From Alan Tomalty we have this question:
No one has been able to separate out the amount of warming due to natural causes and amount due to CO2. Therefore I propose looking at all temperature stations that go back to at least 1880 for their daily maximum temperatures or averaged maximum temperatures. Then you would have 140 years of data, 70 years before 1950 and 70 years after. Since the tangent on the exponential curve of man made CO2 emissions started to move upward violently ~ 1950, this means that before 1950, essentially CO2 had ~ 0 effect but after 1950 a constantly increasing effect if you assume it has an effect at all.
So I propose doing 3 separate regression graphs for each of the 140 yr data temperature stations. 1 graph before 1950, 1 graph after 1950 and 1 graph overall. Do a regressional analysis on each graph with dependent variable as the temperature and date as the independent variable.
The 2 other things that are allowed to be considered are natural warming as 1 other independent variable and CO2 warming as another , but these 2 variables are not graphed. They exist in analysis below. The regression coefficients you come up with for each station are then turned into C degree changes.
Here are the definitions for the 3 way regression of CO2 intervention analysis. Natural warming effect = regression coeff in 1950 graph. Overall effect = regression coeff in complete time series graph. Natural diff is the natural warming effect that you can project into the future ie: is that part of the warming after 1950 that was or will be caused by natural factors.
The CO2 diff is that part of the warming that was or is caused by net CO2 in the atmosphere. Add them together and you have the regression coefficient of the overall complete times series graph which stat you already know. Get an average of Natural diff and CO2 diff from all your temperature stations and then you have the global average difference that CO2 makes versus natural warming.
Here are the equations.
1) Natural diff = Natural warming effect- (CO2 warming effect – overall effect).
2) CO2 diff = CO2 warming effect – natural warming effect.
3) Overall effect = Natural diff + CO2 diff
Interesting idea, but there are some complications. First, regressions can’t prove cause, but when used to suggest cause it makes the assumption of fixed linear forces operating on or overriding other “unimportant” forces. To a first approximation, fixed strictly linear forces (in any system) might be workable—but cannot be convincing without external evidence of both the linearity and the abrupt change in linearity to a new linearity.
Here are some other considerations.
Pick a spot. Your residence, for instance. The best way to ascertain temperature changes at this spot, which is not necessarily a possible way, is to (1) gather as long a series of measurements from that spot as you can, (2) define what a trend or change is, then (3) just look. Either the definition has been met, or it hasn’t.
Chances are the definition of the trend will be too strict if it is formal-model based. A regression coefficient, for instance, is too formal. Again, why would we expect temperature to fit a strict regression from any arbitrary starting point (when your series started) to some arbitrary end point (now)?
Why not just look at what the temperature actually did at your location? Then we can say “Here is what the temperature did.” There is no notion of cause in this, just observation.
We have to remove classical statistical thinking from understanding change. With linear trends, we get the idea the trend is realer than Reality, that the departures from linearity are “noise”. This is not so. Assuming error-free measurements, the measured temperature happened, it was felt by whatever was there at the spot. The lived experience (if we may) of the things embedded in the air is the only reason we’re interested in air temperature in the first place.
We can always look and see what happened. Not that we want to avoid the difficulties of saying why changes occurred, but we want to be careful. For instance, many have pointed out how certain structures, even including cities with all their pavement and so on, have grown up around temperature measurement stations. The structural changes obviously affect the temperature.
Some then ask what the “real temperature” “was” in the absence of these structural changes. Well, that question makes a kind of sense, but it’s irrelevant. The things at the station experienced the temperature that was measured. And if the structural changes were man-made, then the climate (the statistical average) change is also man-made.
I stressed error-free measurements, because many temperature measurements do have error. That error can be accounted for by formal means, which thus increases the uncertainty of the measurements. This uncertainty must always be kept when analyzing any change.
Another form of “error” is change in measurement type. The way it used to be measured is not the same as now, for instance. Again, this can be analyzed formally, and it, too, increases the uncertainty of any change claimed, an uncertainty that must be kept in the final answer, which must then come with a plus-or-minus.
With all those warnings in mind, here is where I more or less agree with Tomalty’s strategy. Gather from as many places as possible the longest series of measurements, accounting for the types of error and changes in measurement types just mentioned. “Line” the series all up, and see how the changes (using the definition of change made above) happened at each station.
Some of the places might have trends or changes and some with have others, and some will have none. This is far superior to looking at some sort of global average temperature (GAT), which is experienced nowhere and which never has any notion of uncertainty attached to it.
And, incidentally, it has to be observable uncertainty, not parametric uncertainty, which is irrelevant and always much lower than observable uncertainty. This is how many fool themselves. See this link about removing parameters.
If it turns out that stations of interest, like your residence, did not change, or could not be said to have changed with sufficient certainty after accounting for error etc., then what has global cooling been to you? Nothing.
Sorry, I meant global warming. No: climate change. Climate disruption, rather. Or was it climate apocalypse? Whatever.
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