The AGW climate feedback discussion

[Andre]

As promised in the now closed CRU-hack thread, this is intended to show why I am convinced that the cause for global warming is overstated and that changes in use of fossil fuel and CO2 production will have little effect on climate. I will try and stick to the scientific method, no room for politics and groupthink. This would include the basic Popperian falsification principle. Maybe in a bit more fuzzy way. If something doesn’t work nearly as strong as advertised, it can’t be held responsible being the main cause for rising temperatures, melting glaciers, rising sea levels, etc.

So we can look at perceived climate changes in the past, geologic records, temperature graphs, oceanic behavior, tree ring, ice cores, anything but correlation is not equal to correlation. You’d still need a physically valid mechanism to explain it, as part of the scientific method.

So doesn’t greenhouse effect exist? You bet it does. However the point is, in what extend? And the whole thing can be regressed to two simple questions:

A: What is the basic climate sensitivity (Planck response) of doubling the CO2 concentration?
B: How is that modified by possible feedbacks?
The “IPCC-answer” to the first question seems to be around one degree Celsius. Sylas explains:

...
The Planck response, or base response is roughly 1 -- or if we are more precise it is around 1.12 to 1.16. The simple blackbody estimate will get into the ballpark okay; a more careful account is given with references in the latter part of [post=2318289]msg #171[/post] of thread "Need Help: Can You Model CO2 as a Greenhouse Gas (Or is This Just Wishful Thinking?)".

It turns out that you can get into the ball park more closely using 4Q/T where Q is the emission from Earth (about 240 W/m²) and T is the mean surface temperature (about 288K). (The Planck response of a simple blackbody with the same emission to space as the Earth would use T as 255K, being the mean emission temperature.) This gives 0.3 K/(Wm^-2). Converting units this is 1.11 K/2xCO₂, with 2xCO₂ being 3.71 Wm^-2, ...

I’m perfectly happy with that. And the main dispute is not about A but about B: How is that modified by possible feedbacks? That’s the key. If the overall feedback is positive the sensitivity value would get “amplified”, whereas negative feedback would reduce the sensivity value. This is what the scientific climate dispute boils down to. In this thread I will show why I think that negative feedback prevails.

 

[sylas]

AI’m perfectly happy with that. And the main dispute is not about A but about B: How is that modified by possible feedbacks? That’s the key. If the overall feedback is positive the sensitivity value would get “amplified”, whereas negative feedback would reduce the sensivity value. This is what the scientific climate dispute boils down to. In this thread I will show why I think that negative feedback prevails.

This may be a really useful thread. I personally think there's a lot more to climate science to this but that's a detail. The matter of feedbacks is a huge open question of major importance.

I look forward to seeing what Andre will offer on the subject. Thanks for a nice tightly focused statement of the problem, Andre! Can I request everyone else help us all keep this as the focus of the thread?

Cheers -- sylas

 

[vanesch]

Just to say that I'm also very interested in learning more about the feedback mechanisms.

 

[Integral]

IIRC James Lovelock addresses the feedback mechanisms in his book Gaia. Searching on Amazon I see he has published a number of updated editions so I am not sure where to start reading now, I read the original work sometime in the '70s. I know that this theory has gained a reputation as new age goobly gook but this came more from the noise made by new age nonscientist who adapted the work. Lovelock is a scientist, his works provide a basis of understanding some of the relationships between the chemical composition of our atmosphere and the salinity of the ocean and lifeforms. This is not a simple connection, and he does attempt to answer every question.

One thing that is certain is that the feedback mechanisms are complex and poorly understood.

The question that keeps running through my mind is that given that we have a feedback based control system; what happens if you introduce a step function change in a basic controlled parameter, ie concentration of CO2. This is a completely separate issue from any temperature change that may occur.

Here is a very reasonable wiki article on http://en.wikipedia.org/wiki/Gaia_hypothesis"

 

[Sorry!]

Thanks Andre

 

[Sorry!]

IIRC James Lovelock addresses the feedback mechanisms in his book Gaia. Searching on Amazon I see he has published a number of updated editions so I am not sure where to start reading now, I read the original work sometime in the '70s. I know that this theory has gained a reputation as new age goobly gook but this came more from the noise made by new age nonscientist who adapted the work. Lovelock is a scientist, his works provide a basis of understanding some of the relationships between the chemical composition of our atmosphere and the salinity of the ocean and lifeforms. This is not a simple connection, and he does attempt to answer every question.

One thing that is certain is that the feedback mechanisms are complex and poorly understood.

The question that keeps running through my mind is that given that we have a feedback based control system; what happens if you introduce a step function change in a basic controlled parameter, ie concentration of CO2. This is a completely separate issue from any temperature change that may occur.

Here is a very reasonable wiki article on http://en.wikipedia.org/wiki/Gaia_hypothesis"

Isn't this the theory that forms the basis for movies such as The Day After Tomorrow? Where the Earths climate goes beserk trying to correct itself?

I find it to be an interesting theory, I don't know much about it but I tend to overlook it I guess. I just think that life formed around original conditions and that spewed life for the conditions the first forms of life took. I do not think that it was 'intentional' in the sense that the organisms all maintain a particular environment to continue life... (Unless of course I misunderstood everything.) I think another thread would be needed to discuss that though

 

[Andre]

Yes Gaia is all about feedbacks but I seem to recall in an idealized hypothetical situation. I'd like to stick to directly identified mechanisms in climate.

And happy to proceed. But most if not all is already in these threads. It's just a compilation. Also it may seen that I'm dumbing down things, but the intention is make a vague attempt to have everybody understand it.

We should adress the following:
1: We need a bit of understanding of the pecularities of feedback first (time delay, persistency etc).
2: identify possible feedback mechanims (hypothesis) identified either by textbooks (Pierehumbert) or in IPCC selected literature or any other logical effect that is not mentioned there.
3: Go over any possible evidence of feedback (lagging CO2 in ice cores, temperature drop after the Pinatubo eruption - Soden and Held, persistency in data series, model calculations etc).

That's for tomorrow.

 

[DrClapeyron]

I may need some sort of definition for feedback. Are yall talking about a biological like feedback?

 

[Sorry!]

Feedback-
# the process in which part of the output of a system is returned to its input in order to regulate its further output
# response to an inquiry or experiment

wordnetweb.princeton.edu/perl/webwn

Climate Feedback-
An atmospheric, oceanic, terrestrial, or other process that is activated by the direct climate change induced by changes in radiative forcing. Climate feedbacks may increase (positive feedback) or diminish (negative feedback) the magnitude of the direct climate change.

http://www.enviroyellowpages.com/Resources/GlobalWarming/globalwarming_glossary.htm

There are generally two types of feedback Positive and Negative... anything elsee

 

[Integral]

Andre,
Have you actually read Gaia or just read about it?

He talks pretty specifically about the chemistry's involved. It has been 30+ yrs since I read it so I cannot get very specific.

More recently he created a model of a very simple ecosystem which behaved as he predicted called http://gingerbooth.com/courseware/daisy.html" article about it.

But again that simple simulation is NOT discussed in the book. The book is short but dense. It may be an easy read for someone with a good chemistry background, mine is weak so I had to slog through a lot of it.

This may be one of the better sources for good information on the current knowledge of feedback mechanisms some what independent of the Global warming group.
You'd love it, lots of graphs!

 

[Xnn]

The “IPCC-answer” to the first question seems to be around one degree Celsius. Sylas explains:

Sylas is a great guy, but last I check he has not been put in charge of the IPCC.

Anyhow, the IPCC's value for climate sensitivity for CO2 doubling is somewhere between 2 to 4.5 C.
One of the reason why the range is as large as it is, is that there is also uncertainty about the Oceans response time to the warming.
The oceans being as large as they do not respond conterminously to warming.
There is also the influence of aerosols on the climate (cooling), that needs to be accounted for.

So, it is not just about feedbacks.

In the meantime, I'll check the math for Planck response.

 

[sylas]

Sylas is a great guy, but last I check he has not been put in charge of the IPCC.

Anyhow, the IPCC's value for climate sensitivity for CO2 doubling is somewhere between 2 to 4.5 C.

Aw, shucks.

But back to the point at issue; Andre is quite right; and I am simply reporting values in the research used by the IPCC. The value we are talking about is not "climate sensitivity", but "non-feedback response", a theoretical value which is useful for breaking down the details of the actual response we experience, and understanding it as a physical theory.

Andre, and I, and the IPCC, all use a value of a little bit over 1K per doubling of CO₂. Andre says "around 1 degree". That's correct, as a reasonable approximate value in discussion. I gave 1.12 to 1.16, which is a range of values from various papers. The IPCC says "around 1.2", on page 631 of the IPCC 4AR WG1 report, in chapter 8. You can think of a value somewhere between 1.1 and 1.2 K/2xCO₂.

Bear in mind that this is not what we actually experience. It's a mathematical approximation corresponding to the unphysical assumption that as temperature changes, nothing else changes that could impact energy balance. The real sensitivity (which is what Xnn is quoting) is much harder to estimate. Most research indicates sensitivity is significantly greater than 1.2 K/2xCO₂, with a strong net positive feedback (that is, what Xnn has given). Some research argues for a net negative feedback, which would give very small sensitivity values less than 1.1.

I've had a fair bit to say about this research in the past, in a number of threads; but on this occasion I think it only fair to leave the floor to Andre, who will be presenting some published investigations or analysis proposing net negative feedback and very low climate sensitivity. I do not want to anticipate that or preempt the discussion by jumping in with my own view again; and I confirm that the number he has given for the non-feedback response is correct and consistent with what is published by the IPCC.

So, it is not just about feedbacks.

In my opinion it is a very positive thing to have tight topic focus in a thread. Andre is not proposing to solve climate science entirely, but to take a focused look at one aspect. Let's not diverge into another sweeping look at all the issues of climate!

Cheers -- sylas

 

[vanesch]

Just to recall what "feedback" is: it is, as Sylas said, an "artificial" way of chopping up the system response of a system, where one has defined an external input signal, and where one is looking at an "output" signal. What actually counts is the input-output relationship.

But sometimes the physical construction of the system is such, that one can discern "subsystems" in it, of which we can define also "input" and "output" signals.

Now, if we discern two subsystems, one which has the same "input" and "output" signals as the overall system, but in which there is also another "backward" subsystem, which seems to take as "input" what we call "output" and which ADDS its output to the overall input, then we have a feedback system.

The wiki entry is rather good: http://en.wikipedia.org/wiki/Feedback

The "forward" subsystem is A, the "feedback" is B.

We have that: (X is external input, Y is output, X' is input to A)

Y = A X' (forward subsystem)

X" = B Y (feedback)

X' = X + X"

From which:

Y = A (X + B Y)

(1 - B) Y = A X

Y = {A / (1 - B)} X

EDIT: I made a silly mistake here !

it has to be:
(1 - A B) Y = A X

 

[Coldcall]

I think Lovelock's original hypothesis has much going for it, particulalrly if like me, one thinks the climate system and in fact the whole Earth is a self-organsing chaotic system.

However i think he's recently contradicted himself by claiming that humans are going to cause the whole system to go out of whack. As humans we are part of the natural cycle, we evolved on this Earth through natural process in line with the larger natural process of climate and biosphere. I think he is wrong to separate humanity from the overall system.

If the Earth is self-organising as he has implied in GAIA, and there exists an attractor maintaining system stability (and i think 3-4 billion years of climate stability is evidence of that) then it would logically follow that the Earth will react with either positive or negative feedbacks depending on what is necessary for that stability to remain.

If it was so unstable that 100ppm increase in human Co2 emisisons was enough to cause the fabled "tipping point" then it would have happened numerous times in Earth's history and we would not be here today, as Earth would as dead as Mars.

So the climate models that project a bias only towards positive feedbacks are clearly not representative of how the real climate and Earth behaves. Again it shows those models are simplistic idealisations and unlikley to have any bearing on physical reality.

 

[Coldcall]

Aw, shucks.

But back to the point at issue; Andre is quite right; and I am simply reporting values in the research used by the IPCC. The value we are talking about is not "climate sensitivity", but "non-feedback response", a theoretical value which is useful for breaking down the details of the actual response we experience, and understanding it as a physical theory.

Andre, and I, and the IPCC, all use a value of a little bit over 1K per doubling of CO₂. Andre says "around 1 degree". That's correct, as a reasonable approximate value in discussion. I gave 1.12 to 1.16, which is a range of values from various papers. The IPCC says "around 1.2", on page 631 of the IPCC 4AR WG1 report, in chapter 8. You can think of a value somewhere between 1.1 and 1.2 K/2xCO₂.

Bear in mind that this is not what we actually experience. It's a mathematical approximation corresponding to the unphysical assumption that as temperature changes, nothing else changes that could impact energy balance. The real sensitivity (which is what Xnn is quoting) is much harder to estimate. Most research indicates sensitivity is significantly greater than 1.2 K/2xCO₂, with a strong net positive feedback (that is, what Xnn has given). Some research argues for a net negative feedback, which would give very small sensitivity values less than 1.1.

I've had a fair bit to say about this research in the past, in a number of threads; but on this occasion I think it only fair to leave the floor to Andre, who will be presenting some published investigations or analysis proposing net negative feedback and very low climate sensitivity. I do not want to anticipate that or preempt the discussion by jumping in with my own view again; and I confirm that the number he has given for the non-feedback response is correct and consistent with what is published by the IPCC.



In my opinion it is a very positive thing to have tight topic focus in a thread. Andre is not proposing to solve climate science entirely, but to take a focused look at one aspect. Let's not diverge into another sweeping look at all the issues of climate!

Cheers -- sylas

hear hear!

 

[Andre]

Please hold your breath for a few hours, I'm working on a big post.

 

[Andre]

Thanks all for the patience and for pointing to the main principles of feedback, so I can take it from there. Vanesch shows how the total gain of a feedback process can be calculated in a steady state but in reality we are looking at constant dynamic transients, as the forcings functions of climate are constantly changing. The process reacts to that with the gain factor A, but with a certain delay, in climate ranging from minutes to centuries perhaps. Feedback uses (part) of the (delayed) output of a process as input and has it's own inertia and gain factor B.

To illustrate what happens when introducing a delay, I have made a very modest little model of the most simplest feedback that uses a step of one to simulate total delay from proces imput to the arrival of the feedback signal to be added or subtracted to the next system input (see attachment). I hoped to be able to use an older version I made a few years ago but I was out of luck so I had to make it again.

As input we use a one dimensional random walk (column C) and we compare the reaction in a zero feedback process (column D) with a gain A (cell C2) , a positive feedback process (column D) with the factor B (cell C3) and a negative feedback process with the factor -B in column F.

Let's look at a certain output, the first 100 steps, with A = 1 and B = 0.5 (green cells)

We see the average total gain for the feedbacks (in steady state pos:2, neg 0.67) are close (2.13 and 0.62). So that's fine. We also count the number of signal reverses for n=1000. The random walk makes 528 reverses (from a positive to a negative step or vice versa), which is close to the expected average of 0.5n = 500. But we see that the positve feedback process makes less reverses (344) and the negative feedback makes more reverses (640). This is obvious and important, as the added previous positive feedback steps tends to increase the deviation from to zero persistently (instable), whereas the negative feedbacks tends to pull the process back to the zero mark (stable) anti-persistent. Because of that we also see that the red positive feedback process is smoother and the negative feedback process is more jerky.

Before the all revealing playing with the parameters it's maybe better to see if we didn't lose everybody/anybody. Still here?

 

[Coldcall]

Thanks all for the patience and for pointing to the main principles of feedback, so I can take it from there. Vanesch shows how the total gain of a feedback process can be calculated in a steady state but in reality we are looking at constant dynamic transients, as the forcings functions of climate are constantly changing. The process reacts to that with the gain factor A, but with a certain delay, in climate ranging from minutes to centuries perhaps. Feedback uses (part) of the (delayed) output of a process as input and has it's own inertia and gain factor B.

To illustrate what happens when introducing a delay, I have made a very modest little model of the most simplest feedback that uses a step of one to simulate total delay from proces imput to the arrival of the feedback signal to be added or subtracted to the next system input (see attachment). I hoped to be able to use an older version I made a few years ago but I was out of luck so I had to make it again.

As input we use a one dimensional random walk (column C) and we compare the reaction in a zero feedback process (column D) with a gain A (cell C2) , a positive feedback process (column D) with the factor B (cell C3) and a negative feedback process with the factor -B in column F.

Let's look at a certain output, the first 100 steps, with A = 1 and B = 0.5 (green cells)

We see the average total gain for the feedbacks (in steady state pos:2, neg 0.67) are close (2.13 and 0.62). So that's fine. We also count the number of signal reverses for n=1000. The random walk makes 528 reverses (from a positive to a negative step or vice versa), which is close to the expected average of 0.5n = 500. But we see that the positve feedback process makes less reverses (344) and the negative feedback makes more reverses (640). This is obvious and important, as the added previous positive feedback steps tends to increase the deviation from to zero persistently (instable), whereas the negative feedbacks tends to pull the process back to the zero mark (stable) anti-persistent. Because of that we also see that the red positive feedback process is smoother and the negative feedback process is more jerky.

Before the all revealing playing with the parameters it's maybe better to see if we didn't lose everybody/anybody. Still here?

Very well explained, thank you.

 

[Xnn]

The process reacts to that with the gain factor A, but with a certain delay, in climate ranging from minutes to centuries perhaps. Feedback uses (part) of the (delayed) output of a process as input and has it's own inertia and gain factor B.

Can't think of any normal climate feedback mechanism that take only a few minutes.
It's a large planet that takes weeks for weather systems to travel around.

 

[Andre]

Few minutes, was to take all option open but in the daily cycle, the forming of low cumulus clouds tempering insolation, which decreases cumulus clouds again (negative feedback) is in the order or magnitude of an hour.

 

[Xnn]

A new study just came out in Nature Geoscience.

Apparently, up to now, mostly short term feedbacks have been included
in climate models. These are those typically thought of such as
water vapor, clouds and sea ice.
However, if longer term feedbacks are included such as vegetation
cover and land ice then climate sensativity is between 30 to 50% greater.

Can't find anything currently on the Nature Geoscience site, but
here is news article about the upcoming study:

http://www.sciencedaily.com/releases/2009/12/091206162955.htm

The authors demonstrate that the increased temperatures indicated by the reconstructions can be explained if factors that vary over long timescales, such as land-ice and vegetation, are included in the model. This is primarily because changes in vegetation and ice lead to more sunlight being absorbed, which in turn increases warming.

Including these long-term processes in the model resulted in an increased temperature response of the Earth to carbon dioxide, indicating that the Earth's temperature is more sensitive to carbon dioxide than previously recognised. Climate models used by bodies such as the Intergovernmental Panel on Climate Change often do not fully include these long-term processes, thus these models do not entirely represent the sensitivity of the Earth's temperature to carbon dioxide.

 

[Xnn]

Here's another article on feedbacks.

http://www.sciencedaily.com/releases/2006/05/060522150948.htm

One complicating factor was that some of the processes that play a role in the feedback loop are quite fast, taking place over a period of years, while others take centuries or even millennia. This implies that the strength of the feedback effect depends on the time scale being analyzed. Another factor was that the modern world looks quite different than it did tens of thousands of year ago, when the ice in the cores was formed.

Therefore, the authors focused especially on relatively recent climatic anomaly known as the "Little Ice Age." During this period (about 1550-1850), immortalized in many paintings of frozen landscapes in Northern Europe, Earth was substantially colder than it is now. This, scientists have concluded, was due largely to reduced solar activity, and just as during true ice ages, the atmospheric carbon level dropped during the Little Ice Age. The authors used this information to estimate how sensitive the carbon dioxide concentration is to temperature, which allowed them to calculate how much the climate-carbon dioxide feedbacks will affect future global warming.

use newly acquired ancient climate data to quantify the two-way phenomenon by which greenhouse gases not only contribute to higher temperatures, but are themselves increased by the higher temperatures. This higher concentration leads to still higher temperatures, in what scientists call a positive feedback loop.

So, "fast" is within a few years while "slow" is up to the millennia length.
And warming from human CO2 emissions may result in higher natural CO2 emissions.

 

[Xnn]

Here's the paragraph from the IPCC Chapter 8 page 631:

The diagnosis of global radiative feedbacks allows better
understanding of the spread of equilibrium climate sensitivity
estimates among current GCMs. In the idealised situation that the
climate response to a doubling of atmospheric CO2 consisted of
a uniform temperature change only, with no feedbacks operating
(but allowing for the enhanced radiative cooling resulting from
the temperature increase), the global warming from GCMs
would be around 1.2°C (Hansen et al., 1984; Bony et al., 2006).
The water vapour feedback, operating alone on top of this,
would at least double the response.6 The water vapour feedback
is, however, closely related to the lapse rate feedback (see
above), and the two combined result in a feedback parameter of
approximately 1 W m–2 °C–1, corresponding to an amplification
of the basic temperature response by approximately 50%. The
surface albedo feedback amplifies the basic response by about
10%, and the cloud feedback does so by 10 to 50% depending
on the GCM. Note, however, that because of the inherently
nonlinear nature of the response to feedbacks, the final impact
on sensitivity is not simply the sum of these responses. The
effect of multiple positive feedbacks is that they mutually
amplify each other’s impact on climate sensitivity.

I believe "surface albedo feedback" is referring only to sea ice.

Next 2 paragraphs:

Using feedback parameters from Figure 8.14, it can be
estimated that in the presence of water vapour, lapse rate and
surface albedo feedbacks, but in the absence of cloud feedbacks,
current GCMs would predict a climate sensitivity (±1 standard
deviation) of roughly 1.9°C ± 0.15°C (ignoring spread from
radiative forcing differences). The mean and standard deviation
of climate sensitivity estimates derived from current GCMs are
larger (3.2°C ± 0.7°C) essentially because the GCMs all predict
a positive cloud feedback (Figure 8.14) but strongly disagree
on its magnitude.
The large spread in cloud radiative feedbacks leads to the
conclusion that differences in cloud response are the primary
source of inter-model differences in climate sensitivity (see
discussion in Section 8.6.3.2.2). However, the contributions
of water vapour/lapse rate and surface albedo feedbacks to
sensitivity spread are non-negligible, particularly since their
impact is reinforced by the mean model cloud feedback being
positive and quite strong.

 

[Andre]

Before addressing alleged feedback processes in climate, maybe it is better to return to topic and observe what our very simple basic model does on various gain parameters.

I also hope that somebody is checking my excel sheet, seeing if it correctly describes the simple zero - order of feedback model with a total delay of one step, (omitting transient behavior) and see if the gain is correctly calculated. I also upload the original version in which the b-column generates a new one dimensional random walk on every calculation cycle.

We have been looking at proces gain A = 1 and feedback gain B = 0.5 giving this,

and we see that positive feedback can change the original signal considerably
Now if we increase the process gain for instance to A = 1.5, the positive feedback (B=0.5) grows to dominant proportions:

We notice also that persistency, or reluctance to reverse, has increased considerably

The reason obviously is that the process gain is bigger than one, hence the amplification can put more energy in the system. However there is no energy source available in climate other than the solar input, that means that attenuation requires that both process gain A and feedback gain B are smaller than one. Sometimes maybe close to one, if the process converts nearly all input energy to the same output energy and dito for the feedback. It can also be much smaller than one if the output is diverted in several other forms and dito of course if feedback is partly positive and partly negative.

So this is median result with both gains A and B on 0.5:

We see the role of positive feedback has been reduced considerably, the total practical gain being only 0.69. Now to see which is dominant A or B, We look at a gain A = 0.2 and B = 0.99 to get this:

Obviously, despite the 'strong' positive feedback factor, the attinuation of A has reduced the effect of feedback considerably and if we reduce the feedback gain B to a 'normal' 0.5, there is not a lot that any feedback does anymore:

It appears that the process gain A as amplification or attenuation determines whether or not the feedback has a big effect. I expect that in climate the factor A is usually not too large. If my ramblings are vaguely into the right direction, maybe that the effects of feedback in attenuated processes are not too dominant, but we would have needed quite some strong feedback to increase the climate sensitivity (Planck response) of about one degrees to about double values or more in attenuated processes.

But there is more, the persistency and antipersistency characteristics of the output signal, can we do something with that? That's for tomorrow.

 

[Xnn]

Here's the abstract from the Nature Geoscience article:

Quantifying the equilibrium response of global temperatures to an increase in atmospheric carbon dioxide concentrations is one of the cornerstones of climate research. Components of the Earth's climate system that vary over long timescales, such as ice sheets and vegetation, could have an important effect on this temperature sensitivity, but have often been neglected. Here we use a coupled atmosphere–ocean general circulation model to simulate the climate of the mid-Pliocene warm period (about three million years ago), and analyse the forcings and feedbacks that contributed to the relatively warm temperatures. Furthermore, we compare our simulation with proxy records of mid-Pliocene sea surface temperature. Taking these lines of evidence together, we estimate that the response of the Earth system to elevated atmospheric carbon dioxide concentrations is 30–50% greater than the response based on those fast-adjusting components of the climate system that are used traditionally to estimate climate sensitivity. We conclude that targets for the long-term stabilization of atmospheric greenhouse-gas concentrations aimed at preventing a dangerous human interference with the climate system should take into account this higher sensitivity of the Earth system.

http://www.nature.com/ngeo/journal/vaop/ncurrent/abs/ngeo706.html


Bottom line; including long term feedbacks results in between 30 to 50% more warming.

 

[GENIERE]

... http://www.nature.com/ngeo/journal/v...s/ngeo706.html…
Bottom line; including long term feedbacks results in between 30 to 50% more warming...

Xnn- All of us are quite capable of searching the web to find information; it is not often that I can sit back and be educated by the likes of Andre, Sylas, and Vanesh. By all means avail us of your personal knowledge of the subject and then include, if you wish, links to supporting literature.

… Let's not diverge into another sweeping look at all the issues of climate!…Cheers -- sylas

Just to say that I'm also very interested in learning more about the feedback mechanisms.

I think your input re: heat transfer via conduction and em radiation will prove essential to the succes of this thread

On feedback -

... This is what the scientific climate dispute boils down to…

<|>

 

[vanesch]

Vanesch shows how the total gain of a feedback process can be calculated in a steady state but in reality we are looking at constant dynamic transients, as the forcings functions of climate are constantly changing.

In that case (as is usual in control theory btw) A and B don't stand for static amplification factors that work instantaneously, but rather for operators working on the entire function. You can switch to the Laplace domain, where A and B then stand for transfer functions, if the systems A and B are linear systems.

However, in the "long times" limit, they reduce to the static responses (technically, in the Laplace domain, we take the limit s -> 0 which gives you the long time static limit of a transfer function).

 

[vanesch]

From which:

Y = A (X + B Y)

(1 - B) Y = A X

Y = {A / (1 - B)} X

I made a terrible, elementary error !

Y = A ( X + B Y)

(1 - A B) Y = A X

so Y = { A / ( 1 - A B) } X

 

[DrClapeyron]

I seem to be lost, the discussion sounds like it has more to do with feedback theory than climate feedback. Let's say we plug in a repsonse to increase temperature due to CO2 levels, like a greater water:ice surface are ratio around the North Pole into the equations. What would the equations have to say? More/less albedo, more/less CO2 sinking?

I am going to throw a big word out there I normally don't use and say that the whole idea of feedback is very abstract. There seems to be some substance missing.

 

[vanesch]

Before addressing alleged feedback processes in climate, maybe it is better to return to topic and observe what our very simple basic model does on various gain parameters.

Ok, good idea.

We have been looking at proces gain A = 1 and feedback gain B = 0.5 giving this,

...

and we see that positive feedback can change the original signal considerably
Now if we increase the process gain for instance to A = 1.5, the positive feedback (B=0.5) grows to dominant proportions:

...So this is median result with both gains A and B on 0.5:

...We see the role of positive feedback has been reduced considerably, the total practical gain being only 0.69. Now to see which is dominant A or B, We look at a gain A = 0.2 and B = 0.99 to get this:

...

It appears that the process gain A as amplification or attenuation determines whether or not the feedback has a big effect. I expect that in climate the factor A is usually not too large. If my ramblings are vaguely into the right direction, maybe that the effects of feedback in attenuated processes are not too dominant, but we would have needed quite some strong feedback to increase the climate sensitivity (Planck response) of about one degrees to about double values or more in attenuated processes.

But there is more, the persistency and antipersistency characteristics of the output signal, can we do something with that? That's for tomorrow.

Yes. I wanted to respond to this, because the explanation is straight forward, and wanted to quote my earlier post, when I realized I made that error there.

Indeed, the overall static gain is A / (1 - AB ) (and not A / (1 - B) as I had erroneously written).

One calls sometimes AB the "loop gain" as it is the effect of a single succession of A and B.

It is the total loop gain that determines the feedback gain.

The "amplification due to feedback" is given by the factor (1/(1-AB))

You get strong feedback if AB is close to 1. If you lower A, you get less feedback amplification.

Let's run the numbers for your cases:

if the process gain is 1, and the FB is 0.5, we get an amplification due to feedback of 1/(1-0.5) = 2 ; the system has an overall gain of 2.

if A = 1.5 and B = 0.5, we have 1 / (1-1.5*0.5) = 4, so twice as much amplification of the feedback (on top of the fact that A itself is now larger, so the system has an overall gain of 6)

if A = 0.5 and B = 0.5 we have a feedback gain of 1/(1 - 0.25) = 1.33 and a total gain of 0.5 x 1.33 or 0.67.

If A = 0.2 and B = 0.99 (say, 1), we have a feedback gain of 1/(1 - 0.2) = 1.25 and an overall gain of 0.2 x 1.25 = 0.25.

That's pretty close to the numbers you had found in your simulation, for the positive feedbacks.

Same goes for the negative feedbacks (although now the formula is of course A / (1 + AB) )

Try it out, I'm pretty sure it works out fine.

(and sorry again for the silly mistake).

 

[vanesch]

I am going to throw a big word out there I normally don't use and say that the whole idea of feedback is very abstract. There seems to be some substance missing.

Well, the thread is about feedback in climate. For that, one has to be clear about what is feedback. For some it might be elementary, for others, they might learn something. We start with the basics, I don't think it is a bad idea! I can help with the basics, I know less about the exact climate feedback systems. It's always a good idea to be clear about the fundamentals even though it may be trivial to some.

To tell you that it is not so trivial for climate science is the nice paper here that has already been discussed if I remember well:
http://earthweb.ess.washington.edu/roe/Publications/BakerRoe_Predictable_Jclim09.pdf

If one can publish a research paper in climate science about the formula A / (1 - A B), and its asymmetrical aspect in B (fig 1 in the paper), then that means it is worth discussing this, to come to full understanding, no ?

 

[Andre]

(1 - A B) Y = A X

so Y = { A / ( 1 - A B) } X

Thanks Vanesch, with that correction the total gain in the different simulation attempts dovetail nicely. So my orginal assessment about transient effects was rather overstated.

Again, the objective of these little demonstrations, were, firstly, showing that significant 'amplification' in attenuated - non-amplified processes, like climate, are unlikely, especially when the total feedback is a combination of positive and negative feedbacks. Hence one could sincerely wonder if the modeled climate feedback claims, so abundantly supplied by Xnn, are either the result of actually modelling the different feedback loops in detail or just dialing in guestimated parameters. This would be an especially valid question about the assessment of IPCC of the positive feedback amplification of the climate sensitivity of doubling CO2 from the Planck response of 1.1 to 1.2 degrees to some 2 -4.5 degrees and this little exercise was to substantiate that doubt.

Secondly, we have introduced persistency and anti-persistency as characteristics of positive and negative feedback response, the tendency to persist in or oppose to the direction of the process output in relation to the average value. And the question arises if it is possible to assess the type of effective feedback of a process on this feature alone. Of course this is rather complex, one has to deal with attractors, autocorrelation, cyclic forcings like diurnal and seasonal cycles, etc. but especially the typical time constant of the different feedbacks. So signal behavior has to be analyzed on many different time scales to assess persistency. Also, for statistical significance, a large sample is required. http://www.aai.ee/~olavi/ has addressed this issue and his researches of many different climate data series all end in the conclusion: anti persistency.

See http://www.aai.ee/~olavi/2001JD002024u.pdf, http://www.aai.ee/~olavi/cejpokfin.pdf and http://www.aai.ee/~olavi/E-Ac-Sci-07.pdf.

There are more as you can see from him home page but linking to those is illegal in these dark ages of global warming groupthink excess.

 

[vanesch]

Again, the objective of these little demonstrations, were, firstly, showing that significant 'amplification' in attenuated - non-amplified processes, like climate, are unlikely, especially when the total feedback is a combination of positive and negative feedbacks. Hence one could sincerely wonder if the modeled climate feedback claims, so abundantly supplied by Xnn, are either the result of actually modelling the different feedback loops in detail or just dialing in guestimated parameters.

That's where I would like to learn more too. But I don't see how an elementary verification of the concept of feedback allows one to conclude that the feedback in the climate system should be small: it all depends on which feedbacks and on their amplitude, right ?

I too would like to know how the feedbacks in climate models are implemented, where they come from, and how their amplitude is determined.

This would be an especially valid question about the assessment of IPCC of the positive feedback amplification of the climate sensitivity of doubling CO2 from the Planck response of 1.1 to 1.2 degrees to some 2 -4.5 degrees and this little exercise was to substantiate that doubt.

A nice review of these values would indeed be nice. Anyone ?

Secondly, we have introduced persistency and anti-persistency as characteristics of positive and negative feedback response, the tendency to persist in or oppose to the direction of the process output in relation to the average value. And the question arises if it is possible to assess the type of effective feedback of a process on this feature alone. Of course this is rather complex, one has to deal with attractors, autocorrelation, cyclic forcings like diurnal and seasonal cycles, etc. but especially the typical time constant of the different feedbacks.

I think it is a bad idea to do black box modelling. We want to understand the climate system dynamics by using explicit physical modelling, not by fitting parametrisable general model classes on existing data, or by trying to extract some general properties from time series analysis. That's something you can do if the complexity of the underlying system is hopelessly beyond comprehension AND if you know that the time series you're analysing are entirely representative for the evolution you want to draw from it. However, this is in fact nothing else but a sophisticated way of "curve fitting and interpolation". You can do that if you need a dynamical model that needs to be used in conditions that are very near to the conditions of where you fitted the data. But you cannot hope to get out of such a thing any general dynamics that is universally valid.

I've been doing such kinds of things for work when I was young. It works rather well in "interpolation" mode and is hopeless in "extrapolation" mode. The reason is that there are myriads of classes of dynamical models which can all agree on the fitted region, and behave wildly differently when outside of that region.

As we want to explore a situation that we don't know much about, namely a quick increase in greenhouse gasses in the atmosphere, there's not much hope of getting the right dynamics out of just a black box curve fitter when such rise was not the case. There's much more hope to learn something by doing "white box" modelling, that is to say, implement physically understood relationships - even if they are rough and simplified - into a simulator and see what it does. It's also much more instructive to do so.

 

[Integral]

This is a simulation of environmental feedback called http://itg1.meteor.wisc.edu/wxwise/radiation/daisyworld.html" . The environment consists of and input luminosity and 2 species of daises one black the other white, The temperature range in which each species can exist is different but overlapping. The black daisies germinate at a low temperature but because they are black absorb heat and raise the temperature. At some higher temperature white daisies germinate. They reflect light tending to lower the temperature. The simulation runs until a equilibrium is reached.

 

[Integral]

That's where I would like to learn more too. But I don't see how an elementary verification of the concept of feedback allows one to conclude that the feedback in the climate system should be small: it all depends on which feedbacks and on their amplitude, right ?

I want to emphasise this statement. For all the pretty graphs there simply no way we can extent this analysis beyond what it is, a simple example of the effects of feedback. It is not a climate model and we should not attempt to draw conclusions about the climate from it.

I too would like to know how the feedbacks in climate models are implemented, where they come from, and how their amplitude is determined.



A nice review of these values would indeed be nice. Anyone ?




I think it is a bad idea to do black box modelling. We want to understand the climate system dynamics by using explicit physical modelling, not by fitting parametrisable general model classes on existing data, or by trying to extract some general properties from time series analysis. That's something you can do if the complexity of the underlying system is hopelessly beyond comprehension AND if you know that the time series you're analysing are entirely representative for the evolution you want to draw from it. However, this is in fact nothing else but a sophisticated way of "curve fitting and interpolation". You can do that if you need a dynamical model that needs to be used in conditions that are very near to the conditions of where you fitted the data. But you cannot hope to get out of such a thing any general dynamics that is universally valid.

I've been doing such kinds of things for work when I was young. It works rather well in "interpolation" mode and is hopeless in "extrapolation" mode. The reason is that there are myriads of classes of dynamical models which can all agree on the fitted region, and behave wildly differently when outside of that region.

As we want to explore a situation that we don't know much about, namely a quick increase in greenhouse gasses in the atmosphere, there's not much hope of getting the right dynamics out of just a black box curve fitter when such rise was not the case. There's much more hope to learn something by doing "white box" modelling, that is to say, implement physically understood relationships - even if they are rough and simplified - into a simulator and see what it does. It's also much more instructive to do so.

I have assumed that the climate modelers were doing what you call "white box" modelling. Am I wrong? For all the noise they make about the complexity of the models they had better be!