September 24, 2019
He continues, of course. And it's a very good read indeed, found here: https://www.powerlineblog.com/archives/2019/09/climate-sense-and-nonsense.php I urge you to peruse the entire article!Since today is Climatepalooza at the UN and in the streets of DC (aside: how many Nobel Peace Prize nominations will Greta Thunberg receive this year?), it might be worth checking in on a couple of serious questions. Like climate modeling on the science side, and decarbonization on the policy side.
In conjunction with the climate hijinks, The Economist put out a special climate change issue, and much to my surprise, The Economist, which usually just parrots the party line, includes a pretty good article explaining the basics of computer climate modeling, and especially their large limitations and defects. Although the magazine tries hard not to sound openly skeptical, it is hard for any unbiased reader to finish this piece and think "the science is settled.†Some useful samples:
Modeling is a complicated process. A model’s code has to represent everything from the laws of thermodynamics to the intricacies of how air molecules interact with one another. Running it means performing quadrillions of mathematical operations a second—hence the need for supercomputers. And using it to make predictions means doing this thousands of times, with slightly different inputs on each run, to get a sense of which outcomes are likely, which unlikely but possible, and which implausible in the extreme.
Even so, such models are crude. Millions of grid cells might sound a lot, but it means that an individual cell’s area, seen from above, is about 10,000 square kilometres, while an air or ocean cell may have a volume of as much as 100,000km3. Treating these enormous areas and volumes as points misses much detail. Clouds, for instance, present a particular challenge to modellers. Depending on how they form and where, they can either warm or cool the climate. But a cloud is far smaller than even the smallest grid-cells, so its individual effect cannot be captured. The same is true of regional effects caused by things like topographic features or islands.
Building models is also made hard by lack of knowledge about the ways that carbon—the central atom in molecules of carbon dioxide and methane, the main heat-capturing greenhouse gases other than water vapour—moves through the environment. Understanding Earth’s carbon cycles is crucial to understanding climate change. But much of that element’s movement is facilitated by living organisms, and these are even more difficult to understand than physical processes.
It gets worse. Most of the models assume no upper limit to the atmosphere, because it would complicate them too much. So the models assume the stratosphere as the top of the atmosphere, and this is not so; the stratosphere ends and space begins, and this has a profound impact on the efficiency of the models.
Chaos theory was created precisely because models designed to predict the weather produced radically different results with the exact same data. We are trying to explain the entire climate with two dimensional models and incomplete data.
Efforts to replicate the current climate using past data have failed miserably using the current models, I might add.
At any point in the process one wrong assumption or bad data will short circuit these models.
And we have no idea how magnetic effects influence climate, or micrometeor bombardment, or changes in the solar wind, or in the wavelength of solar irradiance, etc. There are far too many variables to make these confident predictions of doom. They know it, too.
Posted by: Timothy Birdnow at
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It gets worse. Most of the models assume no upper limit to the atmosphere, because it would complicate them too much. So the models assume the stratosphere as the top of the atmosphere, and this is not so; the stratosphere ends and space begins, and this has a profound impact on the efficiency of the models.
Chaos theory was created precisely because models designed to predict the weather produced radically different results with the exact same data. We are trying to explain the entire climate with two dimensional models and incomplete data.
Efforts to replicate the current climate using past data have failed miserably using the current models, I might add.
At any point in the process one wrong assumption or bad data will short circuit these models.
And we have no idea how magnetic effects influence climate, or micrometeor bombardment, or changes in the solar wind, or in the wavelength of solar irradiance, etc. There are far too many variables to make these confident predictions of doom. They know it, too.
Posted by: Timothy Birdnow at September 24, 2019 10:45 AM (xF38i)
When I was back in the computer world, there was the idea that it was considered kosher to test one's models with an accepted set of test data. The test data was not configured to align with someone's agenda; rather it was expected to produce expected results to mathematical computations. A good set of test data tested "on the margins."
Apparently today's "climate scientists" have not either the time nor the stomach for this. And it appears that some of the models (Hansen's Hockey Stick, for example) will produce its "expected" results no matter what data are fed into the equations. You can get a hockey stick with any numbers, it seems. Cheating, folks!
Posted by: Dana Mathewson at September 24, 2019 10:19 PM (RjlmW)
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