Are the airplane contrails in the sky know as chemtrails (chem trails ) due to a secret government plot, or rathre the byproduct flying at altitudes under certain atmospheric conditons? In this episode, I tackle the chemtrail conspiracy theory, separating fact from fiction, with aviation and atmospheric science and critical thinking.
Learn why and how contrails described as chemtrails form, why they persist in the sky, and why claims about chemical spraying do not hold up under scrutiny. Whether you're curious, skeptical, or want to understand the truth and science behind an enduring myth, this episode provides clear answers and insights into the science of what's happening in the sky.
Show Notes
Intergovernmental Panel on Climate Change (IPCC) titled Aviation and the Global Atmosphere:
https://www.ipcc.ch/report/aviation-and-the-global-atmosphere-2/
Images used in my video (YouTube) version of this episode are credited to http://www.nmsr.org/mkjrept.htm#new2010. I highly recommend those interested in this discussion visit this site. This page contains pictures of aircraft for each of the following conditions:
- Neither a contrail nor chem trail can form.
- A normal contrail from a four-engine jet.
- Lack of contrail formations at high altitudes where humidity is too low for non-turbulent air flow to create contrails.
- An F-22’s condensation trails (contrail) from the rear vertical and horizontal stabilizers. At higher altitudes, these type contrails can be generated with engine created water vapor streaming over the stabilizers and are created by non- turbulent flow conditions that allow the formation of ice crystals and/or water droplets (rapid expansion and cooling of the air).
New Mexicans for Science and Reason. Kim Johnson's Chemtrail Analysis - UPDATED
http://www.nmsr.org/mkjrept.htm
Aerodynamic formation of condensation trails
Klaus Gierens, Bernd Kärcher, Hermann Mannstein, Bernhard Mayer
https://contrailscience.com/files/Gierens_Aerodynamic_poster_060625.pdf
Clifford Carnicom, Aerosol Crimes (Youtube version)
The man you started the chemtrail conspiracy -Clifford Carnicom becameinterested in what became known as 'Chemtrails'. As he mentions in this documentary, he developed his own instrumentation by which he captured samples and had them analyzed. Knowing that something was amiss in the air he continued to investigate the phenomenon and was disturbed by his findings.
https://www.youtube.com/watch?v=-GWOXTLxmxk
Report on Barium in water:
https://contrailscience.com/barium-chemtrails/
The video version of this episode sourced the following images on Wikipedia:
Contrails from propeller-driven aircraft engine exhaust, early 1940s. :United States Air Force - http://www.af.mil/shared/media/document/AFD-051013-001.pdf, Public Domain File: Condensation Trails contrails from Aircraft Engine Exhaust.png, Created: 13 October 2005, Uploaded: 18 July 2024.
Multiple concurrent contrails. How long they last depends upon the weather, especially the temperature, humidity, and wind speed. NOAA - http://www.wrh.noaa.gov/fgz/science/contrail.php, Public Domain, File:Contrails.jpg. Uploaded: 4 November 2005.
Wingtip Condensation Trails. w:United States Air Force - http://www.af.mil/shared/media/document/AFD-051013-001.pdf,Public Domain, File:Wingtip Condensation Trails.png, Created: 13 October 2005.
Transcript
In this episode, we're going to explore how to access and visualize temperature data yourself. This isn't about convincing you whether global warming is real or not. My mother used to tell me, do you believe everything someone tells you?
This is about empowering you on how to explore and see for yourself if something is exactly as you're being told and just to see for yourself. Question everything. With the climate change issue, when we read stories in the news and hear about it, how it's getting hotter by X number of degrees and breaking records and sure it's hot, we're being told something based on someone else's observation. We're being told something based on somebody else looking at the data. But what if you want to see the data yourself?
In 2013, 19 members of the Granite Mountain Hotshots, a firefighting team battling the Yarnell fire in Arizona, died. At that time, I chose Yarnell to get temperature data, because I myself was considering exploring. Okay, let me actually see the data besides seeing on the front page of the newspaper, because news channels around the country were capitalizing on the poor souls that died in that fire, saying global warming had caused it, and we should expect more fires in the years to come. So how bad? How much? You know, how bad is this?
I had to buy the data. There was no free data. By data, I mean 50 or 100 years of data, because as it turns out, most weather stations hardly have 20 years of data. Fortunately, now there appears to be at least one open source that allows us to do this for free - and much easier using Python. For those of you familiar with Python or computer programming, this is going to be amazingly easy. For those of you who have never used Python, no worries. It does help if you have some computer hacker skills. First, you have to install Python on your computer. Details are in the show notes.
I started with the Python script available on mediostat.net. This is the open source project created and maintained by Christian Lampretzsch, founder and maintainer. It's made possible by generous backers and contributors who keep the project going. You can make a contribution as well as become a backer.
For those of you not familiar with Python, basically a backend super Excel fast way of analyzing data and plotting. Most importantly, plotting it. It can plot 100 years of data in a heartbeat.
I used to live in Phoenix, so I'm going to choose Sky Harbor Airport as the location for the weather station because it turns out it's the oldest and has the most, the longest history of data.
What we want is temperature going back is on every day for as many years as possible. 20 years really isn't that far. Even 50 or 100 in the history of man and automobiles and urban development. It'd be nice to go back to when the Industrial Revolution started.
The notes on GitHub make it easy to understand how to change the location of the weather station for where the weather station you're looking at, and to see the data structures like what else besides the minimum and maximum temperature is there, and what days are there data missing and so forth. So you can plot stuff any way you want for anything.
Perhaps you want to see how many days of the year there's missing data, or how far back the actual data goes, what weather stations are available in your area. Now for the fun part, how do you tell if the temperature is actually rising or not? For this, use what we call the fat pencil test. What is the fat pencil test? You take a Sharpie, a magic marker, a highlighter, a fat writing tool, or something, something fat.You put it on the plot, and you try to cover all of the data points. Temperature goes up. In the summer, you're going to get some data points rising, goes down in the winter, lowering. You try to cover all the points of your data. That's called the fat pencil test. If all those data points are within your fat pencil, then you look at the pencil.
Is the pencil sloped up or down? Is your fat magic marker, is it angled up or down or flat? That's the fast and easy way, besides having to analyze it with some other kind of statistical method, which then you kind of throw the baby out with the bath water in terms of is it generally rising or not? If it's generally rising, you're going to have some days that go down, some days that go up, and then it's kind of like two steps forward, one back. There's some super cold winters as well as some super hot summers. In the end, you are led to believe the temperature is rising.
So in the end, if you put this fat marker or Sharpie or highlighter on your data plot, covering all the ups and downs, you should then be able to look at the Sharpie or the highlighter, the magic marker, and see is it pointed up or down or flat. For the city of Phoenix, it appears there was a slight cooling actually in the early 60s. There's also some missing days there. Maybe they changed the weather station, the technology changed. So those are factors too, right? How's it calibrated over a hundred years?
But then it definitely seems like, or it's not actually definite, to me it seems like there's a slight warming trend for the low temperature, but not for the maximum temperature. My theory on this is, as Phoenix developed, and there's more concrete, more blacktop, more cars, more people, the nighttime temperature is, it couldn't cool off as fast. The low temperature is trending upwards. The high maximum temperature in the daytime, to me, doesn't seem to be obviously rising. If you'd like to comment on this, fantastic. That's why I'm doing this. I want you to show me what your plot looks like.
Let me know how you check the data and what you did, if you took any points out, if you added any, if you found they were missing points on certain days, and you did something to account for that.
Links are in the show notes to the example and data plot I did for the city of Phoenix, as well as how to install Python on the package for the Medial Stat. If you have questions or comments, please put them in the show notes. I'd love to hear from you. Let me know if you used more than one weather station and average data for multiple weather stations.
That's what has to be done when you read a headline in the news, such as the temperature in Arizona is rising due to global warming. Either they're misspeaking and they're really meant to say the city of Phoenix temperature is rising. If they in fact did mean to say the state of Arizona, then they must have mixed weather with Flagstaff, a cold climate with Phoenix, a warm climate and weather stations that are new versus old, and have data points missing and not. That's why it's good to see the data for yourself and ask those people, show me your data. I guarantee you, if you ask, find those reporters, they'll stare you towards, oh, I got that from somebody else. And they'll stare you from, oh, you can get that yourself here. I'm like, well, don't you have it? You can do anything you like with this data.
That's what exploration and discovery is all about. Keep track of what you do, document it so others can get the same answer, verify it for themselves. Explore, discover, have fun, think differently, question everything, live fully.