Month: August 2018

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“Even if we cannot definitively attribute a climate change link to a specific event does not mean that we can’t discuss how climate change is likely to impact such events and whether or not we’re seeing changes that are consistent with what is expected.”
Leads to a degree of confirmation bias.
If every extreme event is expected to occur with greater frequency then the mere occurrence of any said extreme event becomes automatic proof of your position, making it a definite attribution.
Seems wrong on some level even if right.
Nothing wrong with talking about the expectation
“Climate change is clearly happening and it is mainly driven by our emission of greenhouse gases (mostly CO2) into the atmosphere. Doing so causes atmospheric CO2 to increase, reducing the outgoing energy flux and causing energy to accumulate in the climate system. This will lead to warming of the surface and troposphere, increasing ocean heat content (and increasing sea surface temperatues), an increase in evaporation, an increase in the frequency and intensity of extreme precipitation events, and a change in the latitudonal temperature gradient that has the potential to influence the jet stream and, hence, weather patterns. This means that in regions that are susceptible to extreme weather events, the conditions will increasingly tend to favour these events becoming more extreme.”
But extreme events have to be rare and rare events are difficult to pin causation on and even a 5% increase is perfectly acceptable within a normal range. As well as fitting an expectation.
It is an argument that can lead to calls of crying wolf.
Wolves are out there but no-one appreciates calls that are not definitely attributable.

 

 

• ngech says:

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  September 6, 2018 at 3:34 am

  Thanks Izen for mentioning a sensible time scale environment.
  Andrew, I don’t understand or do not want to believe or do not get it.
  Your change in temperature for a x4 inst. forcing only goes up very slowly, years in fact to perhaps get to 2 C, then much longer to get higher [ATTP’s centuries+ to get to an equilibrium].
  I want to believe that an atmosphere with a x4 increase in CO2 [and maintained there thereafter] must have a new temperature very close to the maximum achievable and fairly instantly. like within 36 hours.
  I am prepared reluctantly to accept some lapse as the change in heating permeates to its final level. Perhaps a different simpler picture of a non rotating earth would help the imagery. Here the retained incoming heat would rapidly build up and then spread to the dark side over time, hours days until the outgoing again balances the incoming. Obviously some heat would be transferred/ building in the oceans. But this again could balance fairly soon. The oceans being hotter on the surface keep the air warmer and decrease rapidly the amount of heat needed to be put in to achieve that balance as well.
  My point, dense though it is, is that a volume of gas of known composition, on a known backdrop of water/land on one side, space on the other, heated by a known income source [np clouds] has a known expected scientific temperature.
  Earth at 500 ppm CO2 , Earth at 2000 ppm, each has a set expected temp [specifiable in a range if not exact]
  I realise that ocean temperatures can take months to vary but atmospheres are much thinner and reactive. 0 at night to 30C in the day at Alice Springs. Why should not the temperature reaction of a known composition adjust to that expected temperature, or close to. more quickly?
• angech says:

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  September 6, 2018 at 4:33 am

  Steven, “attack the pattern argument”
  I read AD An Estimate of Equilibrium Climate Sensitivity From Interannual Variability
  “It is also worth stepping back and asking what could cause our calculation to be seriously in error.
  It seems unlikely that forcing from doubled CO2 is wrong given our good understanding of the physics of CO2 forcing (e.g.,Feldman et al., 2015).

  Estimates of ? iv,obs and ?iv,obs are derived from observations we view to be reliable,
  so our judgment is that they are also unlikely to be significantly wrong.

  The ?TS/?TA factor comes from climate model simulations, but models have long been able to accurately reproduce the observed pattern of surface warming (e.g., Stouffer & Manabe, 2017), and we have simple physical arguments explaining how the atmospheric and surface temperature should be connected (Xu & Emanuel, 1989).
  Finally, we can compare the models to data (Compo et al., 2011; Poli et al., 2016) to validate their simulation of this ratio.

  Thus, the transfer function seems the most probable place for a significant error to occur. We also argue that while errors may exist in a model (i.e., in the cloud feedback), this will affect both the numerator and denominator and such errors will tend to cancel out.
  While one must be careful about conclusions based on a single model, this nevertheless provides some support for the hypothesis that errors in ? 4xCO2
  will cancel errors in ? iv when the ratio is taken.

  As a preliminary test of this, we have analyzed three different versions of the MPI-ESM 1.2 model that have had their cloud feedbacks modified to produce different ECS (Thorsten Mauritsen and Diego Jimenez, personal communication, 2018). The three versions are the standard model (ECS calculated from an abrupt 4xCO2 run using the Gregory method = 3.0 K), an iris version (described in Mauritsen & Stevens, 2015; ECS = 2.6 K), and a high ECS version, in which the convective parameterization has been tweaked to generate a large, positive cloud feedback (ECS = 5.2 K). ”
  –
  I have cut, pasted and tweaked this somewhat, apologies for those bits left out. It contains numerous items relevant to past discussions on this and other blogs supportive of some of my past statements.
  I have trouble understanding “if the argument turns on which version of ice is used,”
  So I am probably cross discussing..
  Here, with Andy, He has outlined his procedures well. He believes ” models have long been able to accurately reproduce the observed pattern of surface warming ”
  I would argue that reproduction is different to predication, I would hope the models do accurately reproduce. any argument that observations could be put aside when they disagree with the reality of models is fraught. though not untenable.
• angech says:

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  September 6, 2018 at 4:58 am

  I have trouble understanding “if the argument turns on which version of ice is used,”
  Time scales as we both know are very relevant to the accuracy of prediction and expectation. When I argue from a very few years of ice increase from one data set you are more than entitled to laugh at me, and others. When the ice sets [satellites] are of short duration 40 years themselves then the laughter should be stifled though the message is still with the long term trend.
  I have no beef with people having beliefs or predictions. Only sticking to legitimate scientific methods. PIOMAS is currently creating some issues for people wanting warming. They believe it is not accurate now though they were happy to accept it while it was falling precipitously. Skeptics did not believe it when it was falling. Both cut from the same cloth.

 

dikranmarsupial says:
August 17, 2018 at 6:56 am

angech wrote “ECS can only ever come out as it is programmed in. ”

This is just grade A bullshit. If I were a Magrathean and built a replica Earth (as indeed they did), it would have the same ECS as the real Earth because it is an emergent property of the physics governing climate (and the position of the planet in the solar system and the distribution of land masses etc.), not because the Magratheans “programmed it in”. It doesn’t make any difference whether you simulate that physics on a computer (supposing you could do so with infinite spatial and temporal resolution) or whether you simulated them using a replica Earth.

It is the constant stream of this kind of bullshit (and angech is not the only one) that means I am giving up on commenting on blogs, probably permanently. As a scientist, I feel a pressure to respond to this sort of incorrect assertion about science, but at the same time I know it is a complete waste of my time because angech (an others like him) will continue to spread the same bullshit elsewhere or here in a later thread, or move on to some other topic of bullshit. Thus I can’t enjoy just having a reasonable discussion here on a topic I find interesting without being bothered by bullshitters every time (and I mean every time). It appears I should probably stop reading blogs as well. Well done angech.
dikranmarsupial says:
August 17, 2018 at 7:00 am

I should add, it is very easy to show that ECS is not programmed in to climate models. If this were possible, then climate skeptics would just take the code for an existing GCM and twiddle with the parameters etc. until they got a GCM that explains past climate with a low ECS. The trouble is that can’t be done without using parameter values that are either inconsistent with physics or with experiment. Of course this is something that has been pointed out repeatedly over the years. Still waiting.
angech says:
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August 17, 2018 at 10:03 am

angech wrote “ECS can only ever come out as it is programmed in. ”This is just grade A bullshit.”
One could look at 1000 papers like this. They show that ECS outcome, high or low, is very dependent on what initial parameters are put in.
Re examining the Relationship between Climate Sensitivity and the Southern
Hemisphere Radiation Budget in CMIP Models JOHN T. FASULLO
National Center for Atmospheric Research, Boulder, Colorado 8 January 2015
models with the highest values of ECS strongly reduce low-level marine clouds in most regions of the subtropics as CO2 increases, whereas models with the lowest values of ECS actually increase low-level marine clouds in some subtropical regions
The trade-off between a better representation of present-day Southern Ocean or subtropical shortwave CRE in these subsets of models points to choices made in the model development process, rather than robust physical processes. [1]
Consequently, we find no clear physical reason to expect a linkage between subtropical cloud biases and the correlations between ECS and present-day biases over the Southern Ocean.
Instead, the linkage between subtropical and mid-latitude cloud properties is likely an artifact of choices made in model parameterization and tuning [2]
Identifying large model biases in fields physically linked to climate feedbacks remains a promising path for improving models and for potentially narrowing their spread in ECS.[3]
when the correlation between ECS and a present-day climate property arises from a systematic model bias rather than from a real physical process, its utility becomes
questionable [4]
angech says:
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August 17, 2018 at 10:22 am

…and Then There’s Physics says:
angech, You’re just doubling down now. ECS is an emergent property of models.
From RealClimate “Each of these numbers is an ’emergent’ property of the climate system – i.e. something that is affected by many different processes and interactions, and isn’t simply derived just based on knowledge of a small-scale process”
–
“The wording is one issue that I am immediately defeated on. Of course anything that comes out of running a computer program can be said to emerge from it. ” I had this discussion at Lucia’s two years ago with no success. The word emergent is used by many including those promoting the ECS from models view as being an unknown that emerges from the model results. That is it could not be predicted beforehand and yet would fall somewhere in the predicted range.”
–
I feel helpless at this, caught in a paradox. Either we can discuss ideas and be allowed to be wrong but have it explained [I don’t mind being castigated for not understanding] or we can choose to dismiss contradictory views out of hand. Science depends on being explainable. To just say something is emergent means we cannot explain it. In which case what is the point of developing a computer model in the first place if the answer was unprovable?
–
The wording is one issue that I am immediately defeated on.

Joshua.
Sorry about the circles.
Very well put.
“two people can have viewpoints that are mutually exclusive, where both are right”
Godel?
“In this case, we could say that ECS is the function specific inputs chosen by coders, and as such, those coders determine the final value by making choices among specific inputs. Or, we could say that the coders are putting in values that represent our best understanding of physics, and that ECS emerges as an output. Essentially, those views are incompatible, and yet I think both are true.”
You can say both those things.
Some people do.
“You can get anything you like out of a model. If you set it up with scientifically based suppositions about the physics of clouds, based on the existing evidence, and reflective of a scientifically quantified range of uncertainty, they will produce an ECS output range, which likewise reflects a quantified range of uncertainty. This will at some point in the future be modified with scientific improvements.”
is fine.
Note ECS may well be 3 or 4 and better provable and models in the future will, in their arcane way, be the best way of determining it. They must be in the ball park probably inside the bases.
Just they are striking out on the nature pitches.
“You can get anything you like out of a model depending on what you put into it” would be the best interpretation of what I was saying, which I think should be evident yet English is such difficult beast.
“You can only get out of a model what you put into it” is what people here are hearing me say.
Both statements are the same yet mutually contradictory.
Perhaps Willard could explain how this is allowed to happen.
Or ATTP could do a post.

This is the money quote.
IPCC 2007
“These studies highlight some common biases in the simulation of clouds by current models (e.g., Zhang et al., 2005). This includes the over-prediction of optically thick clouds and the under-prediction of optically thin low and middle-top clouds. However, uncertainties remain in the observational determination of the relative amounts of the different cloud types (Chang and Li, 2005). For mid-latitudes, these biases have been interpreted as the consequence of the coarse resolution of climate GCMs and their resulting inability to simulate the right strength of ageostrophic circulations (Bauer and Del Genio, 2006) and the right amount of sub-grid scale variability (Gordon et al., 2005). Although the errors in the simulation of the different cloud types may eventually compensate and lead to a prediction of the mean CRF in agreement with observations (see Section 8.3), they cast doubts on the reliability of the model cloud feedbacks. For instance, given the nonlinear dependence of cloud albedo on cloud optical depth, the overestimate of the cloud optical thickness implies that a change in cloud optical depth, even of the right sign and magnitude, would produce a too small radiative signature. Similarly, the under-prediction of low- and mid-level clouds presumably affects the magnitude of the radiative response to climate warming in the widespread regions of subsidence. Modelling assumptions controlling the cloud water phase (liquid, ice or mixed) are known to be critical for the prediction of climate sensitivity.”
There is more. I truncate only to get a readable comment in. As I recall, possibly from Soden somewhere different models arbitrarily choose a range for absorbtion in thes clouds which can be double another model. I hope most people would agree a doubling or halving gives critically different results and quite a different but predictable emergent ECS.
This is where the input that later forms the ECS occurs.
Worse it implies that 2 wrong assumptions may be being made that somehow compensate each other in that they then produce the right ECS.