What Does Philosophy Have to do with the Science of Clmate Change?

Environmental & Science Education, STEM, Climate Change, Global Change, Sustainability, Earth Science, Earth Systems, History of Science, Nature of Science

Ed Hessler

Many scientists have little to do or regard for philosophers of philosophers of science.

Theoretical physicist Sabine Hossenfelder thinks it should be otherwise. You may not have heard of  Karl Popper but have certainly heard of his claim: "falsifiability is both necessary and sufficient to make a theory scientific...." It all seems so easy to distinguish one, science, from the other, non-science.

In another of her blog posts * on science she writes that she "wants to clarify just when it is scientifically justified to amend a theory whose predictions run into tension with new data." And again you've heard of how this is done:  Occam's razor. Hossenfelder writes that the shaving is done when "two theories ...describe nature equally well you should take the simpler one." And this "means you must discard superfluous assumptions." She continues that "without it we would be "allowed to add all kinds of unnecessary clutter to a theory just because we like it."

And Hossenfelder cites one of the distinguished philosophers of science, Larry Laudan who put it as she says "politely." He wrote, using Popper to distinguish the good from the bad, separate the wheat from the chaff, that it has "'the untoward consequence of countenancing as "scientific" every crank claim which makes...false assertions." Popper can be used "to make arbitrary statements about the future" to make them "scientific.

A common climate denier's complaint is that climate modelers in what they would describe as willy-nilly fashion "adapt models when new data comes in."  Hossenfelder writes that this is a clear example how little deniers know "about scientific methodology".

Revising a hypothesis when new data comes in is perfectly fine in science. In fact, it is what you" must do when you have "more and better data." These data make "higher demands on your theory. Sometimes this means you actually need a new theory. Sometimes you have to adjust one or the other parameter. Sometimes you find an actual mistake (deniers no doubt chortling all the time) and have to correct it. But more often than not it just means you neglected something that better measurements are sensitive to and you must add details to your theory. And this is perfectly fine as long as adding details results in a model that explains the data better than before, and does so not just because you now have more parameters."  And to help in the decision-making are statistical processes and methods which assist in determining which data fit better than other data.(parens mine). 

In response to a reader's comment Dr. Hossenfelder points out that statistics "will not tell you...which parameters are superfluous, but just give you a weight for how relevant they are.

*Take a look for her more extensive discussion and the comments.