No, that’s not when you shoot off all your ammo at the range and have to drive home with an empty gun (yeah, we’ve all done it, nothing to be ashamed about). What I’m talking about here is a series of predictive models which are not only wrong, but hopelessly wrong.
We all know (I think, except maybe for “climatologists”) that the way to test your model is to take all the history available except the most recent set, and feed that back data into your model to measure its prediction against the most recent data. (Back when I used to do this stuff for a living, we used to call it “using history to predict yesterday”).
So here are the conclusions of a whole bunch of predictive models used to predict future temperature change / increase for a specific area:
Looks kinda alarming, doesn’t it? (Okay. what’s alarming to me is the variation in predicted output between the models, but leave that alone for the moment.)
Now let’s drop the actual recorded data for the period and area into the graph, to see where it falls (same graph, plus actual):
Ummmm yes. The only model which came even close to reality (Observations) was “INH-CMS-0”, and even that one overstated actual temperature increase by a factor of almost 40%. Oops.
And by the way? A change of 1.6 degrees over an average decade over four decades (80 40 years) is what we model builders used to call “noise” — i.e. insignificant. Never mind a large area like the U.S. Corn Belt; an increase of 1.6 degrees in air temperature is insignificant in your house.
Here’s the whole analysis, which is long and kinda involved, even for me. The executive summary is what I’ve reproduced above.
And Robert Spencer gives us the summary of the executive summary, which is:
…or as we statisticians used to call it informally:
And John Hinderaker pointed out something else which should make everyone suspicious (it did me when I first looked at the chart):
Someone pointed out with respect to these data – I would credit him, but I can’t now find the reference – that if it were simply a matter of mathematical errors or inconsistencies, one would expect some models to err on the “hot side” and others on the “cold side” of actual observations. But that isn’t the case: all of the models run hot. That suggests that global warming alarmism is a political, not a scientific, movement.
Yup. Let’s worry about something else; this horse (Global Warming Climate Cooling Change©) is dead, and cannot be revived by MOAR HYSTERIA or STILL MOAR WHIPPING.
and those of us who’ve been following this “climate (whatchamacallit)” have been given reason to believe that the “observations” are skewed as well: a temperature recoding device, once in a corn field now immediately adjacent to a black-top parking lot.
In the town I grew up in, at the Junior College I attended, there used to be a weather station in this field. White Shutter walls, on stilts so it was a measured and consistent height above ground. When the College decided it needed a bigger, more modern library, that’s where they built it. I don’t know where they put the weather recording station, but it wouldn’t surprise me if it wasn’t on the top of the library. More wind, but more building heat rising into it.
Objective reality will never convince some/many True Believers. How many cults and religions have survived their own failed predictions? How many Marxists still believe that *this time* they can make it work?
Warm-mongers won’t accept the data, they’re too emotionally invested in the narrative. “We’re all doomed and the only solution is MOAR GOVERNMENT!”. That’s always their answer, regardless of the question.
A good rule when you see the words “DATA MODEL” is to substitute the phrase ” a baised guess dressed up to look legitimate.”
The more complex and more the variables included the Model the less accurate it will be. and the corollary to the rule – The fewer the variables, the less accurate the model will be.
We used to call such data, “PIDOOMA numbers”. “Pulled It Directly Out Of My A–.”
The other term was “Vent numbers”; Rock back in your chair, gaze at the vent until the number you need comes to mind. This is apparently how Michael Man, the unathletic weasel of “Hocky Stick” fame came up with his data, which smoothed out the Little Ice Age, the Medieval Warm Period, the Dark Ages Cold Period, The Roman Warm Period, the cold period that collapsed the Bronze-age trade in about the eleventh or twelfth centuries B.C., and the time before that which was formerly called the equilibrium.
And the big tell of his deceit was his refusal to allow peer review of his raw data.
Without overshooting they wouldn’t get all that sweet grant and research money.
Remember, 97% if all scientists agree with whomever is funding them
Global warming would be mostly a good thing at least for the immense new arable land created in Canada and Northern Eurasia. The downside is that a few hundred million people would be forced to move inland over a 300 year period at a negligible amortized cost. Check out some of Tim Worstall’s older posts for more economics detail. https://www.timworstall.com/
Global cooling is the real risk. If we lose a very few degrees the northern hemisphere will be under literally miles of ice down to the 49th parallel, the oceans will shrink, the Mediterranean will again become a landlocked desert and the vast agriculture around the present Med will dry up. Billions will starve and die and there is no upside whatsoever.
This elementary but accurate risk analysis seems lost on our “intelligentsia”, all of them dumber’n rocks.
You have to remember that for a significant subset of the climate hysteria set, “billions will starve and die” is a feature, not a bug.
Evil bastards.
I remember reading an article in Newsweek in 1971, about the coming ice age. The thing that struck me was the statement “A drop of only x (don’t remember exactly after 53 years, but it was small, like one or two) degrees would eliminate all wheat growing in Canada”.
Then, when they shifted to specter of global warming, I thought “That would mean if the temperature ROSE by X degrees,wheat growing could be extended much farther north, which to some people would be a good thing.”
I can no longer find the source, but a few days ago, I read a quote from some infamous econazi who actually had the gall to say with a straight face [paraphrasing] “The current cool temperatures are masking the actual global warming.”
There’s a standard procedure in training AI models where half the actual data is sequestered from the training, to see if the trained model can accurately predict the actual data. If it cannot, that iteration of the neural net is automatically sent screaming to the bit bucket as unhelpful digital poop.
Too bad there’s no such self corrective mechanism in globular worming modeling.
“…A change of 1.6 degrees over an average decade over four decades (80 years) is what we model builders used to call “noise” …”
So, that explains the expansion of a “decade” from 10-yrs to 20-yrs?
Arithmetic was never my strong suit…. [/1984]
And yet you were doing data research???