New analysis attempts to reconcile differences between satellites and climate models

Posted: November 24, 2022 by oldbrew in Analysis, climate, data, modelling, Natural Variation, opinion, predictions, satellites, Temperature
Tags:

Space satellite orbiting the earth


An academic attempt to gloss over some glaring discrepancies between results from theory-based climate models and observed data. The research paper says: ‘Climate-model simulations exhibit approximately two times more tropical tropospheric warming than satellite observations since 1979’. Over forty years of being so wrong, by their own admission, takes a lot of explaining.
– – –
Satellite observations and computer simulations are important tools for understanding past changes in Earth’s climate and for projecting future changes, says Lawrence Livermore National Laboratory (via Phys.org).

However, satellite observations consistently show less warming than climate model simulations from 1979 to the present, especially in the tropical troposphere (the lowest ~15 km of Earth’s atmosphere).

This difference has raised concerns that models may overstate future temperature changes.

Rather than being an indicator of fundamental model errors, the model-satellite difference can largely be explained by natural fluctuations in Earth’s climate and imperfections in climate-model forcing agents, according to new research by Lawrence Livermore National Laboratory (LLNL) scientists.

“Natural climate variability appears to have partly masked warming over the satellite era,” said Stephen Po-Chedley, a LLNL climate scientist and lead author of a paper appearing in the Proceedings of the National Academy of Sciences.

The results of the study provide an improved understanding of the causes of historical changes in climate and increase confidence in model simulations of continued global warming over the 21st century.

“Although the Earth is warming as a result of human emissions of carbon dioxide, natural variations in the Earth’s climate can temporarily accelerate or diminish this overall warming trend,” noted Zachary Labe, a co-author from Princeton University and the National Oceanic and Atmospheric Administration’s Geophysical Fluid Dynamics Laboratory.

In addition to modulating the rate of warming, natural fluctuations in climate such as the Interdecadal Pacific Oscillation also produce unique patterns of regional surface temperature change.

These surface temperature patterns were key in quantifying the influence of natural variability on satellite-era warming. The research team considered thousands of surface-warming maps from climate-model simulations. The team then trained machine-learning algorithms to relate the pattern of surface warming to the overall magnitude of warming or cooling attributable to natural climate oscillations.

The machine-learning approach was successful in disentangling the component of atmospheric warming due to natural climate oscillations versus warming from other causes, such as human-induced increases in the levels of heat-trapping greenhouse gases.

When this approach was applied to the observed pattern of warming, the prediction from machine learning methods indicated that natural oscillations reduced the real-world tropical tropospheric warming trend by about 25% over the satellite era.

Although climate models simulate such natural decadal oscillations in climate, the timing and sequence of these fluctuations differs in each simulation and will only match the observations by chance.

This partial “offsetting” of warming by natural variability helps to explain why climate model simulations tend to simulate more warming than satellite observations of tropical tropospheric temperature during the last few decades.

Full article here.
– – –
Study: Internal variability and forcing influence model–satellite differences in the rate of tropical tropospheric warming (Nov. 2022)

Comments
  1. […] New analysis attempts to reconcile differences between satellites and climate models — Tallbloke&#… […]

  2. catweazle666 says:

    Really…

    “In sum, a strategy must recognise what is possible. In climate research and modelling, we should recognise that we are dealing with a coupled non-linear chaotic system, and therefore that the long-term prediction of future climate states is not possible.”

    So stated the IPCC’s Working Group I: The Scientific Basis, Third Assessment Report (TAR), Chapter 14 (final para., 14.2.2.2), p774.
    El Niño and La Niña

  3. oldbrew says:

    Bear in mind the paper is about the tropical troposphere warming in particular. When they resort to discussing biomass burning it begins to look unconvincing, IMO.

  4. […] New analysis attempts to reconcile differences between satellites and climate models […]

  5. catweazle666 says:

    ANYTHING that claims authority for evidence based on computer games – sorry, models – is 100% unconvincing notwithstanding references to biomass or anything else, oldbrew.

    Anyone who claims that a purported computer game – sorry – climate simulation of an effectively infinitely large open-ended non-linear feedback-driven (where we don’t know all the feedbacks, and even the ones we do know, we are unsure of the signs of some critical ones) chaotic system – hence subject to inter alia extreme sensitivity to initial conditions, strange attractors and bifurcation – is capable of making meaningful predictions over any significant time period is either a charlatan or a computer salesman.

    Ironically, the first person to point this out was Edward Lorenz – a climate scientist.

  6. Gamecock says:

    It’s word salad, devoid of meaning. Gibberish.

    ‘Rather than being an indicator of fundamental model errors, the model-satellite difference can largely be explained by . . . imperfections in climate-model forcing agents’

    That is what we call ‘errors.’

    And . . . ‘natural fluctuations in Earth’s climate’

    No climate on earth has changed in a hundred years. None. Weather changes; climate doesn’t (which is what makes them useful). ‘Climate’ is a human contrivance; it doesn’t exist in nature. Nature has weather. Man studies weather, and characterizes it for an area or region. It DOESN’T EXIST. Climate is the product of human analysis.

    Like when you average a bunch of numbers. The average is a calculated value; it doesn’t tangibly exist. Climate doesn’t tangibly exist.

    Climate models (sic) don’t work. It can largely be explained by we don’t know enough to model the atmosphere. Every year, they announce new, improved, state-of-the-art models, voiding all past modeling. Remember, whatever model they are using today will be voided next year.

  7. oldbrew says:

    ‘The machine-learning approach was successful in disentangling the component of atmospheric warming due to natural climate oscillations versus warming from other causes, such as human-induced increases in the levels of heat-trapping greenhouse gases.’

    If only they knew what all the natural climate oscillations were and what they’re doing now, but they don’t. So what supposed success are they talking about?

  8. JB says:

    “The machine-learning approach was successful in disentangling the component of atmospheric warming due to natural climate oscillations versus… human-induced increases in the levels of heat-trapping greenhouse gases.”

    HAHAHAHA

  9. P.A.Semi says:

    A preliminary result:
    Earth’s eccentricity “cycle” is quite far from a regular sinusoid…
    (I somehow remember you were writing about this and beats with other frequencies?)

    While we’re now slowly approaching more circular orbit until year 28612, it can’t be said there is some simple regular “cycle” in eccentricity…
    From present 0.01669 it goes down to 0.002289 at June 28612 perihelium, more circular than is Venus now at 0.00675…
    Length of Earth anomalistic year (perihelium to perihelium) stably oscillates with average value 365.259 days, the length oscillation is wider when orbit is circular…

    Presently I’m calculating long time ephemeris, but will need to stop it soon due to disk space concerns, the file takes cca 1 Gb per 10k years, and century takes 9 minutes to calculate… (Not sure when I’ll calculate same into the past, because it takes 2x that disk amount, because it needs to be reversed before usage…)
    This ephemeris calculates EMB (Earth-Moon Barycenter) as one body, instead of separate Earth and Moon, because that accumulated rounding errors and the Moon flew away soon, since Moon and Earth orbit is quite complicated and needs much more orders in Chebyshev polynomials and still is not precise enough…
    Integrating planet attraction (using PPN – Post-Parametrized Newtonian method described by JPL) is done by integrating Chebyshev polynomials, I know of no other sufficiently precise method, simple trapezoid integration and the likes are absolutely insufficient…

    Still not sure, if there may be some error ? (Like if the runaway eccentricity will start to retreat?)

    πα½

  10. oldmanK says:

    PA Semi introduces an interesting factor into the argument.

    However in mathematics the regularity expected of polynomials is not all there is. There is then the abrupt change, the impulse or step change.

    At another site I was introduced to what has been called “Dragon Kings” as in the work of Didier Sornette; see https://arxiv.org/ftp/arxiv/papers/0907/0907.4290.pdf
    In abstract, quote: ‘We develop the concept of “dragon-kings” corresponding to meaningful outliers, which are found to coexist with power laws in the
    distributions of event sizes under a broad range of conditions in a large variety of systems. These dragon-kings reveal the existence of
    mechanisms of self-organization that are not apparent otherwise from the distribution of their smaller siblings.’

    ‘Outlier’ results in tests are usually discarded, when in effect they dictate the new state of a system. In climate systems considered over several centuries ‘Dragon King’ events dictate abrupt change which no polynomial can replicate. One such outlier result was obtained in year 173CE in the measurement of earth’s obliquity, which was at the time, and to date, ignored. Yet it dictated change. The effect of changes in obliquity are more determining on climate than any in eccentricity.

    There were greater D K’s in the earlier past that are now coming to light.
    See here: https://melitamegalithic.wordpress.com/2022/10/31/searching-evidence-astronomy-for-the-heretic/

  11. John Fry says:

    ‘This partial “offsetting” of warming by natural variability helps to explain why climate model simulations tend to simulate more warming than satellite observations of tropical tropospheric temperature during the last few decades.’

    So that’s over the entire history of climate model simulations then, is it not?

    [reply] looks like it, yes

  12. oldbrew says:

    Ned Nikolov writes:

    This is an interesting new paper acknowledging the long-standing and well-known overprediction of the mid-troposphere tropical warming by climate models:

    https://www.pnas.org/doi/10.1073/pnas.2209431119

    It’s been known in the past as the “tropospheric hot spot”, and some climate scientists have denied this important model departure from reality. However, the authors of the above paper adopted a backward method to assess the problem. Instead of training the machine-learning algorithm on observed data, they trained it on climate model output and then applied it to analyze the observations:

    “In applying the climate-model-trained machine-learning framework to observations, we estimate that external forcing has produced a tropical TMT trend of 0.25 ± 0.08 K⋅decade−1 between 1979 and 2014, but internal variability has offset this warming by 0.07 ± 0.07 K⋅decade−1.”

    This approach implicitly assumes that climate models correctly simulate the physical reality including all important forcing, which is demonstrably wrong! For example, climate models are notoriously bad at reproducing observed cloud dynamics, which controls the solar forcing… The real reason for the mid-troposphere tropical “hot spot” to NOT be observed in reality is because “greenhouse gases” such as water vapor do not actually heat the atmosphere as predicted by climate models. The tropical mid-troposphere is mostly heated by convection from the surface, while the surface is heated in turn by the incoming solar flux, which depends on the cloud albedo above the surface.

  13. Phoenix says:

    So the data is different from the models and this “may” show the models are wrong? Climate science really is a joke.

  14. oldbrew says:

    Just came across this 2015 report, looks amusing now…

    Climate scientists find elusive tropospheric hot spot
    14 MAY 2015

    UNSW researchers have confirmed strong warming is taking place in the lowest layer of the Earth’s atmosphere, a phenomenon long predicted in global warming theory.
    . . .
    No climate models were used in the process that revealed the tropospheric hotspot. The researchers instead used observations and combined two well-known techniques — linear regression and Kriging.

    https://newsroom.unsw.edu.au/news/science-tech/climate-scientists-find-elusive-tropospheric-hot-spot

    So – hotspot or notspot 🙄

  15. oldbrew says:

    The new paper says its maths jiggery-pokery has got the percentage of models that are below the upper limit (not the mean) of the satellite temp data up from 12% to 44%. So they admit the other 56% i.e. the majority are still too warm even after the correction(s). This is called ‘reconciling’ their results.

    They then claim some more tweaking with aerosol data could help, but hasn’t been done yet.

    See: A Role for Forcing Biases
    https://www.pnas.org/doi/10.1073/pnas.2209431119#sec-3

  16. ivan says:

    So it boils down to models suffering from the classic GIGO. Since their input is always garbage their output will always be garbage – oh. you can’t predict the future.

  17. Gamecock says:

    No, ivan. It is crap software. The problem is not the input.

  18. Jim says:

    Agreed with the gigo. Warmist keep forgetting, show me a weather satellite prior to the first one of the seventies. Or balloons prior to WW2. That’s how short the history of their story is. Worse yet, was each test done in the same condition, each satellite as good or improved, or different then the previous ones? So you are creating new databases not improving the old ones. Now, are the sensors “improved” or the same, and what are you actually seeing. With how bad the forecast is for for after tomorrow, how accurate they are for next week, and how shoddy the records are kept, and their failure at math, how can you trust them about next year?

  19. Gamecock says:

    Sorry, Jim. Even if they had perfect input data, the models (sic) would still produce junk.

    The joke is they claim that if they had a bigger, more expensive computer, they’d be more correct. The problem isn’t hardware. The problem isn’t input data.

  20. catweazle666 says:

    “The joke is they claim that if they had a bigger, more expensive computer, they’d be more correct.”

    Indeed.
    You can chuck as much computing power at such a problem as you want, all that happens is you get the wrong answer faster.

  21. oldbrew says:

    An earlier paper by some of the same authors said some of the same things.

    Natural variability contributes to model-satellite differences in tropical tropospheric warming (March 2021)

    Our results indicate that even on 40-y timescales, natural climate variability is important to consider when comparing observed and simulated tropospheric warming and is sufficiently large to explain TMT trend differences between models and satellite data.

    https://pubmed.ncbi.nlm.nih.gov/33753490/
    – – –
    They might save themselves a lot of trouble if they just believed the observations reflected natural variability, instead of trying to superimpose theories that clearly don’t work.

  22. ivan says:

    gamecock, I still say it is garbage input – can you prove the input is good when you consider the position of many Stephenson screens in urban heat islands.

  23. Gamecock says:

    I won’t defend the inputs. Our attempts at measuring the earth’s weather are pathetic. Climate scientists (sic) pretend we know.

    But that’s not the problem with GCMs. It DOESN’T MATTER what the inputs are.

    I guess we could have a philosophical debate on it. Inputs are wrong AND the modeling (sic) software is bad. You want to believe GIGO. As a retired computer scientist, I say it’s the software. I assert authority! Calling it GIGO looks amateurish to me.

  24. […] New analysis attempts to reconcile differences between satellites and climate models […]

  25. ivan says:

    I agree the programming is wrong as well as the inputs, after all how do you model an analog system on a digital computer. I started in computers with an analog unit and moved on from there into engineering. The thing is that it is obvious they don’t have a clue what they are doing and just add stupid ideas because that is what is expected by the UN Church of Climatology and what pays the large grants.

  26. Gamecock says:

    “The thing is that it is obvious they don’t have a clue what they are doing”

    True. My point is that if they fix the input, they are no closer to fixing the problem. While the inputs are bad, if I were their boss, I’d tell them to focus on getting the software right

    . . . if I wanted to keep my job.

    If I were honest, I’d tell ’em to turn out the lights and go home; what they were trying to do is IMPOSSIBLE.

    “how do you model an analog system on a digital computer”

    ??? The world is mostly analog. I used to model industrial power cost, which was a function of usage and rates. All analog.

  27. stpaulchuck says:

    if they follow their past pattern, they will “adjust” the satellite data just like the land data and –SUDDENLY– the model and data match!

  28. catweazle666 says:

    “While the inputs are bad, if I were their boss, I’d tell them to focus on getting the software right”

    It doesn’t matter what they do to the software Gamecock.

    Anyone who claims a simulation of an effectively infinitely large open-ended non-linear feedback-driven (where we don’t know all the feedbacks, and even the ones we do know, we are unsure of the signs of some critical ones) chaotic system – hence subject to inter alia extreme sensitivity to initial conditions, strange attractors and bifurcation – is capable of making meaningful predictions over any significant time period is either a charlatan or a computer salesman.

    And you can add as much computing power as you like, the result is purely to produce the wrong answer faster. But for some climate “scientists” I suppose it pays the mortgage…

  29. Gamecock says:

    Simply, Mr Weazle, we don’t know enough about the atmosphere to model it.

    It’s that simple.

  30. catweazle666 says:

    And we never will, Gamecock.
    For the reasons I have enumerated on numerous occasions, being non-linear and chaotic the behaviour of the atmosphere is a computationally intractable problem

  31. oldbrew says:

    They can’t even agree on what they measured. So-called adjustments are the order of the day, then adjustments to the adjustments ad infinitum. No credible science in that.

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