Guest Post by Willis Eschenbach.
I came across an interesting 2014 paper called “The energy balance over land and oceans: an assessment based on direct observations and CMIP5 climate models“. In it, they make a number of comparisons between observational data and 43 climate models regarding the large-scale energy flows of the planet. Here’s a typical graphic:
Figure 1. ORIGINAL CAPTION: “Fig. 7 Average biases (model—observations) in downward solar radiation at Earth’s surface calculated in 43 CMIP5 models at 760 sites from GEBA. Units Wm−2”. The “CMIP5” is the “Computer Model Intercomparison Project 5”, the fifth iteration of a project which compares the various models and how well they perform.
Now, what this is showing is how far the forty-three models are from the actual observations of the amount of solar energy that hits the surface. Which observations are they comparing to? In this case, it’s the observations stored in the “Global Energy Balance Archive), GEBA. Per the paper:
Observational constraints for surface fluxes primarily stem from two databases for worldwide measurements of radiative fluxes at the Earth surface, the global energy balance archive (GEBA) and the database of the Baseline Surface Radiation Network (BSRN).
GEBA, maintained at ETH Zurich, is a database for the worldwide measured energy fluxes at the Earth’s surface and currently contains 2500 stations with 450‘000 monthly mean values of various surface energy balance components. By far the most widely measured quantity is the solar radiation incident at the Earth’s surface, with many of the records extending back to the 1950s, 1960s or 1970s. This quantity is also known as global radiation, and is referred to here as downward solar radiation. Gilgen et al. (1998) estimated the relative random error (root mean square error/mean) of the downward solar radiation values at 5 % for the monthly means and 2 % for yearly means.
So downwelling solar radiation at the surface is very well measured at a number of sites over decades. And surprisingly, or perhaps unsurprisingly given their overall poor performance, the climate models do a really, really bad job of emulating even this most basic of variables—how much sunshine hits the surface.
Now, bear in mind that for these models to be even remotely valid, the total energy entering the system must balance the energy leaving the system. And if the computer models find a small imbalance between energy arriving and leaving, say half a watt per square metre or so, they claim that this is due to increasing “net forcing, including CO2 and other GHGs” and it is going to slowly heat up the earth over the next century.
So their predictions of an impending Thermageddon are based on half a watt or a watt of imbalance in global incoming and outgoing energy … but even after years of refinement, they still can’t get downwelling sunlight at the surface even roughly correct. The average error at the surface is seven watts per square metre, and despite that, they want you to believe that they can calculate the energy balance, which includes dozens of other energy flows, to the nearest half a watt per square metre?
Really?
Now, I wrote my first computer program, laboriously typed into Hollerith cards, in 1963. And after more than half a century of swatting computer bugs, I’ve learned a few things.
One thing I learned is the mystic power that computers have over peoples’ minds. Here’s what I mean by “mystic power”—if you take any old load of rubbish and run it through a computer, when it comes out the other end, there will be lots of folks who will believe it is absolutely true.
For example, if I were to tell you “I say that in the year 2100 temperatures will average two degrees warmer than today”, people would just point and laugh … and rightly so. All I have to back it up are my assumptions, claims, prejudices, and scientific (mis)understandings. Anybody who tells you they know what the average temperature will be in eighty years is blowing smoke up your astral projection. Nobody can tell you with any degree of certainty what the average temperature will be in two years, so how can they know what the temperature will be in eighty years?
But when someone says “Our latest computer model, which contains over a hundred thousand lines of code and requires a supercomputer to run it, says that in the year 2100 temperatures will be two degrees warmer than today”, people scrape and bow and make public policy based on what is nothing more than the physical manifestation of the programmers’ same assumptions, claims, prejudices, and scientific (mis)understandings made solid.
And how do we know that is a fact, rather than just a claim that I’m making based on a half-century of experience programming computers?
Because despite the hundred thousand lines of code, and despite the supercomputers, and despite the inflated claims, the computer models can’t even calculate how much sunshine hits the surface … and yet people still believe them.
Here, after a week of rain, we had sunshine today and we’re looking at a week more. And if you think the models are bad at figuring out the sunshine, you really don’t want to know how poorly they do regarding the rain …
My best to everyone,
w.
““Our latest computer model, which contains over a hundred thousand lines of code and requires a supercomputer to run it, says that in the year 2100 temperatures will be two degrees warmer than today””
I can’t help but think those monstrosities are full of Medusa code – one look and it turns you to stone. I’ve quit (contract) jobs before after I saw what was under the hood.
I think we live in changing times with respect to people’s attitudes towards computer models. Once the entire population is familiar from childhood with what they can do, and what their failings are, then I think we shall see this deference to computer out-put decline.
I really like what this guy, Erl Happ, has done over a ten yr period. https://reality348.wordpress.com/2015/12/20/how-do-we-know-things/
There is about 40 chapters, but each quite short and very clear data.
The best part….no modeling apart from the use of some nullschoolearth pictures.
Great stuff, lots about ozone too.
Earth’s climate systems chaotically evolve in space and time driven by the imposed energy input at the ToA. Mathematical models of climate’s evolution in space and time differ with respect to the continuous equations (PDEs, ODEs, algebraic, and parameterizations) that make up the models. Calculated results indicate that the numerical solutions of the models give chaotic trajectories. That being the case, it is highly unlikely that the set of parameterizations and their numerical values, including those chosen for tuning/calibration, will be the same among the various models.
Beyond the parameterizations, their numerical values, and tuning, the discrete approximations to the continuous equations, and the numerical solution methods applied to these, are different among the various models. Each different approximation, each different solution method, and each different parameterization and associated turning, and each change in parameter numerical values, ensures that none of the models will produce the same trajectories. A given model will not produce the same trajectories if even one numerical value is changed.
Under these conditions, it seems that parameter estimation can not ever be carried out in a manner that gives unique values for any parameters. All trajectories will be different. Averages of the chaotic trajectories remain chaotic; the amplitude and frequency are changed. It is especially unfortunate that parameterizations and turning are necessary for those aspects of Earth’s climate systems that are critical to getting a ‘correct’ response relative to the physical domain.
Weather is the result of the thermally-driven hydrodynamics and the thermodynamics of phase change in Earth’s atmosphere and certain other sub-systems. All GCMs and ESMs and associated numerical methods produce different weather, and averages of the calculated weather produce different calculated climates.
A focus on the parameterizations and turning is a welcome development in climate science. Hopefully, as time goes on, the focus areas will eventually turn to the critically important aspects of the discrete approximations and numerical solutions of these. After all, that’s where the numbers comes from.
In my experience, complicated nonlinear systems have multiple attractors. The one you’re on depends on what the initial conditions are, plus you don’t know which one you’re on until you’ve run a simulation a long time. The modelers have no idea. From a former colleague of Schmidt’s
“This explanation conveniently ignores the fact that there is no way to ever know if a climate model’s attractor is the same as nature’s. When I (Duane Thresher) was at NASA GISS I pointed this out to Dr. Gavin Schmidt, current head of NASA GISS (anointed by former head Dr. James Hansen, the father of global warming) and leading climate change spokesperson. His response was, “We just have to hope they are on the same attractor”, literally using the word “hope”. They are almost certainly not so a climate model can’t predict nature’s climate.”
http://realclimatologists.org/Articles/2017/07/27/More_Reasons_To_Doubt_Climate_Models_Can_Predict_Climate/index.html
A second comment about the coding of the climate models by GISS. It’s as bad as Harry Read Me on the data.
“NASA GISS used to have its own supercomputer to run its climate model but they were so IT incompetent that they had it taken away from them and had to use the supercomputers at NASA’s Goddard Space Flight Center (GSFC). While at NASA GISS, I spent a summer at GSFC, outside Washington D.C., attending NASA’s supercomputing school. After I left NASA GISS, I was talking to a programmer from GSFC and he said they referred to NASA GISS’s climate model as “The Jungle” because it was so badly coded. The results of NASA GISS’s climate model, oft-cited as proof of global warming, are thus still questionable since the model is almost certainly full of bugs.”
http://realclimatologists.org/Articles/2019/01/03/Climate_of_Incompetence/index.html
“Now, bear in mind that for these models to be even remotely valid, the total energy entering the system must balance the energy leaving the system.”
It seems to me, the reason the models don’t work is that they are not taking the largest variable into consideration. For some reason, I rarely hear anyone talk about the heating of our atmosphere being caused by “friction”. (engineers know, all heat is friction)
The downward force in the column of air in a gravity well, creates heat at the bottom of the column. That’s why most heating occurs at the surface. (Day or night)
If the sun is the source of all energy (heat and light) then the laws of thermodynamics must apply. The heating would be from the top, to the closest point to the sun (Mount Everest) getting colder the further you get from the sun (death valley). The opposite is true indicating a different mechanism at play. ( UV, x-rays are consistent with the laws of thermodynamics, the higher you go, the more intense the radiation)
This mechanism is best illustrated by the “Chinook winds”.The dry wind flowing over the mountain heats up 5.4° for every thousand feet it falls in altitude! (day or night).
The higher you go in the atmosphere, the cooler it becomes as the air pressure drops. Camping in the mountains at 8000 feet is 20° colder than the valley below it, at 4000 feet.
You do not need to send up a weather balloon, or an airplane for this affect to occur. It also happens during weather events, like the air pressure drop in the eye of a hurricane, or the cooling affect of a “low pressure system” dropping the barometric pressure as the weather moves over.
To calculate the sun’s influence in real time, it is as simple as subtracting the low of the night from the high of the day, to give you the amount of heat that occurs from just the sun.
There is no better demonstration of how much heat comes from the sun than Antarctica. For six months it receives no sunlight causing the temperatures to plummet to 70° below zero on average. In the summer, with three months of 24 hour sunlight, the temperatures sores the hottest place on earth? NO!
The temperature average is 40° below zero. A 30° difference that usually only takes a few hours every morning any where else on earth.
Now compare the north pole to the south. Under the same conditions, why does the arctic temperatures rise above freezing every summer? It is at sea level.
Antarctica’s height averages near 10,000 feet altitude. Less air pressure generates less heat.
Does carbon dioxide cause any of this heat? Yes. In relation to its molecular “weight” and abundance. About 4/100 of 1%. (400 ppm)
Additional proof that air pressure generates frictional heat to keep our planet warm is to look at the other planets in our solar system. Hottest to coldest is Jupiter, Saturn, Neptune (the furthest from the sun) Uranus, Venus, Mercury, Earth, then Mars. (The gas giants are hotter than the surface of the sun) In short, the thicker the atmosphere, the hotter the planet under its atmosphere.
A comparison between earth and its moon, both in the green zone, reveals that the moon varies 550° (-300° to 250° in the sun) average temperature -50° at the equator. Earth average temperature is near 50°, that’s 100° warmer than the moon even though half of the suns energy does not penetrate our atmosphere!
Does any of this information appear in any climate models?
Friction is the action of turning momentum into heat. That heat then radiates away.
The problem is that unless you have something to keep these molecules in motion, friction will eventually cause them to stop moving altogether. No movement, no friction, no heat.
What you are proposing is indistinguishable from perpetual motion.
All heat is friction????? I’m an engineer, and I don’t know that. Radiation is heat. Chemical reactions are heat.
Compression causes heating, pressure doesn’t.
This would only be true if the sun was heating the earth via conduction. It isn’t. It heats the earth via radiation, and what gets heated is what absorbs the radiation. The atmosphere doesn’t. The ground and water do.
That’s about all the nonsense I can deal with for now.
MarkW: “Compression causes heating, pressure doesn’t.”
Incorrect, see:
Kinetic pressure
and
Ideal gas law
Max
While I’m sure that there is some frictional heating, the process you are describing is explained by the gas laws. Air going up over a mountain range cools, and then heats as it goes back down the leeward side.
Max — “The downward force in the column of air in a gravity well, creates heat at the bottom of the column.”
At a sea level measurement of 14.7 pounds per square inch, the weight of that column of air generates very little heat.
Willis,
In my department we made fatigue testing, measurements and simulations of heavy trucks.
And we had a saying:
“When test or measure results are presented no one believes in them except from the person that made them, and when simulation results are presented every one believes in them except the person who did them.”
It took us a huge amount of workload to solve the problem by geting the different approaches in agreement.
Nice, thanks.
w.
Hey, Willis – or anybody:
Looking at the outputs of the many climate models, I generally see that all of them, but one, deviate more and more from the observations as calculations proceed from about 1990 on. The one that best matches data appears to be INM-CM4, which, I think, is a model produced in Russia. However, looking at the material in the reference you used in your post, that model has biases that are quite large in a number of areas. Got any thoughts on that? Luck?
Willis you have hit the nail on the head. The models are back casting temperature while ignoring equally important climate metrics.
In many places temperature is less important than rainfall, for example. By tuning the models to a single metric the models know more and more about less and less.
Downwelling radiation from an object of low radiance to one of high radiance does not exist. It is akin to saying objects move up hill against the influence of the gravity field.
Measuring downwelling radiation is peak delusion as it does not exist. It is simply the response of a thermopile emitting EMR from its junctions to the cooler target object based on Stefan-Boltzman equation. They are not measuring radiation but inferring from a junction cooling and the electrical potential difference to a reference junction:
https://www.sensorsmag.com/components/demystifying-thermopile-ir-temp-sensors
People who think that EMR will transfer energy against the electric field potential have no clue about the electric field or magnetic field we exist in. There is a kindergarten level explanation here:
http://www.irregularwebcomic.net/1420.html
The key clue is the derivation of the speed of energy transfer in a vacuum or other medium being a simple function of the permittivity and permeability (or electrical and magnetic properties) of the transfer medium. All matter affects the electric field and magnetic field at that speed; commonly known as the speed of light.
For those who have a better grasp of maths there is a mathematical proof that EMR energy can only flow in one direction at any point in time and space:
https://pdfs.semanticscholar.org/c03b/2b493f57e13d3c3e2b58d17c9656d2dee978.pdf
Anything that discusses downwelling radiation from a cool atmosphere to a warmer surface is pure drivel. It is unphysical claptrap.
Measuring downwelling radiation is peak delusion as it does not exist. It is simply the response of a thermopile emitting EMR from its junctions to the cooler target object based on Stefan-Boltzman equation. They are not measuring radiation but inferring from a junction cooling and the electrical potential difference to a reference junction:
https://www.sensorsmag.com/components/demystifying-thermopile-ir-temp-sensors
RickWill:
Attempts at pedantry that stem from fundamental ignorance are especially irritating and only serve to make the attempter look foolish.
First of all, the post is about downwelling SOLAR radiation, which is most certainly NOT “radiation from an object of low radiance to one of high radiance”. So you have beclowned yourself in the very first sentence.
Your second paragraph just sinks you deeper. It is the logical equivalent of saying a mercury thermometer does not measure temperature; it is simply measuring the height of a column of mercury. (Hint: Essentially ALL measurements are indirect in this way.)
But your basic claim, irrelevant as it is to this post that you think you are responding to, is still fundamentally wrong.
You remind me of the kids in my science and engineering classes who would choose the most complex possible method of examining a problem, get lost in the math, and not come close to understanding the underlying physical phenomena.
First example: Let’s say you and I are in a dark room and we shine flashlights such that the beams cross each other at right angles. Your approach would be to calculate the electromagnetic field values and Poynting vectors where the beams intersect to try to predict the follow-on behavior of the beams.
Someone who really understands what is going on would realize that the nature of electromagnetic radiation is such that the two beams will simply pass right through each other, and that computing those properties where the beams intersect is a waste of time for understanding what happens to the beams afterwards.
Second example: In the same dark room, we now shine highly collimated flashlight beams directly at each other. One flashlight has low batteries, so has a filament of lower temperature and therefore lower “radiance” as you put it.
Once again you apply Maxwell’s equations and calculate Poynting vectors, concluding from these calculations that the resulting energy flow is from the brighter flashlight to the dimmer flashlight.
But what you don’t realize is that you have simply performed very difficult calculations to get to the same resulting net transfer as people who use the much simpler “radiative exchange” analysis using Einstein’s “photon gas” paradigm.
EVERY heat transfer textbook I have ever seen uses this “radiative exchange” analysis to introduce radiative heat transfer. Here’s one such textbook available free on-line. It is used to teach mechanical engineering students at MIT.
http://www.mie.uth.gr/labs/ltte/grk/pubs/ahtt.pdf
The quick explanation of exchange is on p32 in the overview chapter. A more detailed explanation is on p487 at the very start of the chapter dedicated to radiation heat transfer.
Both are very clear that radiation “from an object of low radiance to one of high radiance” does indeed exist, but it is just less than the radiation from an object of high radiance to one of low radiance.
Because you get lost in the complex math, and do not have a fundamental understanding of the underlying physical mechanisms, you do not realize that this method is just a different way of calculating the net energy transfer, and it is every bit as valid (and far superior in utility) to your approach.
You haven’t demonstrated where RickWill is wrong, you just wrote paragraphs why you feel he is. Didn’t refute.
For climate to be stable (not change):
Surely I’m right in thinking that this is the key to man-made climate change, models say CO2 causes less OLR to be emitted. More CO2 in atmosphere causes longer retention time for “trapped” LWIR. This causes less OLR to leave earth (go into space). Why does the data show the opposite?
https://ibb.co/hdcX8fZ
So the energy from the sun balances the energy radiated from the planet eh?
So back in the 1600s that still held true(?), even though there was only about ½billion people on the planet, with all their agriculture.
Today there’s 7¼billion people with everything they need (and that’s expanded a lot!) and still it balances. Hay, that means the expansion of humanity (and everything they do) requires no energy from the sun.
And all those peat bogs and all that organic matter that falls to the deep ocean abysses, matter that exchanges solar energy for chemical energy for many millennia does not count?
The expansion and contraction of the totality of all life on this planet (expands and contracts mostly due to weather/climate effects) does not affect this wonderful energy balance, as again it exchanges solar energy for chemical energy in the organic chemical bonds.
Clever stuff this energy balance thing!
Willis: These errors in downward solar radiation could be unimportant – based on you AOGCMs are used.
Step 1. The model is spun up (for 150? year) until incoming and outgoing radiation are in equilibrium and temperature is stable. In a zero-dimensional model:
(S/4)*(1-a) = eoT^4
So the model will contain compensating errors in albedo (a), effective emissivity (e), and T. We know that different models initialize to different temperatures (+/-2 K or +/-6 W/m2 in eoT^4). This error is compensated by errors in a and e as the model is spun up.
To predict warming at equilibrium, we need to know how the planet’s radiative imbalance (Ri) changes with surface temperature – the climate feedback parameter (Ri/dT). In a zero-dimensional model:
Ri = (S/4)*(1-a) – eoT^4
dRi/dT = -(S/4)*(da/dT) – 4eoT^3 – (eoT^4)*(de/dT)
-4eoT^3 is Planck feedback. -(eoT^4)*(de/dT) is the sum of the other LWR feedbacks. Decreased emissivity due to rising water vapor, increased emissivity due to a falling lapse rate and changing cloud top altitude changing effective emissivity. -(S/4)*(da/dT) is the sum of all SWR feedbacks. On paper, a model that gets forcing correct and dRi/dT correct will calculate the correct amount of warming even if it starts with compensating errors in albedo, emissivity and temperature.
Frank January 27, 2019 at 1:54 am
Thanks, Frank, but I couldn’t disagree more.
The idea that things are fine as long as the errors compensate for each other doesn’t even pass the laugh test on my planet. It’s like saying “We modeled the airplane, and the lift on the wings is too low, but the lift on the tail is an equal amount too high … so the total lift is correct and everything is fine”.
Yeah, the total lift is correct … but the airplane will head straight for the ground with those errors.
And it might work in a zero-dimensional model … but a climate model is (allegedly) a 4-d model, with the fourth D being time.
Not only that, but it is an iterated model, where the output at time t is used as the input for time t+1. And those are a bitch to even balance and keep from running off the rails, much less to simulate something.
Folks don’t seem to get it. The climate is far and away the most complex system that we’ve ever attempted to model … and we’re trying to do it without even understanding how it works. Climate has at least six major subsystems—atmosphere, hydrosphere, lithosphere, cryosphere, biosphere, and electrosphere. All of these interact with each other at time scales from millisecond to millennia, and at spatial scales from atomic to planetwide. In addition, all of these systems have internal resonances and cycles. Finally, the action and interaction of all these subsystems is governed by the Constructal Law, which is not taken into account anywhere I can find in the current models.
So no, Frank, it’s NOT ok that they can’t even get something correct that is as basic and important as the amount of sunlight hitting the ground. It is a large and visible symptom of very, very deep problems with the models.
Best to you,
w.
Thanks for the reply, Willis. Thinking simply, warming is controlled by two factors: forcing, which is moderately well understood for GHGs and the climate feedback parameter – how much more LWR is emitted to space and more SWR is reflected to space per degK of surface warming. I’m happy to admit that it is very hard for climate models to correctly calculate a climate feedback parameter, but my opinion is that is unrelated to the model’s ability to spin up perfectly to the correct initial conditions. It is the derivatives (feedbacks) in my equations that are critical to warming in a simple ZDM.
Wills writes: “Not only that, but it is an iterated model, where the output at time t is used as the input for time t+1. And those are a bitch to even balance and keep from running off the rails, much less to simulate something.”
When discussing warming at steady state (ECS), it doesn’t make any different what happened in all of those iterated steps. All you need to know is the temperature that restores a steady state between incoming and outgoing radiation. There are many problems you can solve in physics without calculating all of the intermediate steps along the way. For an object moving along some path in a gravitational field, you can integrate acceleration vs time over the whole path to get a final velocity or simply convert potential energy into kinetic energy and skip all of the intermediate steps. In thermodynamics, the fact that some quantities are state functions means you know how they will change regardless of the path taken between two states.
The rate of ocean heat uptake has a tremendous influence on how long it takes to reach a steady state, but not how much warming is needed. The same thing applies to melting of sea ice. Melting of ice caps and outgassing of CO2 from the deep ocean are problems because they won’t be complete before radiative balance has been restored.
Frank January 27, 2019 at 4:49 pm
Thanks for the reply, Frank. First, please point out to me in your simple (or complex) model the effect of the Constructal Law, which governs all flow systems that are far from equilibrium.
Second, please point out to me in your simple (or complex) model the effect of the emergence of thermoregulatory phenomena like say thunderstorms.
Finally, the climate models do NOT even have the ability to “spin up perfectly to the correct initial conditions”. Instead, they vary by as much as 3°C in the temperature that they spin up to … which is a difference in emitted radiation worldwide of about SIXTEEN WATTS PER SQUARE METRE!
And despite that they claim to diagnose a half-W/m2 in global energy balance … pull the other leg, this one’s got bells on it.
w.
Willis asked: “First, please point out to me in your simple (or complex) model the effect of the Constructal Law, which governs all flow systems that are far from equilibrium.”
Constructal Law began with calculations about heat transfer and has expanded into a philosophy for explaining a surprising large and diverse number of phenomena we observe. I personally have no idea of where this stops being science and begins being philosophy. Based on your comments, I spent some time looking into Bejan with Google Scholar and decided to wait until Bejan is awarded a Nobel Prize before I use Constructal Law to reject otherwise reasonable science. That, of course, may turn out to be too conservative a position.
Willis also asked: “Second, please point out to me in your simple (or complex) model the effect of the emergence of thermoregulatory phenomena like say thunderstorms.”
I used to ask myself why warming in the tropics couldn’t be suppressed by running the Hadley cycle a little faster – carrying heat from the surface to the upper atmosphere where it could easily escape to space. The thunderstorms of the ITCZ that form the heart of your Thermostat Hypothesis are the ascending branch of the Hadley circulation. Some of the air that rises descends locally and some makes the full Hadley circuit. Whether one views individual thunderstorms or the entire Hadley circulation, the fundamental question: Is what controls rate of vertical heat flux that “air conditions” the tropics? Greater flux, lower surface temperature.
Your answer appears to be that surface temperature controls the amount of vertical heat flux. Eventually I decided this couldn’t be right, because vertical convection requires an unstable lapse rate. In that case, the rate at which heat escapes to space from the upper troposphere limits the amount of heat that can be convected upward.
We can express this idea in terms of a global or a local climate feedback parameter: IF 1 K of surface warming results in X W/m2 more LWR escaping to space, then X W/m2/K is the additional amount of heat that must also leave the surface. You can’t have more heat entering the atmosphere from below than leaves from the top indefinitely! Given that latent heat carries about 80 W/m2, then a 1.25%/K increase in latent heat flux (and eventually in precipitation) would be consistent with sending an addition 1 W/m2/K to space as LWR.
However, I’ve oversimplified the problem by ignoring reflection of SWR. The climate feedback parameter is based on the sum of the increased emission of LWR and reflection of SWR per degK of surface temperature rise. Surface warming can result in the vertical convection of 10 W/m2/K of latent heat if 9 W/m2/K of additional SWR is reflected back to space. According to your paper, there is a 60 W/m2 change in SWR in the ITCZ every day! That can allow a massive amount of latent heat to move upward – in a small unique area.
However, once you are above the cloud tops, the only thing left moving heat upward is LWR. At steady state, GLOBALLY, that flux must be about 240 W/m2 and currently is about 0.7 W/m2 short of balance according to ARGO. So it looks like warming over the past half century is sending 2 W/m2/K more OLR+OSR to space, not 1 W/m2/K. The former is equivalent to an ECS of about 3.6 K/doubling and the latter about 1.8 K/doubling. This is simply expressing the output from energy balance models in the terminology of a climate feedback parameter.
Due to the low heat capacity of the NH, it warms and cools much more with the seasons than the SH, producing a 3.5 K warming in GMST. CERES show a 2.2 W/m2/K perfectly linear increase in LWR accompanies this seasonal warming. Unfortunately, global warming is not seasonal warming and 2.2 W/m2/K mostly reflects changes from outside the tropics. Lindzen and Choi and now Mauristen and Stevens have shown that the tropics show an LWR feedback of about 4 W/m2/K. Unfortunately, in all cases, the SWR response is not linear with surface temperature; some components appear lagged by several months. AOGCMs don’t do a good job of reproducing the seasonal or tropical feedbacks we observe from space, so their is no reason to believe the ECS they project.
The weakness of your thermostat hypothesis – as I understand it – is that it doesn’t take into account what happens to surface heat after it is convected above from the surface. Where does descending air come from? When I take into account the need for an unstable lapse rate, I personally envision heat convected upward in response to a cool upper atmosphere, not pushed upward by a hot surface while ignoring the local lapse rate. Tropical islands certainly develop a hot surface during the daytime, but not tropical oceans. And from what I read, there is more precipitation over tropical oceans at night than during the day. And the average molecule of water remains in the atmosphere for an average of 5 days between evaporation and precipitation, not less than a day as one might expected from a thermostat hypothesis limiting maximum temperature every day. However, in the ITCZ, the precipitation rate may indeed be high enough to remove all of the moisture in the column overhead every day. However, most of that moisture appears to be swept into the ITCZ by trade winds after evaporating elsewhere. Obviously I don’t have a very clear idea of how individual intense thunderstorms are integrated into a larger picture of tropical climate as seen from the TOA.
You, of course, have a vast amount of personal experience with tropical weather, both on islands and in the open ocean, below the ITCS with its deep convection, other places where shallow convection predominates and the places where it is dry for half the year. Is the temperature in all of these different locations controlled by your Thermostat Hypothesis or does that apply to only a small fraction of the tropics?
Respectfully, Frank
I spent considerable time quantifying all of the Wild et al. fluxes in terms of the solar constant, atmospheric emissivity (both short and longwave), albedo and non-radiative heat transfers. To solve for atmospheric longwave emissivity, we really need the value of the atmospheric window, and we need time series observations to see changes in the atmospheric longwave emissivity, which is what the fuss is all about. No such observations exist, just a rough estimate currently around 20 W/ m2. That is why you never see an estimate for this rather fundamental value in any of the Wild et al papers 2013, 2014, 2015. Still, I have a lot of respect for Wild et al.’s attempts to quantify the magnitude of the fluxes. You should see in their 2015 paper that there is no attempt to hide the uncertainties in the fluxes Willis. Why don’t YOU solve for the fluxes, if you want to dive in?
Willis wrote: “Finally, the climate models do NOT even have the ability to “spin up perfectly to the correct initial conditions”. Instead, they vary by as much as 3°C in the temperature that they spin up to … which is a difference in emitted radiation worldwide of about SIXTEEN WATTS PER SQUARE METRE!”
Thanks for posting the above Figure showing the range/error in PI temperature exhibited by AOGCMs: 13.5+/-1.5 degC. In terms of oeT^4 for a graybody model, +/-5 W/m2. That is a 2% error in the 240 W/m2 the Earth actually emits to space. That is about the same size as the error in the ability of CERES to measure the emission of 240 W/m2 of LWR! However, we are interested in measuring change in flux, not absolute flux, so you happily post CERES data despite absolute uncertainties this big.
Let’s imagine a graph for RCP6.0, where radiative forcing reaches a plateau (in 2060?) and then approaches steady state warming over the next century or so. (ARGO shows us that we are about 70% of the way to steady state: The current forcing is 2.5 W/m2 and the current imbalance 0.7 W/m2). What will this graph look like? Each of this curves should have reached a plateau, a new steady state. The only thing important to me is how much higher those plateaus will be – steady state WARMING. Think of this as a linear fit, can you obtain a reasonable slope if the y-intercept in your data is wrong? Yes, if the y-intercept is consistently wrong you can get the right slope.
Do the errors in predicting PI conditions say anything definitive about our ability to predict WARMING – to predict the climate feedback parameter. As I showed above, the climate feedback parameters arises from de/dT and da/dT, LWR and SWR feedbacks. These parameters are not directly involved in spinning up to the right PI temperature. Observations prove to me that AOGCMs get feedbacks wrong, so I’m not going to waste my time focusing on less definitive issues.