Tuesday, December 3, 2019

November global surface TempLS down 0.067°C from October.

The Moyhu NCEP/NCAR index fell from 0.475°C in October to 0.408°C in November, on a 1994-2013 anomaly base. That brings it back to about the level of the previous month, but continues the relatively stable warm period since May.

Most of North America was cool, except toward Alaska. far W Europe and most of Russia/Iran also cool. Central Europe was warm, elsewhere mixed. NE Pacific and Greenland were warm.



Saturday, November 23, 2019

A good faith article by a recovering sceptic, but needs care with sources.

There is an interesting article in Reason by Ron Bailey, titled "What Climate Science Tells Us About Temperature Trends" (h/t Judith Curry). It is lukewarmish, but, as it's author notes, that is movement from a more sceptical view. It covers a range of issues.

His history shows up, though, in prominence given to sceptic sources that are not necessarily in such good faith. True, he seems to reach a balance, but needs to be more sceptical of scepticism. An example is this:

"A recent example is the June 2019 claim by geologist Tony Heller, who runs the contrarian website Real Climate Science, that he had identified "yet another round of spectacular data tampering by NASA and NOAA. Cooling the past and warming the present." Heller focused particularly on the adjustments made to NASA Goddard Institute for Space Studies (GISS) global land surface temperature trends. "

He concludes that "Adjustments that overall reduce the amount of warming seen in the past suggest that climatologists are not fiddling with temperature data in order to create or exaggerate global warming" so he wasn't convinced by Heller's case. But as I noted here, that source should be completely rejected. It compares one dataset (a land average) in 2017 with something different (Ts, a land/ocean average based on land data) in 2019 and claims the difference is due to "tampering". Although I raised that at the source, no correction or retraction was ever made, and so it still pollutes the discourse.

A different kind of example is the undue prominence accorded to Christy, and Michaels and Rossiter. He does give the counter arguments, and seems to favor those counters. Since the weighting he gives to those sources probably reflects the orientation of his audience, that may in the end be a good thing, but I hope we will get to a state where these recede to their rightful place.

His conclusion is:
"Continued economic growth and technological progress would surely help future generations to handle many—even most—of the problems caused by climate change. At the same time, the speed and severity at which the earth now appears to be warming makes the wait-and-see approach increasingly risky. Will climate change be apocalyptic? Probably not, but the possibility is not zero. So just how lucky do you feel? Frankly, after reviewing recent scientific evidence, I'm not feeling nearly as lucky as I once did."

Some might see that as still overrating his luck, but it is an article worth reading.








Saturday, November 16, 2019

GISS October global up 0.12°C from September.

The GISS V4 land/ocean temperature anomaly stayed at 1.04°C in October, up from 0.92°C September. It compared with a 0.116deg;C rise in TempLS V4 mesh (latest figures). GISS also had 2019 as second-warmest October after 2015.

The overall pattern was similar to that in TempLS. Prominent cold spot in US W of Mississippi (and adjacent Canada). Warm just about everywhere else (but not Scandinavia).

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

Friday, November 8, 2019

Structure and implementation of methods for averaging surface temperature.

I have been writing about various methods for calculating the average surface temperature anomaly. The mathematical task is basically surface integration of a function determined by sampling at measurement points, followed by fitting offsets to determine anomalies. I'd like to write here about the structure and implementation of those integration calculations.

The basic process for that integration is to match the data to a locally smooth function which can be integrated analytically. That function S(x) is usually expressed as a linear combination of basis functions Bᵢ(x), where x are surface coordinates
s(x) = bᵢBᵢ(x)1
using a summation convention. This post draws on this post from last April, where I describe use of the summation convention and also processes for handling sparse matrices (most entries zero), which I'll say a lot about here. The summation convention says that when you see an index repeated, as here, it is as if there were also a summation sign over the range of that index.

The fitting process is usually least squares; minimise over coefficients b:

(yj-bᵢBᵢ(xj)) Kjk (yₖ-bₘBₘ(xₖ))2


where the yj are the data points, say monthly temperature anomalies, at points xj) and K is some kernel matrix (positive definite, usually diagonal).

This all sounds a lot more complicated than just calculating grid averages, which is the most common technique. I'd like to fit those into this framework; there will be simplifications there.

Minimising (2) by differentiating wrt b gives
(yj-bᵢBᵢ(xj))KjkBₘ(xₖ)3
or in more conventional matrix notation
HwH⊗b = Hw⊗y4

where H is the matrix Bᵢ(xj)), Hw is the matrix Bᵢ(xj))Kjk and HwH = Hw⊗H* (* is transpose and ⊗ is matrix multiplication). So Eq (4) is a conventional regression equation for the fitting coefficients b. The final step in calculating the integral as
A = bᵢaᵢ
where aᵢ are the integrals of the functions Bᵢ(x), or
A=a*⊗(HwH⁻¹)⊗Hw⊗y5

The potentially big operations here are the multiplication of the data values y by the sparse matrix Hw, and then by the inverse of HwH. However, there are some simplifications. The matrix K is the identity or a diagonal matrix. For the grid method and mesh method (the early staple methods of TempLS), HwH turns out to be a diagonal matrix, so inversion is trivial. Otherwise it is positive definite, so a conjugate gradient iterative method can be used, which is in effect the direct minimisation of the quadratic in Eq (2). That requires only a series of multiplication s of vectors by HwH, so this product need not be calculated, but left as its factors.

I'll now look at the specific methods and how they appear in this framework.

Grid method

The sphere is divided into cells, perhaps by lat/lon, or as I prefer, using a more regular grid based on platonic solids. The basis functions Bᵢ are equal to 1 on the ith element, 0 elsewhere. K is the identity, and can be dropped.

The matrix H has a row for each cell and a column for each data point. It is zero except for a 1 where data point j is in cell i. The product HwH is just a diagonal matrix with a row for each cell, and the diagonal entry is the total number of data points in the cell (which may be 0). So that reduces to saying that the coefficient b for each cell is just the average of data in the cell. It is indeterminate if no data, but if such cells are omitted, the value would be unchanged if the cells were assigned the value of the average of the others.

Multiplying H by vector y is a sparse operation, which may need some care to perform efficiently.

Mesh method

Here the irregular mesh ensures there is one basis function per data point, central value 1. Again K is the identity, and so is H (and HwH). The net result is just that the integral is just the scalar product of the data values y with the basis function integrals, which area just 1/3 of the area of the triangles on which they sit.

More advanced methods with sparse matrix inversion

For more advanced methods, the matrix HwH is symmetric positive definite, but not trivial. It is, however, usually well-conditioned, so the conjugate gradient iterative method converges well, using the diagonal as a preconditioner. To multiply by HwH, you can successively multiply by H*, then by diagonal K, then by H, since these matrix vector operations, even iterated, are likely to be more efficient than a matrix-matrix multiplication.

Spherical Harmonics basis functions

My first method of this kind used Spherical Harmonic (SH)functions as bases. These are a 2D sphere equivalent of trig functions, and are orthogonal with exact integration. They have the advantage of not needing a grid or mesh, but the disadvantage that they all interact, so do not yield sparse matrices. Initially I followed the scheme of Eq (5) with K the identity, and H the values of the SH at the data points. HwH would be diagonal if the products of the SH's where truly orthogonal, but with summation over irregular points that is not so. The matrix HwH becomes more ill-conditioned as higher frequency SH's are included. However the condition is improved if a kernel K is used which makes the products in HwH better approximations of the corresponding integral products. A kernel from the simple grid method works quite well. Because of this effect, I converted the SH method from a stand alone method to an enhancement of other methods, which provide the kernel K. This process is described here.

For a long time I used SH fits, calculating the b coefficients as above, for graphic shade plots of the continuum field. But some other advanced methods provide their own fitted functions.

Grid Infill and the Laplacian

As mentioned above, the weakness of grid methods is the existence of cells with no data. Simple omission is equivalent to assigning those cells the average of the cells with data. As Cowtan and Way noted with HADCRUT's use of that method, it has a bias when the empty cell regions are behaving differently to that average; specifically, when the Arctic is warming faster than average. Better estimates of such cells are needed, and this should be obtained from nearby cells. Some people are absolutist about such infilling, saying that if a cell has no data, that is the end of it. But the whole principle of global averaging is that region values are estimated from a finite number of known values. That happens within grid cells, and the same principle obtains outside. The locally based estimate is the best estimate, even if it isn't very good. There is no reason for using anything else.

I described here a rather ad hoc method for iteratively estimating empty cells from neighbors with data, which worked quote well. But the matrix formulation above gives a more scientific way of doing this. You can write a matrix which expresses a requirement that each cell should have the average values of its neighbors. For example, a rectangular grid might have, for each cell, the requirement that 4 times its value - the values of the four neighbors is zero. You can make a (sparse symmetric) matrix L out of these relations, which will have the same dimension as HwH. It has a rank deficiency of 1, since any constant will satisfy it.

Now the grid method gave an HwH which was diagonal, with rows of zeroes corresponding to empty cells, preventing inversion. If you form HwH + εL, that fixes the singularity, and on solving does enforce that the empty cells have the value of the average of the neighbors, even if they are also empty. In effect, it fills with Laplace equation using known cell averages as boundaries. You can use a more exact implementation of the Laplacian than the simple average, but it won't make much difference.

There is a little more to it. I said L would be sparse symmetric, but that involves adding mirror numbers into rows where the cell averages are known. This will have the effect of smoothing, depending on how large ε is. You can make ε very small, but then that leaves the combination ill-conditioned, which slows the iterative solution process. For the purposes of integration, it doesn't hurt to allow a degree of smoothing. But you can counter it by going back and overwriting known cells with the original values.

I said above that, because HwH inversion is done by conjugate gradients, you don't need to actually create the matrix; you can just multiply vectors by the factors as needed. But because HwH is diagonal, you don't need a matrix-matrix multiplication to create HwH + εL, so it may as well be used directly.

I have gone into this in some detail because it has application beyond enhancing the grid method. But it works well in that capacity. My most recent comparisons are here.

FEM/LOESS method

This is the most sophisticated version of the method so far, described in some detail here. The basis functions are now those of finite elements on a triangular (icosahedron-based) mesh. They can be of higher polynomial order. The matrix H can be created by an element assembly process. The kernel K can be the identity.

The question of empty cells is replaced by a more complicated issue of whether each element has enough data to uniquely specify a polynomial of the nominated order. However, that does not have to be resolved, because the same remedy of adding a fraction of a Laplacian works in the same way of regularising with the Laplace smooth. It is no longer easy to just overwrite with known values to avoid smoothing where you might not have wanted it, but where you can identify coefficients added to the matrix which are not wanted, you can add the product of those with the latest version of the solution to the right hand side and iterate, removing spurious smoothing with rapid convergence. But I have found that there is usually a range of ε which gives acceptably small smoothing with adequate improvement in conditioning.

LOESS method

This is the new method I described in April. It does not fit well into this framework. It uses an icosahedral mesh; the node points are each interpolated by local regression fitting of a plane using the 20 closest points with an exponentially decaying weight function. Those values are then used to create an approximating surface using the linear basis functions of the mesh. So the approximating surface is not itself fitted by least squares. The method is computationally intensive but gives good results.

Modes of use of the method - integrals, surfaces and weights.

I use the basic algebra of Eq 4 in three ways:
HwH⊗b = Hw⊗y4
A = b•a4a

  • The straightforward way is to take a set of y values, calculate b and then A, the integral which, when divided by the sphere area, gives the average.
  • Stopping at the derivation of b then gives the function s(x) (Eq 1) which can be used for graphics.
  • There is the use made in TempLS, which iterates values of y to get anomalies that can be averaged. This required operating the multiplication sequence in reverse:
    A = ((a•HwH⁻¹)⊗Hw)•y6

    By evaluating the multiplier of y, a set of weights is generated, which can be re-used in the iteration. Again the inversion of HwH on a is done by the conjugate gradient method.

Summary

Global temperature averaging methods, from simple to advanced, can mostly be expressed as a regression fitting of a surface (basis functions), which is then integrated. My basic methods, grid and mesh, reduce to one that does not require matrix inversion. Spherical harmonics give dense matrices and require matrix solution. Grid infill and FEM/LOESS yield sparse equations, for which conjugate gradient solution works well. The LOESS method does not fit this scheme.

Appendix - code and libraries

I described the code and structure of TempLS V4 here. It uses a set of library functions which are best embedded as a package; I have described that here. It has all been somewhat extended and updated, so I'll call it now TempLS V4.1. I have put the new files on a zip here. It also includes a zipreadme.txt file, which says:

This zip contains:
LS_wt.r master file for TempLS V4.1
LS_fns.r library file for TempLS V4.1
Latest_all.rda a set of general library functions used in the code
All.rda same as a package that you can attach
Latest_all.html Documentation for library
templs.html Documentation for TempLS V4.1

The main functions used to implement the above mathematics are in Latest_all
spmul() for sparse matrix/vector multiplication and
pcg() for preconditioned conjugate gradient iteration



Thursday, November 7, 2019

October global surface TempLS up 0.096°C from September.

The TempLS mesh anomaly (1961-90 base) was 0.87deg;C in October vs 0.774°C in September. This exceeds the 0.056°C rise in the NCEP/NCAR reanalysis base index. This makes it the second warmest October in the record, just behind the El Niño 2015.

The most noticeable feature was the cold spot in NW US/SW Canada. However, the rest of N America was fairly warm. There was warmth throughout Europe (except Scandinavia), N Africa, Middle East, Siberia and Australia. Sea temperature rose a little.

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


As always, the 3D globe map gives better detail.


Sunday, November 3, 2019

October NCEP/NCAR global surface anomaly up 0.056°C from September

The Moyhu NCEP/NCAR index rose from 0.419°C in September to 0.475°C in October, on a 1994-2013 anomaly base. It continued the slow warming trend which has prevailed since June. It is the warmest October since 2015 in this record.

There was a big cold spot in the US W of Mississippi, and SW Canada. WWarm in much of the Arctic, including N Canada. Warm in most of EWurope, but cold in Scandinavia. Cool in SE Pacific, extending into S America.



Thursday, October 17, 2019

Methods of integrating temperature anomalies on the sphere

A few days ago, I described a new method for integrating temperature anomalies on the globe, which I called FEM/LOESS. I think it may be the best general purpose method so far. In this post I would like to show how well it works within TempLS, with comparisons with other advanced methods. But first I would like to say a bit more about the mathematics of the method. I'll color it blue for those who would like to skip.

Mathematics of FEM/LOESS

It's actually a true hybrid of the finite element method (FEM) and LOESS (locally estimated scatterplot smoothing). In LOESS a model is fitted locally by regression, usually to get a central value. In FEM/LOESS, the following least squares parameter fit is made:

The standard FEM approximate function is f(z)=ΣaᵢBᵢ(z) where z is location on the sphere, B are the basis functions described in the previous post, and aᵢ are the set of coefficients to be found by fitting to a set of observations that I will call y(z). The LS target is

SS=∫(f(z)-y(z))^2 dz

This has to be estimated knowing y at a discrete points (stations). In FEM style, the integral is split into integrals over elements, and then within elements the integrand is estimated as mean (f(zₖ)-y(zₖ))^2 over points k. One might question whether the sum within elements should be weighted, but the idea is that the fitted f() takes out the systematic variation, so the residuals should be homogeneous, and a uniform mean is correct.

So differentiating wrt a to minimise:
∫(f(z)-y(z))Bᵢ(z) dz = 0
Discretising:

Σₘ Eₘ (ΣₖBᵢ(zₖ)Bₙ(zₖ)aₙ)/(Σₖ1) = Σₘ Eₘ (ΣₖBᵢ(zₖ)yₖ)/(Σₖ1)

The first summation is over elements, and E are their areas. In FEM style, the summations that follow are specific to each element, and for each E include just the points zₖ within that element, and so the basis functions in the sum are those that have a node within or on the boundary if that element. The denominators Σₖ1 are just the count of points within each element. It looks complicated but putting it together is just the standard FEM step of assembling a mass matrix.

In symbols,this is the regression equation
H a = B* w B a = B* w y
where B is the matrix of basis functions evaluated at zₖ, B* transpose, diagonal matrix w the weights Σₘ/(Σₖ1) (area/count), y the vector of readings, and a the vector of unknown coefficients.

To integrate a specific y, this has to be solved for a:
a=H-1B* w y
and then ΣaᵢBᵢ(z) integrated on the globe
Int = ΣaᵢIᵢ where Iᵢ is the integral of function Bᵢ,

H is generally positive definite and sparse, so the inversion is done with conjugate gradients, using the diagonal as preconditioner.

For TempLS I need not an integral but weights. So I have to calculate

wt = I H-1B* w

It sounds hard, but it is a well trodden FEM task, and is computationally quite fast. In all this a small multiple of a Laplacian matrix is added to H to ensure corresponding infilling of empty or inadequately constrained cells.

Comparisons

I ran TempLS using the FEM/LOESS weighting with 9 modes, h2p2, h2p3, h2p4,...h43,h4p4. I calculated the RMS of differences between monthly averages, pairwise, for the years 1900-2019, and similar differences between and within the advanced methods MESH, LOESS and INFILL (see here for discussion, and explanation of the h..p.. notation). Here is a table of results, of RMS difference in °C, multiplied by 1000:

MESHLOESSINFILLh2p2h3p2h4p2h5p2h2p3h3p3h4p3h5p3h2p4h3p4h4p4h5p4h2p5h3p5h4p5h5p5
MESH0181926201919211817191817171919191922
LOESS1802524221922202220231922212422232427
INFILL1925027191714211413111711111014111012
h2p22624270242526212424262225252626262728
h3p22022192401716161116171612171816171721
h4p2191917251701415128161213101414161720
h5p2192214261614017111161310101206812
h2p32120212116151701616191017171917192023
h3p3182214241112111601011126101211111316
h4p3172013241681116100111194911111216
h5p3192311261716619111101599106048
h2p41819172216121310121115013121513151620
h3p417221125121310176991308101091014
h4p417211125171010171049128061091014
h5p41924102618141219129101510601210912
h2p5192214261614017111161310101206812
h3p5192311261716619111101599106048
h4p51924102717178201312416101098406
h5p52227122821201223161682014141212860


The best agreement between the older methods is between MESH and LOESS at 18. Agreement between the higher order FEM results is much better. Agreement of FEM with MESH is a little better, with LOESS a little worse. The agreement with INFILL is better again, but needs to be discounted somewhat. The reason is that in earlier years there is a large S Pole region without data. Both INFILL and FEM deal with this with Laplace interpolation, so I think that spuriously enhances the agreement.
As will be seen later, there is a change in the matchings at about 1960, when Antarctic data becomes available. So I did a similar table just for the years since 1960:

MESHLOESSINFILLh2p2h3p2h4p2h5p2h2p3h3p3h4p3h5p3h2p4h3p4h4p4h5p4h2p5h3p5h4p5h5p5
MESH0111123171614181312121512111014121216
LOESS1101019141210141099109891091014
INFILL111002116161317131211151110913111013
h2p22319210202020182020201820202120202022
h3p21714162001515131014151311141615151519
h4p21612162015012121071491091312141620
h5p214101320151201511961110101106813
h2p31814171813121501213161013141615161720
h3p3131013201010111209128691111121317
h4p31291220147913901097489101116
h5p31291120151461612100139996049
h2p415101518139111089130991211131418
h3p412911201110101367990791091115
h4p41181020149101494997061091015
h5p410992116131116118912960119913
h2p514101320151201511961110101106813
h3p51291120151461612100139996049
h4p51210102015168171311414111098407
h5p51614132219201320171691815151313970


Clearly the agreement is much better. The best of the older methods is between LOESS and INFILL at 10. But agreement between high order FEM methods is better. Now it is LOESS that agrees well with FEM - better than the others. Of course in assessing agreement, we don't know which is right. It is possible that FEM is the best, but not sure.

Here are some time series graphs. I'll show a time series graph first, but the solutions are too close to distinguish much.





Difference plots are more informative. These are made by subtracting one of the solutions from the others. I have made plots using each of MESH, LOESS and INFILL as reference. You can click the buttons below the plot to cycle through.



There is a region of notably good agreement between 1960 and 1990. This is artificial, because that is the anomaly base period, so all plots have mean zero there. Still, they are unusually aligned in slope.

Before 1960, LOESS deviates from the FEM curves, MESH less, and INFILL least. The agreement of INFILL probably comes from the common use of Laplace interpolation for the empty Antarctic region. In the post 1990 period, it is LOESS which best tracks with FEM.

However, I should note that no discrepancies exceed about 0.02°C.

Next steps

After further experience, I will probably make FEM/LOESS my frontline method.