You didn’t spot my article proving that recent climate changes are likely natural?
I find the CET data rejects the hypothesis of ‘climate change’ (>58%) & current ‘global warming’ (>72%) and that overall global temperature has not changed significantly more than would be expected.
“Myth-busting, inspirational review of Energiewende: Addressing the myths of Germany’s energy transition”
This article is a pack of lies and propaganda.
For eg.: “Germany produces 25% of energy from renewables” – true, but this includes hydro (built before the AGW scare) and boifuel (wood). These sources are exhausted and incapable of increasing. Wind and sun produce only 15% of energy. The fast increase of (theoretical) wind and solar capacity does not result in increase in wind-solar production. The sun produces only 10% of the time (capacity factor 10%), while wind produces some 15-25% of the time – depends on the year (if it was windy).
Emissions have decreased from 1990 to 2000-2005 maybe due to closing of East Germany’s obsolete factories (nothing to do with energiewende). Since 2012 there is a steady annual increase in emissions, despite very massive addition of wind and solar “capacity” (this is theoretical capacity, the true capacity is, as mentioned, 10 to 15% of this).
No matter how much solar and wind capacity you install – you can’t increas much their share of produced energy, and that energy, coming in bursts when it is least needed, is mostly exported at great loss.
The energiewende in Germany is a “dirty failure” – a very expensive illusion.
Jan:
Since I originally raised the Fyfe, et al., study could you please provide me with citations to the contradictory studies you mentioned?
I’m not necessarily disagreeing with your suggested adjustments to the Fyfe, et al., treatment of ENSO, etc. But I do remain impressed by a researcher explicitly testing a null hypothesis rather than assuming it is confirmed. And that is the aspect of the Fyfe, et al., paper I would like to see refuted.
Admittedly, the idea that model simulations have diverged from observations doesn’t seem particularly shocking to me since perfectly modeling the climate is probably impossible. Therefore, the argument that models and observations haven’t “really” diverged (“the internal variability made me do it” defense) is a position that I believe downplays the gaps in our understanding of the climate system.
A slowdown is not a pause.
How about a ‘slowdown’ where the supposed surviving increase is an order of magnitude smaller than the error bar?
Capt Dallas : How can surface air temperature stay flat while the ocean is warming?
Because the heat is staying below the surface, held down by some weird currents thing that defies the normal tendency of warmer water to to rise. Sayeth R Gates, I seem to recall.
Jan,
There’s another related issue, I think, with Fyfe et al. As I understand it, what they did to get the observed trend and its uncertainty was to consider 100 (IIRC) ensemble member realisations of the HadCRUT4 data. Hence, their uncertainty is really the error on the mean of all 100 realisations, not the uncertainty in the trend for a single ensemble member. Their uncertainty is therefore, I think, smaller than it would be had they used the uncertainty in the trend for a single ensemble member, rather than the error on the mean for all 100 ensemble realisations. This, I think, is consistent with what you’re suggesting which is that they’re assuming that the mean trend is the actual forced and that it is unaffected by unforced variability.
So, as I see it, all they’ve really shown is that the mean trend in the HadCRUT4 data is barely consistent with the model trends, but they haven’t really shown that this is necessarily implies anything particularly significant, since they’ve assumed no role for unforced variability.
On a similar note, it is interesting that some use the large uncertainty in the trends to argue that we can’t rule out that there’s been no warming since 1998, and then, when doing a model-observation comparison, use the error on the mean (which is smaller than the uncertainty in the trend) to argue that the models and observations are inconsistent.
By whatever name you choose to call it, something cannot not exist and also be caused by something
+ 10 to all in the sub-thread. + 2 for funny to boot.
And as the authors pointed out, they left off a lot!
And yet Ron C has also found a classical GCM, a Russian model INMCM4, which doesn’t do badly compared with observations, containing, wonderfully, three outlying features for which skeptics have been crabbing for years.
Compared with the true complexity, has this simple GCM model with better parameters than the others, functioned somewhat as the Callendar or Monckton models do to the GCMs?
There’s a question I can’t figure out how to pose here, let alone answer.
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There’s gotta be a joke about a ‘California Pause’ in there somewhere, but from where I sit with the radar gun, the vehicle seems in reverse.
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The difference in the time of observation adjustment between satellite and ground based is the observation time is perfectly recorded on the satellite data so they don’t have to “detect” proximate spatial discrepancies then cancel the outlier by turning it into a new station.
The larger and most difference of course is that satellites cover the whole globe while thermometers cover a small fraction of land surface mostly in the northern hemisphere and precious little else.
Jim, you don’t have to make an attribution statement each time you comment. I’m not a goose. I don’t wake up in a new world every day.
During prohibition trucks filled with booze would cross from Canada into the US along back roads would be stopped by rival mobsters who would say to the driver
“Hi Jack”
Mosher avoiding being hostile is like a fish avoiding being wet.
AFAEK IEHO
Hi Peter Davies
“Without even defining the population and what seems to be an appropriate PDF for such a population, any error bands used on observations cannot be stated with any level of confidence whatsoever. “
Absolute. At the very core of the process–first step.
“IMO the population is non-ergodic and hence not statistically amenable”
I agree. It is difficult to construe the system of interest as stationary and hence it could not be strictly ergodic. Weak stationarity, when suggested, might be exploitable but as is the case in geostatistics such assumptions and there limits have to be clearly quantified.
However, these days that is above my paygrade! :O)
Mushi is very fine fare when delicately prepared; just hacked up can be toxic.
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