In real science, numbers speak, and they talk through what is called a regression. Most people are familiar with the 2nd-grade bean plant experiment where you shine different amounts of light on different bean plants and measure the rate of growth. Y, the dependent variable, is the bean plant height, X, the independent variable, is the amount of sunlight allowed to reach the bean plant, m is the slope or rate of change of the bean plant per unit of light, and b is the constant, which in this case would be the starting height of the plants. This model then defines the change in Y for a change in X. Other than setting the base or “Y-Intercept” the constant b really doesn’t impact the dependent variable at all.
Applying this scientific approach to climate change, temperature is the dependent variable and CO2 is the independent variable over a long time series. The theory goes that over time CO2 increases resulting in an increase in temperature, put another way, temperature is a function of CO2, or T=f(CO2). This model, however, is deeply flawed and demonstrates a disturbing ignorance of science, modeling, and the physics behind the greenhouse gas effect.
The first and likely most serious flaw is that the T=f(CO2) model is defined as linear. It isn’t. CO2 doesn’t cause warming, CO2 is simply a gas. The absorption of long-wave infrared radiation between 13 and 18 microns by CO2 can cause warming through “thermalization,” as well as cooling by facilitating the transport of energy out of the atmosphere through radiation. The real model is Temperature is a function of the outgoing energy absorbed and thermalized by CO2. That relationship isn’t linear, it is logarithmic. T isn’t a function of CO2, T is a function of log(CO2), and that dramatically changes the behavior of the model. Small changes don’t mean much in logarithmic models, and in most cases render CO2 not much different than a constant. CO2 is also constant by latitude, longitude, and altitude, so regional differences can’t be explained by CO2. Temperature change in one location with 400 ppm CO2 can’t be caused by CO2 when the lack of temperature change in another location also has 400 ppm.
Application of this principle can be found in recent research done on the Greenland ice sheet. It found that there were dramatic temperature changes during times of stable CO2 levels. The importance of this is that there must be factors not included in the T=f(CO2) model that are missing. You simply can’t explain such variation in the dependent variable with a constant, or even an independent variable whose impact is measured on a logarithmic scale. Also, there is nothing in the T=f(CO2) model that would ever allow for cooling, the only mechanism by which CO2 can impact climate is by trapping and thermalizing radiation. Climate alarmists completely ignore/deny the cooling effect of radiation.
This period, known as the “last deglaciation,” included episodes of abrupt climate change, such as the Bølling warming [~14.7–14.5 ka], when Northern Hemisphere temperatures increased by 4–5°C in just a few decades…Greenland Warmed By 8-15°C Within Decades During Last Glacial CO2 concentrations remained essentially stable and dangerously low (~180 parts per million) throughout the last glacial (roughly 80,000 to 15,000 years ago). And yet despite the lack of CO2 flux, Greenland’s surface temperatures often warmed by about 10.0°C within a matter of decades during this period. This indicates that CO2 variability is not a detectable factor in abrupt climate changes.
That is a second major flaw in the T=f(CO2) model, it simply leaves out many factors that are more important/significant than CO2. T=(CO2) is like doing a model on weight loss and not including caloric intake and exercise.
When I dug further into the issue, I discovered that an Arctic Hurricane was responsible for the sudden loss of ice. I didn’t even know Hurricanes occurred that far north and the media did nothing to inform me of the event.
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