Waiting Until We Are Sure:
I also write a blog at Wunderground.com. Since November the number of comments on that blog has exploded. Thousands and thousands of words are being written. Some things in the comments are crude, there is some good argument, and complaints about what might be called the climate change machine. Most of the people who write comments at Wunderground.com are people with more than a casual interest in the weather and the environment. They put up maps and figures. It will be interesting to look back on these comments some years from now.
I tried to extract and summarize some of the concepts that were appearing in the comments to the blog. (Here they are.) This blog will address one of the ideas that keeps coming up – uncertainty. There were a number of comments about uncertainty and the fact that our knowledge about climate change is based on model predictions. Several times and in several ways people have said “shouldn’t we wait until we are sure?”
Uncertainty is a part of science. Science does not systematically reveal a list of facts. Science is a process which involves the development and testing of hypotheses. The process is, formally, also transparent so that others can independently test the hypothesis. The process of science, the scientific-method, is not perfect, and it is not independent of the skills and emotions of its practitioners. However, it is, in general, a robust process that is open to challenge and testing. The knowledge generated by the scientific method is subject to change. A good scientific study generates a statement about what is learned, perhaps the knowledge, and a statement about what is uncertain in the determination of that knowledge.
Facts are developed over time, and could be viewed as knowledge whose uncertainty is very low. Theories evolve out of hypotheses that reveal related information.
Models: Models are used in all aspects of scientific investigation. Models are used in architecture and in economics. In fact, models are used in every day life. We use a model when we estimate how long it will take us to drive from home to work. We have the pure model that is distance divided the speed that we travel. We have the version of the model that determines whether or not we take the street with many traffic signals or the longer freeway that does not have traffic signals. We have the version of the model that worries about the possibility of congestion or traffic accidents. We consider rush hour, holiday traffic, the need to stop and get a taco, and whether or not 70,000 people are going to see a re-united Led Zeppelin as the opening act for “An Inconvenient Truth.” We have the model that follows from experience; that is, what have we learned from many years of commuting to work. We have an idea of how long it will take to get to work and some sense of uncertainty. Often we can collect information from traffic reports that help us define and refine the uncertainty for any particular commute. Models are everywhere; imperfect models are everywhere; and we use them to make decisions. (This model of distance = time X speed is not unrelated to a weather or climate model, which calculates the motion of air parcels.)
Types of models:
Intuitive or heuristic models: There are intuitive or heuristic models that come from our experience and observations. These types of models help us, in the beginning, to develop hypotheses. After much study, heuristic models allow us to extract the most important processes that describe the behavior of a set of observations.
Statistical Models: Statistical models are a formalized statement of our experience. We observe the behavior, and we define the mean of the observations and how the observations vary from that mean. We search for relationships that organize and define the variability; often we look for periodicity. Statistical models are often used to describe the stock market, and as William Sharpe said “The key issue is that past performance is a thin reed for how to predict future performance. …”
Physical Models: Another type of model is the physical model, which is based on physics that describe the behavior of the observations. For example, how far an air parcel moves depends on how fast the air parcel is moving and the forces that influence that motion. The strength of the physical model is that we have cause and effect, and with cause and effect we increase our confidence in the predictions that come from models.
All of these models are related; all have their use. For a physical system, like the climate, we often describe the behavior statistically and then work to extract the physical relationship that describes the behavior described by the statistics. The climate of the Earth is a complex system with many interacting physical processes of varying importance. The climate system is made up of many sub-systems. Some of these systems and processes we understand well; some we do not.
Decisions: We always make decisions in the presence of uncertainty. In fact a weakness often pointed out in sociology and management texts is the fallacy of waiting for uncertainty to be eliminated. It is skill or art to know when the uncertainty is at some sweet spot for decision making. It is my opinion that one of the weaknesses of U.S. climate science activities was the idea prevalent in the 1990’s that our investments in science would reduce the uncertainty for “decision makers.”
In decisions that are based on scientific investigation one must realize there will always be uncertainty. Good science includes an estimate of uncertainty. Uncertainty will be reduced for some aspects of investigation. In complex systems new sources of uncertainty will be revealed. This uncertainty can always be used to keep decisions from being made. Uncertainty can always be used to keep policy from converging. It is a form of argument, of rhetoric, of belief. ( Uncertainty and Climate Policy Blog at climatepolicy.org )
If we look at the climate problem then with high certainty we can say that the Earth’s surface will warm. Similarly, we can say that ice will melt and sea level will rise. It is far less certain what we can say about the specifics of regional drought and floods, but we can say with some confidence that the statistical behavior will change. People can and will make decisions in the face of uncertainty.
In the climate problem the non-scientific sources of uncertainty are, now, of much greater concern than scientific uncertainty. One of the reasons than businesses are so interested in the development of policy, is that the uncertainty of what the policy will be impacts the ability to make business decisions. As I pointed out in my report from the meeting on Chicago area businesses, these businesses see that we are past the point of arguing about the basic science; they need to know the policy environment in which they will be operating. ( Corporate Climate Response (Chicago) )
Conclusion: The argument that we must wait until we are sure is not the way we work as individuals or as groups or as a society. We always operate in the presence of uncertainty, and we choose which uncertainties we give priority. In the climate problem it is a point of argument, of rhetoric, or belief. Some will decide that the uncertainty is defined well enough to support their decision making; others will decide that the uncertainty is too large to support decision making. As with virtually every other aspect of life, these will form two groups, which can be described by the number of people in the groups, and if one group is much larger than the other then some will be motivated to say that there is a consensus. (Consensus (American Heritage Dictionary) 1. An opinion or position reached by a group as a whole. 2. General agreement or accord.)
Often if we wait until we are sure, then it is too late.