An Interim Look At Intermediate Sensitivity

Guest Post by Willis Eschenbach

Whenever I find myself growing grim about the mouth; whenever it is a damp, drizzly November in my soul; whenever I find myself involuntarily pausing before coffin warehouses, and bringing up the rear of every funeral I meet; and especially whenever my hypos get such an upper hand of me, that it requires a strong moral principle to prevent me from deliberately stepping into the street, and methodically knocking people’s hats off—then, I account it high time to get to sea as soon as I can.

Ishmael, in Moby Dick.

Yeah, that pretty well describes it. I’d been spending too much time writing about the weather and the climate, and not enough time outdoors experiencing the weather and the climate. So following Ishmael’s excellent advice, I have been kayaking and walking the coast and generally spending time on and around the ocean. During this time I have been considering what I want to write about next. Being on the water again, after the last few years of being boatless, has been most invigorating.

I have chosen to write about my on-and-off investigation of the relationship between changes in surface temperature and corresponding changes in top-of-atmosphere (TOA) radiative balance. I wrote about this previously in a post entitled A Demonstration of Negative Climate Sensitivity. This is an interim report, no code, little analysis, just some thoughts and some graphics, as I am in the (infinitely) slow process of assembling code, data, and results for publication in a journal. Unlike my previous post which used 5°x5° data, in this post I am using 1°x1° data.

Let me start with an interesting question. Under the current paradigm, the assumption is made that surface temperature is a linear function of the TOA imbalance (forcing). But is it true? In particular, is it true all over the world? To answer this, I looked at the monthly TOA radiation imbalance (all downwelling radiation minus all upwelling radiation) versus the change in temperature.

max value r2 temp vs toa imbalFigure 1. Maximum of the R^2 value, temperature vs TOA imbalance. This is the maximum of the individual R^2 for each 1°x1°gridcell, calculated at lags of 0, 1, 2, and 3 months. An R^2 of 0 means there is no relation between the two datasets, and an R^2 of 1 means that they move in lockstep with each other. In the red areas, when the TOA radiation balance changes, the temperature changes in a similar fashion. In the blue areas, changes in temperature and TOA imbalance are not related to each other.

Figure 1 has some interesting aspects.

Figure 1 was created by displaying, for each gridcell, the largest of the four R^2’s, one from each of the four lag periods (0, 1, 2, and 3 months). One interesting result to me was that while the temperature of a large part of the earth slavishly follows the variations in the local TOA balance (red areas), this is not true at all, at any lag, for the area of the  inter-tropical convergence zone (ITCZ, blue, green, and yellow areas). This is evidence in support of my tropical thunderstorm thermostat hypothesis, which I discuss in The Thermostat Hypothesis and It’s Not About Feedback. For that hypothesis to be correct, the surface temperature in the ITCZ must be decoupled from the TOA forcing … and it is obvious from Figure 1 that the ITCZ temperature has little to do with forcing.

Next, I wanted to look at the climate sensitivity. In a general sense, this is the amount of change in the surface temperature for a 1-unit change in the TOA radiation imbalance. There are a variety of sensitivities, from instantaneous to equilibrium. Because I have monthly data, I’m looking at an intermediate sensitivity.

Figure 2 shows the temperature change due to a 3.7 watt per metre squared (W/m2) at various time lags. When the TOA radiation changes, the surface (land or ocean) does not respond immediately. By examining the response at different time lags, we can see the characteristic lag times of the land and the ocean.

temp change from TOA imbalance 0 to 1 mo

temp change from TOA imbalance 2 to 3 mo

Figure 2. Climate sensitivity (temperature change from a 3.7 W/m2 TOA imbalance) for the earth. Sensitivity is determined as the slope of the linear regression line regarding TOA variations and surface temperature for each gridcell, over the period of record. Click on upper or lower image for larger version.

Consider first the land. For most of the land, the strongest response (orange and red) occurs after a 1-month lag. The maximum sensitivity is in the areas of Siberia and the Sahara Desert, at around 0.8° per doubling of CO2. Extratropical land areas are more sensitive to TOA variations than are tropical land areas. The highest sensitivity in the Southern Hemisphere is about 0.3°C per doubling of CO2

Curiously, tropical Africa shows a lagged negative sensitivity. This becomes evident at a 2-month lag, and increases with the 3-month lag.

The ocean, as we would expect, is nowhere near as sensitive to TOA variations as is the land, with a maximum sensitivity of about 0.4°C per doubling, The sensitivity over most of the ocean is on the order of 0.1°Ç per doubling.

Finally, Figure 3 shows the relationship between the climate sensitivity and the temperature. Because of the large difference between the land and the ocean, I have shown them separately.

scatterplots climate sensitivity vs temperature

Figure 3. The relationship between climate sensitivity and temperature. Each point represents one gridcell on the surface of the earth. For each gridcell, I have used the time lag which gives the greatest response. Colors show the latitude of the gridcells.

Here, let me point out that I have long maintained that climate sensitivity is inversely related to temperature. This is clearly true for the land.

As I said, not much analysis, just some thoughts and graphics.

Best to all,

w.

DATA

Sea Temps: NOAA ERSST

Surface Temps: CRU 3.1 1°x1° KNMI

TOA Radiation: CERES data

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92 thoughts on “An Interim Look At Intermediate Sensitivity

  1. The two best days in a boat owner’s life are the day he buys his boat and the day he sells it.

    The days come in pairs but there may be more than one pair.

  2. Welcome back Willis! I, for one, have seriously missed your contributions here.
    as usual, your analysis has excellent graphics supporting your exploration. I’m looking forward to the full paper.

  3. Wow,

    Real data about how the real climate operates. I think you got it here.

    This all fits with what we have seen and know about – some polar amplification, flat equatorial Ocean temps going back 140 years, temperature not rising anywhere near the rate predicted in the theory, Land warming in certain places (mainly high latitudes) with very little in the Oceans, very limited feedbacks operating, much more consistent with paleoclimate data, 1000 times higher spatial resolution than anything that has been done with CERES before.

    You might have rewritten the book here.

  4. Your analysis is always so visual and that’s a good thing for me. The gray of Antarctica leaped out at me when I went to compare the north pole with the south (or the south with all the rest of the world). Looking at the data for the southern region indicates sparse and short term data. I am guessing that is the reason for the the gray southern region. What a pity that we lack data for such an important part of the world if this is the case.

    Bernie

  5. Consistent with phase changes of water that can produce large heat fluxes while simultaneously registering minimal temperature changes.

  6. Great to have you back, Willis! I really hope you take care of yourself as you are a hero of mine.
    Greatings from a snowy and unsually cold Copenhagen.

  7. The observation that there is a significant difference between ocean and land is further evidence that atmospheric CO2 is not a major controlling factor (concentrations are the same over land and sea at the same latitude). The water cycle is the big controlling factor(evaporation/condensation – freeze/thaw).

  8. The lagged negative response in the Sahel (and to a smaller extent in India and northern South America) is presumably a monsoonal effect, warmer temperatures causes the air over land to rise more, which pulls the ITCZ further north with more rains that cools the climate. During previous warmer interglacials this effect caused the Sahara to more or less disappear.

  9. Figure 1, showing blue at the north pole and equator is interesting. It would be interesting to see what is happening at the south pole. The green at the antarctic peninsula hints that the south pole may also be blue.

    The blue region at the north pole indicates that the melting of sea ice at the north pole is not an indication of atmospheric forcings. This has significant implications for climate science on its own, as it has long been publicized that the melting of arctic sea ice was an indication.

    A paper showing that there is little of no correlation between arctic temperatures and climate forcings would perhaps be very significant.

  10. The scatter plots are interesting. They show that one of the most basic of climate assumptions is wrong. The climate models that temperature is a linear function of forcings – that sensitivity is a constant.

    However, what the scatter plot shows is that sensitivity is not a constant. Sensitivity is a function of temperature as well as forcings. That as temperature increases, sensitivity decreases, until it becomes negative at about 30C.

    This suggests that runaway temperature increase is impossible. That the earth has a mechanism in place that operates around 30C to prevent any further temperature increase.

    These scatter plots are very significant because the show that the underlying assumption of constant climate sensitivity is wrong. This would help explain why temperatures have not increased significantly in 16 years. Runaway warming is impossible because as temperatures increase sensitivity turns negative, preventing further increase.

    This has huge implications in climate science and would explain why we have not seen runaway warming in the paleo records, even when CO2 levels were much higher.

  11. Gary says:
    December 10, 2012 at 5:56 am
    If you hadn’t noticed, Bejan and colleagues have done some more work on applying Constructal Theory to climate with this paper”
    ======
    Constructal theory is an interesting idea. Boat owners (mono-hulls) curse it every time they anchor and the wind drops. The boats immediately turn broadside to the swell to minimize the energy used to move with the swell, and in the process roll the guts out of everyone aboard.

    Sailboats hang a “flopper stopper” out over the side on a spinnaker pole to prevent this. This changes the energy needed to roll the boat and the hull will quickly turn to face the waves as it now takes less energy to pitch than roll.

    What this means is the physical objects seek the minimum energy level required to accomplish the work. Water seeks the easiest path as it were. Not only water, but boat hulls and now it seems climate as well.

  12. Great paper. If I read the charts right it means that the tropics has found its temperature, and any amount of CO2 is totally swamped by the effect of water vapor in the tropics. This seems to validate the thermostat hypothesis, and all climate models will have to take into account the effect of clouds and thunderstorms and their strong negative feedback

  13. Willis

    It seems from your plots that the relationship between surface temperature and TOA, that you’re using as a proxy for sensitivity (right?), is a dependent on latitude over land and not over ocean. Therefore, it seems likely that you’re measuring the dampening effects of atmospheric water vapour? In short, you get the greatest response over drier and colder parts of the planet. Was this not a prediction of the original GCMs?

    If so how do your results compare to their predicted (projections) for high latitude warming?

  14. Yes, Welcome back, Willis! Always a thought-provoking exercise–I think you’re onto something big here.

    So as the hydrologic cycle is the vast heat radiator and temperature knob for the earth, can we expect current encroachment of the Sahel upon the Sahara to continue until the sand is overtaken with vegetation? Do we know if this has happened with any/each of the past three temperature maxima that have appeared at regular 1,000-yr intervals, as this current one would suggest? When grapes were grown in England and Vikings farmed Greenland, did goat herds thrive across the Sahara?

  15. Good news! It looks like the IPCC is going to back away from global warming hysteria. Slashdot has the following story:

    “More precise modeling has changed some long term climate predictions: sea levels to rise almost a meter more than present over the next century, but past dire warnings of stronger storms or more frequent droughts won’t pan out. Instead there will be less strong storms, but peak winds in the tropics might be slightly higher. Temperature rise of global average will be about 3 degree C total, including the 1 degree C rise over the 20th century. In places where precipitation is frequent, it will become even more frequent; in arid areas, the tendency will be to become even drier. Some new arid areas are expected to appear in the south of N. America, South Africa and Mediterranean countries. Overall, hardly a doomsday scenario.http://news.slashdot.org/story/12/12/10/0320239/draft-of-ipcc-2013-report-already-circulating

    That story links to an ABC News story: http://abcnews.go.com/International/science-hone-climate-change-warnings/story?id=17906408#.UMVJntHQQSk

  16. I wasn’t surprised to see oceans max out at ~30 degrees; however, I was surprised to see land max out at about 30 degrees also. The oceans benefit from rapid evaporation at that high temperature–is that also the mechanism that operates on land? Or is it merely a function of the granularity of the sample size, with space for relatively few outliers?

  17. Wow, this is really, really interesting stuff. Like Fred Berple, I was especially intrigued by the scatter plots. I’ve long suspected that sensitivity isn’t linear at all – your scatter plots shows it. So much for “runaway warming” – for all we know, we may be close to the upper limit to how warm the earth can become in the current phase of the Milankovitch cycles and with the current continent configuration!

  18. Figure 3 is absolutely stunning. The most amazing effect is the swing into negative climate sensitivity at around 30 C. This occurs over oceans even though higher temperatures should cause higher absolute water vapor concentrations and *increase* the climate sensitivity immediately.

  19. ferd bergle:

    climate models that temperature is a linear function of forcings – that sensitivity is a constant

    Actually they don’t assume sensitivity is a constant. That’s a common assumption by people try to arrive at estimates of climate sensitivity, but it is neither an assumption, nor does it even hold true in the models. see e.g. this

  20. Re: CO2 sensitivity

    So it is not necessary to go to an over-elaborate analysis to
    plot a graph to show that the CO2 saved N. Hemisphere
    or even the world from onset of the new LIA in 1960s.
    or to calculate theCO2 feedback sensitivity at 3 degrees C
    for doubling of the CO2 concentration.

    http://www.vukcevic.talktalk.net/00f.htm

    (acrostichis web page)

  21. Once again, nice work Willis. I also wonder about those areas of negative sensitivity. Is it related to the fact that those are areas of intense precipitation?

  22. ferd berple says:
    December 10, 2012 at 6:20 am
    ….
    What this means is the physical objects seek the minimum energy level required to accomplish the work. Water seeks the easiest path as it were. Not only water, but boat hulls and now it seems climate as well.
    ====================================
    Not just physical objects. That’s the principal of least action http://www.principlesofnature.net/principle_of_least_action.htm

  23. Willis, the elegance blows me away! With heavyweights in climate science tuning multicomponent models, the most principal component being CO2, to take simply net TOA downwelling radiation and 3.7W/m^2 (nothing controversial there!) and show the variability in sensitivity between land, ocean and latitude is an E=mc^2 moment. That the sensitivity turns negative in both cases at about 28 C – the thermostat setting- is the most beautiful demonstration in climate science or maybe even science since the golden age. I knew I was reading something very special when you came out with the thermostat hypothesis – and now you have essentially enclosed this sweet nut into a universal climate theory. How can the linear thinkers who make up the famous 97% wiggle out of this? It will be instructive to see how many of the well known CliSci folk will cling to the scuttled ship.

  24. chris y says:
    December 10, 2012 at 6:48 am

    Figure 3 is absolutely stunning. The most amazing effect is the swing into negative climate sensitivity at around 30 C. This occurs over oceans even though higher temperatures should cause higher absolute water vapor concentrations and *increase* the climate sensitivity immediately.

    Chris, higher absolute atmospheric water vapor concentrations would only act as a positive feedback (increasing sensitivity) if you only consider radiative forcing. But water vapor carries latent heat up past the denser atmosphere and releases that latent heat as it condenses into clouds and more latent heat when/if those clouds freeze at height. At the same time the clouds increase the atmospheric albedo cooling the surface and perhaps drop rain further cooling the atmosphere and surface below.
    The other effect of heightened humidity which everyone ignores is that as you raise atmospheric humidity you raise atmospheric enthalpy. So although the energy content of the air may increase its temperature will not increase at the same rate if at all.

  25. It is always wise to both challenge and critically examine assumptions, we all know that but many fail to do so. Your reminder is both cogent and timely. Alas for all to many it will be ignored as it requires far to many to actually think about something instead of accepting the “pablum” being fed to them.

  26. Good one Willis. Always thought provoking and well presented with it.

    It looks to me, and I am just a Tyro in this game, that the real regulator is the ocean. 1.3 Billion cubic Kilometers and that has to change in temperature if the Earth does. To get some perspective I calculated that each of the 7 billion humans on Earth would have to burn the equivalent of 100 times the amount of fossil fuel we do today over a lifetime of 70 years to raise the ocean temperature by 1 deg Celsius.

    There is no way that atmospheric CO2 can warm anything up more, or faster, than the direct heat of combustion as that would be a perpetual motion machine. I haven’t begun to consider the cooling effect of evaporation, nor have I considered the effect of people currently burning biomass taking up the much more efficient energy from power stations so my 100 times is very conservative. However it is obvious from your short paper that evaporation in the tropics has a huge effect for climate stability.

    So to get back to my point, which is made by Willis’ Fig.2. It really is all about the liquid ocean which acts as the global regulator. It is good that Willis gets to float on it. I walk down to it at least once a week and also contemplate its vastness. It makes me realise just how silly we are to think that we can over power its awsome being.

  27. Thanks, Willis. Excellent analysis of the major factors.

    I wonder which warmers can really appreciate this…

  28. John Doe says:
    December 10, 2012 at 4:20 am

    The two best days in a boat owner’s life are the day he buys his boat and the day he sells it.

    The days come in pairs but there may be more than one pair.

    Thanks, John. In my experience, that’s true for every kind of boat except a kayak. They are just too much fun and too little upkeep to ever be a problem.

    w.

  29. Gary says:
    December 10, 2012 at 5:56 am

    Always good to have you thinking about a new perspective on the climate data, Willis.

    If you hadn’t noticed, Bejan and colleagues have done some more work on applying Constructal Theory to climate with this paper” http://www.constructal.org/en/art/Climate_change_%20in_the_framework_of_the_Constructal_Law.pdf. Any comments on that?

    Much appreciated, Gary. Steven Mosher was kind enough to send me a copy of that paper. It’s a fascinating study, but I haven’t been able to fully wrap my head around it yet.

    w.

  30. ****
    RockyRoad says:
    December 10, 2012 at 6:27 am

    So as the hydrologic cycle is the vast heat radiator and temperature knob for the earth, can we expect current encroachment of the Sahel upon the Sahara to continue until the sand is overtaken with vegetation? Do we know if this has happened with any/each of the past three temperature maxima that have appeared at regular 1,000-yr intervals, as this current one would suggest? When grapes were grown in England and Vikings farmed Greenland, did goat herds thrive across the Sahara?
    ****

    IIRC, the Sahara greening occurs with the ~20k yr precession cycle — summer insolation max in NH expands the monsoon to partially cover it, and min is solid desert like now. I think the last Sahara greening lasted until about ~6000 yrs ago as the summer insolation was declining from the 10000 yr ago max.

    I’m unsure whether this Sahara greening occurs even during the glacial periods (which last ~90 kyrs) in addition to the interglacials, tho.

  31. Note that the average terrestrial TSI is 369 times larger than the estimated direct greenhouse forcing of 3.7 watts/m². I.e., the CO2 forcing is only a very small fraction, about a quarter of a percent, of the TSI. It doesn’t take much variation in the estimation of other climate phenomena to overwhelm any putative effect of CO2. Thus the AGW greenhouse effect, despite its in-laboratory grounding, remains just that: theoretical, and unproven on the larger scale. Small wonder that certain climate scientists resort to mummery in attempting to tease a corresponding temperature signal out of some of the noisiest data on Earth.

    [Note, too, that Perihelion to Aphelion TSI varies by far more than 3.7 watts/m², going between 1,413 and 1,321 over the course of a year, a difference of 92 w/m².]

  32. It has always seemed obvious to me that you should see a bigger temperature rise for the same amount of forcing in cold or dry areas (eg the Arctic and Sahara) for the simple reason that water vapour takes more energy to heat up than dry air. This seems to be what you have shown. Now I wonder if you can turn this around so that instead of calculating the mean global temperature (which has always seemed pretty meaningless to me) a sensitivity weighted average was calculated. This would roughly correspond to global atmospheric enthalpy I guess. My guess is that the only thing that is keeping global mean temperature relatively high at the moment is the fact that somewhere has to be warmer than average, and at the moment it happens to be the Arctic.

  33. A seminal work, Willis. Really excellent. Your conclusion is short and sweet:

    …climate sensitivity is inversely related to temperature. This is clearly true for the land.

    And the oceans have only a 0.1º sensitivity. When averaged with the land, that would mean a ≈0.0º global sensitivity for 2xCO2. Miskolczi got it right.

    Climate alarmism is based on the ‘received wisdom’ that climate sensitivity is directly related to temperature. The only argument has been over the magnitude of the forcing for 2xCO2. By showing empirically that sensitivity becomes inversely related as temperature rises, you have really upset the mainstream climate apple cart.

  34. jorgekafkazar says:
    December 10, 2012 at 10:15 am

    Note that the average terrestrial TSI is 369 times larger than the estimated direct greenhouse forcing of 3.7 watts/m². I.e., the CO2 forcing is only a very small fraction, about a quarter of a percent, of the TSI.

    Jorge, I fear you are comparing apples and oranges here. The greenhouse forcing of 3.7 W/m2 is calculated as a 24/7 global average over the surface area of the earth. TSI, on the other hand, is the solar energy hitting a square metre at the top of the atmosphere (about 1368 W/m2). It is not averaged over the surface of the earth.

    As a result, to convert TSI to the same units as the CO2 forcing, we have to divide the TSI by 4, which gives us a 24/7 global average solar radiation of about 342 W/m2. The CO2 forcing is then about one percent of the (globally and temporally averaged) TSI.

    Having said that, you are correct, that is still quite small. I classify that as a “third-order” forcing. In my classification, a “first-order” forcing changes the total forcing by more than 10%. Clouds are an example of this strength of forcing.

    Second order forcings are those that change the total forcing by between 1% and 10%.

    Finally, third order forcings are those that change the total forcing by one percent or less … which, as you point out, includes a doubling of CO2.

    w.

  35. ferd berple says:
    December 10, 2012 at 6:03 am

    “That as temperature increases, sensitivity decreases, until it becomes negative at about 30C.”

    This fits one of my hypothesis on the effects of CO2, that it’s mainly effective up to a little past 0C, where water vapor takes over.
    The peak co2 wavelength that isn’t swamped by water absorption is ~10um which is ~15C through maybe 14um @~-66C (co2 boiling point is -57C).
    So an Ice ball earth with minimal water vapor, and large areas of ocean covered in ice restricting co2 from dissolving into water, will accumulate co2 in the atm, building until the planet starts to warm, and more water vapor accumulates bringing temps up the rest of the way.

    Also remember that IR coming in at .5u takes 20 times as long to radiate out at 10u, and yet on clear nights air temps drop like a rock, and is limited to how fast the ground can cool.

  36. Here, let me point out that I have long maintained that climate sensitivity is inversely related to temperature. This is clearly true for the land.

    Nice work, and lots of it. At the highest temperature, the mean of the feedbacks is still positive. It looks like a simple extrapolation yields a hypothesis that at at higher maxima than observed now, the mean feedback would be 0. I came up with a similar hypothesis from some simple calculations based on the Graeme Stephan updated energy flow diagram that Dr Curry presented at ClimateEtc a few weeks ago.

  37. Willis-

    Along with everyone else, I am glad to see you back. Good to hear you took some time “to smell the roses.”

    Let me check to make sure I understand your analysis. As I understand your post:

    First you used surface temperature data and the corresponding (in time and space) TOA radiation data to do a linear regression for the whole planet in 1 degree boxes. You state you used monthly data. Question: What time periods did you use? Every month of the year for one year? Average over a number of years for each month? Or a single month averaged for several years.

    You did this for four different temperature lags.

    Then using the best (highest r^2) regression equation for each box ,you calculated the temperature change for a decrease of 3.7 W/m^2 in TOA outgoing radiation. ( I was somewhat confused when you switched to “doubling of CO2,” but assume that a change 3.7 W/m^2 represents a doubling of CO2).

    The resulting scatter plots show the climate sensitivity as a function of some temperature. I assume that the temperature is the average monthly temperature in box. Again, my question is about the time period used for the average monthly temperature. Looks like these plots deserve a lot of study. Lots can be inferred from them.

    Thanks again for your post. It is always edifying to see what can be done with real data.

  38. Until the climatologist community gets over its radiative fantasies and begins to examine the actual dominant factors in climate physics we’ll continue to hear about CO2, which is a minor player if any. Talking about sensitivity “to a doubling of CO2″ just feeds the crocodile when any such sensitivity is so small as to be negligible, especially against powerful negative feedbacks from convection and the water cycle, which apparently have the Climate Illuminati baffled. We can actually expect climate changes well beyond anything attributable to CO2, and because of the all-pervading CO2 radiative phantasm, scientists and academics cannot make credible predictions.

  39. Matthew R Marler says:
    December 10, 2012 at 11:11 am

    Here, let me point out that I have long maintained that climate sensitivity is inversely related to temperature. This is clearly true for the land.

    Nice work, and lots of it. At the highest temperature, the mean of the feedbacks is still positive. It looks like a simple extrapolation yields a hypothesis that at at higher maxima than observed now, the mean feedback would be 0. I came up with a similar hypothesis from some simple calculations based on the Graeme Stephan updated energy flow diagram that Dr Curry presented at ClimateEtc a few weeks ago.

    Matt, good to hear from you, thanks for the comments.

    Rather than looking at it as “feedback”, it is more useful to see the climate as a governed system. “Cruise Control” in a car is perhaps the most common everyday example of a governed system. A governed system is “homeostatic”, meaning it tends to keep a given variable within some range of a target value. In the case of the car, it keeps the speed within some range of a target speed by varying the amount of fuel going to the engine.

    Now, suppose you have a car under “cruise control”. The ground is reasonably level overall, but it contains mild ups and downs. When the car goes up a slight incline, the cruise control increases the flow of fuel to the engine to maintain the speed. When it goes down the slight incline, the fuel flow is correspondingly decreased.

    If the ground is reasonably level, the amount of fuel increase on the inclines will be about the same as the amount of fuel decrease on the declines. This could be described as a net feedback of zero, as you have done above.

    However, in fact what is happening is not zero. It is a series of strong but opposite control inputs which balance out over time, and that is very different from a condition called “no feedback”.

    w.

  40. D Boehm says:

    And the oceans have only a 0.1º sensitivity. When averaged with the land, that would mean a ≈0.0º global sensitivity for 2xCO2. Miskolczi got it right.

    You seem to have misunderstood what Willis has said about the relationship between climate sensitivity and temperature. All that he is saying is that the sensitivity is a (slowly) decreasing function of the temperature. [I’m not saying that I completely agree with Willis’s analysis or even his use of the term “climate sensitivity” in this context, but let’s leave that aside for now and assume what he is saying is correct.]

    This is not actually surprising since the no-feedback climate sensitivity derived from the Stefan-Boltzmann equation is also a decreasing function of the temperature: If the intensity I is proportional to T^4 then dT/dI, which is the inverse of dI/dT is proportional to T^3 where T is the absolute temperature (i.e., temperature measured in Kelvin).

    Climate alarmism is based on the ‘received wisdom’ that climate sensitivity is directly related to temperature.

    No…It is not. Where have you seen it claimed that the climate sensitivity is proportional to the temperature?

    You seem to understand neither what the conventional scientific understanding is nor what Willis has shown here. Not surprisingly, your misunderstandings are in the direction of causing you to believe what you want to believe.

  41. I posted something like this the other week and fits well with the graphics shown on this article.

    The 3.7W/m2 is claimed for a doubling of CO2, yet 324 W/m2 is claimed for all greenhouse back radiation. A doubling of CO2 therefore is just 1.1% of the total. If 33c represents the total for greenhouse gases this just represents 0.36c rise per doubling of CO2. This is being generous because most of the warming from greenhouse gases occurs in the first parts with it being logarithmic. Forgot to put before that this also doesn’t take a water body into account either on the surface. This value fits around the middle between 0.1c and 0.8c mentioned in figure 2.

    There is obviously some disagreement here compared with the theoretical 1c per doubling CO2. The reason is obviously because this is partly derived from ideas over land not the ocean. The 324 W/m2 claimed for all greenhouse gases doesn’t warm a bucket of water in the shade during one day, so 1.1 percent of this even if atmospheric levels in future were reached are so miniscule. No wonder we can’t measure the difference from zero now with many decades until the possibility for a doubling of CO2 is reached.

    Since the 1960’s CO2 levels have raised 90ppm until now so a doubling of CO2 won’t occur until it hits 630ppm. That means we are 28.57% of the target for a doubling of CO2. Therefore CO2 should have since the 1960’s only warmed the planet by 0.1c. This 0.1c being on the high side and other AGW issues bringing this value to around 0.1c (~0.05+0.05). The planet since then has risen 0.4c so only 25 percent at the most has come from AGW; natural makes up at least 75 percent. With 75 percent mainly making up the declining global low cloud albedo and reduced negative swings in the ENSO. NO wonder we are going through a long non-warming period because the natural climate is driving the planet.

  42. Willis:

    I think you’d better leave this to the PROFESSIONALS! You know, like the PROFESSIONALS who invented the airplane (whoops, sorry…amatuers), OK, then the telephone (whoops, sorry, teacher of the deaf, no science training…), alright, the Light Bulb, Phonograph, Motion Picture Camera…(whoops, sorry, 1 1/2 grades of education then home schooled…), OK…the RADIO!!!!, darn, an ENGINEER yes, but for telegraphy…no physics background, no University position.

    WELL, darn it then, how about the INTEGRATED CIRCUIT? Whoops, once again, B.S. Electrical Eng., left at Texas Instrument labs, ALONE during the “holidays”…with all the equipment, newly hired, no specific assignment. Used the lithography/epitaxial transitor process to make a two transistor amplifier, with resistors and capacitors made using diffusion processes in the Silicon substrate. (When he was called in to the “head offices” to explain what he had done, putting a two transistor amplifier in a TQ-5 transitor (top hat)mounting…HE THOUGHT they were going to fire him for “misuse” of equipment…not hardly. )

    But again, no “credentials”, no PEER REVIEWED PAPERS…

    Obviously Willis, the ONLY thing you can do is produce things that WORK or are COMPLETELY correct, based on NATIVE GENIUS.

    You need to be CONTROLLED and held down. (Or start a “stock” company, Willis Inc., so we can INVEST NOW!)

    Max

  43. old engineer says:
    December 10, 2012 at 11:16 am

    Willis-

    Along with everyone else, I am glad to see you back. Good to hear you took some time “to smell the roses.”

    Yes, although this time of year rather than smelling the roses I’m sniffing the sea breeze for rain coming in off of the ocean … my thanks to you and everyone for the good wishes. I generally write only when I can’t stand not writing, and as with many things in my life, the intensity of that comes and goes.

    Let me check to make sure I understand your analysis. As I understand your post:

    First you used surface temperature data and the corresponding (in time and space) TOA radiation data to do a linear regression for the whole planet in 1 degree boxes. You state you used monthly data. Question: What time periods did you use? Every month of the year for one year? Average over a number of years for each month? Or a single month averaged for several years.

    I did a linear regression on a gridcell-by-gridcell basis. Dataset size was limited by the length of the shortest dataset, which is the CERES dataset, about five years of monthly data. For each gridcell, I regressed five years of TOA imbalance data against five years of surface temperature data.

    You did this for four different temperature lags.

    Then using the best (highest r^2) regression equation for each box ,you calculated the temperature change for a decrease of 3.7 W/m^2 in TOA outgoing radiation. ( I was somewhat confused when you switched to “doubling of CO2,” but assume that a change 3.7 W/m^2 represents a doubling of CO2).

    Correct, including the fact that the IPCC says that a doubling of CO2 will increase the TOA downwelling radiation by 3.7 W/m2. I have used their value for simplicity and comparison with other estimates.

    The resulting scatter plots show the climate sensitivity as a function of some temperature. I assume that the temperature is the average monthly temperature in box. Again, my question is about the time period used for the average monthly temperature.

    Good question. Actually, it is the average annual temperature for a given gridcell over the ~ 5-year period of record.

    Looks like these plots deserve a lot of study. Lots can be inferred from them.

    Thanks again for your post. It is always edifying to see what can be done with real data.

    Indeed, it is a most fascinating line of inquiry.

    w.

  44. Damn, in my previous post should be raised 80ppm. so this changes the values through it a little, although the conclusion is still the same.

    These now change to 25.4% of the target for a doubling of CO2 and 0.09c contribution from CO2 since the 1960’s.

  45. @Willis:

    I presume that “forcing” in this posting is defined as “Watts/ M^2″ (with no particular time dimension?).

    Overall, nicely done. Very nicely done.

    Be aware that as you are using monthly AVERAGE data, that averaging process can induce a time lag in the apparent response. (Don’t know if it does in this case, as I don’t know how your average is computed, but it is a common problem with ‘moving averages’ in financial indicators, for example, where the ‘lag’ is critical as you find out about market reversals some time after they happened…)

    Oh, and I just ran into another statistical “issue” with temperature data averages. We all, typically, assume they are Standard Normal Distributions. But if they are not (say they are a Cauchy Lorentz distribution instead), you can have the case where the “mean” is not defined.

    http://chiefio.wordpress.com/2012/12/10/do-temperatures-have-a-mean/

    Some kinds of data distributions are what is called “pathological” and have no valid mean…

    Don’t know if it applies to what you are doing, or not, but it needs thinking about.

    The other thing that would be an interesting extension of this work is to look at the two polar regions on a summer vs winter comparison. I think that matters, and would likely give more clarity to the reasons for the behaviour. During winter, the poles are in one regime that is highly different from the summer regime. I’d expect that they well might have opposite responses to ‘imbalances’. During the winter there is nearly NO “downwelling” anything. (Heck, the height of the stratosphere becomes ‘indistinct’ then and there; as the ‘troposphere’ essentially goes way). During the summer, you have a long persistent solar flux, that ought to show up an ‘imbalance’ even more strongly.

    In short, I think you will find that the Equator response is on the order of “same day” (with the formation of the Thunderstorms you love so much ;-)

    http://chiefio.wordpress.com/2010/12/02/does-convection-dominate/

    http://chiefio.wordpress.com/2010/12/28/ignore-the-day-at-your-peril/

    while the polar regions have quarterly regimes. Doing a time analysis on both with the same monthly time step will work, but will not be as strong a signal as time scales tailored to the local process speeds.

    This paper measures heat flow in Africa and finds it happens from ground level up to the high troposphere in hours, same day:

    http://hal.archives-ouvertes.fr/docs/00/31/68/93/PDF/angeo-19-1001-2001.pdf

    (Oddly, it loaded just yesterday. Today I’m not getting a download. I’ll presume it’s a local issue (not getting a DNS resolution for the server). If need be, I have it archived and can dig out a copy.)

    My read of one of the graphs (in case the server is just gone):

    The upshot of it all to me is pretty simple. Those graphs at lower elevations are darned near constant. There is about a 275 K to 285 K variation in temperature at 4 km altitude over the course of a clear tropical day. It’s the troposphere that has the largest wiggles. At 22.5 km about 200K to 250K as I eyeball the graphs. AND, IFF I’ve read the paper correctly, it says they are due to convections from the surface heating.

    So to capture that will take less than monthly data… Especially the ‘negative response’ of a thunderstorm that comes and goes in hours. (Using a smaller time step ought to show your expected response even more strongly.)

  46. “Under the current paradigm, the assumption is made that surface temperature is a linear function of the TOA imbalance (forcing)”

    No, it isn’t.

  47. tty says:
    December 10, 2012 at 5:22 am
    The lagged negative response in the Sahel (and to a smaller extent in India and northern South America) is presumably a monsoonal effect, warmer temperatures causes the air over land to rise more, which pulls the ITCZ further north with more rains that cools the climate. During previous warmer interglacials this effect caused the Sahara to more or less disappear.

    One can describe the climate as a system that transports heat (enthalpy) from where it is gained to where it it is lost. And this process occurs both vertically and horizontally. IMO the monsoons are an underappreciated and little understood aspect of horizontal transport.

    Willis, if you are an ocean kayaker you might add the Ningaloo Reef in Western Australia to your bucket list. One of the last pristine ocean shorelines left outside the polar regions. I kayaked most of its length 20 years ago and am still amazed at how abundant life is in the ocean when there are no people.

  48. @Pochas:

    I suspect that the negative sensitivity in the tropics is due to the water / thunderstorm “thermostat” process; while that at the pole(s?) is based on the winter lack of troposphere height. In the stratosphere, the CO2 effect is negative (more radiation of heat is caused). During the polar winter, the stratospheric height becomes nearly zero (especially on top of the S. Pole 11,000 foot plateau…) so any increased CO2 or increased energy to the stratosphere will show up as increased radiation…

    https://en.wikipedia.org/wiki/Troposphere

    The average depth of the troposphere is approximately 17 km (11 mi) in the middle latitudes. It is deeper in the tropics, up to 20 km (12 mi), and shallower near the polar regions, at 7 km (4.3 mi) in summer, and indistinct in winter.

    Those polar stratospheric vortex winds can be a cold cold mistress… and the land radiates heat like crazy out the poles. (Most heat gain is at the equator and most heat loss is from the poles).

    @Willis:

    I’m once again missing my boat… Tacking around the bay can clarify the mind in ways nothing on land can do…

    Maybe I need to buy a kayak… with a sail ;-)

  49. E.M.Smith says:
    December 10, 2012 at 12:31 pm

    @Willis:

    I presume that “forcing” in this posting is defined as “Watts/ M^2″ (with no particular time dimension?).

    Indeed, it is a flux.

    Overall, nicely done. Very nicely done.

    Thanks, Chiefio.

    The other thing that would be an interesting extension of this work is to look at the two polar regions on a summer vs winter comparison. I think that matters, and would likely give more clarity to the reasons for the behaviour. During winter, the poles are in one regime that is highly different from the summer regime. I’d expect that they well might have opposite responses to ‘imbalances’. During the winter there is nearly NO “downwelling” anything. (Heck, the hight of the stratosphere become ‘indistinct’ then and there; as the ‘troposphere’ essentially goes way). During the summer, you have a long persistent solar flux, that ought to show up an ‘imbalance’ even more strongly.

    Interesting thought, I haven’t pulled out polar summer vs. polar winter. So many analyses … so little time.

    In short, I think you will find that the Equator response is on the order of “same day” (with the formation of the Thunderstorms you love so much ;-)

    http://chiefio.wordpress.com/2010/12/02/does-convection-dominate/

    http://chiefio.wordpress.com/2010/12/28/ignore-the-day-at-your-peril/

    Thanks for the links. The processes do not go on at annual, monthly, or weekly scales. They go on minute by minute and hour by hour. In that regard, the records of the TAO buoys in the tropical Pacific are quite revealing.

    … while the polar regions have quarterly regimes. Doing a time analysis on both with the same monthly time step will work, but will not be as strong a signal as time scales tailored to the local process speeds.

    Part of the problem with doing that is the shortness of the dataset. Going quarterly means dividing N by three, one third the data. In such a short dataset, that gets you out on shakier ground.

    … The upshot of it all to me is pretty simple. Those graphs at lower elevations are darned near constant. There is about a 275 K to 285 K variation in temperature at 4 km altitude over the course of a clear tropical day. It’s the troposphere that has the largest wiggles. At 22.5 km about 200K to 250K as I eyeball the graphs. AND, IFF I’ve read the paper correctly, it says they are due to convections from the surface heating.

    So to capture that will take less than monthly data… Especially the ‘negative response’ of a thunderstorm that comes and goes in hours. (Using a smaller time step ought to show your expected response even more strongly.)

    Indeed. Some of the TAO buoys have data at 10-minute intervals, I haven’t looked at those yet. So many musicians, so little time …

    w.

  50. Willis –

    Big thank you for this, it really got me thinking, along the lines that this could be really important and deserves the courtesy of some serious critical review. Here are some thoughts:

    TOA imbalance has to be wildly seasonal. If you correlate TOA imbalance with changing temperature, you are correlating two sine waves sampled (if monthly data) 12 times a period. In that case, the climate sensitivity you have measured is the ratio of the average monthly change in temperature to the average monthly change in insolation. But the average change in insolation is huge. For example, at latitude 22.5 degrees north, the TSI/4 average insolation varies by 176 watts, from 285 to 461. In round numbers about 30 watts per month (six months in each direction). So a sensitivity of 1 degree per doubling 3.7 watts is a change of about 8 degrees per month – which is a lot! it isn’t surprising that the sensitivities you measure are low.

    This also explains why the sensitivity over the ocean is lower – the ocean changes temperature slower than the land.

    Next notice the range in the south is higher, because perihelion is in the southern summer. The range at latitude 22.5 degrees south is 233 watts, from 261 to 494 watts. For the same temperature differences, the sensitivities you will measure will be lower in the south, which corresponds to what you see.

    Next, in equatorial regions, there are two peaks in the annual insolation cycle, not one. By the time you are looking at monthly measurements with lags, the cycles can be completely out of synch. I think that’s why you see minimal correlation at the equator.

    Broadly, I think you need to ‘seasonally adjust’ the data somehow, perhaps by looking at annual variations in each item.

    Hope this is some use,

    RERT

  51. RERT says:
    December 10, 2012 at 2:38 pm

    Willis –

    Big thank you for this, it really got me thinking, along the lines that this could be really important and deserves the courtesy of some serious critical review. Here are some thoughts:

    Much appreciated.

    … Next, in equatorial regions, there are two peaks in the annual insolation cycle, not one. By the time you are looking at monthly measurements with lags, the cycles can be completely out of synch. I think that’s why you see minimal correlation at the equator.

    If this were the cause of the low correlation, we would see it quite evenly across the tropics near the equator.

    We do not see the low correlation in the part of the tropics with two peaks in the annual solar cycle (which only extends from about 3°S to about 6°N or so). Instead, we see the lack of correlation only the part of the tropics occupied by the ITCZ.

    w.

  52. Willis Eschenbach: However, in fact what is happening is not zero. It is a series of strong but opposite control inputs which balance out over time, and that is very different from a condition called “no feedback”.

    I agree. You have shown that the effect of increased radiation, whether called “a” feedback or not, depends on the region of the earth surface, and its state.. I erred in not saying that I was referring specifically to the graph of the land relationships. However, I would also conjecture that, at each region of the earth surface, the relationship of radiation change to temperature change is not even constant across seasons, even within temperature ranges.

    One of the desirable consequences of good analyses is that the next questions become obvious.

  53. View from the Solent says:
    December 10, 2012 at 7:31 am
    Not just physical objects. That’s the principal of least action http://www.principlesofnature.net/principle_of_least_action.htm
    ======
    reminds me of quantum mechanics. like stepping into a maze and always knowing in advance which turn to take to reach the exit with the least number of steps. how does nature know how the way? We can describe the path, but what is the underlying mechanism? Perhaps we are a resonance of all possibilities, with “now” being the most likely. Is the big bang and the universal background radiation we see in fact a black hole seen from the inside? is the acceleration due to dark energy a result of our parent universe? are there an exponentially increasing number of universes as black holes reproduce new universes with each generation? Does this give rise to the resonance, a multi-verse of universes. what gave rise to the very first?

  54. John Doe says:
    December 10, 2012 at 4:20 am

    You obviously know what ‘boat’ stands for … Break Out Another Thou$and. :)

  55. Carrick says:
    December 10, 2012 at 6:51 am
    Actually they don’t assume sensitivity is a constant. That’s a common assumption by people try to arrive at estimates of climate sensitivity, but it is neither an assumption, nor does it even hold true in the models. see e.g. http://www.gfdl.noaa.gov/blog/isaac-held/2011/03/19/time-dependent-climate-sensitivity/
    ========
    Interesting result, it shows similar to what Willis has shown. That warm regions of the earth are able to get rid of energy more easily than cold regions. That the effect of GHG is to act as a thermostat. That run-away warming is near to impossible as a result.

  56. joelshore says:

    “You seem to understand neither what the conventional scientific understanding is nor what Willis has shown here. Not surprisingly, your misunderstandings are in the direction of causing you to believe what you want to believe.”

    Yet joelshore says he isn’t in agreement with Willis. From my view then, joelshore is the one who doesn’t understand. Regarding what I “believe”, that is just more projection. I look at empirical evidence above all, and Willis’ article is a gem of empirical evidence. If the evidence showed that CO2 had a significant, measurable effect on global temperature, then I would accept that, because I “believe” testable, empirical scientific evidence and verifiable observations.

    But joelshore does not. He believes in catastrophic AGW caused by the rise in CO2. But the planet is in alignment with Willis Eschenbach’s view — while the planet is deconstructing joelshore’s model-based belief. Which one is correct? Planet Earth, which has not been warming for a decade and a half despite a hefty rise in CO2? Or the IPCC’s models that joelshore cites as his authority? I prefer to listen to what Planet Earth is telling us.

  57. Interesting ‘natural experiment’ in my home town last week: warm westerly winds blew in some high temperatures up to 39C during the clear sunny days … at night with the clear skies the temperatures dropped up to 17C lower than the daily highs. I’m pretty much certain that CO2 concentration was around about constant ; So how come the CO2 didn’t re-radiate the energy back down to keep the temperatures warmer during the night ? /sarc

  58. Very interesting post. I had several questions similar to old engineer. Thank you for the clarifications. Christopher Game at Dr Spencer’s blog has described “compensations” to CO2 flux changes that are similar or identical to your assertion,

    “It is a series of strong but opposite control inputs which balance out over time, and that is very different from a condition called “no feedback”.”

  59. Everyone notices here that the northern continents respond most quickly to a forcing change, so it is very indicative when those are the same areas that are warming fastest in the last few decades. We are in a long-term period of a steady forcing change that now is leaving a distinctive fingerprint on the warming pattern. Not really surprising, but a good way to confirm it.

  60. @Willis:

    Looking at theTao posting (that I’d somehow missed the first go round, dang it…) the comments are closed. So two points here:

    1) That evening ‘shoulder’… check for dew point. IIRC that’s about the time in the evening that things get ‘sticky’ in the tropics as relative humidity gets high… Some enthalpy change instead of temperature change, perhaps?

    2) Why the same start time for the cumulus: Look for solar / water angle of incidence. Up to some hour, light will mostly reflect, then at a critical angle (time of day) it will start to enter / absorb and warm the surface layer.

    Don’t know if those are correct / causal, just what comes to mind to me.

    Also, per what I noticed in Florida on land, the early afternoon warmth comes just about the time the morning moisture has gotten high in the sky, to form rain and give a very cooling mid-afternoon shower. So a time coefficient to travel, condense, rain. ( i.e. not just ‘size of rain’ but time lag to rain). Thus the slight drop after formation of cumulus time. (The moisture heat engine has run a while prior to the delivery of the cool rain cargo ;-) that then overshoots to cooler just a little. Modulo the big hurricanes and any major all night long storms ;-)

    FWIW, after a hurricane (sorry, don’t remember which one) passed off shore, Orlando was significantly cooler in the wake. All the hot air was sucked in, lofted and cooled (rained out) then spread out from the top and descended cooler. If I had to guess I’d guess about 5 F? cooler? Enough that it was more comfortable than the days before the storm passed. Couple of hundred mile radius of effect… A whole lot of heat sucked out of all of land, sea, and air. (Takes a lot of energy to run a cyclone ;-)

    Strange thing was seeing low clouds spiraling in toward the core and high clouds spiraling out at the same time. Rushing toward the eye to rise, then being spread out and disposed once done. Interesting opposite handedness to the spirals motion (speeding up going in, slowing down coming out). At any rate, if you get a chance to watch a hurricane from just outside the storm area, it’s well worth it.

  61. E.M.Smith says:
    December 10, 2012 at 8:14 pm
    Thus the slight drop after formation of cumulus time. (The moisture heat engine has run a while prior to the delivery of the cool rain cargo ;-) that then overshoots to cooler just a little.

    Ditto the monsoon in the horizontal plane.

  62. “During the winter there is nearly NO “downwelling” anything. (Heck, the hight of the stratosphere become ‘indistinct’ then and there; as the ‘troposphere’ essentially goes way).”

    Indeed. There has been serious proposals to put an (remote controlled) IR observatory on top of Dome C in Antarctica rather than on a satellite. In winter there is essentially no water vapour to absorb infrared and CO2 absorption alone does not amount to very much, so it is practically the equivalent of being in orbit.

  63. Philip Bradley says:
    December 10, 2012 at 12:44 pm

    tty says:
    December 10, 2012 at 5:22 am
    The lagged negative response in the Sahel (and to a smaller extent in India and northern South America) is presumably a monsoonal effect, warmer temperatures causes the air over land to rise more, which pulls the ITCZ further north with more rains that cools the climate. During previous warmer interglacials this effect caused the Sahara to more or less disappear.

    One can describe the climate as a system that transports heat (enthalpy) from where it is gained to where it it is lost. And this process occurs both vertically and horizontally. IMO the monsoons are an underappreciated and little understood aspect of horizontal transport.

    The most striking thing about Figure 1 is the way it highlights places where heat is moved horizontally from one place to another. The most prominent locations for mismatch between TOA imbalance and temperature are the locations of: ENSO, the Indian Ocean Dipole, and the Atlantic Equatorial Mode, then, less prominently, the Gulf Stream, the North Pacific and Alaska currents, perhaps including upwelling effects. The subtropical convergence zone to the South shows higher correlation, no doubt because of the slow motion of the water, rather than the 100 million tons of trash reputed to be stuck there. There may be a signature of the Antarctic cirumpolar current, but that’s hard to tell.

    On the land, we can see lack of correlation in equatorial South America and Africa. Monsoon effects are strongly suggested in Thailand and India, with the latter showing a beautiful cutoff at the Himalayas, and at the mountainous border between Pakistan and Afghanistan. We’d expect such heat transfer reduce correlation between TOA imbalance and temperature. The Gulf stream will bring massive amounts of heat to England regardless of whether or not it’s cloudy there. For a lot of the world, this heat transfer signal looks strongly imprinted on the data, so that it would be more of a challenge to find anything there about thunderstorms, or other processes that take place within one gridcell.

    For the equatorial land and oceansareas, in the ITCZ, the convection that drives the Hadley cells (and eventually, the world’s weather as we know it) will carry off a tremendous amounts of heat. Since the convection is driven by solar heating, it will increase or decrease with available sunlight, and damp the temperature effects of varying cloud cover. This will also work to reduce correlation in the ITCZ.

    In the maps that show correlation, or anticorrelation, for various time lags, there is a strong anticorrelation at two and three months in Africa, just North of the equator. These areas have distinct wet and dry seasons, and when you correlate wet-season weather today with dry-season sunshine three months ago, or vice versa, you’ll get a mismatch. With a 3-month lag they’ll be out of phase for a lot of the time. But if that were the only reason, you’d expect to see it show up in other places too. So, perhaps those anomalies have somthing to do with the convection “hot spots” shown here.

    Negative numbers (blue/purple) show rising air; positive ones (pink–yellow) show descending air. It’s not easy to see Africa, hidden under air masses, but three strong convecion areas are prominent. It’s only one month (July 1979) so those hot spots might move around, but the correspondence with the 2- and 3-month lag maps is interesting.

    It’s an interesting post. It’s hard to see how much one can say about thunderstorms until the effects of horizontal heat transfer are accounted for. As for the the Increased cloud formation with heat available is obvious, though nailing down the details could be formidable. A study like this might have something interesting to say in its own right about heat transfer around the globe.

  64. The ENSO fluctuations have been going on for many thousands of years, probably since the isthmus of Panama closed. In all that time there is no evidence that they are the cause of warming or cooling beyond the typical timescale of a few years that the ENSO changes occupy. Certainly no credible evidence exists that they have caused a cooling or warming trend in global temperatures in the past.

    Bob Tisdale now asserts that for the last few decades the extra energy transferred from the oceans to the atmosphere and land by an El Nino event has not been completely lost during any following La Nina so that energy is accumulating. The question then becomes why this change in behaviour and what is causing the reduced loss of energy during a El Nino event.

    Bob Tisdale dates the change to around the start of the satellite record.
    I would suggest that the change dates to the start of the rise in atmospheric CO2 levels which also provides a clear physical process that explains this reduction in energy loss.

  65. Izen says……..

    Bob Tisdale has data showing what happens. He does not assert any “theory”, rather he just shows what the data shows.

    You on the other hand have a “theory” and no data to support it.

    Bob talks facts. I prefer his facts over your assertions.

  66. @- Ed_B says:
    “Bob Tisdale has data showing what happens. He does not assert any “theory”, rather he just shows what the data shows.”

    Correct.
    He provides a description of what has happened without attempting an explanation.

  67. Very interesting. I am trying to get a grasp of what is meant by TOA forcing here. The term ‘forcing’ comes, as far as I know (and I’d very much appreciate any correction here) from computer modellers who found a tractable way to include atmospheric composition changes in their models by the simple device of converting them to instantaneous ‘forcings’ at the model TOA. They then watch while the model re-equilibrates and their new forcings drop to zero.

    In the real world, of course, this does not happen. Atmospheric composition does not change instantaneously, but rather evolves over both time and space. The difference in incoming and outgoing radiation at the real ‘TOA’ has long been established as being net positive inwards in equatorial regions and net positive outwards at polar ones, an effect, not a cause, of poleward transfers of heat and of the spherical shape of the earth (reduced insolation per unit area with latitude).

    Now CO2 largely emerges at the surface, and largely from vegetation in the tropical and subtropical land regions. The CO2 is also transferred polewards in the same tropospheric movements that carry heat. There is a great deal of spatial and temporal variability involved which, again as far as I know, is not taken into account by the ‘forcing’ device. I can appreciate the merit of such a device for making coding problems tractable, but I am struggling to see it as a close analogue of any physical process actually taking place in the system.

  68. Hello Mr. Eschenbach

    Trying to reproduce your results i hit one problem: How did you account for heat exchange between the northern and southern hemisphere, when calculating the sensitivities? One would need the amount of energy “blown” in and out of each parcel.

    The heat exchange between the hemispheres is most significant near the equator, because of the proximity. However it is also warmest near the equator. The inverse relationship between temperature and sensitivity may be an artifact of miscalculating the heat exchange.

    M.B

  69. John Shade says:

    In the real world, of course, this does not happen. Atmospheric composition does not change instantaneously, but rather evolves over both time and space.

    Now CO2 largely emerges at the surface, and largely from vegetation in the tropical and subtropical land regions. The CO2 is also transferred polewards in the same tropospheric movements that carry heat. There is a great deal of spatial and temporal variability involved which, again as far as I know, is not taken into account by the ‘forcing’ device.

    The models can be run either with an instantaneous change in atmospheric composition or a continuous change in atmospheric composition. I kind of doubt the models take the spatial variation of CO2 concentration into account because that spatial variation is frankly very small. They do take spatial variation into account for aerosols and other constituents where the spatial variation in concentration is significant and important.

  70. Ed_B: Bob Tisdale has data showing what happens. He does not assert any “theory”, rather he just shows what the data shows.

    Bob Tisdale performs good and thorough data analyses. However, he does more than that. He asserts that El Ninos erupt as something more than the underlying dynamical processes that generate ENSO, AND that they have generated the excess heat others attribute to CO2. Booth of those are theoretical hypotheses beyond “showing what happens”.

  71. John Shade says:
    December 11, 2012 at 9:20 am

    Very interesting. I am trying to get a grasp of what is meant by TOA forcing here.

    Thanks, John. By “TOA forcing” I mean the imbalance of upwelling and downwelling radiation at the top of the atmosphere.

    w.

  72. Moritz.B says:
    December 11, 2012 at 10:14 am

    Hello Mr. Eschenbach

    Trying to reproduce your results i hit one problem: How did you account for heat exchange between the northern and southern hemisphere, when calculating the sensitivities? One would need the amount of energy “blown” in and out of each parcel.

    The heat exchange between the hemispheres is most significant near the equator, because of the proximity. However it is also warmest near the equator. The inverse relationship between temperature and sensitivity may be an artifact of miscalculating the heat exchange.

    An interesting question. I make no attempt to even touch the issue of heat exchange. I am examining the claim of the modelers that the change in surface temperature is a linear function of the change in TOA forcing. Their fundamental equation is:

    ∆T = λ ∆F

    where delta T (∆T) is the change in temperature, delta F is the change in TOA forcing, and lambda (λ) is the climate sensitivity. I wanted to see where and how much that relationship held true on the earth’s surface.

    The inverse relationship between temperature and sensitivity is indeed related to the export of heat from the ITCZ to the poles. However, that does not imply that there is any miscalculation. I am just looking at their fundamental equation. In the case of the ITCZ, the temperature in that region is NOT a linear function of the forcing. In fact, temperature in that region has little to do with the forcing at all. As you point out, this is because of the export of heat from that region.

    It does raise an interesting question, however, which is the relationship between surface temperature and outgoing radiation … I need to take a look at that one, to see where and how it deviates from the T^4 curve. That should show interesting peculiarities in the ITCZ region …

    But of course, the usual problem … so many musicians … so little time …

    w.

  73. Willis –

    Trying again after my earlier response: you are regressing temperature change on TOA imbalance, which (I surmise) is essentially regressing temperature change on net power in, which is highly seasonal. I think what you get is the (inverse) specific heat capacity per unit area of the cell, modified for albedo. The ocean has high specific heat, so has low ‘sensitivity’, and arid regions have low specific heat, and high sensitivity.

    I still have slight concerns about the granulariy of monthly analysis versus the annual periodicity, but mostly I’m struggling to relate the above, very interesting, data, with what I might need to know to understand better or critque models of CO2 induced AGW.

    Surely no model of AGW concerns itself with the massive swings in TOA inbalance which are seen seasonally? Isn’t the starting point for modelling that TOA must on average stay in equilibrium as a boundary condition, and that changes happen at the bottom of the atmosphere?

    Doubtless I’m missing something, or possibly lots. Any help much appreciated.

  74. Slowly but surely Willis converges on a basic truth: downwelling longwave infrared does not warm the ocean but rather raises the evaporation rate. He also does a nice job of discovering that ocean doesn’t have a lag of many years but rather just several months.

    Nice job Willis. It’s not going to be pleasant admitting to Springer he was right about DWLIR not able to warm the ocean nearly as much as it warms dry land.

  75. John Doe says:
    December 11, 2012 at 2:32 pm

    Slowly but surely Willis converges on a basic truth: downwelling longwave infrared does not warm the ocean but rather raises the evaporation rate. He also does a nice job of discovering that ocean doesn’t have a lag of many years but rather just several months.

    Nice job Willis. It’s not going to be pleasant admitting to Springer he was right about DWLIR not able to warm the ocean nearly as much as it warms dry land.

    John, come back without the snark if you want an real answer. The short answer is, the slower response of the ocean has to do with heat capacity, not infrared absorption, and you are acting like a jerk.

    w.

    PS—For those interested in a complete fisking of the foolish claims that John Doe is making (but doesn’t have the balls to sign his real name to), see my post “Radiating the Ocean“. John, when you can answer the four objections to your cockamamie theory that I listed in that post, come back and discuss your answers, you might have a point. Until then, please take your bad attitude elsewhere.

  76. RERT says:
    December 11, 2012 at 2:15 pm

    Willis –

    Trying again after my earlier response: you are regressing temperature change on TOA imbalance, which (I surmise) is essentially regressing temperature change on net power in, which is highly seasonal. I think what you get is the (inverse) specific heat capacity per unit area of the cell, modified for albedo. The ocean has high specific heat, so has low ‘sensitivity’, and arid regions have low specific heat, and high sensitivity.

    As the map shows, it’s far from that simple. Some parts of the land have higher sensitivity than others, some parts of the ocean have no sensitivity at all. So I can’t just be measuring inverse specific heat.

    I still have slight concerns about the granulariy of monthly analysis versus the annual periodicity, but mostly I’m struggling to relate the above, very interesting, data, with what I might need to know to understand better or critque models of CO2 induced AGW.

    Surely no model of AGW concerns itself with the massive swings in TOA inbalance which are seen seasonally? Isn’t the starting point for modelling that TOA must on average stay in equilibrium as a boundary condition, and that changes happen at the bottom of the atmosphere?

    Good question, RERT. Climate models are just weather models with more variables and run for a longer time. A typical time-step for them is about half an hour, so they assuredly have to be concerned with the “massive swings in TOA imbalance” which occur on daily, monthly, and seasonal scales.

    w.

  77. aaaWillis-

    While this post is getting far down the list at WUWT, I still can’t stop thinking about it. To me this is a really elegant analysis. I hope we can regard this as progress report with more to follow.

    I hope you will follow up on the suggestions about seasonality.

    If I understand your Figure 1, you used all monthly data for the grid cells, with each data point for the regression being one month of paired data, (with the temperature data lagged as described) and the total data points being five years of monthly data (60 data pairs). What if you ran the regression for only 3 months of TOA corresponding to a season (say March, April, and May) with the correspondingly lagged temperature data. Admittedly that would give only 15 data pairs for 5 years, with a smaller temperature range for most latitudes, but it might show if there were significant differences season to season, in both the r^2 and the coefficients.

    For Figure 2, then, use the equation for the season, and use just 3 month temperature averages for all five years so that the average temperature would be calculated from 15 months of data..

    I hate it when someone suggests that I do lots more work. I think “Why don’t you do it yourself” I think I might try to. I could probably handle the spreadsheet development, but I am no so sure about downloading the data and my computer handling- what-64,800 grid cells? Maybe just try a subset.

    Anyway, thanks for an enjoyable several days of expanding my understanding.

  78. Thanks for your reply, Willis (December 11, 2012 at 12:36 pm). But my puzzle is why that imbalance, which as far as I can see is better regarded as an effect, a consequence of the planet’s shape and some fluid dynamics, is called a ‘forcing’. That is the terminology used by computer modellers to introduce what they see as causes, as drivers of temperature changes.in particular.

  79. Old Engineer, thanks very much for your kind thoughts. I don’t think that this work could be done in an Excel spreadsheet, at least in something less than geological time. It’s not only 68,400 gridcells, it’s that many for each of the datasets (ULR, USR, DSR, sea temperature, land temperature, and a couple of land masks). Then you have the derived datasets like the net TOA imbalance, that’s another 64,800 gridcells … the list is long.

    I used the computer language “R” to do the work. I taught myself “R” a few years ago at the urging of Steve McIntyre, and I have never regretted it (many thanks, Steve!). It handles good-sized datasets without breaking a sweat (although you need lots of memory, my machine has 8 GBytes and I wish it could take more). It is designed to do just this kind of heavyweight number crunching on good-sized datasets.

    Anyhow, keep up the good work, I’ll continue my investigations.

    All the best,

    w.

  80. Willis

    May I suggest that you look into the GPGPU acceleration of R. Some of the accelerations are up to 80x faster. Please email me, I have a proposition for you regarding your machine memory bounds.

  81. gymnosperm:

    That blog post that you linked to gets confused here:

    Basically, anything CO2 can do to increase water vapor, water vapor can do for itself. If there were no negative feedback water would have boiled itself out of here eons ago.

    Actually, no. This is the old confusion about the fact that some infinite series have finite sums.

    So, for example, let’s imagine that some extra water vapor gets into the air and this water vapor causes the temperature to rise by 1 C. What happens next? Well, that temperature rise will cause more water vapor to evaporate, which will then cause a further temperature rise…which will then cause more water vapor to evaporate and more temperature rise…and, pretty soon, you have boiled away all the water, right? Well, no, not necessarily. It depends how large the positive feedback is. Let’s say it is such that for each degree rise in temperature, the additional water vapor that evaporates then causes another 0.5 C rise.

    So, the amount temperature rise of 1 C causes the amount of water vapor to rise so that temperature rises by 0.5 C. Then that additional temperature rise of 0.5 C cause the amount of water vapor to rise so that temperature rises by 0.25 C…and so on. In the end, what you have is the infinite geometric series 1 + (1/2) + (1/4) + (1/8) + … and that series does not diverge. Instead, it converges to 2. So, the effect of the water vapor feedback in this case is to double any perturbation (including a perturbation originally produced by water vapor itself).

    There does not need to be a negative feedback to prevent the water vapor feedback from “blowing up” and boiling all of the water on the planet.

  82. Hi Willis –

    Having thought a little more about it after my last reply, I’m happy that it is indeed more complicated than specific heat. Calling this ‘sensitivity’ is also OK, with the one important caveat that it overloads the term with the more common ‘climate sensitivity’, which relates long term global temperature change to CO2 forcing. I don’t see why the rise in temperature in the spring, and its decline in the fall, divided by the increase/decrease in TSI, gives you a number with any bearing on the more usual usage of climate sensitivity other than a similarity of units.

    Again, please help me understand: I can’t believe I’m the only one who is confused, unless I’m misinterpreting enough to be off in LaLaLand….

  83. RERT says:
    December 12, 2012 at 1:11 pm

    Hi Willis –

    Having thought a little more about it after my last reply, I’m happy that it is indeed more complicated than specific heat. Calling this ‘sensitivity’ is also OK, with the one important caveat that it overloads the term with the more common ‘climate sensitivity’, which relates long term global temperature change to CO2 forcing.

    Mmm … well, we can start by noting that “climate sensitivity” is the sensitivity of the climate to any forcing, not just to CO2 forcing.

    Next, we note that global climate sensitivity relates long term global temperature change to long term global forcing.

    Finally, we note that global climate sensitivity is nothing but the global average of local climate sensitivity, which is the local temperature change in response to local forcing.

    In other words, what I am looking at is medium term (one month), local climate sensitivity, which can be averaged to give medium term global climate sensitivity. This relates medium term local temperature changes to local changes in forcing. As my charts show, for most of the earth there is a clear and strong local temperature change in response to local forcing changes, just as the current paradigm says … but not for all of the earth.

    It is in these critical areas around the equator that the heavy lifting of the giant climate engine goes on, and it is in these areas that the temperature is adjusted and regulated by the thermostatic action of clouds and thunderstorms and El Ninos/La Ninas.

    w.

  84. This is my current understanding. Climate sensitivity is something that exists in models If they are sufficiently stable) and is the surface temperature change induced in due course over time to an instantaneous change in the radiation budget at the top of the atmosphere. Often it is shorthand for the change due to a step change in the radiation budget deemed appropriate to a step change in ambient CO2. So in the model, the driving force is the change in the radiation budget and the outcome, the result, is the change in surface temperature. In the real atmosphere the change in the surface temperature would be the driving force, and the change in the radiation budget would be the outcome, the result. In other words, the models do things backwards in unobservable ways, but there is faith that the final results are good enough to justify a colossal, unprecedented disruption to the world’s economic system (esp. production and use of energy) and much else besides, not least the empowerment assumed by some to scare children to raise recruits for this disruption.

  85. Sorry Wilis – my tendency to use sloppy language approximates my tendency to be dogged. Your response above seems completely in line with my understanding of what you’ve done, so I think I’m at least on the right page. But long term climate change is, if you believe it, about changes of the order of a few watts lasting forever. The data you have is for changes of the order of two hundred watts over six months. Somewhere floating arond is the invitation to challenge the 2-6 Kelvin per 3.7 watts with the much lower numbers you present. But the circumstances are so radically different that the comparision is highly tendentious.

    Just as an example of why, consider if summer lasted forever: it would get very very hot. Ditto winter very very cold. The winter-summer temperature spread will be far higher at equilibrium than over a season. So the equilibrium (long term) sensitivity will be higher than the real-world data you measure.

    The data you have extracted is really interesting, but if people misuse it as an argument against the IPCC sensitivity it will be adding to the fog of misinformation which bedevils both sides of this debate.

    Once again, thanks for crunching a great deal of data to produce some real world information.

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