Isolating the Contribution of CO2 on Atmospheric Temperature

Water Vapor

In any serious scientific experiment, efforts are made to “control” for as many exogenous factors as possible. The whole purpose is to isolate the impact of the independent variable on the dependent variable. ΔWeightloss = ΔCaloric Intake + ΔExercise + ΔBase Metabolism + error. To minimize the error of the model (maximize explanatory power), variables outside the model (exogenous factors), are controlled for to make the relationships as close to apples to apples as possible. Factors that impact the dependent variable but aren’t in the model can result in erroneous results and conclusions, for instance, if the person is taking diet drugs during the experiment without reporting them.

Applying this concept to climate change, the ideal model would be ΔTemperature = ΔCO2 + error. The problem is, it isn’t that simple. H2O is a far more potent GHG than CO2, and has a far greater impact on temperature as demonstrated in a previous post. The Urban Heat Island Effect also impacts temperatures. A more robust model would be ΔTemperature = ΔCO2 + ΔH20 + ΔUrban Heat Island Effect + error. For a real scientist to model CO2’s impact on temperature, they would need to create an experiment or model that controls for all factors other than CO2.

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Controlling for The Urban Heat Island Effect:

To control for the Urban Heat Island Effect, the easiest way is to simply look at the areas where there are no urban areas. There are no cities in the oceans. If you look at just the temperatures over the oceans, they show 0.1°C less warming than over land since 1979. CO2 is 400ppm over the oceans and 400ppm over land so CO2 cannot be the cause of that differential.  The vast majority of the earth’s land mass and cities are in the Northern Hemisphere, so the Southern Hemisphere offers another way to control for the Urban Heat Island Effect. Since 1979, Southern Hemisphere Temperatures increased 0.2°C less than the Northern Hemisphere. CO2 is 400ppm over the Northern Hemisphere and 400ppm over the Southern Hemisphere so CO2 cannot be the cause of that differential

Controlling for Water Vapor:

The introductory graphic demonstrates how atmospheric temperature and water vapor are extremely highly correlated. Where there is water vapor, there is warmth, where there isn’t, there isn’t. CO2 blankets the globe with 400ppm so it can’t be responsible for the extreme variations of temperatures, and it certainly can’t explain the high correlation with water vapor.

This effect is so powerful, that it can be observed in local climates. Rainforests have moderate temperatures and a small differential between day and night temperatures. You can comfortably sleep naked in a rainforest. Deserts, on the other hand, have extreme temperature variations between day an night. Temperatures in Death Valley can reach over 100°F in the day, and fall to near freezing at night. If you sleep naked in a desert, you will most likely regret it.

This effect can also be seen in mountainous areas, where the moist windward air is warmer than the dry leeward air on the other side of the mountain. One can see that effect on the Hawaiian Island of Oahu where the temperatures as of this writing are 78°F on the moist windward side, and 71°F on the dry leeward side. CO2 is 400ppm on both sides of the mountain, and the distance between the two spots is about 25 miles. Once again, CO2 can’t explain a temperature differential because it is a constant 400ppm.

Controlling for the Heat Island Effect and Water Vapor:

MSU RSS ArcticAndAntarctic MonthlyTempSince1979 With37monthRunningAverage

Antarctica is by far the best natural control for measuring the impact of CO2 on atmospheric temperature. There are no cities, no roads, no buildings, no lakes and the air is extremely dry and cold. Antarctica is a large area of almost uniform “albedo” or reflectivity, naturally controlling for any distortion caused by the terrain. The major greenhouse gas over Antarctica is CO2, and its temperature is close to the -80°C which is close to the temperature absorbed by CO2 in the 13 & 18µ band of LWIR. Antarctica is like a giant petri dish for climate research.

What do you get when you control for water vapor and the Urban Heat Island Effect? Temperatures have actually FALLEN since 1979 in Antarctica. In fact, temperatures are actually flat over the past 50 years. CO2 has increased from 310 ppm in 1955 to 400 ppm today, and its impact on temperatures in Antarctica is immeasurable. When the impact of CO2 is isolated, and all other significant factors are controlled for, CO2 has no measurable impact on atmospheric temperatures. Antarctica temperature data proves it beyond any reasonable doubt.

People are often making hysterical claims about the polar sea ice disappearing. CO2 is 400ppm over the North Pole, and 400ppm over the South Pole, yet only the North Pole shows warming. CO2 can’t be the cause of the warming, and the warming is due to oceans, not CO2. The 13 & 18µ band of LWIR that CO2 absorbs doesn’t penetrate or warm water.

Other Factors to control for:


Calibration and consistency of measurement equipment and methods are essential for any real science, and it is completely lacking in the field of ground measurement temperatures. What “calibration” is done, is done through universal “adjustments” done in a computer program and the equipment varies from one station to another. The countless network of temperature units was never designed to be used as a global climate database. They were used to report on local temperatures. Satellite measurements are infinitely better and measured scientifically than the ground measurements.

That being said, there are a few data stations that have remained in the same spot and measured the same way for extended periods. Central England is the most famous data set going back to 1650. Summer temperatures in Central England show no increase since 1650, annual numbers show no warming since 1720 and Winter temperatures show minimal warming since 1720, but because of the large variability of temperatures, all periods are most likely statistically identical.

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