The Fundamental Issue for Climate Science
by Edward R. Dougherty
Are we, as a society, up to the challenge? Are we teaching young minds to move fluidly across science, mathematics, statistics, and philosophy, or are we producing technicians who are narrowly confined to work within a specialty and cannot see the forest for the trees? Are we creating an environment where young people are encouraged to look beyond the buzzwords of the moment, like “big data,” and turn their attention to genuine knowledge?
Today, we face a new epistemological crisis. In the realm of natural phenomena, our desire to know has outstripped our understanding of what it means to know. This has serious implications for assessing the data and statistical models presented by climate science.
There has been much debate concerning whether or not the period 1998–2013 experienced a hiatus in global temperature rise. Global warming skeptics point to the asserted hiatus. This is not surprising, since proponents of global warming had predicted warming over the same period. The basis of validation, which establishes the truth of a scientific theory, is agreement between predictions drawn from theory regarding future observations and the observations themselves. From this perspective, if the predicted temperature increases have not materialized, then the theory has been invalidated.
Both sides consider the issue important but disagree on whether the data show a hiatus. Such disagreements can arise based on what data are used and how the data are filtered to correct for bias and noise. Different people may filter data differently, depending on their assumptions regarding the measurement process. Good validation methodology requires decisions on filtering to be made prior to analysis. Unfortunately, this appears not to have been the case. According to Nature News, “The previous version of the NOAA data set had showed less warming during the first decade of the millennium. Researchers revised the NOAA data set to correct for known biases in sea-surface-temperature records.”
But if it does exist, would a hiatus in rising temperatures really invalidate the theory of global warming? Indeed, is it even possible for the theory to be invalidated? That is, can criteria be established and observations made so that the theory can be accepted or rejected based on the degree to which predictions and observations satisfy the criteria? This is a deeper question. It is the establishment of such criteria and the possibility of testing a theory with respect to future observations that makes a theory scientific.
- Is Climate Science a Science?
The predictive skill of a model is usually measured by comparing the predicted outcome with the observed one. Note that any forecast produced in the form of a confidence interval, or as a probability distribution, cannot be verified or disproved by a single observation or realization since there is always a non-zero probability for a single realization to be within or outside the forecast range just by chance. Skill and reliability are assessed by repeatedly comparing many independent realizations of the true system with the model predictions through some metric that quantifies agreement between model forecasts and observations (e.g. rank histograms). For projections of future climate change over decades and longer, there is no verification period, and in a strict sense there will never be any, even if we wait for a century . . . climate projections, decades or longer in the future by definition, cannot be validated directly through observed changes. Our confidence in climate models must therefore come from other sources.
After opening with a slightly vague statement concerning predictive skill about a “model”—rather than a clear statement about knowledge—Tibaldi and Knutti unequivocally state that climate models cannot be scientifically validated and give ironclad reasons why this is so. The paragraph is part of a well-thought-out discussion of modeling, experimentation, and, most importantly, statistics in climate studies.
The closing sentence is telling: Confidence, not knowledge, must come from sources not involving validation via observation. Tebaldi and Knutti go on in the article to discuss other sources of confidence and their shortcomings. But never do they vary from the crucial point that climate modeling cannot be scientifically validated.
Although this is not the venue to go into details behind their reasoning, a couple of general points can be made. First, if a theory is probabilistic—meaning that it does not provide a prediction of outcome for any specific observation but instead gives a probabilistic distribution of possible observations—then the theory cannot be validated or invalidated by any single observation.
Second, contemporary scientists and engineers model highly complex systems involving thousands of variables and thousands of model-defining parameters. Owing to their sheer number, many model parameters cannot be experimentally determined, so they are subjectively entered into the model or left uncertain. As a consequence of this uncertainty, there are an infinite number of physical systems described by an infinite number of models. A subset of these is somehow averaged to provide a description of the phenomena. It is difficult to characterize how this averaging should be done and to quantify the level of uncertainty involved. This can all be done in what is known as a “Bayesian” framework, which provides mathematical rigor, while incorporating uncertainty; however, one is still left without the possibility for scientific validation.
- Towards a Scientific Theory of Complex Systems ....
- A New Epistemological Crisis ....
- Are We Up to the Challenge? ....
Read more: www.thepublicdiscourse.com