The AGW climate feedback discussion

[Xnn]

A good white box model would have to include different time constants for all the different feedbacks. The simple model presented above does not do that.
Instead, it assumes that all feedback is realized during the next time step.

Feedbacks include:

Water Vapor
Lapse Rate
Cloud Cover
Surface Albedo (sea ice)
Surface Albedo (seasonal snow cover)
Surface Albedo (Vegetation)
Surface Albedo (Land Ice)
Surface Albedo (water level)
CO2 emission rates

Each of these have their own magnitude and time constant.

 

[vanesch]

I already quoted this in another thread, but this seems to be a good read about what's in actual climate models:
http://www.iop.org/activity/policy/Publications/file_4147.pdf

I scanned through it, I'm now reading it in more detail. Very good stuff!

 

[Xnn]

Here's another interesting article on climate models which includes a
brief discussion on feedbacks and transient rates:

http://www.iac.ethz.ch/people/knuttir/papers/knutti08natgeo.pdf

 

[Bored Wombat]

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.

Big tipping point between luminosity .93 and .94

 

[vanesch]

I did some playing on my own, but as it pertains more to "chaotic systems" than to "feedback" I put it in the thread there.

 

[Andre]

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.

Thanks, time to resume this thread.

This is a typical controlling/stabilizing effect of negative feedback. Too cold? then there is the black daisy absorbing - low reflectivity - warm feedback. Too warm? then is the white daisy - reflecting - cold feedback. Sign of the feedback opposite to the output.

So what looks like a tipping point to WB is in reality the start of the stable process, steered by negative feedback.

 

[Andre]

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.

But if it works well for intrapolation, would that mean that Karners observation about a predominant negative feedback / stability is valid within the restraints of the extremes?

I would not be too sure that we understand the climate well enough to for white box modelling, how to fit in changes in the not-understood forcings like the http://www.birdpop.org/Media/ENSONAO.pdf .

Maybe that the role of the mobile polar highs with pioneer research of late Marcel Leroux has distinctive effect on the variation in minimum winter temperatures that appear to have some effect on the global temperature variations. Currently the USA appears to be experiencing what that means. As far as I know the MPH are not included in the models let alone its behavior.

 

[vanesch]

Thanks, time to resume this thread.

This is a typical controlling/stabilizing effect of negative feedback. Too cold? then there is the black daisy absorbing - low reflectivity - warm feedback. Too warm? then is the white daisy - reflecting - cold feedback. Sign of the feedback opposite to the output.

So what looks like a tipping point to WB is in reality the start of the stable process, steered by negative feedback.

Indeed. However, the model, simple as it is, is much richer than just "negative feedback". In fact, you get another tipping point around 1.7

If you look at the temperature as a function of "luminosity", then you see that for most values of luminosity, the system controls itself such that temperature is something like 25C.
(do this for several values between 0.94 and 1.70). So there is strong negative feedback here to get temperature to the desired value (more or less): if it is too cold, more black daisies, if it is too hot, more white daisies. Note that there is even "over steering": close to 1.7, the temperature lowers below 20C.
However, a small variation, from 0.94 to 0.92 is sufficient to make temperature drop from this 25 degrees to 0 degrees ; in the same way, a small variation from 1.69 to 1.71, where temperature goes from 20 C to 45 C.

So we see in this simulation that we have a more or less "stable" population occupying 2/3 of the land, of an adapted mix of white and black to keep temperature more or less constant, within two boundaries (0.94 and 1.7), and a total collapse of this system by a tiny change over these boundaries.

 

[vanesch]

I would not be too sure that we understand the climate well enough to for white box modelling, how to fit in changes in the not-understood forcings like the http://www.birdpop.org/Media/ENSONAO.pdf .

The point is that you don't want to "fit that in". You would like to "get it out" from the dynamics of the system itself. If all individual physical local processes are well enough described, the dynamics should "by itself" show this behaviour. It would be a good thing that GCM could reproduce these things (without them being "put in by hand"). It would increase the confidence that one is closing in on the full climate dynamics.

To me, it would be the only way to really be confident in climate predictions: that we just put into a model (essentially an augmented weather simulator as we use it for weather forecasting) all physically relevant information (without any "fitting the data", but just physical modelling), and be able to 1) compute rather accurately current and near-past climate and 2) compute things like these oscillations. Once all that is accurately coming out of the model (without having it put in by hand), I would start to be rather confident that eventual future evolutions would be well-described too.

 

[vanesch]

But if it works well for intrapolation, would that mean that Karners observation about a predominant negative feedback / stability is valid within the restraints of the extremes?

The point is that you don't KNOW if a black-box dynamical model is "having negative feedback" or not. Remember that feedback is the chopping-up of a certain dynamical model into separated chunks (which might be physically motivated), to obtain an overall dynamics. However, by doing black-box modeling, you ONLY fit the general overall dynamics. You don't know if "for real" it is internally chopped up in pieces, that are connected into a feedback loop. That's something you can only do by physical modeling.

Of course, you can FIT an arbitrary feedback model with free parameters onto some data. But nothing tells you that an even rather good fit has any structure that ressembles the "true" structure. The dynamics will reproduce more or less well similar signals as those used to fit it.

My dynamics professor told us: "give me any dynamical model with 12 free parameters and I fit you an elephant ; add a 13th parameter, and I make its trump swing!"

But that doesn't mean that the model is the correct model of an elephant!

 

[Xnn]

Have been toying around with Excel to build a simple model of Earth's climate.
It's physics based with temperature dictated by the Stephan Boltzmann law.
Albedo is based on the fraction of water and ice present.
The model was calibrated to match approximate global temperature corresponding to ice age/interglacial with 180/270 ppm CO2.

Add a little CO2 and emissitivity falls and temp goes up.
Increased temp results in melting a little bit of ice.
The ice doesn't melt immediately to equilibrium, but enough so that albedo goes down a little bit.
In other words, there is a simple feedback mechanism with time delay.

After a couple time steps of rising CO2 levels, levels are held constant.
Problem is that what happens is run away global warming.

So, there has to be some negative feedback mechanism for stablity.
During the Ice age/Interglacial, we know that solar irradiance and CO2 levels fell
and that these kept temperatures from getting too high. However, without those forcings, what is left?

 

[Andre]

Have been toying around with Excel to build a simple model of Earth's climate.
It's physics based with temperature dictated by the Stephan Boltzmann law.
Albedo is based on the fraction of water and ice present.
The model was calibrated to match approximate global temperature corresponding to ice age/interglacial with 180/270 ppm CO2.

Add a little CO2 and emissitivity falls and temp goes up.
Increased temp results in melting a little bit of ice.
The ice doesn't melt immediately to equilibrium, but enough so that albedo goes down a little bit.
In other words, there is a simple feedback mechanism with time delay.

After a couple time steps of rising CO2 levels, levels are held constant.
Problem is that what happens is run away global warming.

So, there has to be some negative feedback mechanism for stablity.
During the Ice age/Interglacial, we know that solar irradiance and CO2 levels fell
and that these kept temperatures from getting too high. However, without those forcings, what is left?

Surely that ice albedo is possible positive feedback loop. Two elements.

1: it happens on the areas with the least insolation at higher lattitudes during the time of minimum insoltation, say spring. Not getting very warm then.

2: It is directy testable: a winter with much snow cover should -statistically significant- be followed by a cold late spring/summer and vice versa, due to that feedback

I'll get back on that dominant negative feedback factor

 

[Andre]

... Once all that is accurately coming out of the model (without having it put in by hand), I would start to be rather confident that eventual future evolutions would be well-described too.

Surely, you're not redefining the scientific method? I'd say that a model can be a good tool to shape hypotheses into detailed quantitative prediction, which has to be verified by testing it eventually to the reality. And even then you can have 100 white swans but if the 101th is black then your all-swans-are-white-hypothesis is falsified (I know the example does not work in Australia - swap white and black)

Also it seems that other specialists like Freeman Dyson or Henk Tennekes have a different view on the skill of models.

Anyway, the thread was about feedback, not models, and I don't think that the discussion about the skill of Karners observations changes from anti-persistent to persistent in this discussion. So if his work suggest anything is that no traces can be found of a dominant positive feedback mechanism, able to push the climate sensitivity for doubling CO2 from the Planck response of 1.1 to 1.2 degrees to some 2 -4.5 degrees.

 

[Andre]

Anyway, the most overlooked feedback factor in my opinion is the latent heat / evaporation. We had a lenghty and difficult discussion about that in this thread in which we dispute whether or not an increased temperature with more of less constant relative humidity would or would not require an additional increase in evaporation rate. If so then the required energy for that would be in the same order of magnitude that the increased greenhouse effect would supply.

But you can't use that energy both for additioal heating and for more evaporation. Hence if the evaporation rate would increase, it would act as a negative feedback, transporting more latent heat to higher levels of the atmosphere where it can be radiated out more easily.

But what goes up must come down and more evaporation also means necesarily an increase in precipitation when dynamic equilibrium is reached and that is testable, see my last post in that thread linking to http://www.sciencemag.org/cgi/content/abstract/317/5835/233, showing that the observed increase of precipitation is roughly consistent with my assumptions (7% per degree kelvin).

However more studies that suggest various increase rates in precipitation:

http://www.agu.org/pubs/crossref/2009/2009GL040218.shtml

We find that the top 10% bin of precipitation intensity increases by about 95% for each degree Kelvin (K) increase in global mean temperature, while 30%–60% bins decrease by about 20% K−1. The global average precipitation intensity increases by about 23% K−1, substantially greater than the increase of about 7% K−1 in atmospheric water-holding capacity estimated by the Clausius-Clapeyron equation.

need to study that,

But also:

http://www.nasa.gov/centers/goddard/news/topstory/2007/rainfall_increase.html

"When we look at the whole planet over almost three decades, the total amount of rain falling has changed very little. But in the tropics, where nearly two-thirds of all rain falls, there has been an increase of 5 percent,"

 

[Xnn]

Actually, my little model runs away in both directions.

Add a little cooling, water freezes to ice and then by albedo changes it runs away to total ice.
Add a little warming, ice melts and albedo runs it away to about 38C. The 38C is just an artifact of how it was tuned.

Albedo changes from Ice/water always re-enforce the initiating forcing (i.e. positive feedback).

Would think that clouds might be a negative feedback at the extremes of warming/cooling.
That is at extreme warming, clouds build up enough to rise albedo.
At extreme cooling, clouds clear enough to counteract the falling albedo.

Problem is that clouds, with some uncertainty, are considered to be net positive feedbacks by conventional climate science. What is supposed to stop run-away global warming in the real world is increased precipitation. From increased weathering this sequesters CO2 and introduces cooling. At the other end of the spectrum, what stops run-away global cooling are volcano's. However, falling CO2 also inhibits plant growth and the lack of plants may halt CO2 levels from falling too much as well.

 

[billiards]

What about the one where all the permafrost melts and loads of greenhouse gases currently trapped are set free?

Or the one where due to increased heat waves we get more forest fires, which then kills a whole load of trees and releases a mass of carbon? (which not only increases greenhouse in the short term but also weakens the planet's ability to capture CO2)

 

[Bored Wombat]

So what looks like a tipping point to WB is in reality the start of the stable process, steered by negative feedback.

Isn't that what a tipping point is? The point between the start of one stable process and the start of a dramatically different one?

 

[vanesch]

Surely, you're not redefining the scientific method? I'd say that a model can be a good tool to shape hypotheses into detailed quantitative prediction, which has to be verified by testing it eventually to the reality.

It's not science, it's engineering. The science was the part where the theory came from on which the model is based. If you do calculations of trajectories of satellites, you also take it on that Newton's laws are valid, and you don't "falsify" the calculated trajectory by sending different satellites and comparing with your calculation. You verify that there aren't bugs in your program, and you are confident that the calculation is going to be right.

Of course, that's a tad too easy, I agree, because you have to make approximations when you do so. You have to decide what's important and what's not if you're modeling things physically. So indeed, it would be better to be able to do some "test casing".

But if you have only one single system (earth's climate), and you don't have a long time (say a few million years of reliable data), then the best you can do is to set up your physical model as best as you can, and take that as the best guess you can make.

Also, you can test subsystems of your calculation. If the main engine is a weather forecast engine that has been tested for several tens of years, this means you master the short-term response well. You can also try to test several other parts, if you have historical data concerning them. And then you have to hope you put it all together correctly.

That's how people build the first atomic bomb too. If you do something totally new, you don't always have the luxury of "prototyping". So you have no choice but to trust your calculations. It's always partly a guess - that you didn't make silly mistakes (but that can be solved by having different independent working groups doing the same thing) - but more importantly that you didn't overlook an important aspect that you thought you could neglect, or that you made a fundamental error somewhere.

Anyway, the thread was about feedback, not models, and I don't think that the discussion about the skill of Karners observations changes from anti-persistent to persistent in this discussion. So if his work suggest anything is that no traces can be found of a dominant positive feedback mechanism, able to push the climate sensitivity for doubling CO2 from the Planck response of 1.1 to 1.2 degrees to some 2 -4.5 degrees.

Feedback is inherently coupled to models. Feedback is a modeling concept. It means constructing a model with a "main" part and a "feedback" part.

You cannot find any "traces of feedback" in pure time series. You don't know if it is inherent dynamics or if the system is structured as a feedback system. What you can do is to try to find/fit a model of the overall system but you never know if that was obtained by a feedback loop or not.

I'll try to explain that later.

 

[vanesch]

To come back to the feedback concept:

In time series analysis and modelling, you have two kinds of linear system descriptions:
"finite impulse response" (FIR) systems, and "infinite impulse response" (IIR) systems.

In fact, one can model almost any (linear) time series systems dynamics both with IIR and FIR systems. Often, IIR systems are more computing-resource-efficient than FIR, but on the other hand, they are a bit harder to "tune".

Well, FIR systems don't contain feedback, and IIR do.

In fact, the definitions are easy:

If x(i) is the input time series (i is an integer indicating "sample number" and represents "time" in most cases) and y(j) is the output time series, then a FIR system is defined by:

y(j) = a0 x(j) + a1 x(j-1) + a2 x(j-2) + ... an x(j-n)

where n is the "order" of the system.

In other words, the output at sample number j is a weighted sum of the n last input samples, and the weighting coefficients are fixed constants which determine the "dynamics" of the system.

For a IIR system, we have:

y(j) = a0 x(j) + a1 x(j-1) + a2 x(j-2) + ... an x(j-n)
- b1 y(j-1) - b2 y(j-2) - ... bm y(j-m).

So an IRR system is a FIR system plus feedback, that is, the output at a certain sample number is composed of, as in the case of a FIR system, a weighted sum of past input samples, but now also a weighted sum of past outputs.

Well, you can approximate any IIR system with a FIR system (of much higher order).

You can't do so exactly, but you can do so within any limit of accuracy. The price to pay is that you will have a very high-order FIR system in some cases to approximate well a rather small-order IIR system.

It is only in the case of unstable IIR systems that you can't build a reliable, long-term FIR system (although you can STILL build a FIR system that approximates the beginning of the divergence). A FIR system can never become unstable. An IIR system can, and that's what makes working with IIR systems somewhat nastier. They are more powerful models, but they are nastier to work with, exactly because of questions of instability.

Andre's simulation was nothing else but an elementary IIR system, something of the kind:

y(j) = G x(j) + F y(j-1)

(so a0=G was the "gain" and b1=-F was the "feedback")

But you can approximate this by a FIR system, by finite substitution:

y(j) = G x(j) + G^2 F x(j-1) + G^3 F^2 x(j-2) + G^4 F^3 x(j-3) + ... + G^(n+1)F^n x(j-n)

In this last system, there is no explicit feedback (the output is not computed using previous outputs).

Note that we see a trace of the feedback loop gain GF :

y(j) = G { x(j) + (GF) x(j-1) + (GF)^2 x(j-2) + ... + (GF)^n x(j-n) }

If GF is smaller than 1 in absolute value, then these coefficients diminish in absolute value as a power series. Hence, the last terms become very small, and truncation is justified. So our FIR system is then a good approximation for the original IIR system.

That's exactly the condition that is necessary for the IIR system to be stable.

And now I come to my original point: if I'm just given time series x and y (inputs and outputs) of the system I'm supposed to model, then I can try to fit a FIR system to it, or I can try to fit an IIR system to it. Now, depending on the quality of the data, that fit will be of good or bad quality. But if the fit is in both cases of good quality, then both system models will reproduce well the behavior of the system, at least for similar signals as the data had.
This is for the very simple case of a *linear* system and one input and output signal.

 

[Andre]

What about the one where all the permafrost melts and loads of greenhouse gases currently trapped are set free?

Or the one where due to increased heat waves we get more forest fires, which then kills a whole load of trees and releases a mass of carbon? (which not only increases greenhouse in the short term but also weakens the planet's ability to capture CO2)

Why would these effects be strong enough to steer climate. Once there was little or no permafrost, when the trees grew at the Arctic coast, http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WPN-45BCR6K-M&_user=10&_rdoc=1&_fmt=&_orig=search&_sort=d&_docanchor=&view=c&_searchStrId=1133189953&_rerunOrigin=google&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=0e829aa6f1e183a5e99a5a7d50e8d656, this was close to biggest methane hydrate decompositon event in the Nordic sea (http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V9Y-4FG2XBV-1&_user=10&_coverDate=01%2F01%2F2005&_alid=1133192856&_rdoc=1&_fmt=high&_orig=search&_cdi=5911&_sort=r&_docanchor=&view=c&_ct=27&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=1e072f80837a7092144b4a463d2c4036.

So already in the Holocene, Earth has seen pretty extreme conditions and it only has got colder since then.

More later, I'm maxed out.

 

[Bill Illis]

Actually, my little model runs away in both directions.

Add a little cooling, water freezes to ice and then by albedo changes it runs away to total ice.
Add a little warming, ice melts and albedo runs it away to about 38C. The 38C is just an artifact of how it was tuned.

Albedo changes from Ice/water always re-enforce the initiating forcing (i.e. positive feedback).

Would think that clouds might be a negative feedback at the extremes of warming/cooling.
That is at extreme warming, clouds build up enough to rise albedo.
At extreme cooling, clouds clear enough to counteract the falling albedo.

Problem is that clouds, with some uncertainty, are considered to be net positive feedbacks by conventional climate science. What is supposed to stop run-away global warming in the real world is increased precipitation. From increased weathering this sequesters CO2 and introduces cooling. At the other end of the spectrum, what stops run-away global cooling are volcano's. However, falling CO2 also inhibits plant growth and the lack of plants may halt CO2 levels from falling too much as well.

Xnn, you can constrain your runaway Albedo values to:

- about 0.245 (the lowest level possible in our current atmosphere corresponding to an Earth with no ice, high sea levels and small continental areas concentrated toward the equator, basically clouds will keep it higher than this level) ; to,

- about 0.550 (the highest possible value corresponding to Snowball Earth where all the continents are locked together at one or both of the Poles and ice covers most of the Earth).

The climate history indicates we have come very close to both levels in the past 650 million years.

 

[Skyhunter]

Anyway, the most overlooked feedback factor in my opinion is the latent heat / evaporation. We had a lenghty and difficult discussion about that in this thread in which we dispute whether or not an increased temperature with more of less constant relative humidity would or would not require an additional increase in evaporation rate. If so then the required energy for that would be in the same order of magnitude that the increased greenhouse effect would supply.

Please see the thread you linked for an explanation of why your opinion is wrong.

Your premise that it requires 80 W/m² to support evaporation is over stated by a factor of 1000.

It only requires .08 W/m² to evaporate 1/2 a million cubic kilometers of water per year.

I see what I did. I forgot to convert to grams.

Irregardless, it is still accounted for, witness the IPCC diagram.

 

[Andre]

okay let's try again.

The anual evaporation is 440.10³ km³ per year = 440,000 km³

the Earth surface is 148,300,000 sq km land + 361,800,000 sq km water = 510,100,000 sq km

So the average evaporation for the complete surface = 440,000 km³/510,100,000 km² = 0,000863 km/year = 0.863 meters/year

This is a cube of 0,863 m³ per square meter, which is 863,000 cubic centimeters or grams water per year

So per second per square meter the evaporation is 863000 grams/(365*24*3600)=0.027 gram per square meter per second.

one gram of water requires http://www.usatoday.com/weather/wlatent.htm hence 0.028 gram requires 68.4 joule per second per square meter or 68.4 watt per square meter. Indeed I was off by some 10 watt but as far as I can see not by a factor 1000.

 

[Sorry!]

one gram of water requires http://www.usatoday.com/weather/wlatent.htm hence 0.028 gram requires 68.4 joule per second per square meter or 68.4 watt per square meter. Indeed I was off by some 10 watt but as far as I can see not by a factor 1000.

where did you get that value. one gram of water requiring 2500 joules for evaporation.EDIT: Nvm see where you saw it lol.

it's just that I seen values around 540 cal/g... so about 2260 joules which would bring the value down to what 63?

I am pretty sure there are a lot more factors thuough which effect this value... like pressure, temperature, density etc. etc. it's been awhile since I've taken my physics course though, lol.

 

[Xnn]

The IPCC pegs evapotranspiration at 78 Watts/m^2.
See FAQ 1.1 Figure 1:

http://www.ipcc.ch/pdf/assessment-report/ar4/wg1/ar4-wg1-chapter1.pdf

Anyhow, this very important mechanism tranports energy within the Earth's atmosphere, its precise value is not a classic feedback.

Feedbacks need to fall into 2 categories:

Albedo
Emissivity

 

[sylas]

okay let's try again.

The anual evaporation is 440.10³ km³ per year = 440,000 km³

the Earth surface is 148,300,000 sq km land + 361,800,000 sq km water = 510,100,000 sq km

So the average evaporation for the complete surface = 440,000 km³/510,100,000 km² = 0,000863 km/year = 0.863 meters/year

This is a cube of 0,863 m³ per square meter, which is 863,000 cubic centimeters or grams water per year

So per second per square meter the evaporation is 863000 grams/(365*24*3600)=0.027 gram per square meter per second.

one gram of water requires http://www.usatoday.com/weather/wlatent.htm hence 0.028 gram requires 68.4 joule per second per square meter or 68.4 watt per square meter. Indeed I was off by some 10 watt but as far as I can see not by a factor 1000.

This is a measure of the latent heat energy flux. Your calculation of this is close, but it is using figures for the ocean only; neglecting evaporation over the land. This makes your result a little too small.

Basically, energy moves up from the surface of the Earth into the atmosphere by three means. Latent heat, convection, and radiation. There is also radiation coming back down from the atmosphere, but we can simply consider the difference between the upwards and downwards flux as the part of the energy moving up from the surface due to radiation.

There has been a fair bit of work on obtaining the various energy fluxes. A major reference is

• Trenberth, K.E., Fasullo, J.T., and Kiehl, J. (2009) http://ams.allenpress.com/archive/1520-0477/90/3/pdf/i1520-0477-90-3-311.pdf , in Bulletin of the AMS, Vol 90, pp 311-323.

The work is summarized with this diagram:

Trenberth et al cites a number of estimates, and the diagram corresponds to 2.76 mm day⁻¹. The surface area of the Earth is 5.1*10⁸ km³. Hence this is using

2.76 *10^-6 * 365 * 5.1*10⁸ km³/year = 5.14*10⁵ km³ per year​

close to Andre's 4.4*10⁵. The difference is the evaporation over the land.

The more serious error is to describe this as feedback, as was explained many times in the other thread.

Consider what happens if we have a temperature rise, for any reason, in the above diagram. The immediate effect is an increase in radiation from the surface. The atmosphere adjusts rapidly to maintain the same lapse rate, so convection remains about the same; and there will be an increase in backradiation from the warmer atmosphere. This is all part of what is normally called the "non-feedback response", which we discussed in the thread already. It is the changes that arise only from temperature change, of the surface and of the atmosphere.

Now consider the knock on changes, which may be part of feedback loops. One effect will be a change in surface cover. Ice melting is the most obvious, but there can be other kinds of effects as well, as vegetation or land cover changes. The ice effect is a significant positive feedback; vegetation cover changes have less dramatic effects but over larger areas; it can be both positive and negative; but overall the surface cover effects are a positive feedback.

More significant, however, is the increased evaporation. This will increase latent heat flux by a small amount, which gives a more effecient transport of heat into the atmosphere. What Andre is missing -- and what he continuously missed in the previous thread as well, is that convection adjusts very rapidly to maintain the lapse rate. So if latent heat effects transport energy more effectively, convection will reduce to compensate. That is why you never find this described as a feedback in any actual scientific literature -- which, I might add, is a requirement for controversial ideas in this forum.

What is a genuine feedback, however, is at least three fold. The increased evaporation corresponds to increased specific humidity... more water in the atmosphere. This leads to:

• More greenhouse effect. Water is a very strong greenhouse gas, and this is a positive feedback.
• Less lapse rate. The moist lapse rate is weaker than a dry lapse rate, and a weaker lapse rate means that the upper atmosphere is warmer; and hence more effective at radiating into space. This is a negative feedback. The combination of lapse rate feedback and humidity feedback is much better constrained than either one individually; the errors tend to cancel. The humidity effect is significantly stronger; so overall this is positive feedback.
• Cloud changes. This one is the hardest to sort out. Cloud can be both a positive and a negative feedback, depending on the nature of the cloud. They are very good reflectors (negative feedback) and also very good greenhouse absorbers (positive feedback). We experience both these effects all the time. In daylight, a cloud shades you and cools. At night, a cloudy sky is invariably much warmer than a clear sky; this is basically a greenhouse effect. There's a lot of work sorting this out, and the effects vary from region to region.

Cheers -- sylas

 

[Andre]

What Andre is missing -- and what he continuously missed in the previous thread as well, is that convection adjusts very rapidly to maintain the lapse rate. So if latent heat effects transport energy more effectively, convection will reduce to compensate. That is why you never find this described as a feedback in any actual scientific literature -- which, I might add, is a requirement for controversial ideas in this forum.

That's a double wrong.

First of all remember that I am not depending on ideas but merely on observations in this thread:

...But what goes up must come down and more evaporation also means necesarily an increase in precipitation when dynamic equilibrium is reached and that is testable, see my last post in that thread linking to http://www.sciencemag.org/cgi/content/abstract/317/5835/233, showing that the observed increase of precipitation is roughly consistent with my assumptions (7% per degree kelvin).

However more studies that suggest various increase rates in precipitation:

http://www.agu.org/pubs/crossref/2009/2009GL040218.shtml

http://www.nasa.gov/centers/goddard/news/topstory/2007/rainfall_increase.html

Secondly, the general idea has been suggested in peer review literature, for instance Chilingar et al 2008

 

[sylas]

Secondly, the general idea has been suggested in peer review literature, for instance Chilingar et al 2008

Gah. Chilingar again.

No, Andre, you're wrong. Chilingar is NOT suggesting a negative feedback effect. He's much MUCH sillier than that.

Before we get stuck into this let me just remind everyone... I do NOT merely dismiss all critics of conventional views of anthrogenic global warming as being cranks. But Chilingar? Complete and utter crank -- except possibly in his own field of petroleum geology; I guess he must be okay at that. But elementary physical thermodynamics of the atmosphere? There's a good reason his paper has zero impact. (Papers, I should say. He published this same nonsense paper in two different journals; which is not kosher. Neither journal is one where you'd expect to see any atmospheric physics being published; but when your paper is this bad...)

Let's review what this thread is meant to be about. It's about feedback. Right at the start, Andre gave the context as follows:

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:



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.

So this whole thread has been about feedback, which can amplify, or damp, the response of climate to some forcing. We started out with the conventional understanding of greenhouse effects and the forcing from CO₂ of 3.7 W/m² per doubling. This number is not in any real doubt. The calculation is intricate, and depends on knowing the absorption spectra of atmospheric gases; but those ARE known, and the thermodynamic consequences have been known for over a century. Adding CO₂ to the atmosphere gives an additional forcing. This number is used quite conventionally by genuine climate scientists who may be critical of other aspects of the majority view on climate response.

Richard Lindzen, for example, uses that same 3.7 W/m² and argues for a zero or negative feedback effect that would make the temperature response very small. He's not got a lot of support for this argument, but it isn't immediately nonsense on the face of it. I had thought that Lindzen and Choi's recent paper was going to be the focus of the thread. Lindzen, after all, is a climate scientist.

Chillingar, however, is no Richard Lindzen. He's NOT proposing a negative feedback at all. Andre is just wrong about that. Chilligar is proposing a negative BASE EFFECT. He argues that CO₂ leads to cooling. He reverses the effect altogether!

That CAN'T be done with feedback.

It also can't be done without rewriting every introductory textbook on atmospheric physics and radiation.

Cheers -- sylas

 

[Andre]

May I enquire how to fit in that observed global precipitation increase of http://www.sciencemag.org/cgi/content/abstract/317/5835/233, suggesting that it was 7%?

Doesn't that indicate that -assuming dynamic equilibrium- the evaporation rate has to be equal to the precipitation rate? Wouldn't a 7% higher evaporation rate require a 7% higher energy level used? I.E. 86.5 W/m² versus 80 W/m² in the observed period? Now how does this relate to the 3.7 W/m² of doubling CO2?

And we are not talking convection here, just evaporation and precipitation rates

Sure I was keeping Lindzen and Choi as reserve. Just tactics.

 

[sylas]

May I, in between all these smoke screens

I have just pointed out where you are going wrong in the stuff YOU are introducing. Don't blame other people for taking up the various issues you choose to raise.

Doesn't that indicate that -assuming dynamic equilibrium- the evaporation rate has to be equal to the precipitation rate? Wouldn't a 7% higher evaporation rate require a 7% higher energy level used? I.E. 86.5 W/m² versus 80 W/m² in the observed period? Now, how can the 3.7 W/m² of doubling CO2 do that?

And we are not talking convection here, just evaporation and precipitation rates

You can't separate them. Convection is whatever is required to maintain the lapse rate. If there is a flux of latent heat up into the atmosphere, there is that much less heat to carry up by convection. This is the same answer you were given previously, and it won't change if you keep asking it next year.

Sure I was keeping Lindzen and Choi as reserve. Just tactics.

I remain interested to see this actually discussed, as I have recommended now quite a number of times. It would be infinitely better than Chilingar.

Cheers -- sylas

 

[vanesch]

Doesn't that indicate that -assuming dynamic equilibrium- the evaporation rate has to be equal to the precipitation rate? Wouldn't a 7% higher evaporation rate require a 7% higher energy level used? I.E. 86.5 W/m² versus 80 W/m² in the observed period? Now how does this relate to the 3.7 W/m² of doubling CO2?

Honestly, we've been going over this several times now. In this thread, YOU stated that

A: What is the basic climate sensitivity (Planck response) of doubling the CO2 concentration?
B: How is that modified by possible feedbacks?

so we were going to discuss the phenomena of feedback in this thread.

Now you come up again with your evaporation rate, which, as we've explained several times again, has nothing to do with the humidity per se.

But to reiterate the answer you already got, heat transport in the lower troposphere consists of 2 contributions:
- radiative
- convective

Convective itself consists of 2 contributions:
- heat as "warmer gas"
- latent heat

In the lower troposphere, the most important part of heat transport is actually convective.

The TOTAL heat flux has to be so that the lapse rate is re-established, because that's what drives convection. There is a strong negative feedback in the troposphere that restores the lapse rate:

If the lapse rate is softer than the adiabatic lapse rate (that is, if temperature doesn't diminish "fast enough" with altitude), then convection stops, strongly diminishing the total heat transport. That means that lower layers get a lot of extra heat that they cannot get rid of through heat transport, and hence heat up, as such increasing lapse rate (==> negative feedback: initially diminishing lapse rate causes increase in lapse rate).

If the lapse rate is stronger than adiabatic, that means that higher layers are much colder, and hence much denser, than they should be in "equilibrium" (adiabatic) conditions. Hence they "fall down", while lower layers are much hotter (and hence much lighter) than they ought to be ==> very strong drive of convection, increased heat transport and hence tendency to cool down lower layers and heat up higher layers ==> lapse rate diminishes ==> negative feedback again, because initial stronger lapse rate is now diminished.

Convection is hence a very strong feedback mechanism that keeps the lapse rate on the adiabatic lapse rate, no matter what.

Now, what happens when there is more evaporation at the surface, is that the latent heat component increases (that's what you calculate). At identical convection rate, this would mean: more heat transport (that's what you calculate). But then the strong negative feedback sets in: this stronger heat transport would make the lapse rate softer. So convection will "slow down" to restore the lapse rate. You can still have your higher latent heat transport, but it will be compensated by a slower convection and hence less "normal" heat transport.

And that doesn't stop you from having more precipitation, because of course with more evaporation must come also more precipitation. And in fact, you don't even need MORE evaporation, you can have in principle ANY evaporation rate and still have higher humidity levels. Because of the strong negative feedback by convection in the troposphere for every attempt at deviation from the adiabatic lapse rate.

In other words, the humidity in the air determines the ratio of heat transport through convection of latent heat and heat transport through convection of normal heat (warm air). This balance can go one way or another, and is determined by other elements, and has nothing to do with the overall heat transport. More of one will automatically mean less of the other, so that the total sum remains OK, because of the strong convective feedback mechanism in the troposphere.I tried to take opportunity from this "feedback" thread to re-iterate the explanation we've given before already a few times, this time in a "feedback" frame.

 

[Andre]

No matter how many times an explanation is given, it doesn't make it righter or wronger. Maybe I should reiterate the working of the Hadley cell and maybe stress a bit more the diurnal variation, with constant radiative cooling and cyclic solar warming, which makes sure that there is no such thing of keeping the lapse rate adiabatic. The next hour, it is different anyway.

And just about important in transport of water (vapor) is advection which is much more complex.

But I think there is little point in it anyway.

 

[Xnn]

Andre;

I think the best thing to do is to look at the diagram that Sylas posted in message 61.
The evapotranspiration value is the same at the surface as it is in the clouds.
Notice it is a flow of energy within the system.

Now look at what is flowing into the system: 341.3 Wm^2

And what is flowing out at the top of the atmospher: 101.9+238.5 = 340.4 Wm^2

The difference is 0.9 Wm^2. This is global warming.

The 80 or 78 or whatever evapotranspiration itself does not affect the difference between the inputs and outputs at the top of the atmosphere.

 

[Xnn]

Another way to look at Sylas's diagram:

The 341 Wm^2 incoming is solar irradiance
The 102 Wm^2 is a function of albedo
The 239 Wm^2 is a function of emissitivity

The thermals, evapotranspiration and surface radiation may look like feedbacks, but they aren't. Instead they are more like eddy currents.

A feedback would have to be something that affects either albedo or emissitivity.

 

[sylas]

No matter how many times an explanation is given, it doesn't make it righter or wronger. Maybe I should reiterate the working of the Hadley cell and maybe stress a bit more the diurnal variation, with constant radiative cooling and cyclic solar warming, which makes sure that there is no such thing of keeping the lapse rate adiabatic. The next hour, it is different anyway.

The added complexity of divergences of the lapse rate from the adiabatic rate does nothing to address the more fundamental confusions involving the simple case. You are trying to run before you can walk; and the big problem is that you don't recognize the problems you are having with walking.

This is not "AGW" theory; it is simply atmospheric physics, the same as is applied for any planet.

There's a reason that the feedbacks which are considered in all the major references for climate feedback never consider latent heat flax as a feedback; but they do consider lapse rate as a negative feedback and greenhouse effects from humidity as a positive feedback.

Lindzen and Choi's paper is still worth looking at. There's nothing wrong with the notions of feedback in use there; though there are other reasons it has been unpersuasive.

Cheers -- sylas