The CSALT model uses the soli-lunar orbital forcings that Scafetta and Loehle recommend, yet they fudge the data to low-ball the sensitivities just like Nic Lewis’ results. How exactly does that work?
Could it be that they have an agenda that they are pursuing?
Hi WHUT,
Like I said, it all comes down to the next few decades. If the temperatures decline similarly like in the 1950s/60s (actually, I predict even more cooling), any significant A(CO2)GW will be very unlikely and CO2 the knob hypothesis completely disproven.
David Springer,
If the hypothesis put forward is that CO2 causes surface warming the null hypothesis is that CO2 does not cause surface warming.
OK, but surely that is falsified by the existence of the greenhouse effect.
No Edim, the CSALT model will fail if that is the case, as the natural variability factors are not predicted to go into a free-fall. Remember that CSALT includes the factors that Team Denier is using as their non-CO2 explanation salvation.
Unless you are predicting huge volcanic eruptions or the abrupt cessation of CO2 emissions in the ear term?
What you need is a method that can give attribution to the various mechanisms — something like CSALT. Unfortunately for the skeptics, the majority of the natural terms oscillate and contribute little to the warming trend. All that is left is the stadium wave and CO2 control knob, and that provides most of the remaining attribution.
I’m not predicting huge volcanic eruptions, just cooling, affected mostly by already weak and declining solar activity and continuation of the oscillatory behaviour of the global temperature indices.
Mosher is arguing semantics again. We are talking about very coarse numerical simulations with largely guessed inputs and a lot of missing physics, not blanket-bans of all poor models everywhere. We are not talking rejection so much as standard % error calculations. We are talking about falsification of the assumptions that the modelers made, not the idea of modeling and correction of models.
It is not about models not being perfect. It is about fitness-for-purpose or even basic adequacy. Indeed it is not even the poor models fault so much as the circular logic that went into assumptions and the huge reliance placed on unvalidated results.
Some models can be made to agree with reality if they use zero positive feedback and non-declining natural variation but we must avoid using any projections of declining fossil fuel use since it seems impossible for the near future. But crucially it seems the need to use any models for climate has withered away because there is no catastrophic scenario. The hypothesis was well tested and it failed all tests. The reason is is one or all of the physics that the IPCC admitted was “unknown”.
More like projection.
Web, it’s their own TCR figure they are promoting.
They are quite obviously and deliberately avoiding the fact that *their own figure* backs the IPCC attribution statement.
It highlights the dishonesty of skeptics and the game they are playing. They aren’t honestly looking for the truth about how the climate works and man’s influence on it. They are concerned only with denying the link between man and climate.
Why hasn’t Judith made any comment on what the TCR figures mean for attribution of warming since 1950? I tell you why: because the answer is inconvenient to her agenda.
They attack the IPCC attribution statement because they don’t want it to be publicly accepted that man is the dominant driver of recent warming. They want to pretend that it’s all uncertain and maybe man only has a bit role in the recent warming.
Their promotion of Nic Lewis’s figure contradicts that stance. And this contradiction should be hammered home at them again and again.
JimD,
What do you mean by Nic’s short period.
His own paper is an extension of the work of Forest et al that covers the whole 20th century. The paper of Otto et al where he contributed covers the period from 1860 to 2009 or more precisely compares the first 20 years of this period with the last four decades separately and as a sum.
The authorship of Otto et al is really impressive. The approach is simple, but that is well justified at least for TCR. Some questions remain on the best way of expressing the outcome, but only at a level of little significance. More refined methods lead to somewhat different results. Some corrections can be tried to correct for specific short term effects, but the applicability of these corrections to this particular comparison must be studied carefully. I believe that the reference period has been chosen to minimize the influence of such corrections.
The paper that Nic wrote alone is a separate issue. He is perfectly right in his criticism of the interpretation of the outcome of Forest et al papers. My own preference is that the results would not be presented at all as a PDF that can be formed only by combining the likelihood function that’s the actual result with a prior. They are, however, commonly combined with an uniform prior in climate sensitivity.
The uniform prior in climate sensitivity is, indeed, suspect, and several arguments have been presented against it. Reaching high climate sensitivities requires a positive feedback close to the point of instability. An uniform prior in CS corresponds to a prior in feedback strength that diverges when approaching the point of instability. Something alone the line of thinking of Jeffreys on the noninformative prior for a scale parameter of the type of climate sensitivity seems to be required.
Jewson, Rowlands and Allen wrote an paper on using Jeffreys’s prior that’s invariant in changes between equivalent sets of parameters of a single model in climate forecasts, and Nic used this proposal to reanalyze the case of Forest et al 2006. Jeffreys’s prior has the nice property that the results are the same independently of the way a fixed model is formulated mathematically as long as it’s the same model. That leads, e.g., automatically to a specific and often plausible way for handling scale parameters. Jeffreys’s prior leads commonly to results that are not badly implausible, but it’s has replaced the dependence on the choice of parameters considered by the dependence on the model used to define it, in case of Nic’s paper on the MIT 2DCM.
As I wrote Jeffreys’s prior is commonly plausible. It’s probably best alternative we have, when a great inherent value is given on the objectivity in the sense that a decision made once removes further subjectivity in the analysis. That does, however, not guarantee at all that the original decision of selecting the Jeffreys’s prior leads to most correct results. At best it can prevent the introduction of really bad priors, when the goal is to get the best estimate for a parameter like ECS, rather than minimizing the subjectivity over all other considerations.
For a parameter that has meaning outside of one single model, as ECS has, each separate analysis based on a different model would have it’s own Jeffreys’s prior. That does not really make sense.
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Is “insufficiently doctrinaire” the same as being a “denier” with and “agenda”?
Lolwot, so it’s very much like negotiating over a car price, only here we have the objective truth which I will not yield an inch over. With science there is no haggling.
Pekka, yes, it looks like you are right that a longer period was used which makes it harder to understand why their sensitivity fails to explain the last 43 years. However, I can’t follow what Lewis is doing to get such a low aerosol forcing, which is the central reason for his low sensitivity.
Would you buy a used car from a guy named WebHubTelescope?
Me neither.
No sale.
NW | December 19, 2013 at 1:41 am |
Lolwot,
Each of 15 clowns measures the heights of 50 women randomly sampled from Manhattan. Each clown independently samples his or her own 50 women. The range of sample average heights recorded by the 15 clowns is 5’2″ to 5’4″. With just the information I have given, can you conclude with 95% confidence that most women in Manhattan are taller than 5’1″?
Show your work. Extra credit for correctly guessing who the clowns are. Special bonus question: How many clowns did the IPCC ask to get the range they stated?
Trust but verify. I’d want to see the data first. Clowns have a tendency to apply adjustments to the data to get the conclusions they want.
Bonus points if you can tell me when a yardstick is not a yardstick.
WebHubTelescope (@WHUT) | December 19, 2013 at 7:30 am |
The CSALT model uses the soli-lunar orbital forcings that Scafetta and Loehle recommend, yet they fudge the data to low-ball the sensitivities just like Nic Lewis’ results. How exactly does that work?
Could it be that they have an agenda that they are pursuing?
Those you mention don’t fudge the data. You’re projecting. Again.
Say, if CSALT is a model what’s it say global average temperature will be in 2016? Loehle and Scafetta 2011 publish a number for that from their model. It’s only fair you do too so we can compare projection to projection, eh?
Bob | December 19, 2013 at 11:26 am |
+many
Jim,
The method that Nic is using, and what Forest et al used previously, has aerosol forcing as one of the three free parameters. The other two are climate sensitivity and deep-ocean diffusivity. All three are determined by the method, not constrained explicitly. They are results of a method that’s opaque in the sense that it’s difficult to follow how the values are obtained. His paper contains more discussion on the deep-ocean diffusivity, joint distributions of aerosol forcing and other parameters are not discussed.
