Murkey science?

In the conversation yesterday, we came across the following statement by climatologist Dr. John Christy: "I've often stated that climate science is a 'murky science'. We do not have laboratory methods of testing our hypotheses as many other sciences do."

My first response was "yes he is right." I am no climatologist, but I have tried to read enough of its foundational literature to understand that climate science is complicated. No, it is extremely complicated. It is often based on enormous data sets, complex statistical analysis, and tedious attention to detail. It is also based upon weaving together the principles of physics and thermodynamics, chemistry, biology, geology, and other disciplines. Weather is the state of the atmosphere for a given location at any given time; climate is the weather of a wider region over a longer period of time. Both are extremely complicated; I admit this.

Have I mentioned that climate science is complicated?

On further reflection, however, climate science is not a 'murky science' simply because it is complicated, nor because it does not have laboratory methods for testing its hypotheses. There are many such sciences. They are called observational, descriptive, or historical. Examples include astronomy, geology, paleontology, epidemiology, and many of the social sciences. The job isn't particularly easy, but scientists engage in this type of investigation all the time. How do they do it?

There are two main techniques in observational science: (1) multivariate statistical techniques, and (2) making predictions of previously unobserved phenomenon based on current knowledge as hypotheses for further testing. Both of these methods are used to good effect in climate science, but both have potential pit falls. In the case of statistical analysis, the proper sequence is far from intuitive. Whether one subtracts before averaging or averages before subtracting can make significant difference in one's viewpoint. The same goes for computing variability before or after averaging. In the case of making predictions, one runs the risk of setting up non-falsifiable hypotheses.

There are many similarities between this situation and the study of cancer, heart disease, and stroke. The causes of these diseases are multi-factorial, meaning that they are complicated. Furthermore, biomedical scientists can't very well do randomized controlled experiments on people and their life-styles. But through sophisticated statistical techniques, epidemiologists have been able to sort out the many variables to show that cigarette smoking is a significant causal factor in all three. Because of the statistical nature of the investigations, however, the results were questioned long after they were conclusive.

I have not always had strong opinions about the topic of climate change. I've not even been a very good advocate for the environment despite my concern for it. By the time I started paying attention to "global warming" a few years ago, it had become so controversial I was not sure what to think. I was sympathetic but also confused. For the past two years, however, I have put significant time and effort into increasing my knowledge and understanding. I am no climatologist and never will be, but where I am today is that the evidence I have seen so far has convinced me that anthropogenic global warming is real and that it will become a crisis for our planet if no changes are made.

Since I did not start out with this belief, I do not consider this a "bias". Nevertheless, because that is my position now, as I proceed to take an even closer look at climate science over the coming months, I will make a concerted effort to sort out non-falsifiable hypotheses and try to avoid unwarranted oversimplifications.

Climate science is complicated (perhaps even more complicated than rocket science), but that doesn't mean we shouldn't try to understand it, and maybe it isn't quite as 'murky' as we are told.