There’s a new paper, discussing climate sensitivity, by Marvel, Schmidt, Miller & Nazarenko called Implications for climate sensitivity from the response to individual forcings. Before I discuss the paper, I should really advertise the lead author’s blog, arguably one of the best around. The idea behind the paper is to consider the efficacy (in a sense, the strength) of the different forcings. It seems to be an expanded version of what is done here and also seems related to what Isaac Held discusses in this post.
The basic idea is that the different forcings (land use, aerosols, GHGs, ozone, solar, volcanoes) produce different temperature responses for the same change in forcing. This seems to be largely because the spatial distribution of the different forcings are different. Greenhouse gases tend to be well-mixed. Land use is obviously over land only. Aerosols tend to be dominant in the northern hemisphere, rather than in the south. The reason this is relevant is that basic energy balance calculations tend to lump all the different forcings into a single term and, consequently, assume that they all produce the same temperature response. However, if the efficacy of the different forcings is not the same, then this will influence the resulting estimate for climate sensitivity.
Credit : Marvel et al. (2015)
Although this is only a single paper, my understanding is that it is generally accepted that the spatial distribution of the forcings, and the resulting warming, means that (as Marvel et al. say)
Climate sensitivities estimated from recent observations will therefore be biased low in comparison with CO2-only simulations owing to an accident of history:
and therefore
when the efficacies of the forcings in the recent historical record are properly taken into account, estimates of TCR and ECS must be revised upwards.
...and Then There's Physics 
“The basic idea is that the different forcings (land use, aerosols, GHGs, ozone, solar, volcanoes) produce different temperature responses for the same change in forcing. This seems to be largely because the spatial distribution of the different forcings are different.”
I tried to point this out months ago and was ignored… Specifically how most Paleoclimate estimates that ignore the distribution of forcing have an upward bias.
-1,
I don’t think you were ignored.
Well… certain comments of mine were deleted when I tried to challenge BBD’s position on Paleoclimate estimates excluding excluding low climate sensitivity and when I suggested that there were flaws in James Hansen’s work, specifically not taking into account distribution of radiative forcing, that caused an upward bias.
I also gave a fairly in depth explanation twice on Judith Curry’s blog (once here http://judithcurry.com/2015/04/22/bjorn-stevens-in-the-cross-fire/#comment-697545) and you never responded. Here is the part relevant to this topic:
The ‘argument’ that is consistently given to dismiss the effect of Milankovitch Cycles is something along the lines of “because changes in global annual solar irradiance are small due to Milankovitch Cycles, they can be neglected”.
This is complete nonsense. For one, global annual solar irradiance is proportional to 1/sqrt(1 – e^2), where e is the eccentricity of the Earth’s orbit. So the above claim basically suggests that obliquity and precession do not matter as they don’t affect global annual solar irradiance. Perform a simple linear regression where global temperature over the Pleistocene is the dependant variable and eccentricity, obliquity and the precession index are the independent variables (add other explanatory factors if you want). You will find that obliquity is by far the most important Milankovitch Cycle, not eccentricity.
Obliquity has an effect on global temperatures beyond GHG or albedo feedbacks. This is due to the Stefan-Boltzman law. The earth’s surface does not have a uniform temperature; polar regions are colder than equatorial regions. Because of this, a change in the incoming radiation in a polar region will have a larger effect on global temperatures than a change in the incoming radiation in an equatorial region as the marginal change in emitted black body radiation due to a change in surface temperature is higher in the equator than in the poles. I’ll demonstrate the magnitude of this effect below:
– – – – –
Not taking into account the unevenness of changes in the distribution of solar insolation can cause significant bias and underestimation of uncertainty in estimates of climate sensitivity. For example, Van Hateren 2012 assumes that a change in solar irradiance will have approximately 0.7/4 (1 – albedo of earth divided by the ratio of the surface area of a sphere to the area of a circle of comparable radius) times the effect of an equivalent change in W/m^2 in GHG forcing. This arguably overestimates the strength of the sun relative to GHG forcing because it doesn’t take into account the fact that extra sunlight in the tropics has less affect on global temperatures than an equivalent amount of extra sunlight in the poles due to the Stefan-Boltzman law.
To illustrate the magnitude of this effect, consider a grey model of earth where in equilibrium:
(1-α)*S(φ) + B = G*σ*T4(φ) + k*(d2T(φ)/dφ2 – tan(φ)*dT(φ)/dφ)
Where α is the Albedo of Earth, S(φ) is the annual solar insolation at latitude φ, B = 0.087 W/m^2 is the heat flux due to the Earth’s internal energy, G is a factor due to greenhouse gasses, σ is the Stefan-Boltzmann constant, k is the constant that determines the rate of heat transfer across the surface of the Earth and S(φ) is the temperature at latitude φ.
If I impose a restriction that the average temperature of this grey earth is 288 K and that the temperature at the equator is 300 K (which gives a temperature profile that is similar to that of Earth), then I get G = 0.1967 and k = -0.0452. If I use this model and vary solar irradiance by 1 W/m^2 then I get an equilibrium global average temperature change that is 5.44% the temperature change I get if I change greenhouse gas forcing by 1 W/m^2 (if you wish to see my matlab code that gives me this I am happy to share it).
Now if the assumption by Van Hateren were valid then the above value should be 0.7/4 = 17.5%, not 5.44%. So not taking the unevenness in the distribution of global insolation and temperature can cause one to overestimate the strength of the sun relative to GHG forcing by a factor of 3; which suggests that Van Hateren’s estimate is an underestimate of climate sensitivity. More realistically, one should take into account the unevenness of albedo distribution and the effect of cosmic rays; if I try to estimate a Van Hateren impulse response function from instrumental data and allow the effect of the sun to vary as a free parameter relative to the effect of GHG forcing, then I find that a change in solar irradiance has about 8% the effect of an equivalent change in W/m^2 in GHG forcing; so the assumption by Van Hateren overestimates the relative strength of Solar Irradiance to changes in GHG forcing by a factor of two.
– – – – –
So clearly, changes in the distribution of incoming solar radiation causes global temperature changes beyond those caused by GHG or albedo feedbacks due to the Stefan-Boltzman law. In addition, the precession index is very relevant because the albedo distribution of the Northern Hemisphere is different from the albedo distribution of the Southern Hemisphere. So to have a decent climate sensitivity estimate using Pleistocene data, Milankovitch Cycles need to be taken into account.
Let’s say I take Dome C data of dO18, CO2, CH4 and N2O. I use Annan and Hargreaves 2013 to convert the dO18 into a proxy for global average temperatures and I convert the CO2 + CH4 + N2O data into GHG forcing. For albedo forcing, let’s assume for the sake of argument that the claim by Hansen et al. 2013 that the radiative forcing due to albedo changes from Holocene to LGM is 3.4 W/m^2 +/- 20%. I can then use a sea level reconstruction/dataset (say de Boer’s ANICE output) and an assumption of linearity to get a proxy for the albedo forcing.
For the effect of Milankovitch cycles, let’s use 3 variables: the change in solar irradiance (which is proportional to 1/sqrt(1 – e^2)), the sine of the obliquity, and the precession index (e*sin(precession). I can then perform a linear regression to estimate the model T = β0 + β1*(GHG + Albedo + 0.05*Solar) + β2*sin(obliquity) + β3*precession_index + model error. If I take into account all my sources of error (model error, temperature error and albedo error) and propagate error correctly my 95% confidence interval for ECS is (2.48 +/- 0.49) C.
And this is an overestimate of ECS since I am using a low value (0.05) of the strength of the sun relative to GHGs (my regressions using the instrumental data suggest this should be closer to 0.08) and I’m not taking into account the fact that the albedo changes are not uniform. As the albedo changes are higher in polar regions than equatorial regions the strength of albedo changes relative to GHG changes should be stronger than what is assumed in the model (due to the Stefan-Boltzman law).
In any case, I think I can conclude that a proper evaluation of the Pleistocene + Holocene ice-core data yields a 95% confidence interval of climate sensitivity that excludes ECS greater than 3 C. So an ECS greater than 3C is excluded at the 2.5% confidence level by Paleoclimate data!
I don’t remember deleting your comments, but playing the ref is tedious. I know you’ve presented this before. Publish it. Repeating it in blog comments is not going to convince anyone.
Actually, this is probably over-confident
We’ve had an ECS range of 1.5 – 4.5C (or 2C – 4.5C) for decades now. I really doubt that a single study (especially one done in a blog comment) is suddenly going to reduce our uncertainty as much as you claim to have done. I also found this paper which considers the Pliocene and Pleistocene and seems to suggest an ECS range that is in the region of 1.5C – 4.5C.
😦 Don’t be like that. I was hoping to have a dialogue.
The main issue with the above (in terms of publishing) is it uses reconstructions of albedo that rely on computer models (if you need to use a computer model to reconstruct albedo changes in order to estimate climate sensitivity, then why not just use that computer model to directly estimate climate sensitivity?). This introduces a large amount of error into the estimate which is difficult to account for. One way I could get around this is to use vegetation reconstructions (that are based directly on fossil data) to reconstruct albedo. There are decent reconstructions for the LGM and the Holocene optimum. I might have some spare time to work on such a paper over the winter break, but other things take priority.
I don’t really know how to have this dialogue. I don’t have enough knowledge of the details to know if the numbers you’re quoting, and the analysis you’re suggesting, is reasonable or not. So, you’re free to post it, but I can’t see the point in going round in circles when I doubt there will be some kind of sudden epiphany. I still think that if you’re that confident that you should be able to get this published.
“I really doubt that a single study (especially one done in a blog comment) is suddenly going to reduce our uncertainty as much as you claim to have done.”
There are biases in the estimate which cause an overestimation of ECS and also an underestimation of its uncertainty (specifically not taking into account radiative distribution of albedo forcing). However, the sources of bias are in such a way that it would basically shift the entire pdf downwards. So while the lower bound may decrease significantly, the upper bound of that 95% CI will either remain as is or go down slightly. That’s why I think I can conclude that it excludes ECS above 3 C. I wouldn’t use my result to exclude ECS values below 2 C. As for the range being 1.5-4.5 C for a long time, most recent estimates are suggesting that ECS is in the lower half of that interval and nearly all of the recent instrumental based estimates are excluding an ECS above 3 C at the 95% confidence level.
“I also found this paper which considers the Pliocene and Pleistocene and seems to suggest an ECS range that is in the region of 1.5C – 4.5C.”
There is a lot more uncertainty for Paleoestimates that use data before the Pleistocene. There is more error about temperature, about CO2 concentrations, often the effect of CH4 and N2O are not taken into account, and platetechtonics starts to become relevant (this greatly affects the transport of heat). I can’t view the paper right now as it is behind a paywall, but I suspect that it has the same issue of not taking into account the distribution of radiative forcing, which will likely cause an upward bias in the estimates since change in radiative forcing over this time period is more concentrated in polar regions (albedo changes).
I actually don’t think the latter is really true, and – if it is – only just. Also, the whole point of this post is to illustrate that these instrumental estimates are biased low.
I really do think that people who make these kind of claims on blogs need to get published. The people who work on this kind of thing are not idiots. If it is as obvious as you suggest then there are two possibilities. Get it published and those who work on it will recognise this. Or, it’s not as obvious as you think. In my experience, the latter is far more common than the former. Hubris is easy.
“Also, the whole point of this post is to illustrate that these instrumental estimates are biased low.”
I agree. They are biased low. Paleoclimate is biased high.
“I really do think that people who make these kind of claims on blogs need to get published.”
I appreciate that you think I should get published. 🙂
“The people who work on this kind of thing are not idiots.”
I don’t think they are idiots. But I do think there are some flaws in the methodology used for most paleoclimate estimates. Distribution of radiative forcing is a major one; I think that if people do a 2nd order approximation to the Stefan-Boltzmann law as opposed to a first order approximation then that should for the most part resolve things.
I know. You keep saying this. Hence my point. Either it’s true, should be pointed out, and will be accepted. Or, you’re missing something and that the experts have thought of this and have realised that it’s not quite as simple as you seem to think it is. I don’t know which it is, but going in circles on a blog is not going to resolve it.
-1 you should have a go at getting it published, if nothing else you will get feedback from some reviewers who actually work in the field (provided you submit to a decent journal).
-1,
Dikran’s right, you should try. It’s always possible that something has become so standard in a field that those who work in that field just haven’t considered something that seems obvious to someone from the outside. On the other hand, maybe they have and there’s a good reason why it isn’t as obvious as it may seem. Either way, you benefit; you could change the field, or you (all of us, maybe) could learn something.
-1=e^iπ,
One problem would be that LGM boundary-condition experiments in GCMs include Milankovitch forcing yet models with >3K 2xCO2 sensitivity produce LGM-Holocene global temperature difference consistent with estimates based on observational constraints.
-1,
Here’s a link to that paper.
Either the size of the carbon release that triggered the PETM was unfeasibly vast, or ECS / 2x CO2 is about 3C.
Also interesting – but irritating (if predictable) – to note that we’ve got a comment thread filled up with contentious, diversionary blog science in response to a study that appears to provide yet more evidence that low estimates of S are incorrect.
BBD,
Technically, -1 seems to be arguing that he can constrain ECS to 2.5 +- 0.5, which just seems overconfident, rather than unreasonably low.
> I was hoping to have a dialogue.
About Dr. Marvel’s paper or about your pet topic, -1?
-1 has almost the entire paper written up right here. How much more work would it be to publish it? And why the resistance to do so?
Speaking of second order, there’s also a second road to getting published:
https://www.documentcloud.org/documents/2642410-Email-Chain-Happer-O-Keefe-and-Donors-Trust.html#document/p3/a265568
Perhaps it would be best to try Judy’s first. If that works well, there’s always the GWPF – they would publish just about anything that can support claims like “Paleoclimate is biased high.”
Now that’s what I call peer review 😉
@-“Perhaps it would be best to try Judy’s first. If that works well, there’s always the GWPF – they would publish just about anything that can support claims like “Paleoclimate is biased high.”
While they might be attracted by the claim that Paleoclimate is biased high, I doubt that either would welcome something that constrains climate sensitivity to 2.5degC.
I suspect that Mr transcendental/imaginary/irrational has stumbled upon the fact that climate sensitivity is a reified global metric that contains within it a LOT of local/regional variation. For instance the poles warm faster than the equator with a GHG forcing, but the climate sensitivity – ECS or TCR – do not reflect this distribution of climate change.
http://www.drroyspencer.com/2015/12/paris-pow-wow-heap-good/#comment-204146
“Well, if the people I’ve talked to (former military) are correct, the government is sitting on some pretty fantastic technology that would transform the planet, if they would release it, but that is not how the game is played. You can’t have a world where people are not connected to a grid you control.
Now, to add some cred to this Obama ordered the release of anti-gravity technology in 2012. It’s been an open secret for decades the military has this. That’s how B2 bombers have such amazing range. Okay, so let’s consider this. We are supposedly on the brink of catastrophic climate disaster. The US has a technology that would cut aerospace consumption of carbon to the bone, and would drastically cut costs, but this is not even mentioned at the Paris conference. If this crisis was real, the technology would be released around the world and fast-tracked. Words and actions are not adding up at all.”
The basic point is people say stuff. One should at least try to get what they say published in a respectable (hard science) journal, if not that, then any journal for the sake of posterity.
Publish enough loopy ideas, cover the Roulette table as it were, and one of those loopy ideas might be right (but regardless the (hard science) house still has the odds in it’s favor).
We now return you to your regular scheduled programming.
(I so like that TeeVee statement, as in, you are being programmed)
Oh, the paper which is the topic of this discussion does appear to be rather a good one. Thanks.
-1
Thou shalt not excise the long fat tail, or touch it in any way shape or form
this is the first commandment
Thou shalt not shift any central measure below 3C.
this is the second commandment.
Thou shalt not achieve the target of 1.5C by any change in science, like lower sensitivity
this is the third commandment
Thou shalt not publish your findings in any open forum like blogs or arxiv first.
this is the 4th commandment.
Thou shalt not use the insight of new papers, to remind people of their prior positions.
This is the 5th commandment
And dont forget the axiom that any comment can be construed as off topic… in true scotsman like manner.
What, like the insight that so-called ‘observationally’ derived estimates of S are probably biased low?
Steven Mosher’s mask seems to have slipped.
Welcome back, Mosher.
“Thou shalt not excise the long fat tail, or touch it in any way shape or form
this is the first commandment”
Actually quite a bit of good work by mainstream climate scientists has been done in recent years to diminish the long fat tail.
“Thou shalt not shift any central measure below 3C.
this is the second commandment.”
NASA GISS Model E: 2.7C
We could go on…
@ Paulskio-
“One problem would be that LGM boundary-condition experiments in GCMs include Milankovitch forcing yet models with >3K 2xCO2 sensitivity produce LGM-Holocene global temperature difference consistent with estimates based on observational constraints.”
You say that yet Annan and Hargreaves 2013 get a median estimate of ECS of 1.7 with a 95% CI of 1.2 C – 2.4 C. Perhaps these studies you are referring to are overestimating LGM-Holocene temperature differences or are not taking into account the distribution of radiative forcing.
@ BBD-
“Either the size of the carbon release that triggered the PETM was unfeasibly vast, or ECS / 2x CO2 is about 3C.”
The uncertainty of temperature changes during PETM and the uncertainty of CO2 changes during PETM are large enough that you can’t exclude lower ECS values. Not to mention there is significant coverage bias with such a temperature reconstruction and often such estimates ignore the CH4 and N2O temperature feedbacks. Pre-Pleistocene paleoclimate data isn’t really accurate enough to be able to well constrain climate sensitivity.
@ ATTP-
” Technically, -1 seems to be arguing that he can constrain ECS to 2.5 +- 0.5, which just seems overconfident, rather than unreasonably low.”
Not quite. I think that the estimate 2.5 +/- 0.5 C is biased high due to not taking into account the fact that the albedo changes primarily occurred in polar regions; so each percentile in the probability distribution likely has an upward bias. ECS probably lies somewhere between 1.5 C and 3.0 C, but until I properly account for the effect of the distribution of the albedo changes (say using vegetation reconstructions), it is difficult to say where the lower bound of the confidence interval is.
-1
Reference please.
Do you mean A&H (2012)? The central estimate is ~2.5C with a range of 1 – 4C (90%).
-1=e^iπ,
You say that yet Annan and Hargreaves 2013 get a median estimate of ECS of 1.7 with a 95% CI of 1.2 C – 2.4 C. Perhaps these studies you are referring to are overestimating LGM-Holocene temperature differences or are not taking into account the distribution of radiative forcing.
You have it the wrong way around. Annan & Hargreaves’ calculation is based on a simple division of global average temperature by global average forcing. It doesn’t take into account the distribution of radiative forcing. They go on to point out that it isn’t robust essentially because of this efficacy issue.
The LGM model runs absolutely do take into account the spatial distribution of radiative forcing. I’m using the Annan & Hargreaves global average LGM temperature estimate (same as was used for that calculation) to define consistency between the model temperature change and observational constraints.
That’s a point. Presumably the PMIP runs are essentially taking into account what -1 is claiming is being ignored.
PMIP my modulz.
Steven Mosher:
I’m not sure where you’re going with that. It makes no sense to run the world in utter chaos. Recommending peer review simply makes sense. Passing peer review by actual experts means the paper is a reasonably formed argument, and merely the first step to take. Pretty much everyone here is saying… “Go do it” to -1.
So… Step 1… Publish. Talk to real experts. While there’s plenty of people that I know and respect here, I’m pretty sure they aren’t experts in this.
After it is published, there will be a slow process by which real scientists will descend on the paper and see what they can do with it.
FYI, You’ll be happy to know we don’t use blogs with people purporting to be experts to design oil refineries, or space shuttles for that matter. Might work… but I dunno, it doesn’t seem wise to me. While we complain about failures is such places, we try to learn from them and move on. Explaining to people after the fact that, “Some guy on a blog told me it was so.” would probably be met with laughter.
thou shalt not suggest nuclear
@BBD-
“Reference please.”
All you have to do is look at the crazy large uncertainties associated with CO2 levels and temperature during PETM. As for asking me to show that no PETM estimates exist that exclude low climate sensitivity, that is like asking me to prove that the flying spaghetti monster does not exist. All I can comment is on the estimates that I have seen. If you think that specific PETM estimates exclude low ECS values (say 1.5 C – 2.0 C) at the 95% confidence level then please provide them.
“Do you mean A&H (2012)? The central estimate is ~2.5C with a range of 1 – 4C (90%).”
No, 2013. http://www.clim-past.net/9/367/2013/cp-9-367-2013.html
@ Paulskio
“You have it the wrong way around. Annan & Hargreaves’ calculation is based on a simple division of global average temperature by global average forcing. It doesn’t take into account the distribution of radiative forcing. They go on to point out that it isn’t robust essentially because of this efficacy issue.”
Yes, this is true. However, as there was more change in radiative forcing in polar regions compared to equatorial regions from the LGM to the Holocene, not taking into account the distribution of radiative forcing will cause an overestimate of ECS. There could be other reasons why Annan and Hargreaves got such a low ECS value (such as a high value of estimated forcing change) but not taking into account distribution of radiative forcing is unlikely to be one of them. Anyway, my main point for bringing up the Annan and Hargreaves result is that I do not think that the Paleoclimate data excludes a low (1.5 C to 2.0 C) ECS, but it might exclude an ECS above 3 C.
@ ATTP-
“-1,
Here’s a link to that paper.”
Okay, I read the paper. Most of it is pretty well done. There are 2 issues though.
1. Doesn’t take into account CH4 or N2O. I argued in that post on Juddith Curry’s blog that this causes an overestimation of ECS of about 16%.
2. How the paper treats coverage bias is questionable. On the 3rd page it claims that the ratio of surface sea temperature change to global mean temperature change is about 0.66. But that ratio is generally what you would get if you had primarily equatorial sea surface temperature data. Since the data used contains many mid latitude sea surface temperature observations, the ratio is likely higher than 0.66. However, the results of figure 4 suggest that the method used to estimate global temperature change and treat coverage bias had a ratio that was less than 0.5. So somehow the global average surface temperature change was more than twice the sea surface temperature change, even during the Pliocene.
The results of figure 4 f-h suggest that the change in sea surface temperature for a given change in radiative forcing was between 0.14 and 0.58 K/(Wm^-2) (95% CI over all 3 sea level reconstructions). If I use a factor of 0.66 to get global temperature change and the fact that a doubling of CO2 corresponds to 3.71 W/m^2 then this suggests that ECS is somewhere between 0.78 C and 3.23 C (95% CI). If one took into account the effects of CH4 and N2O then this would suggest that the 95% CI excludes an ECS greater than 3 C.
The 1.5 K – 4.5 K range reported by the paper isn’t actually a range they get from their data, but a range that previous papers had suggested.
Your link was intelligible until Judith started talking. I sense serious dissonance.
Unless one is trying to sow dissent.
One more thing. The 0.66 value was generally based on earlier estimates of temperature changes over the Pleistocene which were about 25% higher than that suggested by Annan and Hargreaves 2013 (which is the best estimate I know of). This suggests that what I wrote in my last comment should be about 25% lower. If you add the 16% from not taking into account CH4 and N2O then the confidence interval becomes [0.49,2.03] C. Obviously the true uncertainty would be higher than suggested by this confidence interval though.
Oh sorry. 25% higher corresponds to 20% lower. My mistake. The confidence should be [0.54,2.23] C instead.
Houston ,we ‘ave a problem.
Recent studies have shown lowered climate sensitivity.
What! Rubbish. Thump the table dispute the studies. Who did them anyway? Oh that guy,tied in to Curry. That guy , global oil shill. Those guys? They used to belong to us.
What’s going on.
Has to be wrong . Call in ghost busters. I mean Way. No, Way and Cowtan. I mean Marvel,Schmidt et al.
Houston, we ‘ave a solution.
Found a glitch (check spelling) and fixed it.
When a tree falls out in space can anyone hear it?
I prefer lukewarm credos. They’re more positive. It does not say what you ought not to do, only what you ought. Here’s the first one:
I – Thou shalt rip off your shirt.
Writing “thou shalt not” listicles is one good way to do so. CAGW alarmists. Dogma. You know the drill.
-1,
I guess I’ll repeat the point again. You’re throwing out numbers and arguments for why climate sensitivity could be low. Well, yes, I agree we don’t rule out these low numbers, but if you really want to present a convincing argument for ruling out an ECS above 3C, then I really do think you have to get published, both because that adds credibility to your own ideas and because you may learn something during the review process and through the responses.
-1
No, it is asking you to back up the assertion you made earlier. Something you are not able to do. So I will ignore your unsupported argument from assertion and mark you down as a polemicist.
-1
And you are misrepresenting A&H (2013) by selective quotation, exactly as Nic Lewis did, which puts you in the same bin as NL when it comes to reliability on this topic:
I think the main conclusion to take away is that not accounting for efficacy differences in energy balance sensitivity calculations will mean underestimated uncertainty. Even if we assume uncertainty in historical net efficacy is symmetric, due to the nature of the energy balance equation, higher sensitivities become more likely to a greater extent than lower sensitivities.
However, forcing efficacy is believed to be highly model-dependent so we definitely need this kind of study applied to more models in order to find robust patterns, explanations, possibility of observational support, and see if we can apply some constraints to efficacy.
There seem to be some nuances to the results. Although the instantaneous RF and ERF efficacy adjustments return similar final sensitivities, it appears there are different routes taken to arrive there. The iRF result is dominated by large (~ 1.5) efficacy of aerosol forcing. However, a major part of aerosol forcing occurs due to aerosol-cloud interactions in a moving troposphere. Therefore the result is probably more reflective of an erroneously small aerosol forcing calculation rather than what’s typically understood as efficacy.
In the ERF calculation the result appears to be dominated by low efficacy (~ 0.8) of historical GHGs. Aerosol forcing is actually determined to have efficacy lower than unity, which would push the sensitivity calculation towards lower numbers. Given that other GHGs should have a similar spatial forcing distribution to CO2, this doesn’t obviously agree with the view that spatial pattern is the important factor.
I see that the GISS-E2-R GCM is used. Is it still the version used in the CMIP5, which underestimates the water vapour shortwave absorption? See
http://www.nature.com/nature/journal/v528/n7581/full/nature15770.html
This may affect the results. Or is the GISS-E2 model already updated?
Uli,
It’s the same model, I think. That paper, however, is illustrating a feedback issue and appears to be showing that in some models they underestimate how much shortwave energy is absorbed by water vapour and hence over-estimate how much is transported by latent heat (over-estimating precipitation). My impression was that resolving this would simply change the distribution between SW and latent heat, but not change the overall feedback response. If so, it may not influence the work here. I’m not certain of this, though.
@Paulskio: Following up on your post, I haven’t read the paper but my guess is that timing differences among the forcings are contributing to the result. The energy balance method only looks at forcings at the beginning and end of the period of interest, so timing differences among warming and cooling forces will skew the results. In particular, aerosol cooling has been front-end loaded relative to GHG since sulfur emissions peaked around 1970 and haven’t changed much since, while GHG forcing has continued to increase.
I think increasing the water vapour shortwave absorption would increase the water vapour feedback and so the climate sensitivity.
The models in the upper part of Figure 4 show on average a higher climate sensitivity then the models with a very low water vapour shortwave absorption.
Also they say at the and of the methods section that removing the bias reduces the precipitation change per degree of warming (of the ensemble mean) by 38%. But the reduction of the precipitation change is only 25% if not normalized by surface warming. This looks for me as if there were a factor of 0.75/0.62=1.21 more surface warming after the removing of the bias. This factor is for the ensemble mean. It may be greater for the outlier the GISS-E2 model.
Maybe the influence of the water vapour shortwave absorption on climate sensitivity could be checked first on EBMs.
Uli,
But there is an anti-correlation between Short-wavelength anomaly (SWA) and latent heat released by precipitation (LvP).
So, as I understand it, the adjustment would increase SWA and reduce LvP, so it’s not clear that the net-feedback response would be changed.
Yes the anti-correlation between SWA and LvP is the point. The adjustment would increase SWA, that is the SW absorption of the atmosphere, and so increase the albedo reduction per surface warming. That would increase the climate sensitivity. The LvP change is important for the precipitation, of course, but the increase in the SWA is important for the temperature change.
Uli,
I was under the impression that they cancelled; less heating via release of latent heat is compensated for by more heating via shortwavelength absorption. You may be right, though. I’ll have to give it more thought.
This may be approximately true for the atmosphere. But the less heating by release of latent heat is compensated by less cooling of the surface by evaporation. Of course the surface gets also less solar radiation, that is additionally absorbed in the atmosphere.
Most clear is the picture at the TOA, see figure 1. SWd at TOA is not chanced. But if SWu goes down, LWu must go up. LvP has no direct influence at the TOA.
Uli,
Okay, I think I see what you’re saying. So, would this explain why GISS-E2 has a relatively low climate sensitivity?
Yes, it could, I think.
I tried a correlation between the dSWA/dPW, clr-dSWA/dT and the inverse climate sensitivity. And it is about -0.6 to -0.7 explaining about 40% of the variability of the spread.
Assuming the HadGEM2-ES is right with dSWA/dPW and clr-dSWA/dT the correlation would suggest a climate sensitivity for corrected GISS-E2 3.4 to 4.1 instead of 2.1 to 2.3.
Of course this correlation could not replace a real test with an improved SW radiation scheme.
The article says, that there are updates made to the GISS radiation scheme that probable improve the model and refer to
Pincus, R. et al. Radiative flux and forcing parameterization error in aerosol-free clear skies. Geophys. Res. Lett. 42, 5485–5492 (2015)
@ATTP –
Fair enough, with respect to publishing.
@BBD-
I’m not sure how mentioning the estimate of climate sensitivity by Annan and Hargreaves is misrepresenting them. 2.5 C was the result of their 2012 paper, not their 2013 paper, they just mention the result of the 2012 paper in their 2013 paper.
BBD,
As James Hansen may be learning by way of Naomi Oreskes, and stirred (not shaken) by Judith Curry. Sou has the story: http://blog.hotwhopper.com/2015/12/judith-curry-plays-nuclear-politics.html
-1
Oh stop being disingenuous. It’s tiresome. Read the words. I’ve emboldened them to assist you:
That’s why A&N go on to reference their previous paper and the central estimate therein. You aren’t stupid, so you are taking the piss.
BG
God, EnergyBall. I don’t think I can face another EnergyBall thread right now 😉
So much crap, so much vehemence, so little concern for the facts.
BBD: On Nuclear…
BBD,
Han Solo: How are we doin’?
Luke: Same as always.
Han Solo: That bad, huh?
AOM
Been saying it for years but nobody wants to know. That exact argument. And I’ve been called some pretty ugly names for daring to point out the facts.
* * *
BG
🙂
AOM, I cribbed from that JPM document for the convo at Hotwhopper, nice find.
BBD, you know, if things were better than normal I’d probably be more worried than less ….
@ BBD – I don’t disagree with Annan and Hargreaves, so I’m not sure why you are calling me disingenuous. The 1.7 C is simplistic and certainly could have all sorts of structural uncertainty. 2.5 C is what I estimated earlier (see 3rd comment in comment section), which is in agreement with the Annan and Hargreaves 2012 result.
Anyway, back to an earlier point of discussion, do you agree or disagree that Paleoclimate data excludes an ECS below 2 C at the 95% confidence level. If your answer is yes, could you please provide why you think that. Also, do you agree or disagree that Paleoclimate data excludes at ECS above 3 C at the 95% confidence level. If your answer is no, could you please explain why comment #3 and this comment (https://andthentheresphysics.wordpress.com/2015/12/15/forcing-efficacy/#comment-69440) are wrong?
You are disingenuous because you use every opportunity to push for lower sensitivity values based on a mix of argument from assertion and selective quotation. You know it and I know it, so you can stop the messing around.
Yes, eg. Rohling et al. (2012).
No, eg. Rohling et al. (2012).
To paraphrase the Iron Duke, publish or be damned.
BBD,
Rohling et al.’s 95% lower bound is 1.1K.
Yes, the 2.2C lower bound in Rohling et al. (2012) is the lower bound of the 68% confidence interval.
And you are both correct.
Perhaps -1 would like to explain why his preference for a figure at the bottom of the range is justified in the light of a most likely value near the middle of the range.
In a published study.
BBD,
Also, as Dikran has pointed out, an interesting aspect of Nic Lewis’s work is that it largely rules out an ECS below 1C, despite potential issues related to the choice of prior.
@ BBD
“Yes, eg. Rohling et al. (2012).”
This is actually one of the studies I was responding to when I wrote: http://judithcurry.com/2015/04/22/bjorn-stevens-in-the-cross-fire/#comment-697545.
Estimates in Rohling et al. prior to the Pleistocene have high uncertainty and don’t take CH4 or N2O into account. Estimates that use Pleistocene or Holocene data ignore Milankovitch cycles and the distribution of radiative forcing.
“Perhaps -1 would like to explain why his preference for a figure at the bottom of the range is justified in the light of a most likely value near the middle of the range.”
Because I think that an ECS above 3 C is excluded by both instrumental data and Paleoclimate data at the 95% confidence level. The reasons for excluding it at the 95% confidence level for Paleoclimate data are given in this thread. I could go over reasons regarding the instrumental record if you want.
And I think you are overconfident. What is more, you are at odds with the majority of the palaeoclimate community, which is further reason to question the reliability of your conclusions.
The simple test is for you to write it up and submit to a decent journal. Please let us know how your monograph is received.
@ BBD – “And I think you are overconfident.”
But the nature of the uncertainty that is not being taken into account in the 3rd post suggests that it is an overestimate. So other uncertainty sources would not increase the upperbound of the 95% confidence interval.
“What is more, you are at odds with the majority of the palaeoclimate community”
Perhaps there is an issue of confirmation bias in the paleoclimate community to try to support the status quo. Like the paper ATTP linked to in this thread might be a good example. The results of the paper do not indicate a 1.5-4.5 C interval and arguably suggest values at the lower half of the range, yet the conclusion uses the consensus position of 1.5-4.5C rather than giving a confidence interval based on the results of the paper. Why is this? Confirmation bias may also explain some of the discrepancy between climate models and instrumental data.
Really? Take for example Hansen et al. (2013):
What if you were mistaken about other things as well? For example, the degree to which you discount CO2 as the primary driver of the slow Cenozoic cooling trend?
@ BBD –
“Really? Take for example Hansen et al. (2013):”
I was referring to Rohling et al.. A Hansen et al. paper that tries to take CH4 and N2O into account doesn’t debunk that.
But while on the topic of the Cenezoic estimate by Hansen et al. 2013:
1. The reconstructed temperatures are based on scaling to an LGM cooling of 4.5 C. Annan And Hargreaves suggest LGM cooling was 4.0 C +/- 0.8 C. Thus Hansen et al. are overestimating warming by 12.5% and have a scaling uncertainty of +/- 20% that they are not taking into account.
2. They reconstruct temperatures using a polynomial fit, but do not take the uncertainty of this fit into account.
3. Coverage bias is extremely large and not taken into account. Take the LGM where there is far better coverage of global temperatures. Schneider von Deimling et al. 2006 got a best estimate of LGM cooling of 5.8 K, where as Schmittner et al. got a best estimate of 3.3 K (and I think the result of Annan and Hargreaves resolved the issue of coverage bias of LGM for a best estimate of 4.0 K). Easily you have a coverage bias of like +/- 30% for that Cenezoic reconstruction.
4. Measurement uncertainty of deep ocean data used to reconstruct Cenezoic not taken into account.
5. Hansen rounds 3.71 W/m^2 of forcing for a doubling of CO2 to 4 W/m^2 of forcing for no good reason. This results in a 7.8% upward bias in the estimate.
6. Hansen ignores the fact that albedo changes are primarily concentrated in polar regions, so will have a stronger impact on global temperatures than an equivalent change in radiative forcing for greenhouse gases.
7. The change in the positions of the continents over the last 50 million years have slowly moved to an orientation that reduces the transfer of heat from equatorial regions to polar regions. This effect results to a colder earth even if there is no change in radiative forcing due to the Stefan-Boltzman law. This effect is not taken into account by Hansen et al.
But, for the sake of argument, let’s give Hansen et al. the benefit of the doubt and pretend that issues 2, 4, 6 and 7 don’t exist.
Hasen et al. get a temperature change between 50 million years ago and the Holocene of 14 C. The scaling bias and uncertainty suggest that this should actually be 12.4 +/- 2.5 C.
Hasen suggests that 50 million years ago CO2 was between 750 ppm and 1500 ppm. Let’s just give Hansen the benefit of the doubt and pretend this is the case. If we take Holocene CO2 concentrations to be 280 ppm then the change in radiative forcing due to additional CO2 is between 5.27 W/m^2 and 8.98 W/m^2. If we take Hansen’s claim that only 75% of GHG radiative forcing change was due to CO2, then GHG radiative change is between 7.03 W/m^2 and 11.97 W/m^2. If we subtract the 1 W/m^2 due to the increase in solar irradiance and add the 5 W/m^2 of albedo forcing (Where is the uncertainty on this value?) then the change is between 11.03 W/m^2 and 16.97 W/m^2.
This suggests that the ratio of temperature change to forcing is between 0.583 and 1.351 C/(W/m^2). If you include the +/- 30% coverage bias then this becomes between 0.408 and 1.756 C/(W/m^2). Based on 3.71 W/m^2 for a doubling of CO2, this suggests that climate sensitivity is between 1.51 and 6.51 C.
And that’s still an underestimate for reasons mentioned earlier and that is still underestimating uncertainty. So you can’t exclude low ECS values (between 1.5 C and 2.0 C) based upon the Cenezoic result by Hansen et al. 2013.
And remember that when you are trying to add up independent estimate of the same thing, the weight of each estimate should be proportional to the inverse of the square of the standard error of the estimate. So given that the uncertainty of the Cenezoic estimate is about +/- 2.5C, where as the uncertainty of an estimate using instrumental data such as by Nic Lewis is +/- 0.55 C (http://climateaudit.org/2015/03/19/the-implications-for-climate-sensitivity-of-bjorn-stevens-new-aerosol-forcing-paper/), the instrumental data should have about 20 x as much weight as the Cenezoic data.
The Nic Lewis uncertainty I referred to above was 0.575 C not 0.55 C. Sorry for the typo.
@ anoilman
“Is there a particular reason that you are going to the public and blogs with your crazy ideas first?”
To test them in the free marketplace of ideas. You guys (especially BBD) seem so confident that Paleoclimate data excludes lower ECS values and do not exclude higher ECS values. If you guys are so confident then you should be able to debunk my arguments regarding Paleoclimate estimates. I’m a flawed human, I admit I could be wrong. But I don’t really see how I am wrong in this case about Paleoclimate estimates.
You’ve been involved in these kind of discussions long enough to know that this is nonsense. Right?
The free market of ideas thinks that Homeopathy is a valid medical treatment worth billions.
Scientific rigour tells us that homeopathy is bunk.
Which do you think is correct, eyepie?
I don’t dispute that ECS / 2xCO2 might be 2.5C. It might also be 2.8C or 3.2C. None of this makes much difference from a policy standpoint and from such is arguably only of academic interest now.
I do dispute your overheated self-confidence though. For example:
Annan & Hargreaves might be an underestimate. It is certainly something of an outlier. But in your mind it is certain that A&H is exact and the overestimation is in Hansen ea. You might be wrong.
So say you, which is why I’d be interested to see how your stack of assertions and assumptions stood up to expert review. As you are doubtless aware, it is virtually impossible to argue sensibly with stuff like this.
So say you, but the majority view among experts is that CO2 was the predominant driver of Cenozoic cooling. What’s more, I can’t find any references that support this claim.
Once again, the bias is yours. From Hansen & Sato (2012):
And so it goes, on and on: assertions, assumptions, biases etc.
Submit it for publication or pull the plug. Enough is enough.
If you guys are so confident then you should be able to debunk my arguments regarding Paleoclimate estimates.
I debunked them days ago: GCM simulations of the LGM incorporate spatial Milankovitch forcings and produce global average PI-LGM temperature changes consistent with the Annan & Hargreaves reconstruction while having 2xCO2 sensitivity > 3ºC.
You haven’t yet provided an answer to this.
@ BBD –
“None of this makes much difference from a policy standpoint and from such is arguably only of academic interest now.”
Of course it makes a difference from a policy perspective. It would change to optimal level of CO2 emission taxation, for example.
“It is certainly something of an outlier.”
Then what do you call Schmittner et al. with its best estimate of 3.3 K? Schmittner et al. as well as Annan and Hargreaves use more data than previous studies to get their estimates. The main reason why earlier estimates were biased high was because they primarily used land-based measurements and land has a higher sensitivity than ocean.
“So say you, which is why I’d be interested to see how your stack of assertions and assumptions stood up to expert review.”
+/- 30% is roughly the coverage bias that occurred for the Pleistocene when there was more data available, so I don’t see why the coverage bias for the reconstructions of the Cenozoic would be less when the spatial coverage is less. I admit it would be more ideal if those advocating the results of Cenozoic values tried to quantify their coverage bias with a decent methodology.
“the majority view among experts is that CO2 was the predominant driver of Cenozoic cooling.”
CO2 being a major factor doesn’t mean that there are not other relevant factors.
“Once again, the bias is yours.”
So you deny that the increase in radiative forcing for doubling CO2 is 3.71 W/m^2? 3.71 W/m^2 is the IPCC’s position and the mainstream scientific position.
@Paulskio
“GCM simulations of the LGM incorporate spatial Milankovitch forcings and produce global average PI-LGM temperature changes consistent with the Annan & Hargreaves reconstruction while having 2xCO2 sensitivity > 3ºC.”
Just because you can construct a model that are consistent with observations does not mean that the parameters of that model are within the 95% confidence interval of what the data suggests.
What you should do is look for the most probable parameters based upon observations and then construct confidence intervals for those parameters.
-1,
Except your observations are meaningless without some kind of model.
> [Y]our observations are meaningless without some kind of model.
The model is some kind of credo, AT:
V – Thou shalt rope-a-dope from one comment (thread) to the next.
“Except your observations are meaningless without some kind of model.”
Yes, you need a model. For example, I used a simple linear regression model in the 3rd comment.
Flawed. As is well understood. If you wish to discuss the reasons why, we can do so.
No. RTFR before being wrong about it again. Here’s the quote you just blanked:
Read the words.
* * *
Speaking of confidence, here are a few random examples of why I have so little in you.
That’s simply not true. See eg. Hansen13 section 3.
And I think this is mistaken:
Hansen first used the updated benthic istope analysis from Zachos (2008) in Hansen et al. (2008) eg. p. 221:
So I suspect that if the 14C difference is mentioned anywhere in H13, it is still the difference between the HCO and the LGM, not the HCO and the Holocene.
The next quote from you introduces a bias towards your preferred result (the substitution of Holocene for glacial CO2ppm was particularly glaring but multiplying the albedo forcing by 500% was worse):
Here is the text from H13. Compare and contrast:
This fast-and-loose with the facts was a serious problem with your previous appearences here. It does not inspire confidence.
Here’s you, again:
Now, let’s look at what was actually written in Hansen 13:
You have misrepresented Hansen up and down this thread, just as you have done on previous appearences in comments here. It is no wonder that there’s a question mark over how serious you really are.
@ BBD-
“No. RTFR before being wrong about it again. Here’s the quote you just blanked:”
None of what you wrote disputes the fact that Hansen uses 4 W/m^2 per CO2 doubling instead of 3.71 W/m^2. How do you think Hansen goes from a sensitivity of 0.75 C/(W/m^2) to an ECS of 3 C? By using a factor of 4…
“That’s simply not true. See eg. Hansen13 section 3.”
Read it. Measurement uncertainty is not included. Not that I think it is a large source of error (compared to other errors such as scaling uncertainty), but it isn’t included.
“And I think this is mistaken:”
How? From Hansen:
“Our first estimate of global temperature for the remainder of the Cenozoic assumes that ΔTs=ΔTdo prior to 5.33 Myr BP, i.e. prior to the Plio-Pleistocene, which yields a peak Ts of approximately 28°C at 50 Myr BP”
Holocene global temperature is 14 C. 28 – 14 = 14.
“So I suspect that if the 14C difference is mentioned anywhere in H13, it is still the difference between the HCO and the LGM, not the HCO and the Holocene.”
If you are correct (which I don’t think you are), then this just lowers the temperature change between HCO and the Holocene to 10 C, which means that the resultant climate sensitivity estimates I mentioned earlier should be even lower.
“but multiplying the albedo forcing by 500% was worse”
From Hansen “For the sake of simplicity, we use the linear relation in Hansen et al. [5] and electronic supplementary material, figure S4; thus, 5 W m−2 between the LGM and ice-free conditions and 3.4 W m−2 between the LGM and Holocene…. We subjectively estimate an uncertainty of approximately 20%.”
It looks like I used the wrong forcing though as I was comparing the HCO with the Holocene. So I should have used an albedo forcing between 1.28 W/m^2 and 1.92 W/m^2. If I redo my calculations, then this results in ECS between 1.99 C and 9.83 C. Thank you for inadvertently pointing this out.
“Now, let’s look at what was actually written in Hansen 13:”
All your quotation shows is that Hansen does indeed neglect the change in temperature distribution effect.
-1
No, it explains why. As you are concerned about the omission of non-CO2 GHG radiative terms I thought you would favour a CO2e value.
I don’t think the isotopic analysis carried out by Zachos (Zachos et al. 2001) is the main source of measurement uncertainties. I think the uncertainty arises from the interpretation of the proxy which is why I brought up H13 section 3.
Looks like I’m wrong, given the text.
It shows that evidence for such an effect is lacking, not that it was ignored.
“No, it explains why.”
… No it doesn’t. They use CO2 being only 75% of GHG radiative forcing to go from 3 C to 4 C. 4 W/m^2 is used to go from 0.75 to 3 C. 3.71 W/m^2 is used nowhere.
“I think the uncertainty arises from the interpretation of the proxy which is why I brought up H13 section 3.”
Yes, but that isn’t measurement error. It is scaling error.
“It shows that evidence for such an effect is lacking, not that it was ignored.”
It shows both. Would you prefer I use the word neglected instead of ignored?
Not quite sure what you are saying here.
Goalposts move. And what error are we talking about here?
‘Neglected’ would be as incorrect as ‘ignored’, so no.
“Not quite sure what you are saying here.”
Hansen et al. use a radiative forcing per doubling of CO2 of 4 W/m^2 instead of 3.71 W/m^2 and this results in an upward bias by 7.8% in ECS estimates.
-1,
“Hansen et al. use a radiative forcing per doubling of CO2 of 4 W/m^2 instead of 3.71 W/m^2 and this results in an upward bias by 7.8% in ECS estimates.”
Mhm, 4W/m^2 to 3.71W/m^2 would be a inflation to ECS, so there would be a downward bias by 7.8% in ECS estimates, because e.g 10K increase caused by 3.71 or 4 is, that Change in respect to one Watt/m^2 is in 4W/m^2 (10/4) less then as in 3.71(10/3.71).
@ Christian –
Look, this is simple math. First Hansen et al. estimate the ratio of temperature change to forcing change, then they multiply it by the change in forcing due to a CO2 doubling to get ECS. If you increase the change in forcing due to a doubling of CO2 then you increase the estimate of ECS. I admit that the total forcing also depends on the choice of forcing due to a doubling of CO2, but the forcing change also depends on other factors. Thus the net effect is an overestimate of ECS.
-1=e^iπ,
Just because you can construct a model that are consistent with observations does not mean that the parameters of that model are within the 95% confidence interval of what the data suggests.
Then you need to point to specifics on why the model parameters fall outside this confidence level, otherwise you have no support for your 3C upper limit.
-1
As Hansen explains, in paper after paper, the 4W/m^2 forcing for 2xCO2 includes CO2 and non-CO2 GHG feedbacks (CH4; N2O) which scale with CO2. So it represents a step towards improvement of the estimate, not a ‘bias’.
This has been pointed out several times above and I’m surprised that you haven’t yet understood what is going on. If you can’t understand what Hansen is doing, you can’t very well go around claiming that he’s doing it wrong, can you?
-1=e^iπ says:
December 19, 2015 at 6:01 am
“@ anoilman
“Is there a particular reason that you are going to the public and blogs with your crazy ideas first?”
To test them in the free marketplace of ideas. You guys (especially BBD) seem so confident that Paleoclimate data excludes lower ECS values and do not exclude higher ECS values. If you guys are so confident then you should be able to debunk my arguments regarding Paleoclimate estimates. I’m a flawed human, I admit I could be wrong. But I don’t really see how I am wrong in this case about Paleoclimate estimates.”
No… That’s not what I said, this is what I said;
“Recommending peer review simply makes sense. Passing peer review by actual experts means the paper is a reasonably formed argument, and merely the first step to take. Pretty much everyone here is saying… “Go do it” to -1.””
There’s no free market of ideas. Unless you include the hair salon down the road. In fact I’m reasonably certain there aren’t any experts in this material here, although many know it well. (That includes you. You are not an expert.)
[Chill. -W]
@ Paulskio –
“otherwise you have no support for your 3C upper limit.”
I do have a basis. See earlier comments which exclude ECS values above 3 C based on paleoclimate data. For example, I fit the regression equation in the 3rd comment to empirical data and obtain a 95% CI that excludes ECS above 3 C.
@ BBD –
“As Hansen explains, in paper after paper, the 4W/m^2 forcing for 2xCO2 includes CO2 and non-CO2 GHG feedbacks (CH4; N2O).”
Wow I can’t believe you are still trying to argue this point… You are mixing up 4 W/m^2 which was used as the radiative forcing change due to a doubling of CO2 with 4 C climate sensitivity which was used after taking into account non-CO2 GHG feedbacks. 4 C = 0.75 C/(W/m^2)*4 W/m^2 / 0.75; that’s how Hansen et al get the sensitivity of 4 C.
“If you can’t understand what Hansen is doing, you can’t very well go around claiming that he’s doing it wrong, can you?”
The one who doesn’t understand is you, if you think that the basis for using 4 W/m^2 as the radiative forcing change due to a doubling of CO2 is because of non-CO2 GHGs.
[Snip. -W]
-1 What I think people are telling is that it would be better for you to publish your work or go find some scientist you can collaborate with to help you get it published than to let your idea go through review on this blog.
Many thaks for the link to Marvel’s blog– she is wonderfully candid.
Hansen et al. (2005)
Thou Shalt not watch Star Wars with an Astrophysicist sitting next to you.
Scene 1: “Notice how how you can see the planet, but not the back of the ship? The lighting is all wrong.”
Things went downhill from there…
Physicists, always have to ruin everything with ‘reality’ and all that crap. They are sooo annoying. I can see why deniers set on them. 🙂
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-1=e^iπ says: I can’t view the paper right now as it is behind a paywall
If you don’t have access to all the Journals, how can you have done any serious lit review?
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