The Stunning Statistical Fraud Behind The Global Warming Scare

It is the biggest fraud ever committed on mankind

Here's some more fun; The underlying math used in the models to project future warming is based on the Earth being flat. The real science deniers

I read a criticism of this that mentioned that they "averaged" different parts of the globe in their equations, but that doesn't mean shite since the Earth heats and cools differently at different places all the time. It still assumes the Earth is flat.

Oh, and BTW, when have higher co2 levels ever been bad for life on Earth?

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That is, until the NOAA's statisticians "adjust" the data. Using complex statistical models, they change the data to reflect not reality, but their underlying theories of global warming

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Evolutionists did that too.

Not saying evolution isn't true but the advocates in so-called academia created fakes, committed fraud - all in an attempt to convince the public overnight...instead of taking time to see how the theory plays out with normal excavations and studies.

American science is rife with dogma once something becomes Gospel...there's no dissent. Same cloth of the MSM.

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It is the biggest fraud ever committed on mankind

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2nd biggest - Communist/Socialist Utopia takes first credits

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But 97% of scientists, yo

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99% of whom make money off global warming

Hypothetical:

Guy spends 10 years getting a doctorate, focusing on climate science.

Does he get more grant money if climate change is a "crisis"?

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Evolutionists did that too.

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Wow, I'm glad we didn't have to pay for any of Lucy's outfits.

Explaination of how and why there are temperature adjustments

And before the source is questioned (despite the OP coming with IBD in a piece quoting a breitbart writer, a climate skeptic blog, and a random blogger on wordpress), here's a piece about how there's no causation found for a warmer arctic leading to more cold outbreaks: LINK

So it's not just ALARM for every weather event.

Now to quote the important parts of my initial article:

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The adjustments that have a big impact on the surface temperature record all occur before 1950. Here, past temperatures are adjusted up – significantly reducing the warming over the past century. Over the full 1880-2016 period, the adjusted data actually warms more than 20% slower than the raw data. The large adjustments before 1950 are due almost entirely to changes in the way ships measured temperatures.

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A single weather station will likely see many changes over the decades that it is in use. Few stations remain in exactly the same place over very long periods, with most stations experiencing at least one move to a new location. Most stations have also changed the way they measure temperatures, transitioning from liquid-in-glass thermometers to electronic instruments. Stations have often changed the time of day they measure temperatures, and growing cities and urban areas can introduce artificial warming in some stations. Very local factors, such as trees growing over stations or poor station siting, can also cause problems.

To correct for differences in a temperature record caused by these changes, known as inhomogeneities, scientists employ an approach known as statistical homogenization. They compare each station to all of its nearby neighbours and look for changes that are local to one station, but not found at any others in the area. Over long periods of time, climate changes are very rarely local, so localised changes not seen at surrounding stations are most likely due to something such as a station move or instrument change.

For example, if one station is warming over the course of a decade, while all the surrounding stations are cooling, that station would be flagged as “inhomogenous” and its record would be corrected to bring it back in line with its neighbours.

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Since 1990, the way that oceans temperatures are measured has changed once again. While about 90% of measurements came from ships 20 years ago, today almost 80% come from buoys floating in the ocean. These buoys are in direct contact with the water and end up reading temperatures slightly lower than measurements taken in engine room intakes. To correct for a cool bias introduced by adding cooler buoy data to warmer ship data, temperatures are adjusted slightly upwards for recent years. This fact proved quite controversial with the publication of a paper by Tom Karl and colleagues in 2015, but has since been validated by other studies. The net effect of these buoy-related adjustments, however, is relatively small.

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The conclusion?

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With almost 200 years of raw data to work with, adjustments are a necessary part of the methodology for scientists constructing long-term global temperature records. But most of these adjustments are small and they have relatively little impact on temperature records over the past few decades.

The most significant account for the shift from buckets to ship intakes in ocean temperature records in the 1930s and 1940s, and these changes are well-understood by scientists. And, contrary to popular belief, adjustments actually reduce, rather than increase, the amount of warming experienced globally over the past century.

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Here's a chart of adjusted records, performed by numerous different groups:



You might notice the upward trend in all the data since 1970. The UKMet office has shown similar behavior to all the data sets we have.

It's not some grand hoax by NOAA. If it were, you'd think every oil company on earth would have proved it was by now.

You realize that distribution of temperature adjustments gives absolutely no actual data that's helpful. If someone wants to put together a chat of the adjustments correlated to the year the data point is from and how much they changed it and in what direction, that would be helpful. That chart looks great but it just means roughly half the changes were up and half were down. How are those spread out? If the points the lowered were early in the century and the points the raised were in later years, you are doubling your trend

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You realize that distribution of temperature adjustments gives absolutely no actual data that's helpful. I

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That’s not true. As I noted above, we can quantify measurement and model errors and the margin around that error. So if the adjustments are within the quantified range and distraction, that is helpful, even if it doesn’t give a complete picture.

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If the points the lowered were early in the century and the points the raised were in later years, you are doubling your trend

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Well even if that’s true, that in and of itself doesn’t tell use whether those changes are valid and justifiable or not. What is important is if the error and empirical evidence supports the adjustments. If the evidence suggests early instruments and their measurement had an upward bias, then it’s justifable to adjust them downward.

Like I said earlier, I had always been skeptical of those adjustments, but when I read one of the methodology guides, I found their rationale to be valid and evidence-based. That doesn’t mean their own biases didn’t have some impact within the range of possibilities, but the idea that it’s a hoax and the models are fraud, is not evidenced in a broad look at the adjustments themselves.

In order to support the fraud argument, someone would have to actually independently analyze the data and determine that they either didn’t follow their methodology and/or their methodology was not supported by the evidence.

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I want to see all that data plotted. I'm inclined to think that if plotting it out in that way would strengthen the case against objections you would see that graph everywhere.

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There are many reputable scientists who have made valid criticisms of the current theory.

Yet, I can’t think of one who has argued that the data itself is fraudulent. They mostly criticize things like causation and predictions, which are far either far harder to support (e.g., causation is arguably the hardest thing to establish in science) or some we’re just terrible at establishing (e.g., we are terrible at predictions).

Fraudulent data would be much easier to identify, and would be far easier to “prove.” So I think the fact that the extremely capable critics haven’t made this claim probably means it’s not a valid claim.