Guest Post by Willis Eschenbach
Thanks to Nick Stokes, who pointed me to the University of Melbourne Computer Modeling Comparison Project 6 (CMIP6) data warehouse, I got precipitation data from CMIP6 computer climate models. There are 12 models for which they have data covering the entire period 1850 – 2100. Let me start with the average of all twelve models.
Figure 1. Global average annual precipitation as shown by 12 CMIP computer climate models, run once each.
I swear, results like these make me question the sanity of climate scientists. I mean, does anyone seriously think that after one hundred and fifty years of little change in global rainfall, around 2020 it suddenly starts to spike to new highs? Really? Yes, I know that “negation through skepticism” is just circumstantial proof, but we are “Salmon in Milk” level here…
… “Salmon in Milk”?? It seems that in 1849 there was a milkman’s strike, in which it was suspected that milk had been watered in order to increase profits. However, at the time it was difficult to prove. Regarding the strike, Henry David Thoreau famously said…
Sometimes the circumstantial evidence is strong, like when you find a salmon in the milk.
That’s where I find myself related to Figure 1. It’s not the only problem either. Here are the precipitation results from the 12 models, smoothed so we can see the difference.
Figure 2. Precipitation results from 12 runs of the computer model, one at a time. Every initial data is smooth LEAST.
As you can see, the largest result in 1800 is no less than 15-20% higher than the lowest result. I can understand the patterns of mistakes in the future… but when they make mistakes in the past, I get very nervous.
In addition, future precipitation increases over time are quite variable. To illustrate this, here are the data in Figure 2, represented by the anomaly around the 1850-1879 mean of each result.
Figure 3. As shown in Figure 2, precipitation results from 12 runs of the computer model, one for each run, but expressed as anomalies around the mean value 1850-1879.
Note that although they started at the same level, by 1995 they were apart by ~20 mm per year, with some increasing and some decreasing. And as you can see, the expected increase in precipitation varies from +20 mm to 60+ mm, a factor of 3 to 1. In that respect a recent articlesin the journal Science no less, pointed out that…
The projections are based primarily on climate models, and factor the three changes in warming predicted from these models into the tens of trillions of dollars in social costs. As such, most models must be significantly wrong on impact. Does that sound like “solved science?”
And to add to the insult to injury, this is not an element of the three variations between the least and most extreme situations. This is a factor of three variations in one scenario, scenario ssp126 predicting the smallest increase in greenhouse gases.
The simple fact cannot be denied. Existing seasonal climate models are not ready for the critical moment when that requires using them to make trillions of dollars worth of decisions.
Finally, I had another look at the precipitation results. I keep hearing claims that according to models, wet areas are supposed to get wetter, and dry areas are supposed to get drier. Fortunately, the University of Melbourne has regional results for rainfall, which are divided into the following zones.
Figure 4. Areas used by the CMIP6 models.
So I took the average of the 12 models by region, and I looked at both the mean and the mean trend for each region. IF it is true that “wet areas are getting wetter and dry areas are getting drier”, this will show up in the scatter plot of the two datasets. For starters, here are the results, but without the labels, so you can see that there is no statistically significant relationship between the trend and the mean.
Figure 5. Scatter plot, modeled mean precipitation versus modeled decadence trend in precipitation, by region. The dotted lines intersect at the global meanS for averages and trends.
And this is the same number with labeled areas.
Figure 6. As shown in Figure 5, but with each point labeled
You can see the driest areas of the Sahara (SAH), the Gobi Desert of East Central Asia (ECA) and the Arabian Peninsula (ARP) on the left… don’t change much towards wetter or drier .
And on the right are the wettest regions of South America (NWS), East Indian Ocean (EIO) and Southeast Asia (SEA), again showing little overall change.
So it seems that the models aren’t alarming enough for climate alarmism promoters, and as a result, even the model’s results are misrepresented to cause fear…
And here on our forested hill?
Rain. The rain of glory, the fairest and most equal phenomenon, for as the prophet said,
… He created
his sun rises over evil and on good,
and rain fell on the righteous and the unjust.
Well, dear friends, could we ask for a more magical and fascinating world?
Best for all,
w.
PS: Look, there’s enough misunderstanding on the intranet. To keep the count down, please quote the exact words you’re discussing, so we can all be clear just who and what you’re referring to. For example, on my last post someone wrote “Really? Are you all still trying to fake climate science with your pseudoscience trap? What a disgrace to our nation.”
Serious? Who is he referring to? Me? Another person? What “pseudoscientific trap” is he talking about? Such comments go nowhere. Please, quote the exact words you are referring to.
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