP, here and here. In the latter he coded up Pat's simple form (paper here). Roy Spencer says he'll post a similar effort in the morning. So I thought writing something on how error really is propagated in differential equations would be timely. It's an absolutely core part of PDE algorithms, since it determines stability. And it isn't simple, but expresses important physics. Here is a TOC:

Differential equations

An ordinary differential equation (de) system is a number of equations relating many variables and their derivatives. Generally the number of variables and equations is equal. There could be derivatives of higher order, but I'll restrict to one, so it is a first order system. Higher order systems can always be reduced to first order with extra variables and corresponding equations.

A partial differential equation system, as in a GCM, has derivatives in several variables, usually space and time. In computational fluid dynamics (CFD) of which GCMs are part, the space is gridded into cells or otherwise discretised, with variables associated with each cell, or maybe nodes. The system is stepped forward in time. At each stage there are a whole lot of spatial relations between the discretised variables, so it works like a time de with a huge number of cell variables and relations. That is for explicit solution, which is often used by large complex systems like GCMs. Implicit solutions stop to enforce the space relations before proceeding.

Solutions of a first order equation are determined by their initial conditions, at least in the short term. A solution beginning from a specific state is called a trajectory. In a linear system, and at some stage there is linearisation, the trajectories form a linear space with a basis corresponding to the initial variables.

Fluids and Turbulence

As in CFD, GCMs solve the Navier-Stokes equations. I won't spell those out (I have an old post here), except to say that they simply express the conservation of momentum and mass, with an addition for energy. That is, a version of F=m*a, and an equation expressing how the fluid relates density and velocity divergence (and so pressure with a constitutive equation), and an associated heat budget equation.

It is said, often in disparagement of GCMs, that they are not effectively determined by initial conditions. A small change in initial state could give a quite different solution. Put in terms of what is said above, they can't stay on a single trajectory.

That is true, and true in CFD, but it is a feature, not a bug, because we can hardly ever determine the initial conditions anyway, even in a wind tunnel. And even if we could, there is no chance in an aircraft during flight, or a car in motion. So if we want to learn anything useful about fluids, either with CFD or a wind tunnel, it will have to be something that doesn't require knowing initial conditions.

Of course, there is a lot that we do want to know. With an aircraft wing, for example, there is lift and drag. These don't depend on initial conditions, and are applicable throughout the flight. With GCMs it is climate that we seek. The reason we can get this knowledge is that, although we can't stick to any one of those trajectories, they are all subject to the same requirements of mass, momentum and energy conservation, and so in bulk all behave in much the same way (so it doesn't matter where you started). Practical information consists of what is common to a whole bunch of trajectories.

Turbulence messes up the neat idea of trajectories, but not too much, because of Reynolds Averaging. I won't go into this except to say that it is possible to still solve for a mean flow, which still satisfies mass momentum etc. It will be a useful lead in to the business of error propagation, because it is effectively a continuing source of error.

Error propagation and turbulence

I said that in a first order system, there is a correspondence between states and trajectories. That is, error means that the state isn't what you thought, and so you have shifted to a different trajectory. But, as said, we can't follow trajectories for long anyway, so error doesn't really change that situation. The propagation of error depends on how the altered trajectories differ. And again, because of the requirements of conservation, they can't differ by all that much.

As said, turbulence can be seen as a continuing source of error. But it doesn't grow without limit. A common model of turbulence is called k-ε. k stands for turbulent kinetic energy, ε for rate of dissipation. There are k source regions (boundaries), and diffusion equations for both quantities. The point is that the result is a balance. Turbulence overall dissipates as fast as it is generated. The reason is basically conservation of angular momentum in the eddies of turbulence. It can be positive or negative, and diffuses (viscosity), leading to cancellation. Turbulence stays within bounds.

GCM errors and conservation

In a GCM something similar happens with other perurbations. Suppose for a period, cloud cover varies, creating an effective flux. That is what Pat Frank's paper is about. But that flux then comes into the general equilibrating processes in the atmosphere. Some will go into extra TOA radiation, some into the sea. It does not accumulate in random walk fashion.

But, I hear, how is that different from extra GHG? The reason is that GHGs don't create a single burst of flux; they create an ongoing flux, shifting the solution long term. Of course, it is possible that cloud cover might vary long term too. That would indeed be a forcing, as is acknowledged. But fluctuations, as expressed in the 4 W/m2 uncertainty of Pat Frank (from Lauer) will dissipate through conservation.

Simple Equation Analogies

Pat Frank, of course, did not do anything with GCMs. Instead he created a simple model, given by his equation 1:



It is of the common kind, in effect a first order de

d( ΔT)/dt = a F

where F is a combination of forcings. It is said to emulate well the GCM solutions; in fact Pat Frank picks up a fallacy common at WUWT that if a GCM solution (for just one of its many variables) turns out to be able to be simply described, then the GCM must be trivial. This is of course nonsense - the task of the GCM is to reproduce reality in some way. If some aspect of reality has a pattern that makes it predictable, that doesn't diminish the GCM.

The point is, though, that while the simple equation may, properly tuned, follow the GCM, it does not have alternative trajectories, and more importantly does not obey physical conservation laws. So it can indeed go off on a random walk. There is no correspondence between the error propagation of Eq 1 (random walk) and the GCMs (shift between solution trajectories of solutions of the Navier-Stokes equations, conserving mass momentum and energy).

On Earth models

I'll repeat something here from the last post; Pat Frank has a common misconception about the function of GCM's. He says that
"Scientific models are held to the standard of mortal tests and successful predictions outside any calibration bound. The represented systems so derived and tested must evolve congruently with the real-world system if successful predictions are to be achieved."

That just isn't true. They are models of the Earth, but they don't evolve congruently with it (or with each other). They respond like the Earth does, including in both cases natural variation (weather) which won't match. As the IPCC says:
"In climate research and modelling, we should recognise that we are dealing with a coupled non-linear chaotic system, and therefore that the long-term prediction of future climate states is not possible. The most we can expect to achieve is the prediction of the probability distribution of the system’s future possible states by the generation of ensembles of model solutions. This reduces climate change to the discernment of significant differences in the statistics of such ensembles"

If the weather doesn't match, the fluctuations of cloud cover will make no significant difference on the climate scale. A drift on that time scale might, and would then be counted as a forcing, or feedback, depending on cause.

Conclusion

Error propagation in differential equations follows the solution trajectories of the differential equations, and can't be predicted without it. With GCMs those trajectories are constrained by the requirements of conservation of mass, momentum and energy, enforced at each timestep. Any process which claims to emulate that must emulate the conservation requirements. Pat Frank's simple model does not.





Sunday, September 8, 2019

Another round of Pat Frank's "propagation of uncertainties.

See update below for a clear and important error.

There has been another round of the bizarre theories of Pat Frank, saying that he has found huge uncertainties in GCM outputs that no-one else can see. His paper has found a publisher - WUWT article here. It is a pinned article; they think it is a big deal.

The paper is in Frontiers or Earth Science. This is an open publishing system, with (mostly) named reviewers and editors. The supportive editor was Jing-Jia Luo, who has been at BoM but is now at Nanjing. The named reviewers are Carl Wunsch and Davide Zanchettin.

I wrote a Moyhu article on this nearly two years ago, and commented extensively on WUWT threads, eg here. My objections still apply. The paper is nuts. Pat Frank is one of the hardy band at WUWT who insist that taking a means of observations cannot improve the original measurement uncertainty. But he takes it further, as seen in the neighborhood of his Eq 2. He has a cloud cover error estimated annually over 20 years. He takes the average, which you might think was just a average of error. But no, he insists that if you average annual data, then the result is not in units of that data, but in units/year. There is a wacky WUWT to-and-fro on that beginning here. A referee had objected to changing the units of annual time series averaged data by inserting the /year. The referee probably thought he was just pointing out an error that would be promptly corrected. But no, he coped a tirade about his ignorance. And it's true that it is not a typo, but essential to the arithmetic. Having given it units/year, that makes it a rate that he accumulates. I vainly pointed out that if he had gathered the data monthly instead of annually, the average would be assigned units/month, not /year, and then the calculated error bars would be sqrt(12) times as wide.

One thing that seems newish is the emphasis on emulation. This is also a WUWT strand of thinking. You can devise simple time models, perhaps based on forcings, which will give similar results to GCMs for one particular variable, global averaged surface temperature anomaly. So, the logic goes, that must be what GCM's are doing (never mind all the other variables they handle). And Pat Frank's article has much of this. From the abstract: "An extensive series of demonstrations show that GCM air temperature projections are just linear extrapolations of fractional greenhouse gas (GHG) forcing." The conclusion starts: "This analysis has shown that the air temperature projections of advanced climate models are just linear extrapolations of fractional GHG forcing." Just totally untrue, of course, as anyone who actually understands GCMs would know.

One funny thing - I pointed out here that PF's arithmetic would give a ±9°C error range in Hansen's prediction over 30 years. Now I argue that Hansen's prediction was good; some object that it was out by a small fraction of a degree. It would be an odd view that he was extraordinarily lucky to get such a good prediction with those uncertainties. But what do I see? This is now given, not as a reduction ad absurdum, but with a straight face as Fig 8:



To give a specific example of this nutty arithmetic, the paper deals with cloud cover uncertainty thus:

"On conversion of the above CMIP cloud RMS error (RMSE) as ±(cloud-cover unit) year-1 model-1 into a longwave cloud-forcing uncertainty statistic, the global LWCF calibration RMSE becomes ±Wm-2 year-1 model-1. Lauer and Hamilton reported the CMIP5 models to produce an annual average LWCF root-mean-squared error (RMSE) = ±4 Wm-2 year-1 model-1, relative to the observational cloud standard (81). This calibration error represents the average annual uncertainty within the simulated tropospheric thermal energy flux and is generally representative of CMIP5 models."

There is more detailed discussion of this starting here. In fact, Lauer and Hamilton said, correctly, that the RMSE was 4 Wm-2. The year-1 model-1 is nonsense added by PF, but it has an important effect. The year-1 translates directly into the amount of error claimed. If it had been month-1, the claim would have been sqrt(12) higher. So why choose year? PF's only answer - because L&H chose to bin their data annually. That determines GCM uncertainty!

Actually, the ±4 is another issue, explored here. Who writes an RMS as ±4? It's positive. But again it isn't just a typo. An editor in his correspondence, James Annan wrote it as 4, and was blasted as an ignorant sod for omitting the ±. I pointed out that no-one, nor L&H in his reference, used a ± for RMS. It just isn't the meaning of the term. I challenged him to find that usage anywhere, with no result. Unlike the nutty units, I think this one doesn't affect the arithmetic. It's just an indication of being in a different world.

One final thing I should mention is the misunderstanding of climate models contained in the preamble. For example "Scientific models are held to the standard of mortal tests and successful predictions outside any calibration bound. The represented systems so derived and tested must evolve congruently with the real-world system if successful predictions are to be achieved."

But GCMs are models of the earth. They aim to have the same physical properties but are not expected to evolve congruently, just as they don't evolve congruently with each other. This was set out in the often misquoted IPCC statement

"In climate research and modelling, we should recognise that we are dealing with a coupled non-linear chaotic system, and therefore that the long-term prediction of future climate states is not possible. The most we can expect to achieve is the prediction of the probability distribution of the system’s future possible states by the generation of ensembles of model solutions. This reduces climate change to the discernment of significant differences in the statistics of such ensembles. "

Update - I thought I might just highlight this clear error resulting from the nuttiness of the /year attached to averaging. It's from p 12 of the paper:

Firstly, of course, they are not the dimensions (Wm-2) given by the source, Lauer and Hamilton. But the dimensions don't work anyway. The sum of squares gives a year-2 dimension component. Then just taking the sqrt brings that back to year-1. But that is for the uncertainty of the whole period, so that can't be right. I assume Pat Frank puts his logic backward, saying that adding over 20 years multiplies the dimensions by year. But that still leaves the dimension (Wm-2)2 year-1, and on taking sqrt, the unit is (Wm-2)year-1/2. Still makes no sense; the error for a fixed 20 year period should be Wm-2.