Scott Snell

The Climate Change intervention movement, thirty years old and counting, has been based less on actual events than on future projections generated by mathematical models. There are numerous such models, and their projections range from "meh" to "be afraid." Climate activists have enthusiastically seized on the more alarming predictions as a done deal.

Climate modeling is frequently criticized for failing to adequately account for key variables such as cloud cover or solar variance, and for being too coarse-resolution to produce usable results. It's worth noting that of dozens of models in use, the vast majority significantly overestimate the amount of overall warming. Skeptics (of which I am one) have enthusiastically seized on these weaknesses as evidence of their overall unreliability.

As if to underscore this point, recently, a group of climate modeling experts carried out a study in which 37 different climate models were run for a range of possible future temperatures for three different metrics in four different major cities. The cities were Beijing, New York, London, and Mumbai. The metrics were wettest day, hottest day, and windiest day. The range of future temperatures was from +.05C to +4C. The results were so scattered as to be nearly useless, with no identifiable trends. As Jo Nova put it, the results "look like a painting done by a jet engine."

The authors of this study explicitly used these non-results as a cautionary statement to economic planners and investors. In other words, "Don't bet the farm" on these models.

Jo Nova's summary is here

https://joannenova.com.au/2022/09/climate-models-not-good-enough-to-invest-my-superannuation-in-says-modeler-prof-andy-pitman/

And the original article is here:

https://iopscience.iop.org/article/10.1088/2752-5295/ac856f

It should surprise no one that all climate models have major weaknesses. This thing we call "climate" is a fantastically complicated entity involving hundreds of dynamic variables interacting over large swaths of time and distance through enormous volumes of ocean and atmosphere and over millions of square miles of highly variable terrain. It is literally the most complicated problem humans currently contemplate. Even the best models operate at very coarse resolution, take major shortcuts, and rely on highly simplified representations of physical phenomena. It's just too big a problem for our current technology.

So follow their creators' advice. Take these models' predictions with a large allowance. And whatever you do, don't use them to make major economic, technological, or social decisions.

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