Response to Tamino’s ‘More Mathturbation’

I spent a fair amount of time responding to this fool’s diatribes regarding my post on the statistical significance of warming. I took offense at his title ‘More Mathturbation’, which I suspect that he found clever. He also tried to apply Dunning Kruger to myself. Bad move. Let’s see if he posts my response.

Update1: He has, although he doubled down on the gratuitous insults.

Update2: He is now refusing to approve my answers to the objections raised in the discussion. It’s pretty easy to win an argument when you don’t allow answers. LOL. He’s going to regret that one.

I also responded to AlexC over there and reproduced it here. Similar stupidity.

GreenHeretic’s Response to Tamino’s blog posting, ‘MORE MATHTURBATION’

Tamino: If you are going to critique someone else’s blog posting, especially with gratuitous insults, why isn’t it your practice to post something ‘over there’ to alert them? I don’t think much of your ethics.

Did you actually READ my post? Apparently not since you misrepresented why I rejected the Temp=f(CO2) relationship. True, I rejected the original model because of the strong autocorrelation of the errors. However, you are correct that such a deficiency can be ‘compensated’.

In the article I wrote, I rejected pursuing the question down that rabbit hole because CO2 explained no more than a simple time trend model. Real analysts with decades of modeling experience (like myself) understand the importance of that fact.

CO2 has no discernible incremental association with temperature beyond mere correlation over time. Nevertheless, I did waste considerable time exploring, but found nothing worth reporting. That led me to ask the question as to whether an actual trend existed. I am well aware of the dictum, ” It turns out that there isn’t, which is what the article demonstrated and concluded.

You claim that substituting an ARIMA(1,1,0) aka simple change model is not appropriate and say “I’ll bet he learned that in econometrics class.” I learned that in a graduate level advanced regression class in the late 1970s.

When I started analyzing weather in the energy sector in a professional capacity nearly twenty years ago, I validated the application of ARIMA methods for weather. My citation for the appropriateness of analyzing weather data using ARIMA is Daniel Wilks, ‘Statistical Methods in the Atmospheric Sciences’ published by Academic Press in 1995 (First Edition). It’s up to Third Edition today. You can find it easily enough on Amazon. Chapter 8 in my edition is entitled ‘Time Series’ should convince even you that my methodology is accepted by meteorology professionals.

I am not sure what your point was in your discussion of ‘unit root’. If you believe that my analysis has a problem with stationarity, then you should show it, with numbers. Hint: The problem when the dataset fails stationarity is that spurious regression relationships are reported, NOT when no regressions are reported. It’s clear that you have no idea what you are blathering about.

Your point regarding my lack of appropriateness tests for the ARIMA model is actually partially well taken. The ARIMA(1,1,0) shows an annoying negative residual autocorrelation at the fourth lag. A better fit model would have been to add a seasonal term. For other purposes, I would have done that. However, since it didn’t change the outcome (which was to check for statistical significance for the drift term in the ARIMA model), I didn’t include it.

As for your application of the so-called Dunning Kruger phenomenon, I suspect that you should really look in the mirror for the best example of that. You really haven’t a clue what you are talking about. Your multiple insults show a lack of maturity and lack of basic respect for those who disagree with you. Grow up.

AlexC << However, he first differenced the data. Of course there is no significant trend leftover, because first differencing makes series stationary. This is really all that needs to be said >>

Question, AlexC: Did you PASS intermediate statistics? Had you been in the class that I TA’d, I would have failed you on that piece of your final exam. You don’t even understand the rudiments of an ARIMA model.

When you difference, you don’t eliminate the trend in any way. The trend ‘moves’ from a coefficient to become the constant, aka ‘drift’ in time series vernacular.

Update: AlexC has acknowledged my point on data differencing and trend analysis. Good for him. Unlike Tamino, he has integrity. Even if we disagree, I can respect him.

Was July 2015 really the warmest on record?

Note: This article needs some work.

I have been watching breathless agonizing over a NASA report that July 2015 was the “warmest on record”.

Example: Article by Mariano Castillo and Brandon Miller on CNN.

Example: Article written by Ben Guarino at

Guarino wrote, “July 2015 was the hottest since we’ve been keeping records, according to separate data from both NASA’s Goddard Institute for Space Studies….”

In fact, the NASA data cited by Guarino doesn’t say that at all. A quick glance over the data indicates many months where the temperature anomaly was greater than July 2015. In fact, July 2015 was the third smallest (coolest) anomaly in the last twelve months.

Below, you will find a clip of the data. Note that July 2015 shows a value of ’75’ which means that July 2015 was 0.75 degrees higher than the 1951-1980 average.

July2015 NASA DATA

So-called ‘science writers’ have a professional obligation to do basic checks the information that they cite. Their articles are being flung around on social media as gospel. In fact, they are nonsense because the underlying science is mistaken.


Erroneous Report of Record Heat.

Seth Borenstein, AP Science writer published an article on July 20, 2015 that claims that the first six months of 2015 were the hottest on record. A quick analysis of the lower troposphere satellite data contradicts this story.

This empirical data shows that there were six hemi-years that were warmer. For 1998 and 2010, both the first and second halves were warmer. 1998 remains the hottest. The latter halves of 2009 and 2014 were also warmer than the first half of 2015.

What’s more, the data for the surface temperatures for the first half of 2015 is barely in preliminary form. It is far too early to make any such observations.

My question for Mr. Borenstein would be, why haven’t you checked this yourself? It’s not hard. Why are you risking your reputation, perhaps your career, with this sloppy, uncritical reporting?


Has Warming in the Lower Troposphere been Statistically Significant?

“Has there been any warming yet?”

This question should have been the touchstone where every policy analyst should have started from the beginning. The corresponding scientific question should have started with, “Has the observed warming been statistically significant? Astonishingly, that did not happen.

This article examines lower troposphere temperature anomalies in conjunction with carbon dioxide levels using classical regression methods. These techniques are accessible to anyone with no more than a minor in undergraduate statistics. The technical question to be asked and answered is, has the observed warming to date been statistically significant?

Conclusion. At first blush, the empirical evidence appears to support the assertion that there has been warming. However, after conventional model diagnostics and reformulation, the statistical significance disappears completely and we must conclude that the observed warming does not meet any reasonable criterion of statistical significance. The observed warming could easily be the result of simple chance.

– – –

Why examine temperatures of the lower troposphere when it is the surface temperatures we experience?

  • There is no reason to expect any substantive temperature divergence between the surface and the lower troposphere that lies directly above it.
  • Temperatures for the lower troposphere have been consistently and regularly measured by satellites over the past thirty-five years. This is very high quality data.

CO2 and Trend Models. Let’s start with a picture. To anyone who has investigated global warming, this image should be familiar.


The trend line certainly looks convincing.

Next, let’s look at the regression of temperature anomaly on carbon dioxide levels.


The regression looks strong. 42% R-Squared. With a t-statistic of 17.69 and a significance <0.0001, the coefficient for CO2 certainly looks significant.

Diagnostics. What’s left to check? Model assumptions. Are the model’s errors (aka residuals) normally distributed, constant (homoskedastic) and independent?

To cut to the chase, the Durbin-Watson statistic of 0.49 tells us that there is something seriously amiss with this CO2 model. The D-W falls well below the lower bound of 1.65 for D-W from a standard table for >=100 observations. Therefore, we are forced to consider the implications of significant auto-correlation in the model’s residuals.

A check of the ACF (autocorrelation function) and PACF (partial autocorrelation function) on the residuals strongly confirms significant and substantial, even profound, violations of residuals independence at both the first and second lags. The t-statistic for the PACF for the first lag is 15.8 and the second is 5.1. Both have positive signs.


What does this mean? Estimates or inferences that depend on error variance are suspect, at best. That includes any tests of statistical significance. The errors are not independently and identically distributed (iid). We often push the limits on statistical assumptions for normality and constant variance, but not independence.

There is a related point to consider. Carbon dioxide and temperature have both been increasing over this time interval.  So, they are correlated. However, does CO2 level do a better job than a trivial time trend model? If CO2 were a useful explanatory variable, we would expect it to perform at least a little better than a trivial trend model.

Does CO2 do any better than case number? No. Model testing shows that the standard error for the CO2 model is 0.1727 while the standard error for a trivial trend (case number) model is 0.1728. Note: lower is better. An improvement of 0.0001 is no difference. This is a tell to experienced modelers that the CO2’s correlation relationship with temperature is spurious.

Can we even answer the question? All is not lost, of course. We can still move forward and develop a model to learn whether the warming has been statistically significant. In general, how do we reformulate models when our error terms are riven with autocorrelation? We dust off our Box-Jenkins text and try out an ARIMA model.

Cutting through a pleasant afternoon of model exploration, this parsimonious ARIMA(1,1,0) model emerged as adequate for our purposes.


ARIMA(1,1,0) is a simple change model. The middle number ‘1’ means that this is a first difference (simple change) model. The first ‘1’ means that there is a single autoregressive term. That is to say, each observation is closely related to the previous. The ‘0’ means that there is no moving average (MA) term.

In the output table, the ‘Overall Constant’ can also be referenced as ‘drift’. This term corresponds to the trend coefficient in the simple trend model. The (AR)P(1) term is the autoregression coefficient. In mathematical form, this model says:

  • dX(t) = Drift + AR * dX(t-1) + A(t,0,SE)
  • dX(t) = 0.0017 – 0.3359 * dX(t-1) + A(t,0,0.1131)


  • dX(t) is the change for time period t.
  • Drift is the amount of expected change for every time increment. In this model, that would be 0.0017 degrees per month or 0.20 degrees per decade.
  • dX(t-1) references the previous change
  • AR is the coefficient that adjusts the previous change. The -0.3359 value indicates strong reversion or rebound.
  • A(…) refers to white noise for time period t with mean of zero and standard error of 0.1131.

Compare the much improved ARIMA standard error of 0.1131 with standard error of 0.1728 for the CO2 and simple trend models. The ARIMA model passes model diagnostics for normality, independence and constant variance.

Where’s the Warming? The ‘Overall Constant’ is the drift term that corresponds to global warming. If there were warming, this term would show significance. In this model, the monthly change is 0.0017 degrees (0.20/decade) with a standard error of 0.0135. The t-ratio is the ratio of the constant to the standard error.

What does the t-ratio tell us? From the t-statistic we infer the likelihood that a result came about as the consequence of chance. A t-ratio greater than 1.96 (~2.0) indicates a likelihood of <.05 (<5%) that a result was the result of randomness. This is called p-value. Interpretation of t gets more involved when there are fewer than thirty observations.

In academia, the lowest standard for a t-ratio to be considered significant has traditionally been 1.96. In the business world, I have used terms in models with t-ratios as low as 1.2.

The t-ratio for the drift term for this reformulation is  (0.0017/0.0135) = 0.1224. That is to say, the drift is not significantly different from zero. No self-respecting analyst who wanted to keep their job would ever consider retaining a term in a model with a t-ratio of 0.1224.

Does this mean that there has been no global warming? Not at all. What this does mean, however, is that in the thirty six years since we started taking temperature measurements from satellites, there has been no statistically significant warming in the lower troposphere. This is not even a close call. The observed warming could very very easily be the mere consequence of random variation. That is to say, nothing out of the ordinary with respect to lower troposphere temperature changes has occurred.

Nevertheless, this flatly contradicts the models put forward by warming activists. Over the past thirty-six years, carbon dioxide levels in the atmosphere have increased by nearly twenty percent. If that change hasn’t produced statistically significant changes in temperature, then their models lack validity. Their predictions have zero basis in the empirical evidence.

Yes, it really is that simple.

– – –

Data Notes. The data used was acquired from two sources. Anyone can recreate this analysis with these data tables.

Carbon dioxide levels were downloaded from the Earth System Research Laboratory.

Lower Troposphere temperature anomaly records were obtained from National Space Science and Technology Center, hosted at the University of Alabama, Huntsville.

The CO2 data is in weekly form while the satellite temperature anomaly data is aggregated monthly by the NSSTC. The carbon dioxide data is labeled ‘CO2 molefrac’, while the temperature data is labeled ‘Globe’. To get the weekly CO2 data to mesh with the monthly temperature data, the average was taken of the weeks over the month. While there were several missing weeks, there were no entire months with missing data. There was no additional processing or transformation of the data.

Hello world!

Welcome to!

I find it ‘fitting’ to launch my GreenHeretic blog on on Earth Day, 2015. Why? I was an organizer for the first Earth Day in 1970.

How times have changed. I have become increasingly dismayed that the Green Movement has taken on a militant orthodoxy that tolerates no dissent from their point of view. Their materials reek of righteous condescension as the promote their beliefs as though they were incontrovertible fact.

Example? Let’s start with, ‘the debate is over with regard to global warming’. No, it isn’t over. The debate has barely begun. In fact, if we were to conclude the debate today, the empirical evidence supports the Deniers more than the Warmists. The very notion that the ‘debate is over’ smacks of intimidation.

As a consulting professional in the energy sector, I have become further dismayed by the excessive manipulation of information about renewable energy by the Greens. While they have been called out on it many times, that effort has not been effective.

Why have those call-outs been ineffective? Often, those who question the information do not, themselves, understand the issues well enough to counter it effectively. intends to address that. I have the expertise to expose their many canards.

What topics will be addressed? Anything related to energy and the environment where clarification is needed. For the most part, this means correcting mis-information put out by the Greens. However, nobody gets a pass. We will be delving quite deeply into the nuts and bolts of energy and environmental issues. If you have any topic suggestions, by all means contact me!

Why are you anti-Environment? That is a false premise. In fact, as I mentioned in the first paragraph, I am proud of the fact that I was one of the original organizers of Earth Day in 1970 at Carl Sandburg Junior High School in Golden Valley, Minnesota.

For that event, we transplanted dozens of trees from a tree farm owned by a teacher to the school’s campus. Those trees are fully mature today just north of the northeast corner near the main entrance to the school. The GPS coordinates for that stand of trees are: 45.005722, -93.366431. will not oppose green technology, per se. I take a dim view, however, of anyone being compelled to be green themselves. I also do not believe that the Greens should be spreading false information, especially to peddle products or influence public policy.

What false information? I have many articles planned. For example, I will show how the notion that solar energy is converging on grid competitive is not only not true, it is demonstrably silly. Such statements rely on deceptions that do not withstand scrutiny. I will show that the trendline for global warming is not statistically significant. We will discuss wind energy in considerable detail. Most of the articles will illuminate the canards with reference to specific articles and statements.

How is funded? At the present time, out of my wallet. For my day job, I am a self-employed risk management consultant in the electric power markets. I analyze those markets for clients for trading, planning and capital investment purposes. I forecast the value of electricity. I also teach about the business of electricity supply and consumption from a market perspective. While I will be applying information and insights that I learned from that work, none of my electricity business clients support this website.

However, I expect (hope?) to harvest revenue to support this site from:

  • Generic Advertising
  • Grants and Sponsorships
  • Donations

If anyone knows how to get money from the infamous Koch Brothers, please send me contact information! I would be delighted to relieve them of funds to support this effort.

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