I was born into a family of Baptists and raised as a Baptist for all of my formative years. Whether this was always true or not from the time I was old enough to make the distinction I was raised a Southern Baptist. Not that I really understand the distinction now, but at least I know we are a separate group. Southern Baptists are not really as stodgy an fuddy duddy as you may have heard; they can be a fun loving bunch. They do have their limits. On a fine spring Sunday morning back in 1974 or maybe 1973 a fellow church going Southern Baptist and I pushed those limits.
It those distant days people observed a certain solemnity as concerns Sunday morning worship services. For one thing us men folk wore very uncomfortable clothes and shoes. Probably because being uncomfortable and having fun tend to be mutually exclusive things. Young folks were expected to act with decorum. Wearing slick bottom shoes can ensure a certain measure of decorum when traversing waxed and polished floors. Every Sunday the ushers of the church would hand out these stiff, singly folded, color printed pieces of paper known as the Sunday bulletin. That was their first mistake. Parents often allowed the young men to spend Sunday worship service in the balcony. This was their second mistake.
You see, church bulletins are stiff and thick. They are in fact the perfect material for the making of paper airplanes.
In those days I often sat on the first row of the balcony with a friend who would eventually graduate from high school with me named Blake. Blake and I created a number of airplanes. We would place our creations on the edge of the wall demarcating the end of the balcony. Often, when the congregation would bow their heads and close their eyes in solemn prayer these paper construction would creep over that edge as if by accident.
Now, for the most part, paper airplanes made from church bulletins tend to fly pretty good, but the widely recognized method of folding the paper makes an airplane that tends to corkscrew. Most of our creations either nose dived or just turned and flew back under the balcony where few people ever sat. Southern Baptists tend to see relative pulpit position as a badge of faith. Such churches tend to fill up from the front, which seems to be the exact opposite of normal human behavior but what can you do? So, while we felt gloriously naughty and bold our creations in reality were mostly duds.
Until one fine Sunday morning I threw accepted airplane construction technique out the window and created the one, perfect Church bulletin airplane. I won't divulge my design secrets here. Because....
THERE CAN BE ONLY ONE!
My creation found itself in the position of many other such creations. Perched upon that edge marking a separation between the balcony and all that empty air above the heads of some fifteen hundred solemnly serious Southern Baptists, waiting for a moment of prayer. That moment came when all the ushers moved forward to the front of the church for the offering. We call that passing the plate. With the ushers lined up in front of the pulpit, the preacher enjoined the congregation to bow their heads and offer up a prayer. As soon as every head was bowed and every eye closed, my creation leaped into that great empty expanse.
What happened next very nearly cause Blake and I to rupture an internal organ in a supreme act of laughter suppression. My airplane seemed to soar with a will of its own. It never deviated from its course, turning neither left nor right. Instead it soared straight ahead in a perfectly level, gently descending flight. Unseen save for Blake, Me, and God it slowly floated all the way down over all those bowed heads and pegged the pulpit stand dead center with an audible thunk. From there it landed smack dab in the middle of an offering plate.
Seconds later the prayer came to amen, and the ushers came forward. One of them grabbed the plate with the airplane, seemed to pause in confusion, then promptly stuffed it in his pocket.
By this point me and Blake were fairly hurting. It was all we could do. I remember having my head between my knees trying to hold it in.
Shortly after the offering had been given and received and the service got back into full swing we were still in the grip of laughter when suddenly we found our ears gripped between the thumb and forefinger of one of the ushers, a man named Durham Phagan. Which I must say is one heck of a name. I will never forget what he said to us.
"I have been mistaken before, but this time I don't think I am. I do believe there will be no more paper airplanes falling off the balcony from this point on."
Well, all I can say is he was not mistaken. Our career in Southern Baptist Aviation had come to an end. But what a glorious end. It was the one, perfect, church bulletin paper airplane. I bet even God had to smile over that.
It is Thursday 4:14 in the afternoon, and we are coming to you live from the Lanett Alabama Econo Lodge. It has been a long week but things are rapidly coming to a close. Today I propose to show you what I believe will be the last thing you will ever need to see about the myth of global warming. Yes, I said myth. Over the past few posts I have shown you how the proof CO2 is driving temperatures up is weak. I have shown you how the number of monitoring stations active in the world has changed. We have examined station data from Australia, to Wake Island, to Scotland and seen no evidence of catastrophic warming of any kind. If you are scratching your head and wondering why none of this adds up I am going to show you. The answer is in the data and in the text files that go with it.
What does this tell you? It tells you simply they are not reporting the actual temperatures, they are reporting how much the temperatures vary from the 1951 to 1980 average. In fact, according to the text, they reported each month as a deviation from the monthly 1951 to 1980 average. That is how they created a homogenized model of the global average through time.
That is why the plot of their data from the year 1980 shows no seasons. No summer, no winter. Just variations from an average.
This is exactly where they got it wrong. Their model is badly flawed. Remember how I said the number of active stations in the world has grown exponentially during the time their charts show so much rise in temperature? That is the heart of the flaw. In order for their method to work the annual global average can not change because of new stations coming on line or old ones going off line. Meaning if you drop off a station in Antarctica and add 10 stations in the Bahamas you have just caused the global average to go up. It doesn't matter if you move all the data up or down by adding or subtracting 50°, the difference between the land of frost and the land of sunshine remains.
The correct way to homogenize or smooth a large collection of records with varying local temperatures, different starting and ending dates, with no way to determine the true, accurate local temperature back in time, and no way to determine the referential accuracy between instrumentation is to homogenize each individual station record to it's own zero reference point. This way you are asking a simple question. How much temperature rise or fall did this particular location see over that amount of time. Then, when you look at a time frame of the record you are seeing a true record of change on average.
Let me prove this to you. I have created a spread sheet simulation that mimics four temperature stations. Two of these are random numbers between 40 and 50, one consists of random numbers between 30 and 40, and the last one consists of random numbers between 50 and 60. I then processed the numbers using their method and lastly using my method.
When I chart the data, regardless of how many times I generate a new set of random numbers, the result looks like this.
I created my simulation to generate numbers that varied within temperature bands, but always maintained the exact theoretical averages of 45, 55, and 65. That would be the orange line above. It always, always, reflects the reality contained in the data regardless of how I play with the data. I can add station data, or take it away, but the actual trend of the data remains at or close to zero as long as I didn't change the average in any station over time. Even then, it would take a fairly large change in one of the four created stations to make much of a difference on the chart.
The blue line, however, demonstrates their model is highly sensitive to changes in the number of stations active and the differences in local climate. This is their flaw, and it is a big one. Where the stations were added in that big change from 1977 to 2000 makes a huge impact. That would not be the case if they had constructed their core mathematical model correctly.
The evidence I have presented is, in my opinion, conclusive. Their chart of annual global averages shows a likely probability of correlation between temperature and the number of stations on line. Under examination that correlation does not break down as the CO2 correlation did. In fact further examination supports that correlation.
In order for their model to work they would have had to maintained an exact ratio of weather stations to temperature zones, which they did not do. This is why their over all global data does not match any individual records set. If the basic mathematical model for how they engage with the data is so seriously flawed then any conclusions they derive must also be flawed. They are in fact divorced from reality.
Honestly, it is hard to imagine any group of high powered scientists not seeing this basic flaw. It is hard to imagine no one even testing the model against real world results to make sure it was right.
Well, it is 5:41 am here in Lanett Alabama. Not too long now before the alarm goes off and I need to wake up so I can get ready for work. Insomnia bites again. Coffee is coming though. After a good shower & shave and after I get some good, non hotel brand coffee I am sure I will feel human again.
So. What's up today? Well, I was kind of wiped out yesterday after work so I didn't play amateur weather researcher much, but the one hour I did put in was pretty productive. You can read on ahead and decide for yourself.
If you remember last time, I demonstrated there is as good a relationship between so called global warming and the number of weather monitoring stations active in the world as there is with how much CO2 is in the atmosphere. Today I am going to zero in on one station and show you a potential cause for that. Call it progressive error in the system. That location is station 61353 on Wake Island.
Which is really cool, because Wake Island played a role in World War II. Americans died there defending our country. The ones who survived entered a nightmare of captivity under the Japanese. The price was heavy, but they paid it. God bless them. We owe them. We owe their families. Some debts just can't be paid.
Below are pictures of monitoring stations on Wake Island. While these are clearly two different stations, according to NOAA, Smart Tracker, and other sources there is only one station on Wake. It is the only station within 200 kilometers.
I believe this is the older station.
The tower looks like 70's construction.
Looks like solar panels were added at
different times. Plus this is the older picture by
the digital info.
This appears to be a newer station.
It was uploaded after the one above.
The tower appears to match newer construction.
The technology appears newer. The data transmitter
appears to be on the tower.
Below is the temperature record from Wake Island in the NOAA dataset.
In the story of global warming, 1946 to 2004 is a pretty important time frame. The location, smack dab in the middle of no where in the Pacific Ocean, is also pretty important too. The Pacific in particular and oceans in general play a very important role in global temperature and weather. I really can't stress that enough. I may go into detail on why that is a true statement in another post down the line.
I did some statistical analysis of the data and here are the results. I won't get into the details of how and what, though I can if anyone asks. The conclusion matches reality so well I honestly don't think it necessary to prove anything. What appears at first glance to be a general rise in temperature over time resolves into a nearly 1° C jump in temperature occurring in 1979. There appears there may be another such sudden change around 2003 but there just isn't enough data to make the determination. There is a single singularity in the annual readings both before 1979 and after 1979. Other than that the data, when viewed as two separate data sets, shows a high degree of consistency and constancy. No trends in other words. In other words, something happened.
There are only two options here. There was an actual change in the weather or there was a change in how the weather is measured. The evidence presented above shows the monitoring station was moved. No doubt the actual temperature measuring equipment has changed from 1946 to 2004.
There can be quite a difference in temperature from one location to another. That is just a fact. How the a thermometer is positioned, how it is shielded from the sun or wind can make big differences. There is, however, potentially more. Remember, in a prior blog I mentioned my experience in metrology through my nearly 30 year career in Engineering.
Measuring temperature isn't very tricky really, but measuring temperature accurately is. Regardless of instrument resolution, otherwise known as precision, the accuracy of the device depends upon being able to calibrate it to a known standard of much higher accuracy. Unfortunately, temperature isn't something you can carry around. The standard in calibrating thermometers in general use has not changed appreciably since the late 1800's. The standard for the relative zero point on the scale, regardless of what scale you use, is the freezing point of water. That is the general method. The standard for accuracy for normal usage field and regular laboratory usage is ± 1° C. With special care, accuracies of ± 0.1° C can be achieved. With extraordinary care accuracies of ± 0.001° C can be achieved. So, while ultimate accuracy within those parameters can be achieved actual referential accuracy between stations would be twice that. In other words, take two thermometers, regardless of precision, put them in a temperature and humidity controlled environment and you may find they differ by as much as 2° C.
So what does this mean? It is pretty simple really. There has been no significant change in average annual temperatures on Wake Island since 1946.
There is more to be gleaned here, but I am out of time.
Until the next time, be good to one another and remember the boys and men of Wake Island who gave it everything they had and be grateful.
Today's blog is a bit of a travel blog. I am currently residing in the luxurious Econo Lodge of Lanett Alabama. Econo lodge features a continental breakfast as one of it's amenities. You have your choice of Fruit Loops or a hardened granola bar. Beverage choices include juice or water with a splash of coffee in it. Each room, with the free WiFi, is a trip back to a time when dial up ruled the world. Okay, maybe I exaggerate just a bit. But not much.
Last time I put on my nerd hat and traveled into the world of statistics and global warming. It was a fun trip for me, but hey that's me. This time we are going to delve a bit further. I have gone data spelunking with great success. Millions of records kind of success. I had to ice down my computer and turn the AC up full blast kind of success. My data this time comes straight from the National Oceanic and Atmospheric Administration (NOAA). I now have raw station data from around the world from 1764 to 2017.
First, I would like to show you something. If you remember on my last blog I showed you a graph of temperature and CO2 along with some statistics indicating a possibility of a relationship. Today I am doing the same thing but instead of CO2 I am looking at the number of active temperature monitoring stations per year in the NOAA records. If you will remember, the correlation between temperature and CO2 was about .7. Call that a 70% chance if you will. It turns out it is equally as likely we are causing global warming by building temperature monitoring stations. Yeah, really.
The correlation coefficient between temperature as a global average and the number of active stations is .698, which is really danged close to .7. There isn't any real difference. The fact of the matter is if I had a record of how many cell phones were in use each year I might get a fairly decent correlation out of that too. Because even when things appear to have a high likelihood of being tied together in a cause and effect relationship it turns out not to be true. The ultimate test of that is being able to make it happen. Turn the faucet knob right, more water comes out and vice versa. That is what conclusive stuff looks like.
Looks like a pretty good match, does it not?
I am going to go a step further. I believe that statistical match between the number of active monitoring stations and the global temperature averages they are reporting is more than just a happy accident. In 1900 there were 495 active stations. In 1925 there were 819, and increase of 65.5%. From 1925 it went to 1305 in 1950, and increase of 59.3%. From 1950 it went to 2828 in 1975, an increase of 116.7%. In 2000 it went to 4515, and increase of 59.7%.
During the last quarter century the biggest jump in stations occurred in 1998 to 1999, with 465 stations coming on line. By 2004 the number hit 4627 and has stayed there through 2017. Now, what are the odds that 1998 or 1999 just happen to be the point climatologists and experts all over the world point to as the beginning of the pause? That period of time over the past, well, 17 to 19 years global temperatures have failed to rise.
Actually, I don't know the odds. It just seems very coincidental. Especially in light of the moderately high correlation above. Again, when you see that kind of correlation it means the idea has merit and is worth exploring. I fully intend doing so.
In the mean time, since I happen to have a wealth of information at my finger tips I decided to see how individual station data stacks up to the overall picture. I can answer that right now. It doesn't.
Below you will see graphs from stations located from Scotland, to Germany, to Australia, and to the Sudan in Africa. You will see three plots per chart corresponding to the yearly average, the highest monthly average in the year, and the lowest monthly average. This gives you a far more complete picture than you will see anywhere else, as far as I have seen.
With the except of Sudan, each graph shows the annual highs to be either flat or slightly decreasing over time. In the Sudan the annual highs have been generally increasing but jumped slightly towards 2008 after apparently going haywire. Who knows. They may have seen a 10 to 15 degree jump in one year, but that seems unlikely.
In all locations the annual lows have been steadily increasing at rate of .9° C to 2.7° C (which is Sudan) per year. Meaning the summers are generally staying about the same or lower, while the winters are gradually getting warmer.
What you do not see in any of these places widely scattered across the planet is this big hockey stick. It just isn't there.
Unravelling this big kettle of noodles is going to be really hard to maybe impossible, but I am going to try. The fact of the matter is there are not so many continuous records to look at covering that 1970 to 2000 or better time frame. Each year new stations came on line, but each year older stations went off line. Over 700 in one year in fact. I don't know how these folks are doing it, but they are splicing a lot of fragmentary records together. So far, it doesn't appear to be worth a tinker's damn. Which could mean the crisis exists only in the playing with the data while not really existing in the data at all. That would really be a man made crisis. Kind of like a man freezing to death because his thermometer broke.
It has certainly been a while since I have blogged. What can I say? Holidays, tax season, vacations, work, putting in a home recording studio, spring fishing.....Hey, stuff gets in the way.
This post is going to be different. I am going to dust off a hat I very rarely wear in public without getting paid for it. I am going to go full tilt nerd. Yeah, very surprising I know. Whatever... We are going to talk Anthropogenic Global Warming (AGW). You will note I am sticking with the original terminology. It changed to climate change for whatever reasons, but the heart of the debate is still the original concept. CO2 is a primary driver of temperature and we are polluting the world with the stuff. For every addition 100 PPM (parts per million) of C02 in the atmosphere temperatures go up. Thus we are cooking the planet and we are all going to die. They tell you the science is settled. It is the settled or proven part I am going to address. I am not going to prove CO2 doesn't make the temperature go up. I am not going to prove it does either. I am going to demonstrate why I think the issue is far from settled science and nothing more.
Before we begin I am going to address some key issues. I have discussed and debated this subject many times and whenever you do that people who support the theory of AGW always, always, make these ad hominem statements. So let's get that out of the way up front. No I am not a climate scientist. I never claimed to be. No, I have never published a peer reviewed article. Neither did Isaac Newton and he was no hack. I am, however a degreed mathematician. I have worked in an engineering field for some 30 years. I have extensive experience in statistics and statistical analysis as well as metrology. Metrology, by the way, is the science of measuring. Now that isn't all I have done, but it is a big part of what I have done. Okay? By way of comparison, I am at least as qualified to study and discuss the subject as Bill Nye. I equal him in education and I believe I exceed him in terms of experience. If you think about it, we should all thank Bill Nye for setting the precedent of elevating someone of less than overpowering qualifications to the level of national expert and spokesperson. Golf clap, Bill, golf clap.
As to questions on my data. The temperature data comes from Berkeley Earth which is a non profit eco organization. I have pulled data from a number of sources in the past from the CET, to the English MET, NOAA, and so forth. The folks at Berkeley Earth have basically compiled these data sets. My data on global CO2 comes from the Institute for Atmospheric and Climate Science (IAC) at Eidgenössische Technische Hochschule in Zürich, Switzerland. The data represents what is the acknowledge global record of temperature and CO2 for 265 years beginning in the year 1753. The temperature data shows temperature variations from the 1950 to 1984 average in degrees Celsius.
So shall we begin?
The chart below shows temperatures in blue and CO2 levels in grey from 1753 to 2016. This is no different from any graph you have ever seen from proponents of AGW. Except most graphs I have seen do not go back past about 1978. It is true, looking at this graph you will say it all looks pretty clear to you. I can assure you it is not. So don't stop here! In the world of statistics looking right is usually only the beginning. Can you prove it is right is the real question.
The next graph is something you probably haven't seen. This is called a scatter diagram and is a graphical representation of the measure of correlation between CO2 levels and temperature. This chart in fact does indicate a moderate amount of correlation. Not strong and certainly not beyond any doubt or debate. Call it a 70% chance of correlation. That isn't precise, but it does give you an idea. It does indicate further work is warranted. The hypothesis has not been rejected, but it hasn't been proven either. We are at the point of saying the theory appears likely. I will break this down as we continue.
Prior to this point the data we have been looking at was yearly averages through time. From this point onward we are going to be looking at monthly global averages through time. There are a number of reasons to look at the data this way, but to me the biggest reason is this. Looking at an average, especially one generated over data showing a great deal of inherent variability, does not yield a clear picture of what is really happening. If I tell you last year was a lot hotter than the year before what do I mean? Was the summer really hot? Was the winter really mild? Was it a late spring? A long fall? What does it mean to say one year is hotter than another? Well, if all you look at is just the average temperature for the year it could very well mean any or all of those things and more besides. To understand why something is happening you really must understand what is happening. The charts below will, I believe, answer some of those questions.
The next chart shows the strength of correlation between CO2 levels and temperature variations over time both for annual yearly averages and annual monthly averages. What I am charting is known as a correlation coefficient. This is a statistical measurement of correlation strength ranging from 0 to 1 for positive correlation, 0 being no correlation at all and 1 being perfect correlation. A scatter diagram with a CC of 1 would be a straight line with no deviations. Generally speaking a CC of .5 is the point at which statisticians consider there to be some evidence of correlation between two factors.
You will notice the CC for the annual averages appears to be somewhat significant, but that is not true for the monthly averages. In fact, there is a pretty wide variation between months. How can that be right? Well, I will explain that. But first, lets look at some monthly data side by side so we can see what is really happening.
The graphs below depict the historical temperature records for July and December from 1753 to 2016. The first thing you will notice is neither month bears any strong resemblance to the chart of global averages we started off with. Secondarily, you will notice there is a very distinct difference between July and December. There doesn't appear to be any appreciable change to July temperatures. It started off just below 1° and ended up just below 1°. December's data shows a general steady rise in temperature with minor cyclical oscillations. .
Let's see what happens when we compare August and March temperatures through time. Again, August appears to have netted not much of a difference between 1753 and 2016. The results are pretty similar to what we saw looking at July and December.
I won't put a chart for every month up, they are all similar to the four proceeding charts. Below is a closer look at the data for January. Notice how the graph generally fluctuates above and below the linear average creating four essential symmetrical sections. Weird, huh?
So, here is the conclusion to my piece. There is no clear proof of correlation between CO2 levels and temperature. Therefore it is reasonable to reject the hypothesis of correlation between CO2 and temperature. That doesn't mean the issue is settled by the way. It is a rejection based upon a failure to prove the hypothesis because the case for it is just not strong enough to accept.
So what explains the seemingly strong correlation between CO2 and the annual averages?
A correlation coefficient of .7 is fairly strong, but is by no means definitive. It indicates you might be on the right track and the inference is reasonable to fairly likely. Further work would be required if you wish to declare the issue settled. But, that is what I did, I performed a deeper examination of the data.
What I have found indicates there is a flaw in the basic methodology of looking at global temperatures as an annual average. As such, the picture or model of reality thus created is inaccurate and flawed.
The data infers whatever effect CO2 may have on temperature as an annual global average is not uniform throughout the year. This would indicate there are additional factors involved capable of either enhancing or counteracting whatever affect CO2 might have. Therefore it is necessary to understand that effect on a seasonal basis.
This leads me to conclude there are other factors which may skew the data and create an inaccurate picture. Such as seasonal differences between the northern and southern hemispheres, location with respect to the equator, and so forth. The simple fact is more study is needed.
From the moment I first became aware global warming was a thing, from the moment I first saw a graph showing annual temperatures and CO2, I have been interested in the subject. And yes, as crazy as it sounds, I actually enjoy looking at data and doing statistics. In my career as an Engineer I did a great deal of data analysis. Back in the day we didn't have anything like the type of programs nearly everyone has on their computers today. Programs like Excel for instance. I wrote programs to do statistical analysis myself, because such programs were not common. I used these tools to analyze and resolve real world problems where the proof of the results of my work was, as they say, in the pudding. It made the difference between good product or bad product rolling down the assembly line. It made the difference between machines making good part or spitting out piles of scrap. It is what I do. In my world looking at just an average rarely ever yields a complete picture of what is really going on. Looking at the average of a target that moves in cycles as an average over several cycles is just bad statistics. Looking at the average of results produced over several different processes is just bad statistics. Yet, that is exactly what all these charts you see supporting the theory of AGW do.
I will no doubt continue looking at this as I have been doing for quite some time. Actually presenting the results is a new and novel idea to me. I will no doubt do so again. Maybe.