by Patrick Michaels
Criticism of Judah Cohen’s recent articles in Science linking the catastrophic February cold outbreak in Texas to global warming
I’ve always had trouble with the notion that warming causes cooling. It left me with the chills I get when my hometown neighbors insist that putting hot water in the ice cube tray makes ice faster. It’s really a test you can run, and I can guarantee it certainly isn’t (though arguments may appear in the comments).
But it is much more difficult to conduct a similar experiment, such as the hypothesis that an unusual and costly ($200 billion) cold outbreak in Texas last February was caused by global warming. bridge. Ignoring much of the damage caused by significantly unprotected power generation equipment—both conventional and renewable—it was very cold and windy, even by Texas green north standards. You can’t just put a little bit warmer Texas in the fridge to see if it freezes faster.
Predictably, the champions of cold-heat-caused anomalies have emerged, with Judah Cohen, a consulting atmospheric scientist, with his theory that Arctic sea ice changes and October snow-induced changes in Siberia conspire to extend the stratospheric polar vortex down to Texas, for example. Somehow, his tools always work New York Timesmaybe not a measure of its quality, but another thing to turn on their climate change alarm (which it rarely goes off).
Cohen concludes:
“Therefore, a change in the Arctic could contribute to an increase in SPV [Stratospheric Polar Vortex] protracted events, including one just before the Texas cold in February 2021.”
How he came to this conclusion is a common story. Let’s first break down some target variables (in this case 100mb height) into feature samples, and then use a General Periodic Model (GCM) to explain its behavior. While Cohen and his four co-authors say the models are from “a machine learning technique,” it is actually good old-fashioned cluster analysis, something that has existed around physical geography since the ice age. River.
Guess. The amplitude of some clusters is going up, others are going down and 40% has not changed statistically. Cohen then compared these changes to the Eurasian snow cover in October.
Given that Cohen has had some success comparing the amount of snow in Siberia in October and the geographic advance in cold outbreaks in the US (along with the reduction of ice cover in the Arctic Ocean), he sought to “prove” the relationship with “a simplified GCM… well suited to isolating the response of the atmosphere to idealized thermal perturbations”. The model is abbreviated as MiMA, which means Model with Ideal Humid Atmosphere.
The word idealization is neither defined nor reasoned, so we must refer to Chaim Garfinkel, fourth author of Cohen’s paper, and first author of paper describes MiMA, where we find that it is “idealized” because existing GCMs are “tuned” so much that they become unstable:
“The [general circulation] however, models tend to be less flexible and easy to adjust, so removing too many relevant spares leads to unstable behavior. “
What is a good guess as to what is “tuned” in GCM leading to unstable behavior get out of MiMA – it’s cloudless. Albedo (think of the “reflection coefficient”) of clouds produces a real cooling especially over latitudes away from the tropics. MiMA artificially reduces the earth’s albedo because of the lack of clouds, from a constant 27% to about a constant 20% (which is practically constant), representing a large 25% increase in solar irradiance heat the earth’s surface.
So to this simulated climate, Cohen et al. altered (elevated) albedo of Siberia and East Asia in early autumn, to compensate for the increase in October snow cover that has been detected since 1979, as well as the increase in the temperature of the model’s Arctic Ocean. to make it take more ice .
And, prior to the change, the modified model would extend the polar stratospheric vortex in the winter to somehow reach Texas by February 2021. How useful this will be for your company. he makes money by pre-sale winter forecast. Just think how many billions of dollars (and lives) could be saved the next time he makes such a forecast!
Indeed, Cohen went on to note: “Third, our analysis is informative for policymakers.” He concludes by noting that it is unwise to prepare for “severe winter weather that only eases” (there is some evidence Texas has done this, judging from the performance of projected gas plants). their rooms, which are too cold to function), when the swirling stratosphere can extend as far as the Lonely Star State, as shown by his constant, cloudless model of what can only be definitely related to the earth’s climate.
So could Cohen really handle the cold outbreaks in Texas better in a cloudless atmosphere and constant temperature? Except for Siberia, which he has lightened, where everything else is equal, will become colder due to the increasing amount of snowfall due to the precipitation cloudless atmosphere. This allows for large, seasonally cold high-pressure systems in Siberia to become larger, increasing the likelihood that the vortex will transport some of its cold air down into Texas.
If you’re scratching your head after reading this, think of the hair on the back of my neck when I read Cohen’s article. It has a lot of beautiful images that look captivating until you get into the details of how they are ultimately applied by the MiMA model.
The bottom line is that Cohen et al. It will take much more convincing before I believe that a month’s snowfall in Siberia will push the weather away thousands of miles and months.
