Contact
Subscribe
February 8, 2007 
This is the second of a multi-part series
Part II.
Overview of the IPCC Climate change methodology.
Let’s start with what a couple of examples of modern, acceptable science look like today. The efficacy of birth control is generally considered effective if it works 98 - 99.9% of the time. The results of DNA testing are used in death penalty cases when the results demonstrate a 99.5% - 99.9% or higher probability of matching a criminal defendant. In many criminal cases that hinge on DNA, the statistical odds that the DNA matches someone else are nearly equal the number of people on the planet, or one in several billion.
Why should we accept anything less when discussing something of such global significance, which liberals argue will imminently kill us all in less than a generation if we don’t stop it ? Acceptable statistical probabilities generally hover in the 95% certainty range and this is an obstacle to the acceptance of any statistical evidence as conclusive proof of anything.
Now that we have something for comparison, let’s examine the science behind the IPCC; the reports themselves.
Previous Assessments
The First Assessment Report (FAR) in 1990 dealt primarily with the impacts of climate change and the response strategies, but did not substantially attempt to address the question of man’s influence. It claimed a measurable rise in temperature, but questions remained over the accuracy of the climate models, the unknown nature of possible mitigating anthropogenic (or human-induced) cooling effects and the lack of longer periods of historical data.
The Second Assessment Report (SAR) in 1995 began to suggest that man may have a discernible influence on the environment, but it cited many uncertainties, such as doubts about future estimates of emissions and aerosols and their properties. It also stressed the need for further development in the modeling of clouds, oceans, sea ice and vegetation and also in further long-term observations of the climate. The SAR also began to heavily suggest the “transfer” of technology to less developed nations.
The Third Assessment Report (TAR) in 2001 continued to build upon the work of past reports and continued to inch towards the conclusion the UN had already reached in the late ’80’s. As stated in Part I, there is no published methodology for the 2007 report until May, so we’ll have to look at the 2001 (TAR) results which form the basis of the latest science, because the 2007 report will no doubt cite the improvements since the 2001 report as the primary basis for their firmer conclusion.
Numerous red flags which call the IPCC’s whole conclusion into question were uncovered when critiquing the science behind the global warming theory. Among them was the IPCC ’s definition of detection and attribution with respect to man’s influence on his environment.
First, they stipulate that true, scientifically valid attribution of man to the warming of the planet would actually require pouring various greenhouse gases, in various concentrations, in controlled experiments, over varying time frames into the atmosphere and that this approach is –strictly speaking– practically impossible. [1]. This, many of us knew already. Next, they acknowledged that natural climate variability exists and is a complex phenomenon, which is not completely understood, in scope or frequency. [2]
Other red flags include the acknowledgment that current models cannot account for differences with the actual observed warming trend and that some studies actually suggest that the warming observed at the beginning of the 20th century may have been significantly influenced by natural factors such as solar irradiance. Lacking directly observable evidence, the IPCC must rely on the the study of paleoclimatology, the observation of tree rings and various forms of geological evidence or “proxy data”, such as carbon dating, ice cores or sediment layers, along with multiple inferences about the meaning of this proxy data. The proxy data is then combined with a very limited historical record in an attempt to support the conclusions. The problem with relying on these substitutes for hard observation is that they tend to also have seasonal as well as decadal variations that are, in themselves, not readily explained by modern science.
Therefore, the IPCC must approach global warming as a statistical signal-to-noise problem. [3] In simple English, mankind’s stress on his environment is the “signal” to be separated from the “noise” that is all of those other things; solar cycles and polar shifts, volcanic and geothermal activity, plate tectonics, natural convection, cloud dynamics and all of the other influences on the Earth’s temperature. This approach is flawed, because it implies that there is a detectable signal (man’s interference) out there among the noise to begin with and then sets about using other controversial methods to validate that assumption. The way that the IPCC sets out to actually separating that signal from the noise of natural climate variability doesn’t involve hard science, but some rather squishy statistics and questionable scientific techniques.
Statistical Analysis and the Null Hypothesis
The method of null hypothesis testing has as many detractors as supporters. Simply put, it involves advancing a theory to be disproved by advancing an alternate theory. The incorrect use of the null hypothesis can introduce bias. For example:
H0 (Null hypothesis) Humans AREN’T causing global warming
H1 - Alternate hypothesis - Humans ARE causing global warming.
With some flawed and incomplete climate models and a lot of inferences about conditions millenia before humans appeared, it is quite a simple matter to say that you detected increased warmth due to increased atmospheric CO2 that has only come about since the burning of fossil fuels. On the surface you have refuted the null hypothesis, which some would imply that humans cause global warming. But in reality there has been no establishment of cause and effect relationship. That would require additional layers of testing and validation.
Even if you were to detect a significant temperature change; and even if that change was scientifically proven to have happened in the last 100 years, that makes progress towards, but still does not meet the threshold of unequivocal scientific proof that man is causing it.
Multiple regression
In order to provide enough data to make your conclusions even meaningful, in the case of global warming, your models must simulate what the atmosphere used to be like, and what the effects of increased heat and CO2 would do to the planet. We only possess a little more than a century of actual records to go by, which magnifies the problem. The technique that the IPCC team uses to account for the unknown natural or internal forces that may be warming or cooling the planet is referred to as multiple regression [4]. Discussion of regression techniques is beyond the scope of an opinion piece, but the local library and the Internet are treasure troves of information on this technique and its applications and limitations. The point here is that there is no clear scientific consensus on the use of multiple regression as scientific proof. Many scientists over the course of history have dismissed it.
Climate Models
I was previously a Systems Programmer/Analyst working on some very critial systems with low tolerances for error. One of the cardinal rules of data processing is expressed in the acronym G.I.G.O., or Garbage In, Garbage Out.
If the data input into a computer program contains errors to start with, those errors will be carried over to the end result. But in the complex world of 1’s and 0’s; of hexadecimal and binary arithmetic; there is simply no way to determine what the effects will be. The error could be negligible. It may produce perfectly valid results, but results that are double, or half of what one expected, leading to faulty conclusions. In principle, if variables aren’t defined with enough precision, no one knows the results. I have personally seen errors propagated in computer code by humans that went unnoticed for decades until some observant analyst spotted a problem and then dug deep. It happens all the time.
Assumptions are deadly in the world of data. A single assumption calls the results of ANY data into question no matter how many hundreds of scientists bristle at the suggestion. What I have seen over the course of 150 hours of research in terms of the assumptions made about the data in the IPCC’s conclusion makes me cringe. According to the IPCC itself, the models and the conclusions make scores of assumptions raising serious doubts in my mind about the report’s validity.
There are, according to the IPCC, also many aspects of climate models themselves that are less than ideal. They still cannot adequately explain –much less model– the mitigating effects of vapors, clouds, vegetation or the cleansing properties of the hydrocycle. The models have come a long way, but still lack many of the inputs necessary to make a scientifically valid conclusion about man’s influence.
In short, the science involved in the entire IPCC report drafting process is quite simple:
150 years of available records are combined with the available information from sediment beds, ice cores and tree rings and plugged in to climate models to draw conclusions about the climate over the previous thousand years. That information is combined with a greenhouse gas inventory based on similar faulty logic as well as the actual measurements of CO2 and other gases that we can now take. A conclusion is then drawn that the Earth’s temperature has risen roughly .3 to .6 °C over the last century or so and that it is likely due to increased atmospheric CO2, so the statistical conclusion is then drawn that it very well must be caused by man.
I will submit to you that there are just as many scientists that –absent political and financial pressure– would reject these conclusions as absurd.
There are simply too many uses of the words and phrases like assumptions, large uncertainties, gapsand confidence ellipsoids. It is riddled with internal bias, and is not worthy of the title “science”.
1.Climate Change 2001: The Scientific Basis - Chapter 12 - Detection of Climate Change and Attribution of Causes 12.1.1 the meaning of detection and attribution
2.Climate Change 2001: The Scientific Basis - Chapter 12 - Detection of Climate Change and Attribution of Causes 12.5 Remaining Uncertainties
3.Climate Change 2001: The Scientific Basis - Chapter 12 - Detection of Climate Change and Attribution of Causes 12.2.2 Internal Climate Variability
4.Climate Change 2001: The Scientific Basis - Chapter 12 Appendix 12.1 - Optimal Detection is Regression
More resources on the scientific techniques used by the IPCC:
All IPCC Publications - All you need to know about this scam
Multiple Regression http://www.statsoft.com/textbook/stmulreg.html
Commentaries on Significance testing http://www.indiana.edu/~stigtsts/
A Sensible formulation of the significance test http://forrest.psych.unc.edu/jones-tukey112399.html
The Tyranny of ‘global warming’ - WorldNetDaily
