- - Monday, July 30, 2018

Have mathematical models replaced good old-fashioned scientific testing?

An understanding of the big picture in a field of study helps to frame and give essential perspective to that field. Take the field of natural science for instance. A big-picture look at the overall operation of the natural science profession has traditionally been seen in the “scientific method,” which consists of observation, hypothesis and testing. Rigorous testing of a hypothesis eventually leads to a “theory.”

This makes sense from an objective point of view. Although there is no particular set order to the arrangement of observation-hypothesis-testing, a good example of scientific practice would be the observation of a phenomenon in nature, hypothesizing the cause of the phenomenon, then testing (many times in many ways) the hypothesis. Sufficient confirmation of the hypothesis results in a theory that is tentative, subject to any future negation.

Of late, mathematical modeling, an essential investigative tool, appears to have taken over the world of natural science. And with the ascension of modeling, the focus in scientific endeavors — particularly in the practice of atmospheric science — may have shifted away from the rigor of testing to verify a hypothesis and toward constructing a model to represent a theory.

Here’s a climatic example of the traditional observation-hypothesis-testing arrangement. Based on an observation of increasing global average temperatures over a decade, a hypothesis may be proposed, such as: “Excessive carbon dioxide (CO2) concentrations in the atmosphere will lead to long-term, catastrophic, global warming.” Given the extended nature of climate, which is officially based on 30-year means, a reasonable testing period can be set up to see if the hypothesis can be substantiated.

This example fairly matches recent history. With the milestone 30-year anniversary of the declaration by James Hansen of NASA at a June 1988 congressional hearing that “the greenhouse effect is here and is affecting our climate now,” there has been a minimal amount of time to begin to test the hypothesis of disastrous climate change. So far, comparing dramatically increasing atmospheric levels of CO2 with a substantially smaller than expected increase in global average temperatures and typically mixed extreme weather events across the globe, it can certainly be said that the jury is still out on what long-term catastrophic effects, if any, increasing CO2 has on the planet.

Yet, climate hysteria continues with increasing alarm. After all, the worst is yet to come, so say climate crusaders buttressed by their faith in climate models — the same models that performed dubiously when predicting the global-mean temperature trend during the past 30 years.

At least part of the problem of predicting reality can be attributed to the apparent abandonment of the observation-hypothesis-testing construct and replacing the hypothesis component with theory and the testing component with modeling.

And yet, models have a big role to play in our understanding of the atmosphere.

In the introduction to his acclaimed book, “A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming” (MIT Press, 2010), Professor Paul Edwards, a supporter of the “consensus” view of climate change, asserts that “Everything we know about the world’s climate — past, present, and future — we know through models.” He also notes that “without models, there are no data.”

Models have become integral to modern scientific practice. In many fields, Edwards says “computer models complement or even replace laboratory experiments; analysis and simulation models have become principal means of data collection, prediction, and decision making.”

Such is the contemporary world of science aided by the powerful tool of modern computers. The three basic components of the scientific method — observation, hypothesis, and testing — still hold, but in many cases the testing portion has been abetted, if not in some cases usurped, by models.

Still, when it comes to running models to foresee the Earth’s distant future climate, the eminent atmospheric scientist, Reid Bryson, probably gave the best observation: Making a forecast is easy. Being right is the hard part.

• Anthony J. Sadar is a certified consulting meteorologist and author of “In Global Warming We Trust: Too Big to Fail” (Stairway Press, 2016).

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