by Judith Curry
Circa 2003-2005, we had the “hockey wars”. In 2005-2006, we had the “hurricane wars”. It looks like this is the season for “cloud wars.”
Cloud wars
Stephanie Pappas at Environment on msnbc.com provides a good overview of the summer cloud wars:
Call it the Cloud Wars. In the largely political debate over global warming, the role of clouds in the climate system is a perennial topic of argument. Basic research — such as a recent early investigation of the effect of cosmic rays on cloud formation — gets taken out of context, used to support arguments far beyond its scope. Climate blogs blow up with angry back-and-forth banter. As soon as it simmers down, another controversial paper restarts the cycle yet again.
The paper provides good, basic background information on the controversies surrounding the impacts of clouds on climate.
The various climate “wars” are characterized by papers on a timely topic receiving substantial attention in the MSM and the blogosphere, often with a “windshield wiper” effect (Revkin’s term) going back and forth with each paper from opposing sides in the debate. Pappas’ article reflects journalistic maturity in dealing with “wars.” Most of the papers in these wars are not of fundamental scientific importance, in the sense that they will stand the test of time and garner a substantial number of scientific citations [the CERN papers will certainly stand the test of time]. Rather, most of these papers achieve a brief period of fame via press release, blogospheric flame wars, and MSM attention, which dissipates fairly quickly in most instances, unless the the paper becomes immortalized by the IPCC (e.g. MBH).
Cloud radiative effect
There is a new paper by Richard Allen on the cloud radiative effect, which is a solid contribution to the literature. Before discussing that paper, I would to clarify some misunderstandings surrounding “cloud forcing” and “cloud feedback.” This very common confusion was evident in the original WUWT post on this subject (see the comments in that thread).
For those of you that are confused by the terminology and can handle partial derivatives, I recommend Chapter 13 from my text Thermodynamics of Atmospheres and Oceans, specifically 13.4 on Cloud Radiation Feedback. Here is an attempt at an excerpt, minus the equations:
Changes in cloud characteristics induced by a climate change would modify the radiative fluxes, thus altering the surface and atmospheric temperatures and further modify cloud characteristics. The feedback between surface temperature, clouds, and the Earth’s radiation balance is referred to as the cloud-radiation feedback.
To the extent that the net radiative flux at the top of the atmosphere is linearly related to cloud fraction, the sensitivity term can be related to a parameter called the cloud-radiative effect. [Footnote: The term “cloud forcing” is typically used to refer to the cloud-radiative effect. We believe that the word “force” is a misnomer for this effect.]
The cloud-radiative effect is defined to be the actual radiative flux (which depends on cloud amount) minus the radiative flux for cloud-free conditions, all other characteristics of the atmosphere and surface remaining the same. The values of the cloud-radiative effect are negative for cooling and positive for warming. The cloud-radiative effect is most often defined in the context of the net radiative flux at the top of the atmosphere, although the cloud-radiative effect can also be defined in the context of the surface radiative flux. In addition, we can separate the cloud-radiative effect into longwave and shortwave components.
The cloud-radiative effect provides information on the overall effect of clouds on radiative fluxes, relative to a cloud-free Earth. The cloud- radiative effect can be evaluated exactly using a radiative transfer model, where fluxes obtained from a calculation for a cloud-free but otherwise exactly similar atmosphere is subtracted from a calculation for the actual cloudy atmosphere. Determination of the cloud-radiative effect from satellite is accomplished by separating the clear from the cloudy observations. In spite of the simplicity of evaluating the cloud-radiative effect at the top of the atmosphere using satellite data, such evaluations are somewhat ambiguous. Ambiguities arise since the distinction between clear and cloudy regions is not always simple (particularly in polar regions) and because other characteristics of the atmosphere (e.g., water vapor amount, atmospheric and surface temperature) change in cloudy versus clear conditions, even in the same location.
Table 13.1 provides some estimates of the mean annual global cloud radiative forcing at the top of the atmosphere. Clouds reduce the longwave emission at the top of the atmosphere since they are emitting at a colder temperature than the Earth’s surface. At the same time, clouds decrease the net shortwave radiation at the top of the atmosphere because clouds overlying the earth reflect more shortwave radiation than does the cloud-free earth-atmosphere. Because of the partial cancellation of these effects, the net cloud-radiative effect has a smaller magnitude than either the individual longwave or shortwave terms. Both satellite and model estimates agree that the net cloud-radiative effect at the top of the atmosphere is negative and that shortwave effect dominates, i.e. that clouds reduce the global net radiative energy flux into the planet by about 20 W m-2.
Table 13.1. Estimates of the mean annual, globally averaged cloud radiative effect (W m-2) at the top of the atmosphere derived from satellite observations and general circulation models.
Basis Investigation CFLW CFSW CFnet
satellite Ramanathan et al 1989 31 -48 -17
satellite Ardanuy et al. 1991 24 -51 -27
models Cess and Potter 1987 23 to 55 – 45 to -75 -2 to -34
Combining satellite data and models to estimate cloud radiative effect at the surface and in the atmosphere
Richard P. Allan
Abstract: Satellite measurements and numerical forecast model reanalysis data are used to compute an updated estimate of the cloud radiative effect on the global multi-annual mean radiative energy budget of the atmosphere and surface. The cloud radiative cooling effect through reflection of short wave radiation dominates over the long wave heating effect, resulting in a net cooling of the climate system of -21 Wm-2. The short wave radiative effect of cloud is primarily manifest as a reduction in the solar radiation absorbed at the surface of -53 Wm-2. Clouds impact long wave radiation by heating the moist tropical atmosphere (up to around 40 Wm-2 for global annual means) while enhancing the radiative cooling of the atmosphere over other regions, in particular higher latitudes and sub-tropical marine stratocumulus regimes. While clouds act to cool the climate system during the daytime, the cloud greenhouse effect heats the climate system at night. The influence of cloud radiative effect on determining cloud feedbacks and changes in the water cycle are discussed.
The paper is published in Meteorological Applications. The paper is available online [here].
Overall, I find this paper to be a useful addition to the literature on this subject. Here is what I like about the paper:
- the paper is clearly written and provides a good historical overview of the topic
- cloud radiative effects at both the top of atmosphere and surface are considered; the surface effects are important in the context of understanding feedbacks in the coupled system (not just the atmopshere, but also the land, oceans, and cryosphere).
- a careful job has been done in analyzing the satellite data, and a novel approach is provided for determining the surface radiative fluxes
- the results are interpreted in terms of variations with latitude, over the diurnal cycle, and interannual variations. No surprises here, but they provide a nice overview.
My only objection to the paper is that it is lacking any kind of an uncertainty analysis. The results of the top of atmosphere cloud radiative effect are not surprising, they fall within the range of previous studies as outlined in Table 13.1 from my text.
JC conclusions: Clouds and their feedbacks are arguably the source of greatest uncertainty in climate model simulations. The sources of uncertainty range from interactions of atmospheric aerosols in the water/ice nucleation process to unresolved turbulent and convection scales in the climate models. Good progress is being made on the treatment of cloud microphysical processes in climate models (including interactions with aerosols). Stochastic cloud parameterizations are the source of some progress in dealing with unresolved scales of motions, as is increasing the resolution of climate models. But the uncertainty surrounding cloud feedbacks is not going to go away soon. Studies such as Allan’s help the targets for evaluating climate model simulations.
I think I’ve never heard so loud
The quiet message in a cloud.
=======================
The message was recorded in meteorites, too,
Cosmic rays from the Sun were hard to chew!
Bilderberg said it was a ball of gas,
Surely that group is not an a_ _!
Cosmic rays in meteorites point to the source,
Hidden by glittering deception, of course.
http://apod.nasa.gov/apod/ap110918.html
Like elements in a stellar funerary urn
Carbonaceous, then rocky and finally iron
The sun is a mass of incandescent gas.
1. And glitters like interstellar diamonds!
Nature 326, 160 – 162 (1987)
http://www.nature.com/nature/journal/v326/n6109/abs/326160a0.html
2. For information more
Check author # four
And the discovery of
Technetium in Nature!
Nature 176, 299 – 301 (1955)
http://www.nature.com/nature/journal/v176/n4476/abs/176299a0.html
3. When you become old
You too will be bold!
4. Unafraid of demise,
Like solar system lies!
Nature 303, 286-286 (1983)
http://tallbloke.files.wordpress.com/2010/01/swart-1983.pdf
Talking of clouds and uncertainty, a contribution from one of the usual suspects, dated Dec 10, 2010:
As they say, there’s always a first victim in every war. No prize to guess what it is.
To perpetuate the ‘cloud wars’, here’s a new criticism of Dessler 2010/11 and his use of CERES clear sky with CERES all sky.
here’s the link
I suspect this will run on for a while.
yes troy does GREAT work.
This calls for Joni Mitchel| – Both Sides Now
Rows and flows of angel hair,
And ice cream castles in the air,
And feather canyons everywhere,
I’ve looked at clouds that way.
But now they only block the Sun,
They rain and snow on everyone.
So many things I would have done,
But clouds got in my way.
I’ve looked at clouds from both sides now,
From up and down, and still somehow,
It’s cloud illusions I recall,
I really don’t know clouds, at all.
Nicely sung by Judy Collins on, from memory, “Wildflowers.”
__/__/__/__/__/__/__/__/__/__/__/__/
“These results suggest that current observational diagnoses of cloud feedback-and possibly other feedbacks-could be significantly biased in the positive direction …
“Our understanding of how sensitive the climate system is to radiative perturbations has been limited by large uncertainties regarding how clouds and other elements of the climate system feed back on surface temperature change (e.g., Webster and Stephens 1984; Cess et al. 1990; Senior and Mitchell 1993; Stephens 2005; Soden and Held 2006; Spencer et al. 2007)…
“It is significant that all model errors for runs consistent with satellite-observed variability are in the direction of positive feedback, raising the possibility that current observational estimates of cloud feedback are biased in the positive direction…”
(Spencer, RW; Braswell, WD. Potential biases in feedback diagnosis from observational data: a simple, journal of climate. 2008: V21, 5624- 5628)
__/__/__/__/__/__/__/__/__/__/__/__/
I expanded on my comments over at WUWT, regarding Watts confusing the net cloud radiative effect for the cloud feedback, on my blog:
http://ourchangingclimate.wordpress.com/2011/09/20/net-cloud-effect-cloud-feedback-wuwt-confused/
Soloman 2010.
Abstract
Stratospheric water vapor concentrations decreased by about 10% after the year 2000. Here we show that this acted to slow the rate of increase in global surface temperature over 2000–2009 by about 25% compared to that which would have occurred due only to carbon dioxide and other greenhouse gases. More limited data suggest that stratospheric water vapor probably increased between 1980 and 2000, which would have enhanced the decadal rate of surface warming during the 1990s by about 30% as compared to estimates neglecting this change. These findings show that stratospheric water vapor is an important driver of decadal global surface climate change.
This paper is quite interesting for anyone interested in this topic.
Regarding Allan’s paper and his statement that it does not deal with cloud feedback, I posted this on WUWT:
I am a little surprised Allan says the paper is not about cloud feedbacks when it mentions cloud feedbacks four times in the article, including in the abstract and the conclusion.
The abstract reads: “The influence of cloud radiative effect on determining cloud feedbacks and changes in the water cycle are discussed. ”
So is Allan now saying he did not do what the abstract says he did?
From the body of the paper:
“Thus, the radiative effect of changes in cloud cover or properties is highly sensitive not only to cloud type (height, optical thickness, extent) but also to the time of year and time of day at which the changes in cloud properties take place. This is of importance in assessing cloud climate feedbacks which contribute substantially to uncertainty in climate prediction (Bony et al., 2006).”
So is he now saying he is wrong? Is he now saying the radiative effect of cloud changes does not play a role in assessing cloud climate feedbacks?
From the conclusion: “These analyses are vital in constraining cloud feedback processes further and in linking to future changes in the water cycle (Stephens, 2005; Bony et al., 2006; John et al., 2009).”
So is Allan now saying his analysis does not “constrain cloud feedback processes?”
It is fine to say the Allan paper focuses on the radiative effects. It is wrong to say the paper does not say anything about feedbacks. It clearly does.
Perhaps this paper is just a pre-print and Allan will make corrections to it prior to publication like Dessler is doing?
Will Judith apologize to WUWT?
The paper has a few comments on feedbacks, that is very different from the paper being about feedbacks or providing any conclusions about feedbacks.
So … none of of the below is about feedabcks?
“Thus,
the radiative effect of changes in cloud cover or properties
is highly sensitive not only to cloud type (height,
optical thickness, extent) but also to the time of year and
time of day at which the changes in cloud properties take
place. This is of importance in assessing cloud climate
feedbacks which contribute substantially to uncertainty
in climate prediction (Bony et al., 2006).”
“Bony et al. 2006
Processes in the climate system that can either amplify or dampen the climate response to an external perturbation are referred to as climate feedbacks. Climate sensitivity estimates depend critically on radiative feedbacks associated with water vapor, lapse rate, clouds, snow, and sea ice, and global estimates of these feedbacks differ among general circulation models.”
He is discussing forcing. These variations are of relevance to cloud climate feedbacks. Sort of like saying a rainfall paper is about farming if the author says rainfall is relevant to farmers.
I agree that he is quantifying forcing and not feedback. That does not change the fact he linked forcing to feedback in both the abstract and the conclusion. Truthfully, it would be hard to ignore the link between forcing and feedback. I don’t understand why Allan made the comment to Bart that he does not link the two in his paper.
you counted words to see if there was anything substantive on feedbacks? jeez o pete.
People get things wrong. Stop trying to defend simple mistakes. You sound like Mann.
Steven,
I am not defending Anthony Watts. He is a smart guy and able to defend himself. I have never claimed Anthony was right to say the Allen paper supports SB11. My purpose is to try to determine what Allen is saying about the link between cloud radiative forcing and cloud feedback and why he later denied he linked the two in an email to Bart.
The linkage is significant enough in Allen’s mind that he wrote of it in both the abstract and the conclusion. The are the two most important sections of any paper and are written with the most care. The abstract is the author’s chance to sell the reader on why he should read his paper. The conclusion is the author’s chance to sell researchers on why they should cite his paper.
I found this interesting quote in AR4 which also links cloud forcing and cloud feedback.
“Figure 10.11a shows globally averaged cloud radiative forcing changes for 2080 to 2099 under the A1B scenario for individual models of the data set, which have a variety of different magnitudes and even signs. The ensemble mean change is –0.6 W m–2. This range indicates that cloud feedback is still an uncertain feature of the global coupled models (see Section 8.6.3.2.2).”
It seems clear to me cloud forcing and cloud feedback are linked and to pretend they are not linked is a disservice to science.
“These analyses are vital in constraining cloud feedback processes further and in linking to future changes in the water cycle (Stephens, 2005; Bony et al., 2006; John et al., 2009).” From Allan’s text.
“Changes in cloud characteristics induced by a climate change would modify the radiative fluxes, thus altering the surface and atmospheric temperatures and further modify cloud characteristics. The feedback between surface temperature, clouds, and the Earth’s radiation balance is referred to as the cloud-radiation feedback.” From Judith’s book, along with, “To the extent that the net radiative flux at the top of the atmosphere is linearly related to cloud fraction, the sensitivity term can be related to a parameter called the cloud-radiative effect. [Footnote: The term “cloud forcing” is typically used to refer to the cloud-radiative effect. We believe that the word “force” is a misnomer for this effect.]” and of course Allan’s paper uses both cloud radiative effect and cloud radiative forcing.
So there is a little bit of confusion :)
“The feedback between surface temperature, clouds, and the Earth’s radiation balance is referred to as the cloud-radiation feedback.” Is perfectly correct. Whether or not “cloud forcing” is a misnomer is up for grabs. Dessler and Trenberth’s berating Spencer for not knowing the difference between a feedback and a forcing appears to be based on gamesmanship rather than the accuracy of the use of the term forcing.
In S-B’s 2010, “Only in the idealized special case of instantaneous and then constant radiative forcing, a situation that probably never occurs either naturally or anthropogenically, can feedback be observed in the presence of unknown radiative forcing.” An unknown radiative forcing is just that, unknown. If was found to be cloud feedback due to an unexpected change in surface temperature it wouldn’t be unknown anymore and you could argue over if it was an unexpected cloud radiative effect. If the change in the cloud radiative effect was significantly different than expected, non-linear for example, then that could be termed a cloud forcing due to an unknown mechanism.
With global interest in the subject, it is just a little bit petty to assume your usage is correct and not attempt to interpret what is being communicated.
The feedback/forcing issue for clouds depends on how you define the system. If the system is the atmosphere, then clouds are internal to the system and it makes no sense to refer to clouds as a forcing, this is just bad terminology. When one of those guys writes a text book on thermodynamics and makes a coherent argument regarding the usage of those terms, then I will pay more attention to their terminology. I devoted 3 threads to the Spencer & Braswell paper, including an interpretation of how they got tangled up in their argument.
But isn’t CO2 part of the atmosphere? And so, likewise, internal to the system? Why is CO2 a forcing but not clouds?
In models where CO2 is specified, it is a forcing. In models that include an interactive carbon cycle, then carbon is no longer a forcing but a feedback. Again, it depends on how you define the system.
All climate models include interactive clouds.
You are enormously correct that the difference between a forcing and a feedback depends on how the system is defined. But when you have a chain of responses this distinction may not be possible. That is the problem with complex systems.
Roy Spencer has studied this problem to death. Can we get him on here?
You are doing a marvelous job of clarifying matters, as usual. However, wouldn’t it be better just to drop the terms ‘forcing’ and ‘feedback’? Both are multiply ambiguous and are traps for those not well versed in the theory. The way to drop the terms is to display the full set of hypotheses that define the various causal relations in a given theory (not model).
I agree with you, but I can sympathize with their entanglement. Cloud radiative effect, aerosol radiative effect are better terms. Neither are a source of radiant energy and both can be a positive or negative feedback to the atmosphere. Since you have written the book, do you think aerosol forcing is more appropriate than aerosol radiative effect?
I find it a bit of a conundrum that change in aerosol “forcing” is acceptable while a change in the percentage of cloud cover is an “unforced” variation. Or should I consider aerosols external to the system?
i think aerosol radiative effect is better, especially when referring to aerosol indirect effect (interactions with clouds).
It depends on how you define the system. Models have interactive clouds with prognostic equations for cloud water. Aerosols are most often specified externally, although some models with advanced chemistry modules have interactive aerosols, in which case they are not external to the system
So external and internal have nothing to with physical boundaries. That is just a misleading metaphor. It is all about the equations. Do we have a formal definition that captures this distinction? Or is it all metaphorical?
That is were it starts getting confusing and confusion over terminology is most of the problem. Not a new problem. In modeling you can force any variable you like in any combination, say you have 3 variables you tweaked in a run, no problem. However, if I am evaluating your model and say it likes like clouds are forcing your results, don’t think I am a dumb butt.
Is it not an intellectual trap to use this definition of ‘forcing’, something that cause the Earth to warm up and then to restrict the actual forcers as the Sun and CO2?
I also think you don’t quite know what a cloud is. Big white thing in sky is big transfer of energy from sea level against gravity well, turning sea level brine into atmospheric fresh water = work.
Rain drops falling from sky = loss of potential energy and atmospheric cooling due to evaporative cooling.
Big white fluffy things are dynamic transfers of huge amounts of energy. On rotating planet, this is rather important.
Roy Spencer has come back with 140 model curves to compare with the satellite data. See
The Rest of the Cherries: 140 decades of Climate Models vs. Observations
And who were the “winners” in the Hockey & Hurricane Wars?
MBH 98/99/08 (aka, the ‘hockey stick’ graph) is a proven scientific fraud and famous ‘denier’ Bill Gray, hurricane forecasting expert, has lived to see the global warming hoax die.
The hurricane wars were negotiated, where both sides decided to be polite to each other and acknowledge the uncertainties. Science was the winner.
The hockey war rages on, given juice by the climate gate emails. Science is the loser in this particular war.
What’s most important is that humanity be the winner (along with other species), “good” and well-explained and understood science is a means to that end.
The Hockey Stick war is over. The M&M boys won.
Apart from Mann’s indefensible statistical gimmicks, the sceptics have shown that you cannot get a hockey stick without using tree ring data of proven unreliability.
This is a horrible comment. First, nobody has shown you can only get a hockey stick if you include tree ring data (there are papers other than those by Mann). Second, it wouldn’t matter if they did. There is nothing wrong with using tree ring data. Telling people to ignore one of the best sources of data for an issue is just silly.
Now then, this is what has been shown. It has been shown the original hockey stick originated entirely from a (relatively) small amount of trees in limited geographical areas. These trees in no way are known to be representative of the globe as a whole, and yet they were the sole source of the original hockey stick. Papers following this have been shown to have the same general problem, namely, a small amount of data is given undue weight in order to (consciously or subconsciously) generate hockey sticks. In addition, in most of these cases, the data relied upon is known to questionable.
More has been shown, but that’s the main point. The hockey stick war hasn’t been about tree rings, statistical tricks, or anything like that. In the end, it has always boiled down to one thing. A hockey stick only comes by picking out a small subset of data. That data supports a conclusion some people “like,” and thus, it is considered “right” while the rest of the data is effectively ignored.
before declaring that the fat lady has sung on this one, lets see what shows up in the AR5 on this topic.
To a certain extent, this may not be necessary. The IPCC has to rely on published work, so one can just follow the papers which get published regarding millennial temperature reconstructions.
As it stands, I can see three ways the IPCC could maintain it’s previous position. One, it could rely on some new paper which gets published between now and whatever deadline it uses. Two, it could rely upon papers with known problems, simply choosing to ignore the problems (which may be easier as often those problems aren’t acknowledged in peer-reviewed literature, even if they are acknowledged elsewhere). Three, it could look to papers which haven’t received much attention so far.
None of those options seem wise, and I don’t see any other choices. It will certainly be interesting to see what the AR5 says, but I don’t think it will offer any meaningful support for the hockey stick. It will most likely just determine whether the “consensus” stands by an indefensible position or “surrenders” the point.
Wrong. Though they’ve demonstrated that whenever the series contain any hockey stick, Mann’s black box would give it an enourmous weight in its “averaging”. And that if ever that hockey stick is directed downwards, Mann’s magic algorthm direct it upwards…
Historically (going back 1,000s of years), climate change caused wars. Now, it is fear of climate change that leads to conflict:
“… In recent years, historians and climatologists have built evidence that past societies suffered and fell due in connection with heat or droughts that damaged agriculture and shook governments… ‘We can speculate that a long-ago Egyptian dynasty was overthrown during a drought. That’s a specific time and place, that may be very different from today, so people might say, “OK, we’re immune to that now.” This study shows a systematic pattern of global climate affecting conflict, and shows it right now.’”
http://www.earth.columbia.edu/articles/view/2842
Drought usualy happen in cold times. So did the end of many past civilizations.
Judith you write ” Studies such as Allan’s help the targets for evaluating climate model simulations.” You are also very complimentary to the study. I dont pretend to know a lot about climate science, but I do know about modelling.
It seems to me that if Allan has added significant inew nformation on the way clouds “work”, then one of two conditions ought to apply. Either clouds are so poorly modelled that this new knowledge does not help refining how clouds are modelled. In which case, I suggest the output of the models is useless for predictive purposes (which I believe anyway). Or clouds are modelled in a sufficiently sophisticated way, that this new knowledge requires that the way clouds are modelled needs to be significanly changed, and previous studies using such models need to be reviewed.
Or maybe I have no idea what I am talking about.
Allen hasn’t provided any new information about how clouds work. He has provided better estimates of the cloud radiative effect, particularly at the surface. this does not help us better model clouds. It helps us evaluate how we model clouds against observations.
What if this forcing vs. feedback debate is just a symptom of the fact that there is no such a thing as a “pure” climate forcing (i.e. a variable that is truly independent from all other variables), Sun excluded perhaps?
External inputs to the system, like solar radiation, is an external input. Calling any of this “forcing” is a misnomer, think about the units of “force”
Forcing is correct when referring to CO2. We are assuming that the doubling of CO2 will “force” the system into a new state. Since the object of the models is to determine the “forced” state due to CO2, everything else would be an input, output, feedback or indirect feedback or noise.
If I built a model to determine if CO2 would be stronger than solar in determining a new state, then I could call both solar and CO2 “forcings” but it starts to get silly. The least confusing way is just to define what knob you are cranking (forcing) then everything else is input, output or feedback.
Dallas I am surprised at this comment. I think that CO2 is usually considered a “forcing” because it is considered to be an essentially external condition (people burn underground carbon injecting it into the atmosphere). Might as well be space aliens sucking air in or out. Think, ‘It’s Mega Maid, she’s gone from suck to blow!’. So JC has it right when she says somewhere above on this thread (paraphrasing) that once you consider a model with an interactive carbon cycle, CO2 isn’t really a forcing any more. I suppose you could say A CO2 is still a forcing if you consider anything A to be driven by an external agent (man). Likewise anthropogenic aerosols. And volcanoes I suppose. Like it or lump it I think that’s the terminology.
I think it is misleading to consider anything with an interactive cycle a forcing. I would prefer to think of clouds as a feedback, recognizing that by the definition of feedback they change the radiative balance. Whether they are a feedback due to temperature, cosmic rays or whatever. A condition arising in response to a forcing (or feedback) doesn’t become a feedback until it affects the radiative balance.
BillC, The difference is modeling versus the real world. In my model world I control what I want to change to see a response. In the real world the only thing we are changing is CO2 and depending on how we change CO2, we are changing aerosols to some degree. If you want to take the real world to the simplest level, everything is an input or a response. The input can be constant or variable, the responses can be linear, non-linear, direct, indirect, any level of complexity. Effect is the most appropriate term for real world responses, feed back appropriate for the model world.
So in the model world I may “force” CO2 and Solar to determine a system response for that model to those two “forcings”. Aerosols are “forced” in most models to obtain more realistic results. Assuming your model parameters are correct, aerosol “forcing” a model allows you to estimate the system response to aerosols which can give you an estimate of the real world aerosol effect.
Most of the confusion comes from trying to explain a real world effect that differs from a model world’s expected response or feedback to a model adjustment of “forcing”.
Dallas: “In the real world the only thing we are changing is CO2…”. I don’t understand what you mean here. Maybe I have missed the point. But in addition to CO2 humans are doing many other things which could have an effect on atmospheric temperature and climate generally. Even IPCC accepts this. Examples are the changing pattern of rural land use, the extension of the built environment (the so-called UHI effect), deposition of black carbon on glaciers and ice sheets. As far as I know the effect of none of these processes has so far been adequately quantified, but can they not all be regarded as ‘forcings’ in IPCC parlance, in that they provide external inputs to the energy budget of the earth’s atmosphere?
Coldish, sorry that comment was brief. Man has of course made many changes. CO2 is consider the most significant. In the climate models, climate sensitivity is based on an anthropogenic change in CO2, with land use, man made aerosols, etc. considered, but anthropogenic CO2 is the reason the general climate models to determine climate sensitivity exist. The term forcing was coined because the models were “forced” with an instantaneous doubling and an estimate of sensitivity to that doubling to see what would happen in the future. So in my opinion, the only true forcing in both the model world and the real world is doubled CO2, if you are considering climate change.
In models, anything being studied can be a forcing. The forcing elements are defined by the modeler. In a regional weather model, UHI could be a forcing, for example. What is called a forcing in a GCM is defined by the modeler and is now being consider a real world phenomenon.
What is defined as a forcing and how that is used in the models versus the real world is the issue. For example, aerosols are both man made and natural. Most man made aerosols are related to the method of producing energy, which in turn is related to the amount of CO2 produced. In the models, aerosols are tweaked, a selective version of forcing, to match the model to observations. Because of that, aerosols in general are called a forcing. Cloud cover is not tweaked in the models, yet anyway, so cloud effects are only feed backs, or responses to a forcing.
So any variation in climate not associated with a defined forcing, is an unforced variation, in the minds of the modelers. Is that true in the real world?
Looking at the estimated raw flows VS net flows of CO2 in an out of the atmosphere, I’d say C02 is mainly (by far) a feedback…
Dr Curry,
“Forcing” is actually a new terminology introduced by climate scientists that were trying to speak the language of complex systems’ theory. But this word of “forcing” has actually no sense regarding the standards of Control theory.
A “forcing” is nothing but an independent input to the system. This input is external to this system and cannot be influenced by any of the outputs of this system.
To that extent there are only very few “forcings” in climate systems :
– The sun is a forcing (indeed the main and almost only one !) as it is the only source of energy.
– Other (big) planets are a forcing since influencing the parameters of Earth’ Orbit (eccentricity, tilt, precession) that are directly responsible for glacial / interglacial cycles.
– Volcanos eruptions are a forcing since not influenced by climate system but influencing this system because of produced aerosols
Actually even manmade GHG emissions cannot be formally considered as a forcing since the amount of manmade GHG emissions is directly linked to the status of climate system: a cooler climate will induce higher GHG emissions for heating houses, whereas a warmer climate will also lead to higher emissions (but not as much as for cooler) for air conditioning !
All other parameters such as clouds, water vapor, CO2, oceans’ oscillations… are fully internal to the climate system and cannot be named “forcing”. They are all outputs of this system but are actually more or less acting on each other, i.e. on the system itself, through complex feed-back loops.
AGREED. Ramanathan introduced forcing in his 1989 paper, and it stuck, much to the confusion of everyone.
I like the Dallas distinction. Every tunable knob is a forcing, but only when it is turned. In short, this is a math concept, not a physical one. Loops have no beginning (and no end). You can tweak them anywhere, and things happen.
Here is what Ramanathan stated quite succintly.
Cloud radiative forcing (CRF) is defined as the difference between the radiation budget (net incoming solar radiation minus the outgoing long wave) over a cloudy (mix of clear and clouds) sky and that over a clear sky. If this difference is negative clouds exert a cooling effect, while if it is positive, it denotes a heating effect. Five-year average of the cloud radiative forcing [1] is shown in Fig. 2. The global average forcing is about –15 to –20 W m-2 and thus clouds have a major cooling effect on the planet.
An important part of the papers 1989 and 1997 is the observation of the clash of doctrines (thermodynamic/dynamicist eg Dahan) and the problems with transport.
The enormous cooling effect of extratropical storm track cloud systems Extra-tropical storm track cloud systems provide about 60% of the total cooling effect of clouds [2]. The annual mean forcing from these cloud systems is in the range of –45 to –55 W m–2 and effectively these cloud systems are shielding both the northern and the southern polar regions from intense radiative heating. Their spatial extent towards the tropics moves with the jet stream, extending farthest towards the tropics (about 35 deg latitude) during winter and retreating polewards (polewards of 50 deg latitude) during summer. This phenomenon raises an important question related to past climate dynamics. During the ice age, due to the large polar cooling, the northern hemisphere jet stream extended more southwards. But have the extra tropical cloud systems also moved southward? The increase in the negative forcing would have exerted a major positive feedback on the ice age cooling. There is a curious puzzle about the existence of these cooling clouds. The basic function of the extra tropical dynamics is to export heat polewards.
While the baroclinic systems are efficient in transporting heat, the enormous negative radiative forcing (Fig. 2) associated with these cloud systems seems to undo the poleward transport of heat by the dynamics. The radiative effect of these systems is working against the dynamical effect. Evidently,we need better understanding of the dynamic-thermodynamic coupling between these enormous cooling clouds and the equator-pole temperature gradient, and greenhouse forcing
The is a troublesome problem with the use of so called universal parametrization in the abscence of any uniquness physical laws.and scaling problems with fitting and the ex creation nihlo problem ie precipitation without clouds.
Ramanathan found a nice observable from satellites: the flux measured at the top of the atmosphere, which could be discriminated into clear sky and cloudy scenes. The unfortunate part of this is that he labeled it as a “forcing.” The coupling of large scale thermodynamics and dynamics is indeed important, and it is this idea that I think S&B are going after. But it is not all simple to diagnose these couplings, and it is definitely a chicken and egg situation, trying to identify a single causal factor is probably impossible, it is all part of the nonlinear dynamics of the system. My problem with S&B is that they are attempting to ascribe clouds as the primary causal mechanism. Changes in clouds are caused by changes in dynamics and thermodynamics, which in turn can modify surface temperature and changes in the thermodynamics and dynamics of the atmosphere and ocean.
I’m going to repeat here something I said above and ask for JC’s blessing unless it’s asinine.
Something which is a feedback (e.g. clouds) cannot also be a forcing. However, it can be a feedback to a different parameter (surface temp, cosmic rays, etc.).
If a changed condition doesn’t have an effect on the radiative balance, it is neither a forcing or a feedback. Even if it arises in response to a forcing.
It does seem that the climate scientists would have been better off starting with something well-known and well-characterized – like systems analysis methodologies.
Eric Ollivet
Thank you for that clarification.
I thought I was going mad with the definitions earlier.
One field’s jargon is not that of another field.
Deja vu the various definitions of validation and verification from earlier threads.
We need a glossary thread.
GCRs should also be considered “forcing” if they have effects one the climate (e.g. if GCRs-LCC theories are validated).
Nice one. More generally, because economic development and welfare, civilizations extension and population growth are largely due to the Sun’s activity, so to the positions of the big gaseous planets in the solar system. So we human, like many species, are a “climate feedback”.
I wouldn’t be so sure of the Sun being a “forcing” either.
Who’s going to demonstrate that the atmosphere will respond predictably and progressively if the Sun input to it varies, and everything else remains equal?
For all we know, the Sun could be a positive forcing up to a point, then negative, then positive again, or simply positive but by different amounts following a complex multi-step function that moves up and down, all according to the atmosphere’s initial status.
Actually, we can be pretty sure of all that complication, thanks to the Mpemba effect (and the Leidenfrost effect).
I think that the sun is external to the Earth’s climate system and must be deemed to be a forcing and this forcing can be either positive or negative depending on what state(s) the Earth’s climate initial conditions happen to be (averaged out for each region: Ouch! my head is still trying to get around the concept of global climate) :)
Forcing vs feedback, I have just realized, is not just an idle “philosophical” discourse. If there is no such a thing as a forcing in the physical world, it’s a small step to demonstrate that the maps-vs-territory distinction does not apply to climate.
That is, there is perhaps no such a thing as a simplified-yet-working model (representation) of the climate, and the only way to hope to understand it is by considering it whole (I have blogged about that here).
Actually, the are plenty of simplified working models of climate. They are the most reliable. A Stochastic control model is very useful. http://en.wikipedia.org/wiki/Stochastic_control based on linear-quadratic-Gaussian is kick butt. What people miss is the Gaussian. Get over the fear of Bayes and they work. Try to force a distribution on a control system and you get surprises, er.. Big Surprises. Forcing though is a modeling world thing.
Speaking of clouds and models, a good model may not give you the right answer or the answer you want, it is no better than the inputs. But the answer is still worth something If you look hard enough.
http://noconsensus.wordpress.com/2011/09/21/ceres-and-era-interim-fluxes-in-dessler-2010/#more-12377
It seems WRT the Sun’s radiation reflected into space that that “clear sky” number would be pretty well known. Then it is just a matter of measuring the solar input vs reflected output and adjusting for the clear sky baseline.
Michael Wallace writes on Making statistical sense of an imperfect world to explain “how his backroom work on measurement error can make the difference between a cure and a wrong conclusion.”
For example statisticians at ClimateAudit have been examining and weighing in on the significance or lack thereof on Dessler (2010).
In examining the statistics of Dessler 2010, Steve McIntyre has found results with twice the significance with the opposite sign.
Similarly see Troy: Dessler(2010) “artifact of combining two flux calculations”
We still have the major questions of which comes first: the warming or the CO2?
Are clouds the cause or the consequence?
Will we ever have significant evidence that one or the other or both of these cloud conundrums are significant?
“statisticians at ClimateAudit have been examining and weighing in on the significance or lack thereof on Dessler (2010).”
and completely missing the point. Dessler was not claiming that his regressions showed the slope was positive, or different from zero – the paper notes that it could be negative. But that’s what they tested.
Dessler was claiming that it could not be sufficiently negative to counter water vapor feedback, as requirede by L&C and S&B. That’s the proposition you need to test.
It is you missing the point. This is a sensitivity test. The result of the test is the conclusion that Desslers claims are unsupportable because of the huge effect of the input choice on the resultant linear regression.
However, regardless of the papers worth, Dessler in his articles to the press is not shy of claiming he has demonstrated positive feedback from clouds and one of these articles is linked to above. Your argument is therefore equally unsupportable.
I’m an old aerospace engineer, with an education that dates to the time that analog, rather than digital simulation was primary. When I first encountered the concept of CO2 forcing in the early days of climate blogs, I recalled being chastized for my insistence that any parameter, such as CO2 or water vapor or clouds could be treated as a forcing for modeling purposes. The blog-master insisted that only CO2 could be a forcing. I believe the concept of CO2 forcing is only a modeling convention, which will reveal something about the behavior of a particular model, but perhaps not so much about the real world. To say that clouds cannot be a forcing is naive in the modeling word, where any parameter can be forced.
I think in discussions of the real world, the concept of forcing needs to be discarded, since there is no real distinction between forcings and responses.
Are clouds a cause or a consequence? Yes. That’s why the puzzle is fun :)
Looking at the glacial/interglacial periods for the past few 100Ka, with the estimate of change in solar absorbed, it is pretty obvious how potent albedo is. Then if you look at the estimate of no greenhouse temperature, -18 C, 30% albedo is included in the estimate, with about 50% of that due to clouds. As a perfect blackbody, Earth’s no atmosphere temperature would be 5.3 C. This should not impact the accuracy of the Watts/m^2 per degree C change very much, but a snow ball Earth with no atmosphere should have an albedo of about 36% (it’s an ocean/land positioning thing). Water vapor would be in the neighborhood of 20% less, but the sensitivity of climate to water vapor during a glacial, per Dessler, is nearly the same. Humm? I thrashed the spread sheet I was messing around with, but it tended to push the percentage of the greenhouse effect due to CO2 toward the lower estimated range. The data I was using was no were near accurate enough and my remaining math brain cells no were near active enough, but it was interesting.
It is obvious to me that doubling CO2 would be considered a forcing because it is unconditional on climate, as were the IPCC AR4 scenarios where CO2 was specified as a function of time only. This is similar to how solar changes and volcanoes are forcing, as are anthropogenic aerosol inputs. Clouds, on the other hand are just responses to other changes, including surface temperatures, atmospheric relative humidity and natural and anthropogenic aerosols.
In a model with a carbon cycle (ocean, land and biosphere sub-models) instead of a specified CO2 amount the forcing becomes the even more well defined process of anthropogenic CO2 emission from fossil fuels.
Forcing is an extremely important concept in climate change as distinct from natural internal variability. Without these concepts, the arguments would be very confusing.
It seems that only anthropogenic CO2 could be considered a forcing. The climate system does have feedbacks WRT CO2 – the oceans absorb it, plants utilize it, bacteria produce it, etc. In fact, if you consider man to be natural and not of divine origin, man is just another organism in the carbon cycle and it’s all feedback.
Gaia hatched us for the Carbon Liberation Wars.
==========
How about a delta CO2 war?
I did a careful removal of the periodic component from the Mauna Loa CO2 data ( http://woodfortrees.org/plot/hadsst2gl/from:1960/plot/esrl-co2/from:1960/derivative ) by applying both a kernel average and a phased sine wave and its first harmonic reconstruction.
http://4.bp.blogspot.com/-nIBeKLJvgOE/TnrcZwnlw0I/AAAAAAAAAgk/fc12Gr_krdM/s1600/kernelCO2.gif
Again I am amazed by how close the temperature anomaly tracks delta changes in CO2 level at the granularity of about 1 year.
The cross-correlation is very apparent:
http://img683.imageshack.us/img683/311/kernelc2.png
Apparently someone at WUWT also posted about this a few years ago:
http://wattsupwiththat.com/2008/12/17/the-co2-temperature-link/
Really, what’s up with this data cross-correlation? And is this giving information on feedback or is it a result of a forcing function?
I don’t know, a delta CO2 war may be the war to end all wars. CO2 in the atmosphere does vary with temperature and biological activity by a fairly significant amount. Surface temperature through a chain of heat transfer processes does increase with increased CO2. The CO2 increase due to temperature and biological activity does not appear adequate to create enough radiative effect to significantly amplify the change in temperature. So there is a time dependent process involved. This opens up a brand new can of worms if you look at it one way or the way I look at it implies the system is not ergodic, Tomas will argue.
This d[CO2] issue I think this is a huge hole in the overall argument, and even though it has been known (ignored?) for some time, I haven’t seen a clear explanation. What is compelling about this path is that the cross-correlation is strong and I haven’t seen a proxy for temperature that behaves quite like this. It’s almost like the first guy that figured out how to calibrate mercury rising in a thermometer, ain’t it? But the weird thing is that it is delta CO2 against the temperature anomaly, not absolute CO2. One would think that it should be showing up as delta CO2 against delta T, and it in fact does for the longer term temporal time trend, but this is really in addition to that. What it is saying that if there is a linear rise in absolute temperature, then absolute CO2 rise would be quadratic, i.e. like a parabola.
Not to be too loose with the analogies, but If temperature is like a voltage and CO2 is like a current, then something is acting like an inductor in an RL circuit. That is why I can model it as a Proportional-Derivative controller:
http://theoilconundrum.blogspot.com/2011/09/sensitivity-of-global-temperature-to.html
The ratio of the Fourier Transforms between d[CO2] and Temperature is also perfectly flat across the spectrum (see the graph on the above link).
Granted, if it turns out to be a feedback element and d[CO2] is just responding sensitively to global temperature, then this is still a great proxy measure for warming and it becomes an independent verification of the hockey stick. No way that this information can be simply coincidental. It is truly an independent way of looking at temperature. It’s a fascinating data analysis and until someone explains what is happening, call me stumped.
Dallas, As you know well from experience, a fresh batch of worms catches the biggest fish :) They are the wriggliest.
It is interesting, like popping a coke open and slamming it versus sipping it with a little Bourbon. I am biased toward the bourbon.
The other thing this behavior reminds me of is the transfer function of a sample-and-hold system, which is a kind of integrator. The adjustment residence time acts like a sample-and-hold IMO.
My question, D, is whether this Anthro CO2 is from a shaken bottle of Champagne or not. I see no evidence of prior shaking.
=======================
Kim, Bourbon and Champagne tastes nasty mixed, but if you like the temperature dependence CO2 could be naturally effervescent Champagne, Moet preferably, and the Anthro CO2 could be Bourbon and Coke (More likely a seltzer bottle with Krusty the Clown in charge).
Pop the cork on the Champagne and there is no fizzing if you know what to do and want to enjoy or you can get festive. Festive, with a rapid uncorking, there is a quick surge of CO2 with a rapid reduction in the rate of flow. If you don’t drink at a responsible rate it goes flat, that would be alcohol abuse. Open the Champagne properly and the initial rate of CO2 flow is less, so it stays bubbly longer – if you keep it cool, you can sip more leisurely, maybe mix with some fresh OJ while having brunch. You don’t need to shake in either case, it is the rate of uncorking that makes the difference in the rate of CO2 loss from the bottle to the atmosphere.
I see no foaming neck; the plants drink to your health.
===============
WHT, The sample and hold relationship is not a bad analogy. I was looking a discrete thresholds like the relationship between temperature and and PDO index. The PDO index is pretty arbitrary, but something happens when it is positive and negative, but how? I was thinking that North American precipitation proxies might provide a clue, but there is not a direct correlation. With thresholds there appears to be a relationship. Could be cyclomania though.
I don’t think it is cyclomania so much as one observable (T) closely following another observable (d[CO2]). I also did a Fourier Transform on each waveform, and the ratio between the two was flat across the spectrum,
http://img827.imageshack.us/img827/9890/ftco2t.gif
So it seems like it doesn’t really matter what the frequency is and they will track.
But then again knowing exactly what causes the longer range cycles is like getting the keys to the castlke.
WebHubTelescope | September 22, 2011 at 8:55 am
“This d[CO2] issue I think this is a huge hole in the overall argument, and even though it has been known (ignored?) for some time, I haven’t seen a clear explanation.”
WHT,
It’s great that you find the T-dCO2 correlation interesting. Keep it up. I think there is already a clear explanation, but it’s not really accepted. I think Salby’s paper will bring some weight to it.
Accoirding to Pekka there is a clear explanation that answers the question satisfactorily and it is also found in the IPCC docs. This could be the same thing that Salby proposed. The issue becomes one of asking why no one has done the detailed cross-correlation that would quantify the scale of the effect.
WebHubTelescope
Compliments on very clearly showing the correlation between temperature and dCO2.
David Stockwell finds a Phase Shift in Spencer’s Data
Now look at Spencer’s: Deep Ocean Temperature Change Spaghetti: 15 Climate Models Versus Observations
Note the rapid temperature change at the surface decaying with depth.
Compare solar and dCO2 with
1) Cloud modulation (by GCR or ocean temperature?) and/or
2) Sea surface temperature?
Compare the difference between the logarithmic variation in atmospheric depth with CO2 + H2O.
Miskolczi finds very little change in global optical depth with CO2. See Slide 16, 17.
Compare that with the increase in atmospheric CO2 (decline in dissolved CO2) with increasing ocean temperature .
Can we tell anything from the correlation (magnitude and especially sign) and phase lags between dCO2 and both sea surface temperature and global temperature?
Happy hunting
Stockwell linked to David Stockwell Key evidence for the accumulative model of high solar influence on global temperature August 23, 2011http://vixra.org/abs/1108.0032
David,
Thanks, I have been studying some of the links you supplied. After reading Fred Haynie’s presentation, I decided to try deconvolving the seasonal effect on Mauna Loa CO2, and it worked out fairly well. So I will get to these as well and see what happens.
WebHubTelescope
Excellent insight and remarkable results.
Thinking of Fred Haynie’s analysis, I think it would be insightful to see how the correlation and phase lags change with the strong variation of CO2 signals magnitude and phase with latitude from the south pole to the north pole.
Might the annual dCO2 vs T correlation and Proportional-Derivative change in magnitude and/or phase across that range? That might give further clues as to causation.
Another interesting one would be the variation of dCO2 direction with cloud fraction or LW.
Look forward to your further explorations.
I can imagine that the more spatial dimensions we add the more constrained the solutions will become. They will have to all interlock in a consistent way if there is a strong forcing function that has the same spatial dependence (in this case latitude). It’s a lot of work but it probably needs to be done.
But back to the question: How can the derivative of CO2 track the temperature anomaly so closely?
This is just a working hypothesis, but my take is that if the underlying basis for the strong correlation is a CO2 forcing function, the argument goes like this: Assume that fresh CO2 is the forcing function. An impulse of CO2 enters the atmosphere and it creates a response function over time. Let’s say the impulse response is a damped exponential and the atmospheric temperature responds relatively quickly to this profile.
However, the CO2 measured at the Mauna Loa station takes some time to disperse over from the original source points. This implies that a smearing function would describe that dispersion, and we can model that as a convolution. The simplest convolution is an exponential with an exponential, as we just need to get the shape right. But what the convolution does is eliminate the strong early impulse response, and thus create a lagged response. As you can see from the Alpha plot in the link below, the way we get the strong impulse back is to take the derivative. What this does is bring the CO2 signal above the sample-and-hold characteristic caused by the fat-tail. The lag disappears and the temperature anomaly now tracks the d[CO2] impulses.
http://4.bp.blogspot.com/-BlQSr-A4FgM/TnvjJeSUEII/AAAAAAAAAgo/csV3lWEhX8A/s1600/co2-conv-der.gif
If we believe that CO2 is a forcing function for Temperature, then this behavior must happen as well; the only question is whether the effect is strong enough to be observable. And then we need the causative proof of carbon emissions. Do carbon emissions cross-correlate with CO2? Perhaps, according to this plot:
http://img30.imageshack.us/img30/6048/co2ccemissions.gif
The cross-correlation isn’t as strong as d[CO2] with T, but it is at least as strong as some other correlations I have seen.
As far as I can see this question has been answered satisfactorily by the main stream science. The answer is:
– Long term changes are driven by the anthropogenic releases of CO2. That influences the global temperature through the cumulative CO2 concentration very slowly. Therefore the effect is totally invisible in short term variations, but is the reason for the slow increase in temperature.
– On shorter term the climatic variations influence mainly land based biosphere in a way that results in the variability that you observe in the CO2 data. The derivative of the CO2 concentration is affected by weather patterns. That effect is in part directly due to temperature and in part with correlated changes in precipitation.
The fact that the short term causal relation is opposite to the long term causality explains the observations in a natural way.
Thanks. Nice to see more settled science. After reading what you said, I decided to do some more digging and found this link:
http://www.skepticalscience.com/print.php?r=145
which points to work by (Bacastow and Keeling 1981) and Section 7.3.2.4 of the IPCC AR4 Working Group 1 report. The latter has all the details:
Those years do indeed match, just odd that no one ever thought to actually plot the numbers and show the cross-correlation. This seems like such obvious scientific book-keeping that words fail me to explain the lack of a published plot (except for the WUWT blog post and recent stuff here).
Take a look at figure 7.9 in particular and one can see how the trends change quite a bit for CO2 measurements over ocean versus land. Curiously Mauna Loa is in the ocean yet it shows the strong correlation of the land stations.
Nonetheless, it would be interesting to find out whether a cross-correlation technique can be used to separate out anthropogenic fraction from natural variability. The argument, which I find very subtle, is that the short-term fluctuations correlated to d[CO2] contribute to the integration over the long term in a different fraction than the absolute change in [CO2]. You can see that in my blog post here. This is the technique of fitting to a Proportional-Derivative model and minimizing the variance between model and data. Perhaps this is also in the mainstream science and someone has used this to figure out the fraction of temperature change due to man-made forcings. I do this as a hobby and can imagine that if someone who is actually making a career out of it, could go through all the data (both land and ocean based) quite meticulously and make some real headway.
WHT,
When I wrote “answered satisfactorily”, I didn’t want to imply that all details would be known or that there would not still be much to learn. I wanted only to point out that the issue has been studied, and that a reasonable qualitative agreement between the data and the explanation has been found.
The references you give are along those lines. The resent thread on “Detection of Global Economic Fluctuations in the Atmospheric CO2 Record” discusses ongoing research that’s likely to provide more quantitative details.
WebHubTelescope
Thanks for the references and for your further modeling.
“It’s a lot of work but it probably needs to be done.”
Anyone know a graduate student seeking a challenging thesis of fundamental importance?
Re:
As I understand it, Fred Haynie’s analysis appears to show that strong forcing as a function of temperature by Arctic ice sheet variation.
Re: “If you realize that the noisy data below is what we started with, and we had to extract a non-seasonal signal from the green curve, one realizes that detecting that subtle a shift in magnitude is certainly possible.”
May I suggest that that “noisy data” actually has a wealth of phase and amplitude information buried in it that may help extract the driving parameters. e.g. especially the phase and sign of dCO2 relative to the annual solar variations and the sea temperature and Arctic sea ice extent. Stockwell’s analysis suggests a simple temperature cycle would be 90 deg or 3 months lagging the annual solar variation. If that drove the CO2, then the dCO2 may similarly show a 3 month lag in the Arctic from the solar insolation (“forcing”). Conversely, if the anthropogenic CO2 cycle drives the optical depth, then there will be a 3 month lag to the global temperature if fossil fuel >> ocean temperature driven CO2. etc. Similarly, if the land bio CO2 sink dominated, that would be in synch with the land weighted insolation (dominant No. hemisphere). If sea bio CO2 dominated, that would be in synch with the sea weighted insolation (dominant so hemisphere.)
That’s the thread that started all this discussion in my mind. There are plenty of leads, both false and positive strewn through the commentary that could, as D.H. suggests, provide a good project for an enterprising graduate student to sort out.
What is weird too is that I have long frequented http://TheOilDrum.com blog, and a couple of years ago there was a commenter by the name of Memmel who kept on claiming that delta atmospheric CO2 concentrations were a good indicator of oil consumption. He would point out ripples in the [CO2] curve and said this would reveal actual oil production. At the time I said it might be possible but that you would have to subtract out the coal, seasonal adjustments, etc. He compiled his thoughts into his blog (of which there is really but this one entry) at http://mike-emmel.blogspot.com/. He had a chemistry background and this was one passage:
He can’t write his way out of a paper bag and there were no units on any of his graphs. If someone else can figure this out, thanks.
This is a more complete story on explaining d[CO2]:
http://theoilconundrum.blogspot.com/2011/09/fat-tail-impulse-response-of-co2.html
Changes in fossil-fuel emissions have some effect on d[CO2] but definitely not as cross-correlated as the short-term variation in global temperature.
WHT,
Nice analysis that quantifies much of what was discussed in the earlier messages.
I only comment on your sentence: “Curiously Mauna Loa is in the ocean yet it shows the strong correlation of the land stations.” There is nothing surprising in that. The strong circulation of the atmosphere most evident in the Westerly jet streams provides rapid longitudinal mixing for each latitude. The mixing is slower across latitudes and specifically across the equator, but even that doesn’t take many years.
In the middle of the jet stream it takes less than two weeks for a parcel of air to go around the Earth.
Pekka, I guess it depends on the latitude that the jet-stream is in then, right? I am assuming that Hawaii happens to be at the right latitude compared to the ocean sites that show less of the CO2 fluctuation as shown in Figure 7.9 of the IPCC report. This aspect seems very confusing to me, because Hawaii is in the northern hemisphere yet the ocean sites show very small d[CO2] in that figure, in each of the Baker, Rodenbeck, and Bousquet ocean data curves. I suppose I could track these references down and check the latitudes, but asking here is more convenient at the moment.
WHT,
The rate of mixing depends certainly on the latitude, but I don’t think that the related time constants are more than two or three months at any latitude. The large scale winds are weakest near the equator, but Hawaii at 20N is not that close. The Hadley cells effect also longitudinal mixing at the latitude of Havaii.
There is also some difference between winter and summer (stronger winds in winter).
Pekka & WHT
re my comment above on expecting 180 degree differing phase lags between No. and So. Poles.
See: Fred Haynie’s Slide 16/59 which shows about a 3 month or 9 month lag between Arctic and Antarctic ice extremes. That looks reasonably close to the 0.25 year and 0.75 year lags (0.25 + 0.5) between insolation and temperature minimums predicted by Stockwell’s solar accumulation theory. Correspondingly, in slide 20/59, the Sea Ice thaw rates look to be in synch with the respective north and southern pole insolations.
Now see Fred Haynie’s slide 10/59. By eyeballing, the Arctic CO2 minimum is ~ 0.60 of year. The Antarctic/So. pole minimum is at ~ 0.20 of year. (Note similar lags in the Alert Carbon Isotope Depletion in Slide 11/59.) Adding the 9 day or 0.3 mo difference between the winter solstice (Dec 22) and January 1st, nominally gives 0.23 year (3 mo) and 0.63 year ( 7.6 mo) delays from the insolation minimums to the CO2 minimums.
The CO2 curves also look like a ~0.4 year or 5 month difference between them. That is close to the 0.5 year (6 mo) difference expected from the North versus Southern hemisphere annual solar cycle. These phase lags suggest the CO2 pulses are driven by the temperature changes which lag the insolation by 90 degrees (2.5 years or 3 months) supporting Haynie’s evidence. See Haynie’s slide 18/59:
Pekka you noted:
Would that make any significant differences in Haynie’s data and fits?
David L. Hagen,
Bravo. Nice discussion of Haynie and Stockwell. I am in agreement.
David,
When I fit the Mauna Loa data via Fourier Analysis, I get a strong seasonal period and a significant harmonic at twice the period. Starting from the beginning of the year, the fit is
phase shift in radians
I think what this is doing is making the sinusoidal curve more asymmetric, as the seasons aren’t symmetric.
The flipping from northern hemisphere to southern occurs because the seasons flip at the equator, right? And the size difference is due to differences in land masses (more in the north), right?
WebHubTelescope
Thanks or the analysis. See my outdented response below
Haynie has many nice slides, but I think that he is not giving proper weight on what happens to the land based biosphere. His explanations based on oceans and ice cover are in some cases in contradiction with the isotope data that he presents. It seems, that he didn’t notice that some isotope ratios didn’t follow at all his explanations.
I haven’t studied it myself, but I believe that the land areas are much more important for the internannual and year-to-year variations than oceans or ice cover. There are all kind of correlation, which makes it difficult to draw conclusions of such comparison alone that Haynie presents. Thus it’s not surprising that he may find nice correlations and interpret them erroneously to indicate direct causal relationship, when the true reason is elsewhere and affects both sets of data.
I think that the most recent analysis of WHT is quite reasonable, but finding out, how the causal relationships work requires a different approach that includes analysis of the actual local processes. Comparing data at the level that Haynie has done is not nearly detailed enough. Such an analysis may provide new ideas, but cannot tell, whether they are true.
Thanks Pekka
Do you have any suggestions to good reviews?
In a quick search for “co2 fluxes phase lags northern southern annual”, I happened across the following:
Changes in the Phase of the Annual Cycle of Surface Temperature A.R. Stine et al.
Ocean has a 56 day lag, the land a 29 day lag. They see the phase shifting earlier and amplitude changing. The IPCC models do not show that.
Regional CO2 and latent heat surface fluxes in the Southern Great Plains: Measurements, modeling, and scaling
They explain some of the issues. e.g. see Fig 7.
I cannot propose anything specific in addition of what WHT has already referred to. The problem is that fully realistic analyses get very complicated. The data is fragmentary, but it’s total amount is rather large. Checking, what agrees with all the fragmentary information cannot be done so easily. In practice the big models of the Earth system are used very much in that. The big models have been built to summarize very much of the fragmentary knowledge.
The big models have their weaknesses, but the are very helpful in tasks like the interpretation of variations in CO2 concentration assuming that such a version of the models is used or developed by addition of some new modules that is detailed enough on the particular issue being studied. In doing such research the most relevant details must be checked carefully, because anything that hasn’t previously be subject of a detailed study may be represented poorly in the model. The great value of the model is in it’s capability of providing the general settings correctly enough to allow for a reasonable interpretation of the specific details.
Without the help of a big model one is likely to overlook some very essential mechanisms or to be unable to take them into account at an even remotely correct way.
The models provide a laboratory infrastructure for studying detailed ideas is roughly realistic general settings. When that’s done, it’s often possible to extract the relevant details and state with fair confidence that nothing essential has been overlooked. It’s even possible to make additional points to prove that the most likely issues don’t affect the conclusions too much. Such a combination of the use of models and direct arguments is often a very valuable approach. When that’s taken, the direct arguments explain the point and the role of the model is to confirm that nothing essential has been overlooked.
Trying to do the same without the support of the model leads often to overlooking something essential, or at the minimum leaves a great uncertainty on, whether that has happened.
Using the same models all around leads to a risk of confirmatory bias, but the severity of this risk varies greatly depending on the issue considered. In many cases on can be confident that the risk is small, but in some others it’s so large that the approach doesn’t work in the same way. I think that the issue of variations in CO2 concentration belongs to the first class, while the problems on the role of clouds in warming belongs to the second.
WebHubTelescope
Thanks for the CO2 fit at Mauna Loa. That phase lag is
Re:
Solar alone varies approximately by calendar symmetry. BUT apply Kepler’s elliptical orbit and that modulates insolation by the inverse square law for accurate calculations. For details see:
Solar Position Algorithm for Solar Radiation Applications, Ibrahim Reda and Afshin Andreas Jan. 2008 NREL/TP-560-34302 which includes orbital calculations.
Partly:
1) Thermal heat capacity varies due to differences in land vs ocean.
2) Terrestrial vs ocean biomass CO2 absorption and albedo vary with land vs ocean and time.
3) Arctic ice cycling varies temperature and salt concentrations, which in turn vary CO2 desorption / sequestration.
4) Earth’s magnetic field varies differently from geographic field. Consequently cosmic rays and solar wind interaction are modulated differently north vs south
5) Cloud levels and cloud feedback vary differently between north/south hemispheres because of 1, 2, 3 and 4.
So while the differences are approximately due to the “land masses”. As an interested observer, I think there is much more detail that needs to be included to sort out CO2, H2O, vs insolation vs clouds as climate drivers.
Your fossil fuel – diffusion model provides an interesting correlation.
However, I understand that David Stockwell’s solar accumulation model provides similar or better correlation than CO2. It further explains the 90 deg lag of temperature behind solar insolation.
The solar (insolation) differences north vs south appear to give corresponding 90 degree temperature lags.
They also appear to give 90 degree CO2 lags.
I interpret these factors together as appearing to give better evidence for Stockwell’s simpler solar accumulation model with a better fit and better predictions, compared with IPCC’s fossil fuel CO2 driven model.
Do you have any better explanation for the ~90 degree temperature and CO2 lags behind the insolation? See Pekka’s comment on needing further detail than Fred Haynie provides.
PS Thanks Edim for the affirmation that my “eyeballing” was in the “ballpark”.
Sure, it is one step at a time for me. I am still on the track of explaining the CO2 completely and then looking for other physics evidence to tie it to the temperature. Or not, as the case may be, as it could dead end and lead to a solar model.
Solar and orbit eccentricity are still I think the best candidates for the long, long term changes in climate. Whether it can do it in short term, beats me.
Temperature on the scale of dozens of years tracks absolute [CO2]
Temperature fluctuations track d[CO2] fluctuations at short term intervals.
So a 90 degree phase shift is always possible if the observation is sensitive to changes as a response function.
Stockwell has a plot showing cumulative insolation. I have never seen a cumulative plot that isn’t monotonically increasing so I am not sure what he is driving at there. The cumulative seems to go both up and down. These little quandaries usually stop me dead in my tracks.
WebHubTelsescope
Re: “cumulative plot that isn’t monotonically increasing”
Apologies for not being precise:
Stockwell summarizes:
ie subtract the mean to get the anomaly. That eliminates the monotonic increase. Accumulating the variation about the mean shows the correlation with temperature lagged by 90 deg (Pi/2). i.e. 2.75 years lag for 11 year solar cycle, or 3 months for annual cycle.
Pekka & WHT
Well, speaking of “unexpected”, see:
Plants gobbling up CO2 – 45% more than thought
Lisa R. Welp, Ralph F. Keeling, Harro A. J. Meijer, Alane F. Bollenbacher, Stephen C. Piper, KeiYoshimura, Roger J. Francey, Colin E. Allison & Martin Wahlen (2011): Interannual variability in the oxygen isotopes of atmospheric CO2 driven by El Niño.
29 September 2011, Vol. 477, Nature 579, 579-582. doi:10.1038/nature10421
Matthias Cuntz (2011): A dent in carbon´s gold standard.
29 September 2011, Vol. 477, Nature 579, 547-548.
I don’t doubt it. The time for CO2 to cycle through the land and atmosphere is much shorter than the cycle time through deep sequestering stores. That is the difference between residence time and adjustment time. The biota never have been able to adjust to the extra carbon outside of the regular carbon cycle, and needs that slow deep sequestering to remove the excess.
BTW, I finished up a real neat derivation yesterday that expresses the time profile of carbon as it does a random walk into deeper layers.
http://theoilconundrum.blogspot.com/2011/09/derivation-of-maxent-diffusion-applied.html
It is one of those entropy-based derivations that is so elegant that it likely captures the fundamental statistical physics spot on. I have a feeling that no one has done this before because it uses not one but a pair of mathematical identities to generate the long-tail solution.
WebHubTelescope
Thanks for the derivation of CO2 diffusion.
I expect that CO2 diffusion is likely similar to thermal diffusion(?). e.g., See Spencer’s ocean diffusion models.
I think that is a good way to think about it. Unlike charged particles, which can move about due to an E-field, or mass particles, which can move around by gravity, thermal energy can only move by diffusion. That is the basis of the Heat Equation. That being said, heat can also move by convection but the motion is controlled by the medium and not by diffusion across a thermal gradient in that case, i.e. heat is only being carried along.
I used to do experiments and theory of dopant diffusion for semiconductors. The diffusion of dopants matches that of CO2 because dopants are uncharged particles in which gravity plays no effect. Dopants diffuse through a solid while CO2 diffuses through earth or water (and also moves around by the convection of ocean currents).
So the scenario is exactly the same as a dopant atmosphere: a carefully controlled vapor pressure of dopant gas is applied to a heated semiconductor wafer inside an evacuated chamber. Dopants fly back and forth between the surface and the chamber interior, while only a few of the dopants start diffusing into the bulk of the semiconductor. That is the same as sequestering the dopants (i.e. sequestering the carbon). The dopant atoms at the surface also go through a mini “carbon cycle”, hopping about the surface until they decide to fly-off and later re-ttach, but those are irrelevant to to the dopants that start migrating deeper into the surface. The migrators are the only ones that matter to sequestering of the dopant population.
The math on this diffusion behavior is very well known and if it wasn’t, none of these computers we use would be as fast and reliable as they are. I am simply taking the math from that discipline and applying it on a grander scale, smearing out the diffusion coefficient to match the different diffusion rates and smearing out the earth/atmosphere interface.
I have written papers on some very weird diffusion behavior corresponding to dopant ion implantation, but this CO2 adjustment time is just classic run-of-the-mill Fokker-Planck diffusion. The impulse response has the correct characteristics of a fast drop-off but then a long-tail.
Murray Salby is probably right on his results, but I don’t think he realizes how insignificant that is with respect to what actually happens during diffusion.
WebHubTelescope
On CO2, see Net Primary Productivity, especially Ocean Productivity
Note the major net primary productivity differences with latitude:
Primary Productivity in the Oceans
Contrast land net primary productivity the peaks in August in the No. Hemisphere.
We are so familiar with the No. temperature cycle that we may not notice the southern dominance of the ocean productivity for the other half of global productivity.
The ocean productivity is large, but the lifetime of phytoplancton is very short. The annual productivity (67 Gt) is 20 times larger than the amount of marine biomass at any moment (3 Gt). Thus there cannot be much accumulation of carbon in ocean biomass. The total amount of land biomass may vary very much more than the amount of marine biomass.
The overall picture is not quite that simple, because there is also other organic carbon in oceans and it’s amount is much larger. This paper tells more about that and it’s role in carbon cycle.
http://www.tos.org/oceanography/archive/22-4_hansell.pdf
Thanks Pekka for that insightful article.
Even more amazing is 662 Gt of dissolved organic matter or 10 times the annual productivity:
WHT
Pretty picture?
Well, you make a very interesting statement, “..44 years is only a median, and a mean diffusion time does not exist..” which — a median with no mean — always makes an impressive graphic.
I admit, I’d guessed wrong about how dynamical CO2 residence time would be.
If your 44 year median stands up, does imply some conclusions might be possible for questions of the cost of reducing CO2 now vs. the cost of reducing CO2 later.
That’s some math I’d love to see through your point of view.
Either way, you appear to have considerably reduced much of the uncertainty of residence time questions. Good monster wrangling.
44 years? Who’d have guessed?
I guess the point is that you can’t deduce statistical moments from a diffusion-driven sequestering. I mentioned the median because half of the CO2 will get sequestered by the median time. It will take a long time for the random walk to eliminate the rest, and a moment calculation will diverge, so a median is all we get. That really describes the nature of a random walk. I don’t know why they can’t come out and say that instead of mentioning some meaningless number for a mean that can’t exist.
These are curves for media=50 years and you can see how it matches the mix of exponentials used by MaierReimer
http://img577.imageshack.us/img577/6595/maierreimerhasselman.gif
MaierRemer don’t even allow the concentration to go below a certain level.
WHT
Nicely done!
I won’t comment on utility (which speaks for itself), or take a run at the beyond-my-present-competency-math, but the format, presentation and tone are an excellent example.
Postma ought be taking notes.
Will you be adding pretty pictures?
BartR, Give me an idea of pictures that you would like to see. Everything I do usually gets a good typesetting markup for posterity, which is actually what is nice about Postma’s reports. But then again, making something look pretty is irrelevant if the theory turns out wrong.
Judith and Eric Ollivet are perfectly right.
The notion of “forcing” comes from the notion of “external force” where the word external relates to the boundary of the system being studied.
As the boundary may be arbitrarily chosen, by simply changing the choice of the boundary external forces become internal and vice-versa.
That’s why the use of the word “forcing” will always be ambiguous, misleading or outright false if it is not systematically accompanied by the full definition of the boundaries of the sytudied system.
Now the climate science often defines the system in a very curious and arbitrary way in that it is almost everything but the sun, volcanoes and the anthropogenic CO2. Sometimes but not always also aerosols are excluded.
Strangely the non anthropogenic CO2 stays internal to the system.
No wonders that with such unnecessarily contrieved boundaries, the arguments get confused.
If the studied system is the Earth, then there are only two external forces and it is the extraterrestrial radiation (stars&Co) and the gravity (solar system) . If the low boundary is the litosphere, then it makes sense to add volcanic events because the dynamics of Earth’s interior is usually considered as not coupled to the rest (and it is not predictable anyway).
Everything else – clouds, temperatures, pressures, velocities, concentrations, biomass are the many fields internal to the system and coupled with each other in a non linear way. None of these fields is “cause” of another, they interact with each other and the patterns we observe (f.ex the oceanic oscillations or the cloudiness variations) are results of the internal dynamics imposed by the couplings.
It is perfectly possible and certainly clearer to talk about the system’s behaviour without ever using the words “forcing” or “feedback”.
By George, I think you’ve got it. Communication is 99% of the battle. (That is a descriptive use of a statistical term meaning “it’s a lot” in case James Annan is lurking).
I think there is a practical and needed distinction of forcings. If you are trying to model the climate, you will write down a set of relations, mostly pde’s that you want the state variables to satisfy. There will be variables that you want and expect to emerge from the solution of the system. And there will be some variables whose values you prefer to specify. The criteria for selecting those are:
1. They must be directly measurable
2. You need to be confident that in specifying them, you are not overriding one of the system relations that you want satisfied.
3. The values should be poorly determined by the system (otherwise let it solve for them).
Items 2 and 3 make the practical distinction of externality.
Here is the GISS Model E set of forcings. You can see that there are are included primarily under the third criterion – eg stratospheric water vapor. It’s partly anthropogenic, but as a practical matter, it’s variations are hard to couple to the climate system, and it can be measured.
Nick Stokes (24): you remind me of that old management issue, the risk of managing what one can measure instead of managing what one should manage, un-measurable stuff included.
Judith,
Interesting…still considering the planet as a cylinder and NOT an orb in circular motion.
AR5 on the HS? Is this a joke? How about reading what Mao had to say about the Great Famine?
Judith Curry
I think posters here are getting wrapped around the axle on “what Allan said”.
After reading the paper several times, I’d agree that Allan specifically discusses cloud forcing – not feedback.
However, as others have remarked here, and a poster named PaulM elaborated on the WUWT thread, certain statements are made in both the abstract and conclusions regarding cloud feedback. These imply a net negative feedback from clouds.
Nitpicking on the difference or on “what Allan really wanted to say”, as some are doing here, does not get us anywhere.
The easiest way to clear up “what Allan really wanted to say” is to ask him that question directly.
Naturally caused changes in cloud forcing would have the same impact on climate as changes in cloud feedback to natural forcing (or changes in cloud feedback to anthropogenic forcing, for that matter). So this is essentially a pointless “chicken and egg” discussion.
S+B 2011 concluded:
As I recall, you basically agreed with this conclusion.
To summarize my understanding:
60% to 70% of our planet’s surface is covered by clouds at any one time (several sources).
The net forcing from clouds is strongly negative (Allan and others), due to the reflection of incoming SW radiation, which far exceeds the GH effect of slowing down outgoing LW radiation.
How clouds react to warmer surface (or tropospheric) temperatures is uncertain; some studies based on satellite observations (S+B 2007) suggest they exert a net negative feedback, but IPCC (data prior to 2006) concedes ”cloud feedbacks remain the largest source of uncertainty”. IPCC models all estimate a net positive cloud feedback.and no natural change in cloud forcing.
Whether or not natural changes in cloud cover have exerted a natural forcing is not clear, but rather probable based on Earthshine observations (Pallé 2006), although the exact mechanism for this is not established.
The CLOUD experiment at CERN may give us some partial answers to some of the unresolved questions.
Is my (obviously simplified) understanding correct?
Max
all ok except for this statement:
“However, as others have remarked here, and a poster named PaulM elaborated on the WUWT thread, certain statements are made in both the abstract and conclusions regarding cloud feedback. These imply a net negative feedback from clouds.”
Forcing (or whatever it is called) cannot be used by itself to infer a sign of the cloud feedback
Even changes in forcing can’t be used to infer the sign of cloud feedback. This is because changes in forcing may be accompanied by changes in the clear sky response that are masked by the clouds and which would alter the final direction of the effect – see, for example
Cloud Forcing and Feedback.
In the above comment, I used “forcing” to refer to the phenomenon of “cloud forcing” that Judith Curry appropriately considers misnamed. Maybe a law should be passed making it a crime to use the words “forcing” and “clouds” in the same sentence.
I think I’ve never heard such force
As when a cloud is in its course.
==========
Judith Curry
I do not want to get into any kind of debate on the cloud forcing versus feedback issue, which may, in fact, be more of a discussion of semantics or arbitrary definitions.
However, I would just point out that Allan states (regarding his Figure 7, which shows a significant drop in the cloud forcing anomaly during the warmer than normal El Niño years 1998 and 2010):
and, further down:
Poster PaulM on WUWT interpreted this as a possible indication of negative cloud feedback with warming. But, again, we may have a “chicken and egg” problem here.
That is where the suggestion has come from, right or wrong.
Max
Dr. Curry,
You write “Forcing (or whatever it is called) cannot be used by itself to infer a sign of the cloud feedback”
I do not think I can agree with this. If the radiative forcing is only minimally cooling, then I could agree. But the net cooling, as estimated by Allen, is so strongly cooling that I do not think it is possible to avoid a negative cloud feedback. While Allen has not attempted the calculation, it appears he thinks so too or why would he mention cloud feedback in both the abstract and the conclusion?
The AR4 clearly links forcing to feedback. See this quote from AR4
“Figure 10.11a shows globally averaged cloud radiative forcing changes for 2080 to 2099 under the A1B scenario for individual models of the data set, which have a variety of different magnitudes and even signs. The ensemble mean change is –0.6 W m–2. This range indicates that cloud feedback is still an uncertain feature of the global coupled models (see Section 8.6.3.2.2).”
In this case, AR4 is predicting only minimal cooling from radiative forcing changes (and some models apparently show radiative warming). But the conclusion is that feedback is uncertain. A clear and unavoidable link there.
Go through the mathematics in chapter 13 of my book, it will become clear that you cannot infer feedback from a forcing. Further Allen addresses only one component of cloud forcing, associated with cloud area and vertical distribution (does not include the optical depth “forcing”.)
That is certainly a discussion killer. Unfortunately, I haven’t the time to read chapter 13. However, based on my experience, if one of the inputs is very high it alone can be enough to dominate and determine whether the result is positive or negative. The estimate by Allen is much higher than I normally see. It may be that optical depth forcing may be large and approaches Allen’s estimate of radiative forcing, but I doubt it. Based on Allen’s abstract and conclusion, i think he doubts it too.
Judith
I found your Ch 13 Sect. 13.4 Cloud Radiative Feedback at
Thermodynamics of atmospheres and oceans By Judith A. Curry, Peter J. 1999, Webster (c/o Google Books)
Have you or others extended your discussion to include solar/galactic modulation of clouds? ( I did not see it my rapid scan.)
Can the impact of solar Forbush events on clouds with the time lag to cloud changes and the time lag to ocean temperature changes and CO2 changes be used to identify feedback?
Forbush decreases – clouds relation in the neutron monitor era
(Would “cloud modulation” be a usable term?)
PS See WebHubTelescope’s post above and his remarkable correlation of dCO2 with T with zero lag.
Compare Stockwell’s 90 deg lag of temperature behind solar in the solar cycle and in both SW and LW in Spencer’s data.
Items 2 and 3 make the practical distinction of externality
Certainly not.
The boundaries of the system are not changed by an arbitrary decision to explicitely prescribe this or that variable in a model
These variables might indeed be external to the computer (because I force them inside by hand) but they still stay perfectly internal to the real system and therefore it’s inappropriate to call them “forcing”.
Call them “variables specified by hand in a model” because that’s what they are but not “forcings” because this term had been reserved for forces external to the real system for centuries.
Of course it is also possible to specify the system’s boundaries so that this or that real phenomenon becomes external instead of internal but this would be clumsy at best.
It is really not necessary to add to the confusion by using misleading terms.
The various partial derivatives of X wrt Y, with almost everything else assumed constant and found floating around in Climate Science, and generally limited to a linearized approach, are zeroth-order sensitivities, no other label is necessary.
Labels of forcing and feedback are additional examples of extreme over-simplifications that have been employed to make the situation seem to be completely understood, in depth. Analogous to the use of temporal chaos concepts, sucked into Climate Science solely by osmosis and lacking any firm theoretical foundation. And analogous to use of surface temperature data and models when all the important radiative-energy transport physical phenomena and processes occur far from the surface. And analogous to use of two-box models for which the boxes do not correspond to the correct locations in physical space relative to the physics said to be modeled and lacking a model of the physical processes that couple the boxes in the physical world.
While simple, limiting-case approaches might provide some information in some special situations, such approaches must be connected with the physical world of interest in order to provide useful insight.
I add on more view on, what are feedbacks and forcings.
The more general basic division is between external input (or control) and feedback. External is anything determined outside the chosen system boundary and feedback is everything in the reaction of the system, which is not considered to be direct influence of the external input. External inputs include solar radiation and changes in the environment caused by human actions. Human choices are in this considered not as unavoidable reactions to changing world, but independent of that and therefore external to the system.
Forcing is not part of that division, but an additional concept that may be related both to the external input and the feed back. More specifically the discussion is concerns usually radiative forcing. Radiative forcing is the contribution of any specified factor on the radiative energy balance of the Earth system.
Radiative forcing of additional CO2 is the change in radiative energy balance, when everything else is kept fixed.
Radiative forcing of the clouds is the difference in the energy balance between the actually present clouds and no clouds. The forcing can be determined also for a change in cloudiness.
Cloud feedback is the effect on the temperature of the change in clouds, which results from the warming initiated by whatever reason. It’s the difference in the change in temperature in two cases: the actual change and the hypothetical alternative, where other factors change, but clouds are unchanged.
Clouds cannot be external input, but they can be influenced also by natural variability. Thus the cloud forcing can vary both as feedback to external input related warming and as part of natural variability.
Pekka
You have just confirmed to me that the distinction between “forcing” and “feedback” is not an absolute scientific distinction, but one of relative definition or even semantics.
If “forcing” in W/m^2 is defined as having to come either from outside anthropogenic factors or directly from the sun by changes in solar irradiation, then that is an arbitrary restriction that excludes any other forcing factors of which we are not aware.
Feedback, on the other hand, is the response to a change in temperature in W/m^2 degC (from the GHE of added water vapor assumed to have evaporated due to warmer temperature following Clausius Clapeyron, for example)
For convenience, IPCC lumps all feedbacks into a model-derived (2xCO2) climate sensitivity.
Have I understood this correctly?
Max
Max,
My view is that the concepts are in different dimensions. Thus it’s doesn’t make sense at all to discuss, whether there is a distinction between the concepts.
There can be forcing from direct influence and there can be forcing from a feedback. The concept of forcing is neutral on the source of the the radiative influence.
The total energy balance at TOA is the sum of all forcings. Thus the net forcing is zero, when there’s no change in the heat content of the Earth system, while it’s positive, when the heat content of the Earth system is increasing.
Doubling suddenly CO2 would lead to the imbalance of about 3.7 W/m^2 as an immediate effect, which would change when feedbacks start to influence the outcome. The Planck response would make the overall reaction to be a reduction in the forcing.
All divisions of the overall forcing to components are defined as a difference between two states. At least one of these states is always hypothetical and dependent on the details of the definition.
Pekka
Thanks for your explanation.
As you see, I have gotten a slightly different one from Fred, to which I have responded.
It appears to me that the definitions of “forcing”</em and “feedback” are not absolutely defined.
As I understand it, IPCC sees “forcing” as an external peturbation caused either by volcanoes, solar irradiance or anthropogenic factors, with all other peturbations, such as GH forcing from added water vapor, or the net impact from changes in cloud cover, classified as “feedbacks”.
This is a convenient categorization for theoretical considerations and model simulations but may be meaningless as far as the actual climate system is concerned.
Max
Max, the definitions in chapter 13 of my book are consistent with control theory as used by engineers and also by those that first applied the concept of feedback to the earth’s climate system. Your impression of how IPCC sees feedbacks is not correct.
Judith Curry
Thanks for tip.
I will read the cited reference in your book. If I still have questions or difficulties understanding how IPCC differentiates between forcing and feedback I’ll get back to you.
I was surprised that Fred and Pekka gave me two different explanations.
Since there appears to be some general uncertainty among the posters here, a separate post on this subject might be helpful.
Max
It is not uncertainty, it is a conceptual confusion. Uncertainty suggests that research can resolve it, but the concepts themselves are incoherent in practice, in complex systems.
”the distinction between “forcing” and “feedback” is not an absolute scientific distinction, but one of relative definition or even semantics”
As I understand the basic principles of semantics, words have no inherent meaning, but derive their meaning from the way they are used. Lexicographers generally accept a definition as clearly established when a word is used in a consistent fashion by individuals knowledgeable about the subject.
In the case of “forcing”, there appears to be some inconsistency, but at the same time, there is fairly general agreement about the most common use that I find convenient. By that usage, a forcing is a change in the radiative flux balance at the tropopause or at the TOA imposed by some cause external to the climate system. That includes changing solar irradiance, volcanic activity, and the extraction and burning of fossil fuels to yield greenhouse gases as well as anthropogenic aerosols. It would also include land use changes resulting from human activity. It would exclude changes arising within the climate system, including changes occurring in response to the above forcings – the latter are designated as feedbacks even though they too often alter the radiative balance. Again as a matter of usage, human decisions to burn fossil fuels based on climate factors are not considered feedbacks, because they don’t represent a response based on the laws of physics.
Now, one could define forcings in some other manner, but that would be inconsistent with the semantic principle that definitions are determined by how words are consistently used. In addition, the established use of the word happens to be quite convenient in distinguishing climate responses such as feedbacks from changes in radiative balance that are not responses to some previous climate change. I think it’s therefore reasonable to continue to use the tem in this manner.
Because a forcing refers to an imposed change in the radiative balance, it’s important to note that its magnitude (in W/m^2) is not necessarily synonymous with the magnitude of a radiative imbalance. The two would be the same only if the climate were in balance before the forcing was imposed. In IPCC usage, forcings are often described as a change since 1750, with the implication that at that time, the system was relatively balanced. That was probably not wildly inaccurate, but it’s unlikely that the radiative flux imbalance at that time was exactly zero.
Finally, because a forcing is defined as an imposed radiative perturbation, it can only change if the perturbing factor changes. For example, if CO2 is instantaneously doubled, the estimated forcing is 3.7 W/m^2, meaning that the system balance has been perturbed by that amount. The climate will respond to this imbalance by warming, so that the flux imbalance will decline back in the direction of zero imbalance. However, the forcing will not change – it will remain 3.7 W/m^2 as long as there is no further change in CO2 or other forcing modality. Forcing and perturbed flux balance are identical only before the system responds. This is important because at any given moment, the climate will be responding in proportion to the existing imbalance and not to the forcing if the two are different. When a balance is restored, the climate will no longer respond to the imposed forcing, although the restoration will presumably have occurred via a change in temperature.
To add to my statement above that “Forcing and perturbed flux balance are identical only before the system responds”, I should mention that the system response includes feedbacks that will either amplify or diminish the response to the forcing itself. The 3.7 W/m^2 for doubled CO2 represents the change from the forcing alone, before any modification by feedbacks.
Fred,
The use of the word “forcing” has included both the cloud forcing and the forcing due to the doubling of the CO2 concentration. Both fall within the definition that I propose – and I proposed it, because it agrees with usage in a consistent way more generally than the one related to external influence.
While the overall imbalance varies, when the state of the Earth system changes, the specific components may remain unchanged, because they are defined through artificially defined hypothetical comparisons that don’t correspond to any pair of actual states of the Earth system at two different times.
Because there are many different forcings, further specification is always needed and there is nothing to loose by allowing that to include also effects related to factors internal to the Earth system.
Pekka – I see the term “cloud forcing” as inconsistent with the general use of the term forcing. I think it arose as an historical accident, but like Dr. Curry, I believe the term cloud forcing should be abandoned, and a different term substituted – e.g., “cloud radiative effect”. Most climatology literature that addresses forcing per se includes ghgs, aerosols, solar changes – i.e., externally applied perturbations – but does not include clouds. In theory, forcing could be redefined to include both externally imposed and internally arising perturbations, but that isn’t what the word means today as it is most often used.
As long as different uses of the word continue, the best we can do is to point out that they are being used to denote different concepts.
Here is the wikipedia description of Radiative Forcing as an externally imposed perturbation. Their description is consistent with the uses I’ve encountered in the literature (as well as the IPCC use). I think it’s convenient to use the term that way, although we probably couldn’t change it even if we wanted, but again, this is a matter of the process by which words are defined. It doesn’t change the physics.
Fred,
Actually the article of Wikipedia supports my view that the radiative forcing is equally applicable to all factors that influence the balance at TOA. Of course Wikipedia is not an authority on the issue, but it’s usage is likely to be rather common.
The article states that the IPCC usage is more restrictive than the general usage. In addition the IPCC usage includes also indirect effects, which are strongly influenced by the feedbacks.
Thus we disagree on the actual usage of the word. So far I have seen many cases, where it’s used also for effects that are due to internal mechanisms of the Earth system and no evidence that such usage should be discouraged.
From the wikipedia article: In the context of climate change, the term “forcing” is restricted to changes in the radiation balance of the surface-troposphere system imposed by external factors
That is the usage I’m familiar with. For example, in the thread regarding the Padilla paper, forcing involved solar changes, volcanism, and anthropogenic emissions. Clouds were not included, but were subsumed within the analysis as feedbacks. I think that if the term forcing is to be extended in a particular discussion, that should be made clear.
The problem is largely due to several different implied system boundaries related to the word forcing.
Radiative forcing operates at the physical boundary of the Earth system at TOA and applies to one variable only: the heat content of the system inside this boundary. In a sense we consider a system of only one variable. All other variables are outside this “system boundary” including all details of the Earth system. Clouds influence the radiative balance and contribute to the radiative forcing.
The other concept is external forcing on the climate system. For this clouds are internal for the system, and any change in clouds is response to something, which can be external forcing or some change in other internal variables of the climate system.
Cosmic rays provide an external forcing that operates through clouds (I do not wish to imply anything about the significance of this influence, it’s just a clear example). Clouds are the internal factor in the climate system that’s influenced most directly by the cosmic rays. Thus clouds transform the external forcing in form of cosmic rays to a form significant for the climate system. The change in clouds is the most direct mechanism for radiative forcing due to cosmic rays.
Some changes in ocean circulation may also operate most significantly through clouds.
Fred
Without asking you to restate the whole of our understanding of climate science, why does everything have to be couched in terms of radiative balance? There is more to the climate system than just temperature, surely? Who is to say this conceptual framework of radiation is the correct one to use when it seems to preclude many of the other working parts or relegates them to the status of causal effects? Like clouds for example.
Rob – I agree that not all climate change involves radiative balance, but I wasn’t trying to describe all climate change. Radiative balance is important to an understanding of the relationship between agents known to change it and temperature. I was trying to state my understanding of the terms used to describe this.
Fred Moolten
Thanks for clearing up how you see the meaning of “forcing”.
As you have described it, this would include flux imbalance resulting from changes in cloud cover caused by a natural mechanism (whether or not that particular mechanism has as yet been clearly defined or quantified).
If the changes in cloud cover were caused as a result of temperature change, which in turn was caused by another (natural or anthropogenic) external forcing factor, then the resulting flux imbalance would be defined as cloud “feedback”.
So much for the theory. The problem will be, of course, to separate the two .in real life (as I believe S+B 2011 have concluded and our host here has agreed).
Max
The definition I see most often used restricts forcing to changes imposed from influences external to the climate system. Clouds would not be included.
To amplify on the foregoing, aerosol effects include direct changes and changes mediated by the effects of aerosols on clouds (e.g., longer cloud lifetime and smaller droplets). However, it is the aerosols in each case that are the forcing agents.
Sorry to reply in such piecemeal fashion, but I do agree with your general statement “this would include flux imbalance resulting from changes in cloud cover caused by a natural mechanism”, provided that the “natural mechanism” is external to the climate system. In that case, it would be that mechanism and not clouds that is the forcing agent. Some have argued for cosmic rays as a forcing mechanism, and although there are doubts about its magnitude, it would fit your definition.
Fred,
“To amplify on the foregoing, aerosol effects include direct changes and changes mediated by the effects of aerosols on clouds (e.g., longer cloud lifetime and smaller droplets). However, it is the aerosols in each case that are the forcing agents.” Which aerosols? The natural occurring ones or the anthropogenic ones? R. Allan uses the term cloud radiative forcing. If you Google “cloud radiative forcing” you get a bunch of hits.
To me, since the terminology is based on control theory and has been used in Meteorology, “Cloud forcing (sometimes described as cloud radiative forcing) is, in meteorology, the difference between the radiation budget components for average cloud conditions and cloud-free conditions. Much of the interest in cloud forcing relates to its role as a feedback process in the present period of global warming.” , the first usage was not climate science. That quote is from Wikipedia BTW.
In Dr. Curry’s book, cloud radiative effect is used to avoid confusion. You can use any definition you like, but you should consider the term in the context of the discussion.
Dallas – I agree that the existence of the term “cloud radiative forcing” introduces confusion into our ability to distinguish different concepts, because “forcing” is used there in a manner different from its ordinary use to denote a flux perturbation imposed from outside the climate system. That is one reason why I, like Dr Curry, would prefer a different term for the cloud effects.
If you read the literature, you’ll find, I believe, that outside that particular deviation from ordinary usage, forcings are all external. They include both anthropogenic and natural factors – the former include ghgs and anthropogenic aerosols, and the latter include solar changes and the aerosols from volcanoes. What I don’t believe you’ll find in the literature often, and perhaps never, is a conflation of the two uses in the same context. For example, in the transient climate sensitivity thread, papers by Padilla et al and Gregory and Forster both combined the forcings I’ve described above, but in neither case did they add a fifth forcing – clouds. To do so would have been totally confusing, because it would have become impossible to add their effects, cloud changes already having been included in the responses to the listed forcings.
Not only is their nothing wrong with evaluating the effect of clouds on radiative flux, but it’s necessary for a complete understanding of climate change. What is confusing would be an attempt to add that together with external factors in a way that allows the same cloud effects to be counted twice – once as an independent variable and again as a response to external factors. My experience with the literature, as exemplified by those two papers, is that this isn’t done, and the phenomena are treated separately, with “forcings” reserved for the externally imposed perturbations. Until this usage changes, I think it would be make it easier for everyone to understand “forcings” to mean the same thing, and use a different term for something outside that concept.
If you can find counter-examples in the literature, where ghgs, aerosols, and solar changes are listed together with clouds, all as forcings in the same context, I would be interested in the reference. I think there would be few such examples for the reasons I cited.
Fred, G&F and Padilla are both modellers, they should not have an issue. The issue is communicating with the rest of the scientific community and the general public. You have been studying the climate modelling side for quite some time, you feel you are fine with the terminology. Meteorologists, engineers, etc. are the ones evaluating the model results and having issues. How do they communicate to you that natural variation produces cloud radiative forcing in the meteorological sense. To the modelers, natural variation is an “Unforced variation”, but that natural variation can produce decades long changes in the system with significant impact on “climate” not “weather”.
Fred Moolten
Thanks for additional clarification on distinction between “forcing” and “feedback” as seen by IPCC.
So any natural mechanism causing a change in cloud cover, for example, would be considered a “forcing”.
If this is the case, it would obviously apply whether or not we are absolutely sure of the magnitude (or even the direct root cause) of such a “natural mechanism”.
Which translates to me that natural mechanism “X” may well be causing the changes we have observed in cloud cover (Palle et al.). but, we have simply not yet been able to identify and quantify natural mechanism “X”.
This would apply equally well for the case that an empirical correlation has been observed between the postulated natural mechanism “X” and our climate’s response (as Svensmark et al. have shown is the case for the “cosmic ray” example you cite) or that no such correlation has at yet been identified, simply because we are still in the early stages of figuring out what makes our climate do what it does.
This would tend to confirm the conclusion of S+B 2011:
Seen in this light it all makes sense to me.
Max
Fred
Further to earlier comments:
As regards the fine line in practice in our climate system between “forcing” and “feedback”, the French might say:
(A reaction our host here might support.)
Max
Sorry for typo. Should be “incertitude”, of course.
So any natural mechanism causing a change in cloud cover, for example, would be considered a “forcing”.
Yes, as long as the natural mechanism is external to the climate system.
Again, not to belabor the point, but this is simply so that the word “forcing” is understood to mean the same thing in the mind of the person who writes it and the person who reads it. Mechanisms internal to the climate system are important, but are not ordinarily included in the forcing concept.
Fred Moolten
Agree with your last post that a “forcing” is a <em"natural [or anthropogenic] mechanism [which] is external to the climate system".
IOW changes in clouds themselves cannot be a “forcing”; these are induced by some external natural -or anthropogenic – mechanism, in which case that “mechanism” becomes the “forcing”.
As seldom as this may be the case, it looks like we agree on this one.
Does this mean that “increased atmospheric CO2 levels” are not a “forcing” in themselves (anymore than “increased cloud levels” in the previous example), but that whatever “external mechanism” is responsible for these increases (human CO2 emissions?) is the actual “forcing”?
Max
PS I’m still going to read Judith’s book to see if I got it right..
Max,
Your recent comment tells reasons for my view that the usage of Fred’s liking is problematic in practice.
CO2 contributes to radiative forcing, clouds contribute to radiative forcing. Humans contribute very little directly to radiative forcing and cosmic rays even less. Human influence and cosmic rays are, however, inputs to the climate system and lead indirectly (through atmospheric CO2 and clouds) to radiative forcing.
As far as I know forcing is not a common concept in control theory. Thus control theory doesn’t provide guidelines for the proper use of this word. It would be best to use it only for radiative forcing and for that as a synonym for the radiative imbalance at TOA (or some other surface). Making a search with words forcing and feedback in Goolgle brings mainly climate papers. In the few other articles the relationship between forcing and feedback is mostly not that of external input and feedback.
Much of the use of the word forcing in climate science is in accordance with that wish, but all too often is confusion created by other usage.
Pekka
Just when I thought I’d finally gotten it regarding the definition of “forcing” and “feedback” in climate science parlance, you throw me a new slant…
Fred gave a fairly succinct distinction between the two, IOW changes within the climate system themselves (i.e. increased cloud cover, higher atmospheric water vapor content or higher atmospheric CO2 content) cannot be a “forcing” per se; these are induced by some external natural -or anthropogenic – mechanism, in which case that “mechanism” is, by definition, the “forcing”.
A “feedback” is a reaction [in Watts per square meter per degree C] to changes in temperature [degrees C] caused by a “forcing” [Watts per square meter].
But you appear to see this rather less definitively than Fred.
For now I;ll stick with Fred’s definition (which I can understand), but I’ll keep in mind that it may be more complicated than that.
Max
Max,
All concepts have their own problems. The whole concept of feedbacks is taken from connections described well by a small number of variables, while the climate involves an infinity of variables.
In control theory we consider typically one controlling signal and one variable that describes the state of the system. Sometimes there are a few more variables, but most of the discussion is based on the minimal case. The interest in often in to the dynamics of the system, while the steady state problem is so simple that it requires little attention.
This is brought to the climate system having the two variables of radiative forcing and average surface temperature. It’s not possible to restrict the radiative forcing to any specific sources, because it’s one of the two variables needed.
The analog with control theory is as follows:
1) An external influence causes the radiative forcing to change.
2) The change in radiative forcing leads to warming, i.e. change in the temperature.
3) This change in temperature modifies through the feedback the radiative forcing.
The loop of steps 2) and 3) continues until a new balance is reached.
The point is that the step 3) modifies the radiative forcing, which means that internal processes influence the radiative forcing. Changes in that variable are not restricted to an external input.
Terminological confusion is created, when the word “forcing” is used both for the variable influenced by the feedback and restricting it to the external input alone. From the point of view of rate of change in temperature the imbalance is always radiative forcing. Forcing means here an energy flux that forces the temperature to change (this is really the meaning of the word.)
Taking the case of cosmic rays. One cannot say that there is a feedback to cosmic rays, because no feedback affects them. The feedback is again to radiative forcing, where the initial change in radiative forcing is induced indirectly by cosmic rays, but in this case the initial change involved also clouds, which are an internal part of the climate system. The feedback loop closes at the level of radiative forcing leaving the cosmic rays unaffected.
Many people, including Andrew Lacis, have used the concept of control knob to describe the role of increase in CO2, and it could be used also for cosmic rays. This is actually a better analogy, that leaves the radiative forcing to be just one of the two variables used to describe the state of the climate system without reference to the source of the change in its value. It’s still possible to calculate individual contributions to the radiative forcing like the 3.7 W/m^2 from doubling the concentration of CO2, when everything else is fixed or the feedback responses of various nature.
So there is apparently a great deal about the role of clouds and even the meaning of the term “forcing”.
And we are supposed to accept the idea this is good enough to make radical choices about public and industrial policy based on this?
>sigh<
"So there is apparently a great deal of doubt about the role of clouds and even the meaning of the term “forcing”.
Hunter, “So there is apparently a great deal of doubt about the role of clouds and even the meaning of the term “forcing”.” There is some “doubt” about the role of clouds, but the inconsistent use of “forcing” is a problem. Pekka’s example is a proper way to consistently use the modelling term “forcing” related to real world events. If you call aerosol reflection of incoming solar a forcing in the real world, you cannot say that cloud reflection of incoming solar is not also a forcing in the real world. Arbitrarily defining your system external parameters to allow one and not the other is silly outside of your specific model.
I do wonder how the model versus real world issue has impacted understanding of the real world. “Unforced variations” should be purely a modeling term IMHO. Natural variations can and do change radiative impact both by varying outgoing LW and incoming SW. The mechanism causing the natural variation may not be fully understood, but its impact should not be ignored.
I realize this is off topic but has anyone seen SkS attempt to debunk Salby’s upcoming paper last was discussed last month? I tried bringing it up on the appropriate thread but no one has responded.
http://www.skepticalscience.com/settled-science-humans-are-raising-co2-levels.html
Pekka’s clarification aptly sums up the use of the term forcing but IMO does not clarify the use of feedback, which also IMO needs further clarification.
I disagree with the use of the term ‘cloud forcing’ though I can understand why it is used in modeling circles. I think the better way to look at it is clouds are a feedback in the real system, meaning they RESPOND to forcings (warming, volcanoes, solar radiation, GCRs etc.) AND IN TURN AFFECT the radiative balance (both by reflecting and absorbing various radiation, as well as convective effects).
I don’;t really know control theory but I feel like this is the basic vernacular definition of a feedback. By contrast, lots of variables respond to forcings but aren’t feedbacks because they don’t affect the radiative balance when they change. Like, rising or falling sea levels (i’m not talking about ice/albedo).
Thus I think it’s meaningless to discuss separating cloud forcing from feedback. As a random engineer (with apologies to randomengineer) not a climate scientist. It’s a non-sequitur unless I deliberatively redefine words I’m used to using.
Actually I think there is a perfectly good reason to consider the “effects” of all the components in the system. For example, aerosols are both natural and anthropogenic and can create positive and negative impacts on the system. Clouds have natural fluctuations and should have fluctuations due to anthropogenic influence. CO2 is increasing due to anthropogenic activities and fluctuates naturally. There are some and quite likely many more indirect effects due to the interaction of various natural and anthropogenic changes. The system is complex enough without adding semantics. Science existed prior to climate science so climate scientist can be consistent with the use of borrowed terminology or create their own like every other new field,
I would like to know if Dr. Curry considers “Celestial driver of phanerozoic climate?”, Shaviv & Veizer 2003, to be an important paper standing the test of time. With citations continuing (google scholar tallies 161 and climbing), it would not seem to be a paper that authors are ignoring. Integrated over millions of solar cycles, there is a clear association of global temperatures with galactic cosmic ray flux, about 7 degrees C peak to peak, hothouse to snowball Earth.
The citation of Richard Alley in the post leads me to recall the handwaving at his Dec. 2009 AGU talk, where he stated there’s nothing besides CO2 to blame for the heat of the Permian-Triassic extinction (except maybe the coincident GCR flux minimum for the entire Phanerozic?), and that the lack of a temperature signal for a 10Be anomaly in the middle of the last major glaciation proved there was no role for GCR in climate study The latter is more subtle… Svensmark reasonably claims the cooling effect of clouds on our polar regions is reversed, since clouds are less reflective than snow and ice. Therefore, as the planet changes from a warm interglacial to a cold glaciation, the cooling effect on the planet of more GCR-generated clouds would lessen and even reverse to a warming if most or all of the planet were frozen, and the 10Be increase without a visible temperature dip in the middle of the last glaciation isn’t so convincing.
Is CLOUD just an early investigation, as Pappas states, or are we here because there’s been a steady drumbeat in the literature over the last 20 years suggestive of linkages to GCR ? To many, this looks more like the endgame. ANY role of GCR in the climate over the past 60 years only weakens the argument for catastrophic runaway warming, and the argument was weak to begin with.
It wasn’t “Richard Alley”; it was Richard Allen who was cited above. My error.
Greg Goodknight
You were right the first time.
Dr. Richard B. Alley gave the presentation entitled:
“Biggest Control Knob – Carbon Dioxide in Earth’s Climate History” at an AGU meeting on 15 December 2009
http://www.agu.org/meetings/fm09/lectures/lecture_videos/A23A.shtml
Watch the lecture yourself and find the boo-boos (you’ve pointed out one, already)..
Max
No, I was right the first time, My claimed error (sorry if I was not clear) was not in remembering the Richard Alley talk correctly; the error was thinking jcurry had cited Alley in her post. She had cited a Richard Allen.
In Shaviv & Veizer, we have an astrophysicist and a geochemist who independently found the same periodicity, one for 14C, the other for tropical sea surface temperatures, over a 500+ million year period, and it’s typically just ignored by the climate community as being something of concern only over geologic time.
I could question the presence of any feedback loop in the climate system in the absense of a divine, a biological, or more narrowly, a human component, so I shall.
There is a definition of a feedback loop based on information processing. The use of information about an outcome to modify the system in such a way as to bring it closer to the intended outcome.
For me, the notion of intent is fundamental.
The system may well contain elements that are regenerative, degenerative, or homeostatic by nature but their presence by themselves does not make for a feedback loop but maybe seen to operate as such with respect to some notion that an equilibrium (stable or unstable) is a control signal, an intention.
I would challenge whether an equilibrium or similarly self determined state represents information or at least external information.
There are many ways in which we can construct systems that produce outputs that are stabilised against fluctuation without those outputs being feedback. These may contain comparison of some system value to some reference value and a response proportional to the difference but that does not of itself imply that the output has been feedback.
Should we decide on our intent regarding the climate, our actions and the climate response could constitute a feedback loop.
Alex
I’m not sure we can look at the state of Easter Island today and the evidence of Easter Island’s ecology in the past and call its ecocide a myth.
The island hardly qualifies as pristine, and significant extinctions are catalogued for it.
All the discussion so far merely differs on mechanical detail; some of which have certain mythic elements.
Don’t we mean, “Myths About Easter Island’s Ecocide?”
Whoa? How’d that happen?
Is WordPress funky or what?
A reply landing in entirely a different thread.
So from Chapter 13:
“The relationship between the magnitude of the climate forcing and
the magnitude of the climate change response defines the climate
sensitivity. A process that changes the sensitivity of the climate response
is called a feedback mechanism. A feedback is positive if the process
increases the magnitude of the response and negative if the feedback
reduces the magnitude of the response. ”
This implies that climate sensitivity is variable. That, in turn, implies that if a value for climate sensitivity is given, it should be qualified for a time period, or even for a set of state variables of climate. I don’t remember it being presented this way anywhere.
I think you’re right, it’s just another one of those many things that is confusing in the discourse. I guess if you postulate stable climate for 10,000 years or whatever, the difference is more or less moot if the time frame is +/- now.
“This implies that climate sensitivity is variable.” Interesting huh?
Yes it is, but isn’t this another case of semantic tripping? So, sensitivity is usually specified where the given set of state variables is “whatever we have now, double CO2”. When we hear it should be the same for say, during the MWP, that’s based on 10,000 years of stability.
So to me, this is another time where the vocabulary in the field, doesn’t quite match my naive vocabulary as an average tech person, instead it’s defined in a specific case and then used like a general term.
http://www.realclimate.org/index.php/archives/2005/12/natural-variability-and-climate-sensitivity/ That links to a post by Raypierre on realclimate in 2005. While showing how silly Richard Lindzen was, Ray mentioned work in progress to estimate climate sensitivity during the last glacial maximum. You can take the estimates with a grain of salt because of the quality of the reconstructed data, but this paper, http://mgg.coas.oregonstate.edu/~andreas/pdf/S/schmittner11sci_man.pdf estimate a considerably lower climate sensitivity 1.5 – 3.5 with a 66% probability and a 0% change of sensitivity over 6 C. Current estimates are leaning toward the 1.2 to 2.5 range.
So whether it is terminology or not I don’t see clear communication of either the apparent reduction in the range of sensitivity or possible non-linearity of climate sensitivity. If climate sensitivity is significantly non-linear, it would seem likely it would decrease with warming temperatures.
Dallas – I have seen no evidence that current estimates of climate sensitivity have changed much in either direction from those of recent years, with a typical range of 2 to 4.5 C, and a mid-range estimate of 3 C or slightly below. The Padilla et al and Gregory and Forster papers discussed in the transient climate sensitivity thread, when translated into an equilibrium climate sensitivity (ECS) value, support these estimates. Because they are less subject to assumptions, their support is actually fairly strong evidence.
I’m referring to sensitivity estimates for CO2. Estimates regarding short term ENSO variations may be different, but probably have little relevance to the ECS values for CO2.
Actually Raypierre is at odds with the Belgian school in this area .A good example is Bergers pupil eg Crucifix 2006.
Four simulations with atmosphere-ocean climate models have been produced using identical Last Glacial Maximum ice sheets, topography and greenhouse gas concentrations. Compared to the pre-industrial, the
diagnosed radiative feedback parameter ranges between -1.30 and -1.18 Wm^2K-1, the tropical ocean sea-surface temperature decreases between 1.7 and 2.7C, and Antarctic surface air temperature decreases by 7 to 11C. These values are all compatible with observational estimates, except for a tendency to underestimate the tropical ocean cooling. On the other hand, the same models have a climate sensitivity to
CO2 concentration doubling ranging between 2.1 and 3.9 K.
It is therefore inappropriate to simply scale an observational
estimate of LGM temperature to predict climate sensitivity.This is mainly a consequence of the non-linear character of the cloud (mainly shortwave) feedback at low latitudes. Changes in albedo and cloud cover at mid and high latitudes are also important, but less so.
Fred, I imagine you haven’t. You look at the Padilla paper then figure how to multiply to get the standard spread for ESC. I look at the more recent estimates for ECS on Barton Paul Leviston’s site, compare them with Padilla and see a lower range.
Maksimovitch, “Changes in albedo and cloud cover at mid and high latitudes are also important, but less so.” I agree, it is interesting though that arctic cloud cover is increasing currently. Scaling ESC from the LGM to now is not something I would do. I do think that ECS should decrease with rising temperature, so it should be somewhat less than the LGM, but I am not sure how accurate the modeled LGM sensitivity is.
Dallas – My perspectives on ECS are not derived from those two TCS papers, although they are informative, but from the multiplicity of studies that have emerged over recent years. I haven’t seen much change since a few years ago, but if you have a comprehensive list of ECS studies relating to CO2 during very recent years that change the estimates from, say, the 2008 Knutti and Hegerl review, please cite your source or reproduce the recent (post-2008) list. I would have to say that the range from 2 to 4.5C is still a reasonably good estimate, even though one occasionally sees outliers in either direction. A 1.2 to 2.5 range is almost certainly too low for CO2, although not necessarily for short term forcings from ENSO or other internal climate fluctuations.
This is one subject where the temptation to engage in cherry picking is strong because of the range of estimates. It’s not a good idea, and I’m reluctant even to quote the two TCS papers except for the fact that there are few of these, and so the fact that two yield similar estimates is informative.
“This is one subject where the temptation to engage in cherry picking is strong because of the range of estimates. ”
That’s very true, and one reason a key question is: “How low would climate sensitivity have to be such that a business-as-usual path would not cause dangerous warming (conventionally estimated at +2C)?”
This answer is: really, really low. And even if the central estimate were that low, it’s hard to imagine that higher estimates could be confidently excluded.
That’s why I think climate sensitivity is basically irrelevant to the public policy side of the discussion (although fascinating as a science problem). Whatever realistic value you plug in, “Dangerous warming on a business as usual path” comes out. It’s as if you had been shot three times, and someone offered to call an ambulance for you, and you said, “Wait, what caliber was it?”
Robert
You wrote:
Let’s run that through a quick reality check.
The observed climate sensitivity lies somewhere between 0.8 and 1.5°C, depending on whether one accepts the conclusion by several solar studies that around half of the warming observed to date can be attributed to the unusually high level of 20th century solar activity (highest in several thousand years), or whether one accepts the IPCC estimate that this represented only 7% of the total forcing.
Hansen et al. postulated in 2005 that almost half of the expected warming since the late 19th century was still “hidden in the pipeline”, but recent ARGO measurements are casting this hypothesis in serious doubt.
This was based on model estimates of climate sensitivity compared to actual observed warming.
So, applying the circular logic employed by Hansen, we would arrive at a 2xCO2 climate sensitivity at “equilibrium” of 3°C (his model’s estimate).
On the other hand, if the Hansen assumption is incorrect and the solar scientists are right, it is 0.8°C.
That’s a pretty wide spread, but let’s stay with it for now.
IPCC has assumed several model “scenarios and storylines” for arriving at year 2100 CO2 concentrations.
The most realistic is “B1”. This is the “business as usual” case with no “climate initiatives”. It assumers that CO2 will continue to increase at the same exponential growth rate (CAGR) that it has most recently or over the past 50 years. This is 0.43% per year CAGR. On this basis, the 2100 level is around 580 ppmv and temperature would increase by 1.8°C, using the 3.2°C ECS as assumed by the IPCC models.
If we use the observed climate sensitivity instead, with IPCC’s assumption on solar impact, we arrive at 0.9°C.
And if we use the observed climate sensitivity, with the estimate of various solar studies on solar impact, we arrive at 0.5°C.
So you see that we have a range of 0.5 to 1.8°C, all of which are below the 2°C threshold you have arbitrarily described as “dangerous warming”.
Just to inject a bit of reality into all this, Robert.
(And, hopefully, to calm you down a bit.)
Max
Manaker you write “The observed climate sensitivity lies somewhere between 0.8 and 1.5°C,”
I am not sure that this is correct. I think the lower limit could be somewhere close to zero. The fact of the matter is that we do not understand all the factors which affect global temperatures, and so it is by no means impossible that none of the observed change in temperature has been caused by increasing levels of CO2. Only when we know precisely what causes all changes in global temperatures, on whatever time scale, can we claim that we know how much of the change is caused by factors other than CO2.
When you add the fact that there is no physics that allows us to estimate how much the change in the radiative balance actually affects surface temperature, and I find lower limit of 0.8 C to be very, very suspect.
Jim Cripwell
The climate sensitivity may well be below the range I cited as you stated.
My basis was
a) the observed HadCRUT3 temperature change from 1850 to today (with all its warts and blemishes, ignoring the possible distortions from UHI, land use changes, changes in measuring stations, etc.)
b) Mauna Loa observation for atmospheric CO2 level today
c) estimate of CO2 concentration in 1850 cited by IPCC (AR4) based on Vostok ice core data
d) 1. in one case, the highly questionable IPCC (AR4) assumption that 93% of the warming was caused by anthropogenic forcing and that this was equal to forcing from CO2 alone, with only 7% of the observed warming from natural (solar) forcing
d) 2. in the second case, the estimate by several solar studies that 50% of the observed warming can be attributed to the unusual high level of 20th century solar activity (highest in several thousand years), with the implicit suggestion that the remaining 50% was caused by human CO2.
I used these bases to show Robert that it is unlikely that warming by 2100 will be a problem, in order to ease his fears.
If we only look at the period since the new millennium started (January 2001) we see that the 2xCO2 climate sensitivity was around 0, so there is even less reason for fear of rampant global warming over the next nine decades of the 21st century.
Even IPCC, using an arguably exaggerated assumption for 2xCO2 climate sensitivity, only arrives at 1.8C warming by 2100 with a “business as usual” assumption on CO2 increase (and 0.6C, if CO2 was held to 2000 levels).
So there is really no rational justification for Robert’s fear, and I was just trying to calm him down a little.
Max
The concept of “radiative forcing” has been addressed with many comments in this thread. I thought it might be worthwhile to summarize my own perspective on the subject in one place, where it can be compared with perspectives stated by others
Let me start by listing three phenomena important to an understanding of temperature change as an element of climate change.
(1) Changes in the radiative flux balance at the tropopause (or less frequently estimated at the top of the atmosphere) imposed by an agent external to the climate system. The latter might be anthropogenic greenhouse gas emissions, anthropogenic aerosols, changes in solar irradiance, or aerosols emitted during volcanic eruptions. If the balance changes in the direction of an excess of incoming over outgoing energy (either via an increase in incoming or a reduction in outgoing), the climate will tend to warm. In the reverse case, it will tend to cool. As examples, either an increase in solar irradiance or a reduction in outgoing infrared radiation caused by an increase in CO2 can serve as a warming influence.
(2) Responses of the climate system to a temperature change, in which the response itself changes the flux balance. An example is an increase in water vapor due to warming, which itself mediates more warming because water vapor is a greenhouse gas. The subject of this thread, clouds, is often considered another example. These are generally referred to as “feedbacks”, although there is some objection to this terminology. Whether clouds play any other role was the subject of a previous thread and I won’t address it again here, but there is no controversy about the fact that they can act as feedbacks.
(3) Natural internal fluctuations of the climate system that are not clearly examples of either of the above, and often have an oscillatory character. ENSO, and the PDO and AMO are examples.
The term “radiative forcing” has typically been applied to item 1 above. This definition is evident in the wikipedia description of Radiative Forcing. In fact, that description includes the statement, “In the context of climate change, the term “forcing” is restricted to changes in the radiation balance of the surface-troposphere system imposed by external factors“. However, even that article is not unambiguous, because different descriptions within it of “forcing” are not completely identical. Different usages in this thread reflect some of these ambiguities. (However, I would add that the term “cloud radiative forcing” seems to conflict with the general nature of the definitions in that article, and as stated in earlier comments, there are reasons why that term is not the best way to describe cloud radiative effects. “Cloud radiative forcing” won’t be addressed further here.)
My own experience with the climate science literature echoes the description in wikipedia. As mentioned earlier, for example, both the Padilla et al and the Gregory and Forster papers in the recent transient climate sensitivity thread use “forcing” to refer only to the externally mediated radiative flux perturbations. I find this usage convenient for two reasons. First, it is the most common in my experience, and so if I read the term “forcing” in a paper, and assume it is restricted to external influences, I will probably have correctly understood the intended meaning. Second, there is a homogeneity to the concept that is useful, because the external influences share common features – they are not dictated by the climate state or a response to it, and they each impose a certain flux perturbation independent of each other, measurable in W/m^2, so that their contributions can be added together as a good first approximation of net effects.
If the term “forcing” is extended beyond this to include some of the “feedback” perturbers of radiative flux described in item 2, the utility of the term diminishes, in my view, because both independent causes of flux change and responses to those causes are being mixed together in ways that are hard to disentangle and may overlap. The same applies to item 3 – natural variations. By keeping the concepts separate, it becomes easier to estimate the way they interact with each other, and I suspect this is an important reason why the literature generally does this. I have seen forcings listed as the effect of ghgs, solar changes, and aerosols, and I have seen feedbacks listed as water vapor changes, cloud changes, and snow-ice/albedo changes, but I have never seen a paper that lists forcings as due to ghgs, aerosols, and solar changes, and additionally cloud changes and water vapor changes. If the usage of the term changes in the future, we will have to adapt to it, but I would be content to see that not happen.
An important element of the radiative forcing concept is that it is defined as a flux perturbation induced by an external cause whose magnitude is the quantity experienced by the climate before the climate has had a chance to respond (with the exception of some stratospheric adjustments). The common example is a doubling of atmospheric CO2, which is estimated to induce a positive imbalance of 3.7 W/m^2 in the absence of a climate response. The climate response to that imbalance will be to reduce the imbalance in the direction of zero (no imbalance) by warming to the point where outgoing radiation is once again sufficient to equal incoming radiation. Therefore, although the forcing will remain at 3.7 W/m^2 (by definition,) unless CO2 changes further, the flux imbalance will decline – i.e., “forcing” and “flux imbalance” are not synonymous. The conceptual value of a fixed value of forcing resides, among other things, in its utility in calculating a temperature change that at equilibrium (the perturbation completely erased) will have reduced the imbalance by the amount specified in the forcing.
Feedbacks (see item 2) are climate responses to forcings (and sometimes to natural variations), and act in the direction of restoring equilibrium by reducing imbalances. The principal restorative phenomenon is the “Planck Response”, which is the tendency of a body to radiate in proportion to the fourth power of its temperature (in degrees K) as described by the Stefan-Boltzmann law – i.e., a warmed climate will radiate more energy, a cooled climate will radiate less, and in each case therefore tend to reduce an imbalance. This can be expressed as a “rate”, where “rate” is not a function of time but of temperature. For the Planck Response, at climate temperatures, this is approximately 3.4 W/m^2/K, sometimes expressed reciprocally as about 0.3 K/W/m^2. “Positive feedbacks” that amplify a warming or cooling effect (e.g., a water vapor feedback) reduce the rate to less than 3.4W/m^2/K, so that a greater temperature response is needed to erase a given flux imbalance. Conversely, negative feedbacks, which diminish an effect, increase the rate to more than 3.4K/W/m^2, so that a smaller temperature effect can eliminate an imbalance.
Clearly, there are alternative ways to look at all these things, but I believe the perspective I’ve described has the virtue of consistency, and matches the use of terminology that is most common in the literature.
In the above, the statement “conversely, negative feedbacks, which diminish an effect, increase the rate to more than 3.4K/W/m^2“, should replace 3.4K/W/m^2 with 3.4 W/m^2/K, consistent with the use elsewhere in the comment..
Fred and Robert, I don’t know which reply thing will match so I brought it down here.
The Annan and Hargreaves paper made perfectly good sense, the fat tails are skewing the estimates. Per their paper the upper limit of 4.5 is highly unlikely and 4.0 is the likely upper limit. In the 2008 Knutti and Hegerl review you will notice that the fat tails are extremely fat in many cases and quite a few of the estimates are totally unrealistic, (near flat estimates ranging toward infinity it looks like for some.) The BPL estimate from 2000 to the end average 2.7-2.8
As far as the TCR estimates, the in the pipeline uncertainty is not evident in the OHC data. The ARGO data may not be perfect, but it has to be better than buckets.
Robert, One of the biggest issue is uncertainty. Having a more realistic working range allows better decision making. If you feel that inflating the uncertainty is desired, well hey!
Guys I was trying to a link to peer reviewed article on why it may be time to consider dropping the high end sensitivity fat tail. Well, it is poor form to site yourself, but I can’t find anyone that has used this example, so here is my justification for thinking about dropping the fat tail.
http://captdallas2.blogspot.com/2011/09/monte-hall.html
Knutti and Hegerl say this much about the choice of prior (emphasis mine):
The fat tails are due to two reasons:
1) The prior is usually chosen to be totally flat. Thus the prior doesn’t restrict the tail at all over the range shown as result of any particular analysis. (There is typically an artificial cutoff at some value outside the range shown.)
2) Empirical evidence is weak on the tail, because climate sensitivity is obtained by dividing the temperature change by the forcing, and because the lower limit of the forcing is typically low. It may be impossible to exclude any positive values. Actually the empirical setup is typically such that the value of zero for forcing and values of wrong sign (negative forcing for positive warming) cannot be fully excluded either. The probability of the wrong sign for the forcing is then the value of resulting conditional probability curve at infinite sensitivity. The tail of the conditional probability is so fat that it will stay above that positive non-zero value to infinity. The conditional probability curve can very well be non-integrable.
When we combine points 1) and 2) we get a non-integrable posterior probability distribution unless we cut the prior artificially at some value. That’s exactly, what’s being done and what Annan and Hargreaves criticized strongly. Without that artificial cutoff the standard methodology gives an infinite expectation value for the climate sensitivity and the posterior probability of exceeding any specified finite value is one. These conclusions are canceled by the artificial cutoff, and the results depend totally on the cutoff.
That result is not quite as nonsensical as it may seem, because the infinities reflect the possibility of run-away warming. The real run-away warming would be a nonlinear effect, but the infinities are the corresponding effect in linearized world. If we give any finite probability for the run-away case in our linear model, it must give an infinite expectation value for the climate sensitivity.
While I give above an interpretation for the infinite results, that doesn’t resolve the whole issue. The flat prior in climate sensitivity is still not acceptable, because it gives results very strongly dependent on the cutoff. The strength of the dependence may not be fully evident, when the cutoff is moved from 10 to 20, but try to move it to 1000 or to one trillion. Moving it to infinity gives the infinite expectation value. Thus moving it to a very high finite value will also give very large expectation values. When the cutoff is put to an extremely high value, we find that the likelihood of S being less than 10 becomes very small. The infinite finite tail wins ultimately in every analysis that cannot totally exclude the possibility of the wrong sign for the forcing. No Gaussian in the inverse of climate sensitivity excludes totally the negative values in spite of the fact that the Gaussian as a thin tail, and most empirical data is presented by a Gaussian in that variable.
The right way to resolve the problem is to use some kind of expert prior. The flat prior with cutoff is an expert prior of a very stupid expert. It doesn’t solve the problem, it just hides it under the carpet.
Pekka,
Models and statistics are tools. If one indicates there is no fat tail and the other does, you don’t believe either, you look for evidence of which is more trust worthy.
Is the expert prior from a stupid expert (emeritus expert may be more PC)?
Are there constraints that realistically limit the output of the system?
The implications of a fat tail are enormous, both in how they may effect our world and our decisions.
Dallas,
I’m not saying that the tail cannot be fat. I’m only saying that the box-type distribution, which is constant up to a point and zero beyond, cannot be defended, when the cutoff affects essentially the outcome and when there are no reasons to choose one value of the cutoff over another.
In other words, nobody should avoid stating openly, what is his preferred prior and reasons for that. The argument must be something more reasonable than a random choice of cutoff.
The box-type prior may be ok, when the cutoff doesn’t make much difference, which means that it may be used, when the conditional probability obtained from the experiments has a thin enough tail to provide an effective cutoff closer to the center of the distribution.
For climate sensitivity the data is not conclusive enough. Therefore the point must be discussed openly and open discussion means that either a more justified prior must be chosen or one must conclude that no posterior PDF can be produced, only the conditional probability curve, which is not a PDF unless its combined with a prior.
Anyone that does dispersion analysis will eventually run into this. For a box-type dispersion of velocities where the measuring reference frame is time, the observation of time will show a “1/t^2” fat-tail PDF as the lower limit of the velocity box gets closer to zero. The mean observation time will blow up logarithmically. So how that box gets selected is important. Interesting that if one picks a mean for the dispersed velocity and then let the maximum entropy principle choose the shape of the box, then it becomes fat-tail by a simple derivation.
This is just an analogy but the application of dispersion to propagation of uncertainty arguments is interesting.
This is all dependent on the transformation that one is working on. I still don’t have a good feeling of what the overall transform for this sensitivity analysis is all about, but I think I understand the ideas of uncertainty propagation fairly well. Putting it pedantically, something in the transform is getting stuck in the denominator and that is giving a fat-tail. If we want to be able to explain this to the masses, we want to understand in basic terms what this factor is.
I guess I want to have a simple practical understanding and I have less interest in just punting and saying that this is what the Monte Carlo GCM models say. Again there must be an identifiable dispersion-like factor that we can point to for the overall uncertainty.
Let me give another example: In analog electronics, a parallel resistor(R)/inductor(L)/capacitor(C) circuit will resonate and the quality of that resonance is given by the Q-factor. However, the riddle is which one of R,L, or C will give the fat-tail in Q if we have uncertainties in the component values. I know this because it is a standard engineering calculation, but everyone else can figure it out for themselves. That is essentially the problem, is that we are not exposing the primary propagation of uncertainty path. I might be talking past everyone here, but that is just my bunny-rabbit mind at work.
Pekkas, Bayes is interesting. I may see more potential in it than most, because I am a bit of a gambler. I like to know my odds and my outs, but I don’t need perfect accuracy because always know the limits, I set them. When I set my limits, I use the best available information and I stick to them with one exception, I will stand up before I hit my limit, but never will I stay in the game past my limit. I learned that the hard way.
So, “In other words, nobody should avoid stating openly, what is his preferred prior and reasons for that. The argument must be something more reasonable than a random choice of cutoff.” I agree completely.
That is way I think Monte has potential. You can compare the three ranges of probability and figure out the first door with goats.
Possible limits of the system that my cut the fat tail, Albedo is the most probable. OH uptake is another. My computer allowed me to do some rough calculations on OH uptake. At 4 W/m^2 positive radiative feedback it would take 139 years to raise the average ocean temperature 1 degree C. 4C is the temperature of salt water which has the maximum density. The rate of over turning will increase as the 4 C limit is approached.
My computer has been known to lie to me thanks to some glitch in open office so you may want to check my calculations, but to me that implies a limit depending on the amount of anthropogenic gases we can add to the atmosphere.
Dallas
Assuming your calculation is correct (i.e. that at 4 W/m^2 positive radiative feedback it would take 139 years to raise the average ocean temperature 1 degree C):
2xCO2 radiative forcing per IPCC = 3.71 W/m^2 (Myhre et al.)
From 1750 (280 ppmv CO2) to 2011 (390 ppmv CO2) the CO2 radiative forcing = 1.77 W/m^2
There are enough optimistically inferred fossil fuel resources on our planet to reach around 1065 ppmv atmospheric concentration (according to a 2010 report by the World Energy Council)
Atmospheric CO2 has been rising exponentially since the 1960s, at a compounded annual growth rate (CAGR) of 0.43% per year. This is the current rate of increase, as well, so it is reasonable to assume that (despite a dramatic projected decrease in the rate of population growth) this exponential rate represents an upper limit to the rate of increase in atmospheric CO2.
IPCC has used this basis for its “scenario and storyline” B1, with atmospheric CO2 reaching 580 ppmv by year 2100.
On this basis, we would expect to reach 1065 ppmv in 235 years, or by year 2246. At that point, the radiative forcing from CO2 would be 5.37 W/m^2.
Your estimate would then show that the ocean temperature would have increased by 1°C sometime in the late 22nd century, say around 2180, and would continue to warm by another fraction of a degree by 2246, when human-induced CO2 increase and AGW would stop.
So let’s say that all the inferred fossil fuel resources of our planet could theoretically raise the ocean temperature by 1.5°C over the next 235 years.
This does not sound too alarming to me.
Max
If you use R, here is a book on wavelets in R.
http://www.springerlink.com/content/978-0-387-75961-6#section=741521&page=6&locus=99
Dallas
Assuming your calculation is correct (i.e. that at 4 W/m^2 positive radiative feedback it would take 139 years to raise the average ocean temperature 1 degree C):
2xCO2 radiative forcing per IPCC = 3.71 W/m^2 (Myhre et al.)
From 1750 (280 ppmv CO2) to 2011 (390 ppmv CO2) the CO2 radiative forcing = 1.77 W/m^2
There are enough optimistically inferred fossil fuel resources on our planet to reach around 1065 ppmv atmospheric concentration (according to a 2010 report by the World Energy Council)
Atmospheric CO2 has been rising exponentially since the 1960s, at a compounded annual growth rate (CAGR) of 0.43% per year. This is the current rate of increase, as well, so it is reasonable to assume that (despite a dramatic projected decrease in the rate of population growth) this exponential rate represents an upper limit to the rate of increase in atmospheric CO2.
IPCC has used this basis for its “scenario and storyline” B1, with atmospheric CO2 reaching 580 ppmv by year 2100.
On this basis, we would expect to reach 1065 ppmv in 235 years, or by year 2246. At that point, the radiative forcing from CO2 would be 5.37 W/m^2.
Your estimate would then show that the ocean temperature would have increased by 1°C sometime in the late 22nd century, say around 2180, and would continue to warm by another fraction of a degree by 2246, when human-induced CO2 increase and AGW would stop.
So let’s say that all the inferred fossil fuel resources of our planet could theoretically raise the ocean temperature by 1.5°C over the next 235 years.
This does not sound too alarming to me
Max
Looks like this message got posted twice.
Sorry.
No need to be sorry, it is worth posting twice.
Dallas – Because of the enormous thermal inertia of the deep ocean, many centuries would be needed for equilibration after a persistent forcing from CO2 or other modality, and at equilibration, the deep ocean temperature will remain lower than the surface ocean temperature and will dominate any average value. For these reasons, calculation of a “mean ocean temperature” (even assuming equilibration) is not very informative as to the change at the surface, which will be larger.
The paper a few months ago by Hansen addressed some of these ocean equilibration and temperature issues, albeit with a considerable degree of speculation.
The point you rasie is largely opinion and necessarily fact correct Fred?
Hansen’s conclusion “ The two dominant causes are changes of greenhouse gases, which are measured very precisely, and changes of atmospheric aerosols.” Is opinion and not a proven fact Fred.
Can’t possibly know the aerosol effect, don’t know the CO2 effect, ignored the sun’s effect. Yet, he’ll picket coal mines, civilly disobedient. Some call that bravery.
This is the man who claimed regional skill for his climate model before Congress in 1988. Show me the model with regional skill even today.
Yeah, tell me about the war on science.
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Rob – The basic principles regarding ocean equilibration, deep ocean thermal inertia, and temperature differentials are based on a combination of measurements and fundamental physical principles, and so, no, they are not based on opinion. For example, the conclusion that the deep ocean heat capacity is overwhelmingly greater than that of the mixed layer is not controversial, that it essentially dominates any “average” is not controversial, and that temperature at the surface during any positive forcing will be higher than the average and increasing faster is not controversial. It is some of the detailed quantitation that is more uncertain, but the basis of estimates is still not “opinion” per se, but rather a question of data interpretation.
This subject is too huge to address in its entirety here, but if you have specific data sources providing evidence on some of the individual points, they would be worth reviewing. I also think that the thread in this blog dealing with the Hansen paper dealt with some of these concepts, and you might want to start there if you are interested.
Fred
Actually, I conclude that much of what you wrote on the impacts of the deep ocean is probably correct.
What I object to- is your and others like Hansen failing to include qualifiers in your conclusions for which you do not have adequate data to support.
When Hansen concluded –“The two dominant causes are changes of greenhouse gases, which are measured very precisely, and changes of atmospheric aerosols.” His conclusion is his opinion and not a proven fact Fred.
A part of his conclusion regarding the fact that greenhouse gases are measured “very preciously” is also untruthful (or at least inaccurate) if you conclude that he is commenting on the key issue of human released greenhouse gases. The amount that humans are releasing is absolutely NOT preciously measured, but only very roughly estimated. The amount of the natural variation of these emissions is only barely understood.
And a global mean surface temperature is? The deep ocean has a 4C maximum density layer analogous to the atmospheric tropopause. The “mean ocean temperature” is a minimum time require to warm the ocean. The 4C boundary can in effect regulate the ocean heat uptake. In and of itself, mean ocean temperature and mean surface temperature are not very informative, unless the impact of regions and layers are considered. Mean surface air temperature is increasing more in the northern extent than the southern extent, why? Because of the ratio of land to water. The 700 meter ocean heat content varies between the southern pacific and the Indian ocean, why? How can an increase in cloud cover cause a flat line in OHC so noticeable that scientists are questioning the accuracy of ARGO?
As far as Hansen paper goes, I think it is as informative as the Spencer and Dessler rebuttal fest.
manacker, the human contribution to atmospheric CO2 is doubling every 33 years. It is not an exponential based on 0 ppm, it is one based on 280 ppm. If continued, this gives 1000 ppm by 2100. Most don’t think it will continue because it requires developing new fossil fuel sources, but it is not unreasonable to say 1000 ppm is an upper limit.
JimD
Forget the “human contribution to atmospheric CO2” for a moment.
What we are discussing here is the increase in atmospheric CO2.
Since Mauna Loa measurements were installed, this has increased at around 0.43% compounded annual growth rate (CAGR).
Most recently (past 5 years) it has also increased at this same exponential rate.
Over the period 1960-2010 human population has increased at a CAGR of 1.71% per year.
The UN estimates that the human population growth rate will slow down drastically, to around 0.45% CAGR over the 21st century (to around 10.5 billion by 2100), or around one-fourth the rate seen from 1960 to 2010.
So if one assumes that humans are largely responsible for the increase in atmospheric CO2 levels, it is quite reasonable to assume that the rate of increase of atmospheric CO2 will be no higher than it was during the past period of very rapid population growth. One could safely assume that the CAGR of 0.43% per year would be an upper limit.
This happens to be the rate at which IPCC assumed CO2 growth to year 2100 in the business-as-usual “scenario + storyline” B1.
I’d agree that “it is not unreasonable to say 1000 ppm is an upper limit” (some day in the far distant future, when all fossil fuels are essentially used up).
Based on the cited WEC estimates on “inferred possible total fossil fuel resources in place”, I figured this would be at 1065 ppmv (when they are all consumed), and this would be in year 2246 (at the same exponential rate of increase of 0.43% per year).
If you have better figures, I’d like to see them (and the bases for them).
Max
First, I would say population growth or its future slow-down, don’t necessarily correlate with CO2 production. The reduction in population growth is a sign of increasing development, and increasing development itself increases GDP and this is probably better correlated to CO2 production (growing numbers of people with cars and more power use). There are large populations gradually moving into the developed world either physically or as nations.
Coal and oil can’t get us up to 1000 ppm, especially by 2100, so it would need oil shales or more undiscovered oil and gas reserves, and these add up to more than coal. At the current production rate it would be nearly 2200 by the time we reach 1000 ppm, but the CO2 production rate will continue to accelerate bringing that horizon to 2100, unless fossil fuel growth is prevented from continuing at its current rate.
I kind of agree that CO2 production correlations with temperature are really only one aspect of the story. How atmospheric CO2 levels works as an excellent proxy for work productivity through its direct connection to fossil fuel production is an amazingly useful tool.
The fact that performing a single convolution of the historical FF production with a diffusive impulse response for adjustment time, gives exact agreement with atmospheric CO2 concentrations is an eye-opener.
Jim D
The WEC estimate of “inferred possible total fossil fuel resources” includes worldwide tar sands and shale oil and gas. It is an optimistic estimate of ALL the fossil fuels existing on our planet. There is no more.
This equates to around 675 ppmv additional CO2 in the atmosphere (around 6X what we have put into the atmosphere to date) = an estimated 1,065 ppmv when they are all gone. That’s it, Jim. Ain’t no’ mo’.
And at anticipated future consumption rates, these reserves would last us ~250 years (~350 years at current consumption rate). Obviously, these resources will become increasingly expensive as resources become more difficult and costly to extract, so we can probably expect a reduction in consumption rates, especially if economically and politically viable alternate energy sources can be developed over those many years. This shift will occur naturally without the need for top-down action today.
Now to population growth slowdown.
Population growth is anticipated by UN to slow down to one-fourth of the CAGR we saw from 1960 to 2010, from 1.71%/year to around 0.45% per year.
As the GDP of nations increase, so do the carbon efficiencies. The industrially developed economies (Europe, North America, Japan, Australia, New Zealand) currently generate four times the GDP per ton of CO2 emitted as the developing nations, such as China, India, etc.
http://farm6.static.flickr.com/5011/5500972088_54742f12be_b.jpg
This will undoubtedly increase, especially as fossil fuels become more difficult and expensive to extract and politically viable alternate energy sources become economically competitive. Nuclear power is the “wild card”. If the current post-Fukushima hysteria dies down and as new technologies, such as fast-breeder reactors using thorium or even nuclear fusion are commercialized, this could change the future dramatically.
During the past 50 population boom years, global GDP increased from $7.2 to 62.9 trillion or a CAGR of 4.4% per year (or 3X population growth rate), while CO2 increased at 0.43% CAGR (or less than one-third of population growth rate).
If we assume that global GDP will grow to $300 trillion (in constant $) by 2100, this is a CAGR of 1.8% (or 3X population growth rate). Global per capita GDP would triple, but unlike the period 1960-2010, most of this growth will occur outside the developed economies.
But, no matter how you slice it, Jim, we cannot get much above 1,065 ppmv CO2, because there is no more. Whether this happens in 200 or 350 years (or never) does not matter that much.
Max
The Dessler analysis is just plain dumb. Use of proper analysis tools shows quite clearly that the cloud feedback is negative and significant.
Steve McIntyre has posted an very interesting analysis which is relevant to this discussion. Dessler and others have been using a monthly centering technique which Steve questions.
See http://climateaudit.org/2011/09/28/monthly-centering-and-climate-sensitivity/
We don’t need no stinkin’ information.
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