Guest Post by Willis Eschenbach
Thanks to Nick Stokes, who pointed me to the University of Melbourne Computer Model Intercomparison Project 6 (CMIP6) data repository, I got data on rainfall from the CMIP6 computer climate models. There were 12 models for which they had data covering the entire period 1850 – 2100. Let me start with the average of all twelve models.
Figure 1. Average global annual precipitation as shown by 12 CMIP computer climate models, one run per model.
I swear, results like that make me question the sanity of climate scientists. I mean, does anyone seriously think that after a hundred and fifty years of little change in global rainfall, around 2020 it suddenly started skyrocketing to new highs? (Note that the issue I’m addressing is not the amount of the change, which is not that great, but the shape of the change—level for 150 years, then within the space of only a couple years, suddenly increasing almost vertically. What changed?)
Really? Yes, I know that “negation through incredulity” is merely circumstantial evidence, but we’re getting to “trout in the milk” levels here …
… “trout in the milk”?? Seems that in 1849 there was a dairyman’s strike, during which there was suspicion that the milk was being watered down to increase profits. However, at the time it was hard to prove. Regarding the strike, Henry David Thoreau famously said …
Sometimes circumstantial evidence is very strong, as when you find a trout in the milk.
That’s where I find myself regarding Figure 1. Nor is this the only problem. Here are the rainfall results from the 12 models, smoothed so we can see the differences.
Figure 2. Precipitation results from 12 computer model runs, one from each model. Each is a LOWESS smooth of the original data.
As you can see, the largest results in the 1800’s are no less than 15-20% higher than the lowest result. I can understand models getting the future wrong … but when they get the past wrong, I get very nervous.
In addition, the amount of rise in future precipitation over the period is quite variable. To illustrate this, here is the data in Figure 2, expressed as an anomaly around each result’s 1850-1879 mean.
Figure 3. As in Figure 2, precipitation results from 12 computer model runs, one from each model, but expressed as an anomaly around the 1850-1879 mean value.
Note that although they start at the same level, by 1995 they differ by ~ 20 mm per year, with some increasing and some decreasing. And as you can see, the projected increase in rainfall varies from +20 mm to 60+ mm, a factor of three to one. In that regard a recent article, in Science magazine no less, pointed out that …
Projections rely largely on climate models, and the factor of three variation in predicted warming from these models amounts to tens of trillions of dollars of societal costs. Thus, most models must be significantly wrong about impact. Does that sound like “the science is settled?”
And to add insult to injury, this is not a factor of three variation between the least and most extreme scenarios. This is a factor of three variation in one scenario, the ssp126 scenario which projects the smallest increase in greenhouse gases.
Simple undeniable fact. The current crop of climate models is far from ready for prime time when that entails using them to make trillion-dollar decisions.
Finally, I took a different look at the rainfall results. I keep hearing claims that according to the models, the wet areas are supposed to get wetter, and the dry areas are supposed to get drier. Fortunately, the University of Melbourne has regional results for the precipitation, divided up into the following regions.
Figure 4. Regions used by the CMIP6 models.
So I averaged out the 12 models region by region, and I looked at both the average values and the average trends for each region. IF it were true that the “wet areas are getting wetter and the dry areas are getting dryer”, this should show up in a scatterplot of the two datasets. To start with, here are the results, but without labels, so you can see that there’s no statistically significant relationship between the trend and the mean.
Figure 5. Scatterplot, modeled average rainfall versus modeled decadal trend in rainfall, by region. Dotted lines intersect at the global average values for mean and trend.
And here is the same figure with the areas labeled.
Figure 6. As in Figure 5, but with each point labeled
You can see the driest areas of the Sahara (SAH), the Gobi Desert of Eastern Central Asia (ECA), and the Arabian Peninsula (ARP) at the left … not changing much toward either wetter or drier.
And on the right are the wettest areas of South America (NWS), East Indian Ocean (EIO) and Southeast Asia (SEA), again showing little common change.
So it seems that the models are not sufficiently alarming for the promoters of climate alarmism, and as a result, even the model results are being misrepresented to jack up the fear …
And here on our forested hill?
Rain. Glorious rain, the fairest and most egalitarian of phenomena, for as the prophet said,
… he maketh
his sun to rise on the evil and on the good,
and sendeth rain on the just and on the unjust.
Ah, dear friends, could we ask for a more marvelous and entrancing world?
Best to all,
PS: Look, there are enough misunderstandings on the intarwebs. To keep the number down, please quote the exact words you are discussing, so we can all be clear just who and what you are referring to. For example, on my last post someone wrote “Really? You all are still trying to pretend away climate science with your pseudo-scientific clap-trap? What a disgrace to our nation.”
Seriously? Who was he referring to? Me? Someone else? What “pseudo-scientific clap-trap” is he talking about? Such comments go nowhere. Please, quote the exact words you are referring to.