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Comment on Reconstructing a dataset of observed global temperatures 1950-2016 from human and natural influences by frankclimate

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Javier: 1. The smoothing from 1958 on hides the incease around 1960 IMO. 2. If the smoothing would start in 1947 we would see a much stronger decline also in the smoothed line above? 2. If the solar forcing, measured from SSN or 10.7 cm flux ( on shorter timescales ,i.e. 1950-2016 as it was done in the mainpost) would have an stronger impact on the GMST why is there not some valuable energgy trace at 11 years in any fourier analysis ot it? As I wrote, the limitaions ot the method imply that we can’t deduce longer timescales. As long as we can’t we must take what we have IMO


Comment on Reconstructing a dataset of observed global temperatures 1950-2016 from human and natural influences by frankclimate

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David: ” In that case you get a CO2 sensitivity of zero,” This is an indication that there is something wrong with your proposed method?

Comment on Reconstructing a dataset of observed global temperatures 1950-2016 from human and natural influences by coolclimateinfo

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oops – “please take the ..”

For anyone who doubts the solar cycle TSI influence, here is another graphic indicating the cross-correlation of TSI with climate indices over a twelve year window, covering 40 years of data:

The reason the correlations aren’t higher is due to irregular solar activity.

I’ll be busy today with other things and will return over the weekend.

Comment on Reconstructing a dataset of observed global temperatures 1950-2016 from human and natural influences by coolclimateinfo

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Sorry about the huge image size, it was made for a 4’x6′ poster.

Please note in the image there is a solar cycle onset El Nino, followed by La Nina, then El Nino(s) due to the build-up of solar energy at the top of the cycle, with lags.

Comment on Reconstructing a dataset of observed global temperatures 1950-2016 from human and natural influences by David Wojick

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Frank: It looks like this procedure depends heavily on the order in which the regressions are carried out. In particular, you seem to assume that the CO2 forcing is fully effective, which is certainly a questionable assumption. Natural variability then becomes a “residual.”

Try it this way. Start with a simple natural variability that explains the most of the change. Then apply the volcanoes and such, ending with the CO2 increase explaining the residuals. I suspect you get a very different answer.

Comment on Reconstructing a dataset of observed global temperatures 1950-2016 from human and natural influences by David Wojick

Comment on Reconstructing a dataset of observed global temperatures 1950-2016 from human and natural influences by frankclimate

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David: I would do so if we would know nothing about forcing, But some generations of scientists spent their time in research about it. However, I used the best available data for the ERF and the GMST and mentioned the sources. You are free to make a different approach.

Comment on Reconstructing a dataset of observed global temperatures 1950-2016 from human and natural influences by David Wojick

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Frank: We know a lot about what a small forcing does in a complex non-linear system. What it often does in counter intuitive. What is seldom does is manifest itself in a simply linear way, which you seem to assume.

It is your procedure so I am simply asking you to try several variants. The first is on the satellite record. The second is on the surface estimates with natural variability leading.

These seem like simple requests. Think of them as sensitivity analyses.


Comment on Reconstructing a dataset of observed global temperatures 1950-2016 from human and natural influences by oiltranslator

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So “forcing” is the new rectifying, faking, fudging, counterfeiting, altering and varnishing of data so the forcer sees what he wants to see? Orwellian!

Comment on Reconstructing a dataset of observed global temperatures 1950-2016 from human and natural influences by David Wojick

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No forcing is real enough, but it is just a small part of the equation of change. For example, the forcing of gravity will cause a feather to fall as fast as a cannonball in a vacuum. But on Earth the feather will not do that. If the wind is right it might even go up, not down. And so it is with CO2 forcing.

Comment on Reconstructing a dataset of observed global temperatures 1950-2016 from human and natural influences by Wagathon

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Meanwhile… global warming alarmists’ fears that our children would by now, never know snow again, seem deranged.

Comment on Reconstructing a dataset of observed global temperatures 1950-2016 from human and natural influences by Hans Erren

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Hi Frank,
Why did you use 3.8 W/m2 for CO2 doubling. In the Myhre CO2 forcing formula 5.35 ln(2) is 3.7 W/m2.

Comment on Reconstructing a dataset of observed global temperatures 1950-2016 from human and natural influences by frankclimate

Comment on Reconstructing a dataset of observed global temperatures 1950-2016 from human and natural influences by frankclimate

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from 3.7 to 3.8W/m² of course, sorry, fat fingers :-)

Comment on Reconstructing a dataset of observed global temperatures 1950-2016 from human and natural influences by frankclimate

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“The sat record shows nothing but natural variability, specifically a step function coincident with the giant ENSO at the end of the century. There is no warming before that ENSO and none after, but the second period of no warming is about 0.3 degrees C warmer than the first period.”
I don’t see this:

I see a record with warming from 1979 to 2018 with a rate of 1.3°C/centrury but very more volatile vs. ENSO as one would await it for the troposphere with much less thermal inertia than the GMST.


Comment on Reconstructing a dataset of observed global temperatures 1950-2016 from human and natural influences by JCH

Comment on Week in review – science edition by Atomsk's Sanakan (@AtomsksSanakan)

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Re: “Your carbon religion is fake, and only cares about the money. What a surprise.”

What an incredibly rational and non-paranoid response from you. You’ve truly blown me away.
[/sarcasm]

Anyway, you’re still sticking to your usual distortions (which CarbonBrief avoided) including:

1) Overlooking the fact that IPCC FAR over-estimated post-1990 CO2 levels. You need to account for this when looking at FAR’s temperature projection. Instead, you obscure this fact by claiming that CO2 emissions match FAR projections.

2) You treat the FAR’s GHG-induced warming trend as being linear, when it actually increases with time. That would be fine if you were just comparing the projection’s average linear trend over a specific period time with the trend in observational analyses over that same period of time. But that’s not what you’re doing. You’re instead comparing the projection’s average linear trend over an entire century with observational trends that are less than a third of a century. That makes no sense:

“1990 IPCC FAR: “Under the IPCC ‘Business as Usual’ emissions of greenhouse gases the average rate of increase of global mean temperature during the next century is estimated to be 0.3°C per decade (with an uncertainty range of 0.2°C – 0.5°C).” See here, page xi.”
http://archive.vn/O52h9#selection-541.0-547.10

I’ve put up a Twitter thread on what you’re doing, so folks can learn from how ludicrous it is:

Comment on Reconstructing a dataset of observed global temperatures 1950-2016 from human and natural influences by Javier

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Frank, the smoothing does not affect the polynomial fit in the least. Here you have the entire series unsmoothed and fitted with a third order polynomial:

There is some border effect at the beginning and end of the series, but it is clear that the decline in solar activity is taking place in the 21st century. There was a small dip in activity in the late 60’s to early 70’s, that also coincides with another period when there was no warming.

Comment on Week in review – science edition by Javier

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Like I give a hoot. Only 6 years to 2025. Where is the promised degree? What a joke.

Comment on Reconstructing a dataset of observed global temperatures 1950-2016 from human and natural influences by greggvankipnis

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Thank you. Very helpful. I am an economist/econometrician by training (1963-68) so, though stale in practice, I can spot foolishness in calculation, estimation and regression. It is non-scientific to make the calculation of ∆F as a residual, that is, the net effect of dozens of other variables, whether they are related to the dissipation (atmospheric insulation) of solar energy or not. You have made ∆T a relationship with a single regressor, i.e., an instrumental variable ∆F, as if it is exogenous. However, there is good reason to believe ∆F is itself a function of other independent and dependent variables. Econometricians struggle with this all the time and wish they con run controlled experiments.

In addition, the estimation of ∆F used in these studies does not take account of the different linear and non-linear effects each of the non-specified (exogenous) underlying explanatory variables may have on the calculation of the coefficient on ∆F.

I would have expected, and therefore wrongly assumed, that the insulating effect of H2O, CO2, CH4, or other gases, would have been calculated in the laboratory under controlled conditions. That is normal practice in science when trying to determine the insulating properties of many substances. For example, the insulating efficiency of building materials is expressed as an R factor. Its calculation controls for all other factors.

I can now see why this is really ‘unsettled’ science.

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