# Y = mX + b

One of the most basic statistical techniques used in science is the linear regression. The linear regression defines the relationship between the independent variable (cause) and the dependent variable (effect). The mathematical relationship is Y = mX + b, where Y is the dependent variable, m is the slope of the relationship, X is the independent variable and b is the Y-Axis intercept. There is also an error component, but for the sake of simplicity, we will stick with just Y = mX + b.

# Chart #1: The Independent Variable X

In the IPCC models and the anthropogenic global warming theory, CO2 is by far the most significant greenhouse gas and responsible for the majority of warming since the start of the industrial age. The “Hockeystick” comically demonstrates a very stable climate over 900 of the past 1,000 years and then a sharp spike in temperatures over the last 100 years.

Here is an actual formula used in one of the IPCC models, demonstrating that CO2 is really the only factor in the models of any significance. Also, note the “linear extrapolation” comment at the bottom of the graphic.

The important point being that CO2 is a **linear independent variable**. While it has annual cycles which vari by about 5 to 8 ppm from peak to trough, the overall “trend” is nearly a straight line with a slope of about 3 ppm/yr.

# Chart #2: The Results of the IPCC Models Demonstrate a Linear Relationship

What happens when you plug a linear independent variable/X (CO2) in the formula Y = mX + b? You get a linear dependent variable/Y (Temperature estimate). That is exactly what the IPCC models do. The black line is the temperature estimates of 73 IPCC models. None, I repeat, None with a capital N came close to modeling actual observed temperatures, and all demonstrate a positive highly linear relationship between CO2 and temperature.

# Chart #3: The Dependent Variable Y

What do you do if you define a linear relationship between CO2 and Temperature, and 100% of your models fail? Do you admit your theory is flawed and go back and try to find a better explanation for the warming? Hell no!!!, what you do is “adjust” the dependent variable data to become more linear so the model works. The above data demonstrates how the GISS data has been “adjusted” over time to make temperatures more linear. As you can see from this chart, the “linearization” of the temperature is almost complete.

Current temperature charts are wildly different from the raw and historical data, and the following graphic highlighting the “adjustments” demonstrates an almost complete bias towards steepening and straightening of the data. That would never happen if one was correcting for random errors in data. These “adjustments” demonstrate a clear bias. More importantly, the “adjustments” tie very closely to the CO2. With an R-Squared of 0.98, you will never find greater and more convincing evidence of scientific fraud.

# Chart #4: CO2 and Temperature Aren’t Linearly Related

Why can I claim the “adjustments” are clear evidence of scientific fraud? Easy, CO2 doesn’t cause atmospheric warming, the amount of energy absorbed by CO2 is what causes the warming. While CO2 may be a linear variable, the absorption of energy by CO2 is logarithmic. The underlying physics of the CO2 molecule and the greenhouse gas effect simply aren’t captured in the IPCC models. The IPCC model is Temperature = m(CO2) +b, the actual model is Temperature = mLog(CO2) + b. Those models give extremely different results. The Temperature = mLog(CO2) +b model would make CO2 basically irrelevant to the variation in temperature. The result of that model would be very similar to what the satellite and long-term CO2 and temperature records show, that being that temperature variations aren’t highly correlated with CO2.

# In Conclusion:

I am not a climate scientist, and because I’m not a climate scientist I should not be able to make predictions that are far more accurate than the climate “experts” and “scientists.” Clearly, if this “science” is worthy enough to boast that title, bloggers like me should have no chance what so ever of proving the “experts” wrong. From my explanation above there is 0.00% chance that the IPCC models will ever produce results that match the “unadjusted” satellite and balloon data. The only way the IPCC models will ever work is if they continue to “adjust” the NOAA/GISS/HadCRU data to make it more linear and steep. If I am correct in properly identifying the motives and intent of the fraud, the divergence between the ground measurements and satellite data will continue to widen with time. In 10 years, an understanding of the crime detailed above and an update of the following chart is all Congress should need to present an open and shut case against the climate alarmists that have defrauded the American taxpayers, corrupted real science, and destroyed the credibility of our media and educational system.

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Y = mx + b. Y is a function of X. Does time make climate? No. Therefore the trends shown have no meaning. That is, y = f(X) functions in statistical trend analysis only works on dependent variables. So, the only valid graph would be X = CO2 and Y = temperature data set. Graph that. The relationship is pure noise. This settles the relationship. Times series essentially means the author has no clue what is making Y behave as it does. Just as the price of silver has strong linear trends for intervals of time that can change at any point to any other trend, climate and time does the same thing. Trends of independent variables are not predictive as their relationship can appear to correlate for short intervals only, and outside factors will change their relationship unpredictably. Similarly, overlaying a CO2 time series and temperature time series resolves and predicts nothing.

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The entire argument behind the AGW, IPCC and the trillion of dollars wasted is that as CO2 has increased OVER TIME, the time period being the INDUSTRIAL ERA, temperatures have increased. Show me any CO2 and Temperature chart that doesn’t include time as the X axis. That is why they are always referring to the HOTTEST YEAR RECORDED HISTORY. History is a period of time. Also, they are making claims of the climate 100 years in the future. The only way they do that is by creating a forecast, which requires extrapolating a time series analysis.

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