A pertinent climate question that will be judged impertinent

28 March 2021 Climate Comments (8)

Not so innocent as it looks, a pertinent question is asked by Judith Curry on Twitter:

How much of a change in cloudiness would it take to account for the 0.53 W/m2 increase in TOA radiative forcing since 2003?

https://twitter.com/curryja/status/1375144537522204672

She asks it in relation with a recent article accepted for publication on Observational evidence of increasing global radiative forcing (Kramer et al., 2021).

This question touches a central point of climate science because it cannot be an experimental science in which one can play with parameters in isolation from each other. Only a few limited ongoing instrumental observations and Palaeolithic reconstructions may serve to try to distinguish natural from anthropogenic processes, in particular radiative forcing processes. However, most of this job, if not all of it, takes place in silico.

The question can also be formulated in a more general way:

Is it at all possible, at global scope and by instrumental observations, to distinguish the causes of radiative forcing difference of 0.53 W·m-2 over a time period of 15 years? 

To the cloudiness suggestion:

  • From a simple, two-layers energy balance budget it can be estimated that, all other things remaining constant, a 1% increase in cloudiness (which amounts to approx. 66% overall) may induce a temperature increase of 0.54 °C at the Earth surface and of 0.45 °C at the top of atmosphere (TOA).
  • Without consideration for any system feedback, a radiative forcing of 0.53 W·m-2 would induce a temperature rise of 0.11 °C at the surface, and 0.18 °C at TOA.
  • To obtain a same temperature increase, thus to respond to a forcing of 0.53 W·m-‑2, it would take a change in cloudiness by 0.27 % for the surface, or by 0.4 % for the TOA.
  • Is cloudiness, or change of cloudiness, measurable with such accuracy and precision at the aggregated global scope? What was it in 2003, and in 2018?

From an overall energy balance perspective:

  • In general, and to simplify, modelers estimate all in- and outgoing heat fluxes, and let any remaining quantity warm or cool the oceans, thus reporting a so-called accumulated ocean heat or “heat content anomaly”. 
    According to NASA, over the 1993–2019 period, a heat flux anomaly of 0.36 to 0.41 W·m-2 for the first 700 m of depth would have accumulated. Over time, other heat release periods should also occur so that the imbalance does not let us boil or freeze for ever (it never did).
  • Over this time period of 26 years, this heat flux would have implied a temperature change to a well homogenized 700-meter water column of 0.10 to 0.11 °C, a hard to measure change.
  • A question, similar to the previous one, arises regarding instrumental observation: is it at all possible to measure such heat accumulation precisely, accurately, and at the aggregated global scope (by localized temperature monitoring or any other valid method)?

In all these evaluations, errors will have to be taken into account; those arising from instrumental imprecisions and inaccuracies, those that are embedded in the data massaging process (averaging over time and locations), and systemic ones deriving from incomplete and imperfect model designs, their parametrization and simplifications.

Said differently: the resulting balance sheet of any model should entail an account for garbage; but it appears that it is at the same time the energy accumulating in oceans. The NASA-Goddard simplified representations does not show any, others (Trenberth, Fasullo, & Kiehl, 2009) show an “net absorbed” of  0.9 W·m-2 or the U.S. Global Change Research Program (USGCRP) indicates a “Surface imbalance” of 0,6 ±0.17 W·m-2 (one appreciates the margin precision). However, taking into account all potential errors, the true range of validity of this imbalance may well be of the order of hundreds of percent, thus challenging the narrative of a ticking time bomb accumulated in the ocean depths.

One final question must be addressed to the climate science community:
will the heat accumulated in the oceans ever be trashed?

Kramer, R. J., He, H., Soden, B. J., Oreopoulos, L., Myhre, G., Forster, P. M., & Smith, C. J. (2021). Observational evidence of increasing global radiative forcing. Geophysical Research Letters, 48(e2020GL091585). https://doi.org/10.1029/2020GL091585

Trenberth, K. E., Fasullo, J. T., & Kiehl, J. (2009). Earth’s global energy budget.
Bulletin of the American Meteorological Society, 90(3), 311–323. https://doi.org/10.1175/2008BAMS2634.1

This post was published simultaneously on Climate Etc.


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8 thoughts on “A pertinent climate question that will be judged impertinent”

  1. I’m afraid your climate calculator is wrong. It does not take into account that if cloud area fraction is reduced LW is also reduced, by almost the same amount SW increases.

    Reply

      1. Thanks, ok, it’s worse than I first thought, and the opposite of what I first thought.
        Your link to Stocker08 does not work, but I found it. It says:

        “Die ca. +33°C sind dem natürlichen Treibhauseffekt der Erde zuzuschreiben, der hauptsächlich durch Wasserdampf verursacht wird.
        Das soll an einem zweiten, leicht komplexeren EBM illustriert werden (Fig. 2.7, rechts). Wir nehmen an, dass die Abstrahlung sowohl von der Erdoberfläche auf Temperatur T1, als auch von einer höheren Fläche (“Cirrus Wolken”, die die Albedo nicht beeinflussen) auf Temperatur T2 erfolgt. Die hohe Wolkenfläche ist nicht
        vollständig, sondern belegt einen Anteil c der Gesamtoberfläche.”

        So, this cloud cover is a simulation of the greenhouse effect, high clouds not affecting SW and absorbing most of the LW. It is nothing like the real cloud cover consisting of only 10% (CAF) high clouds, 30% low clouds and the remaining in between (300mb to 700mb).

        The cloud radiative effect of the real 66% cloud cover is -44 W/m2 in the SW-band and +27 W/m2 in LW, so your cloudiness parameter doesn’t make sense. Change it from 0 to 1 and Ts increases more than 50 degrees, which of course is not simulating the real planet earth.
        When you include a cloudiness parameter of 0.66 most people will think its the real total cloud cover, not a thought experiment containing high clouds only, with an absurd 0.66 coverage.

        Reply

        1. You’re right… and wrong.
          Yes, this model is too simple, I should have written simplistic. It even lets calculate temperatures in absence of atmosphere!
          But you’re wrong to try to dissect it.
          Everybody who is a little informed, and apparently you are, knows that its validity is near nil. But it has one quality: the relevant drivers of the energy balance (solar irradiation, albedo, cloud cover, surface emissivity; but nothing about the atmosphere and its composition) are summarized, with orders of magnitudes that can be grasped. Not more, but also not less.
          Fact is that cloudiness is important and its measurement over time and locations is approaching mission impossible; its role on the climate remains quite equivocal (cloud feedback at 0.3 ±0.7 [W m2 / °C], according to IPCC).
          66% cloudiness ? See here: https://isccp.giss.nasa.gov/analysis/zD2BASICS/B8glbp.anomdevs.jpg (outdated website, clouds don’t pay much in climate science).
          But this is just an example. The whole issue is that in silico calculated confounding factors remain confounded, above all when confronted with reality.
          Another point: the “33 °C warming thanks to greenhouse gases” is a gross exaggeration: an atmosphere deprived of them would already get temperate by conductive and convective heat transfer between the ground and the air. However, I don’t know how to calculate such a hypothetical situation. Anyway, with just oxygen and without water and CO2, there would be no life. But attributing all 33 °C to water and CO2 gives the appearance of a higher climate sensitivity to GHGs.
          Thank you for the comment on the link. It had changed and is now corrected.

          Reply

          1. The point is, your modelling of clouds is not “too simple”, it’s plain wrong. Like I said, it assumes all clouds (66% CAF) to be thin high clouds transparent to SW and opaque to LW, which of course is wrong. Even the sign of your result is wrong. More clouds will decrease temperature, not increase it like you claim. (the composition, high/mid/low is of course essential, but with only one cloudiness parameter we have to assume unchanged mix)

            I have put some CERES data here: virakkraft.com/CERES_CRE_CAF.xlsx
            Suggesting two methods to get an estimate closer to reality:

            Method 1, using the trends in CERES CRE and CAF. CRE/CAF = 181/-847 = -0.21 W/m2/%CAF
            Method 2, todays 66% CAF gives -17 W/m2. -17/66 = -0.26 W/m2/%CAF

            i.e. data from the real world indicates you need to change the CAF around 2% (or 3% of the 66% already there) to get a 0.5 W/m2 change in forcing. Your model is off by a factor of 5, and even the wrong sign. Not “too simple”. Plain wrong.

          2. More clouds cool the surface, and less warm it ? This is new to me.
            In this case, Tomas Stocker’s lecture is plainly wrong (equation 2.4a,b of his lecture), and everybody’s experience of temperate cloudy nights vs. cool ones at clear sky has the wrong sign.
            The monthly time series of CERES CRE data on your Excel sheet has a r2 correlation coefficient of 0.0006, which means no trend at all, or any trend is a good one.
            This lets open the relationship between observed warming and CRE or CAF.

          3. Well, Stocker’s lecture can be very misleading if you don’t read it carefully. c in his equation is high clouds only, which he sets to 0.6 coverage to get a proper result. 0.6 is 6 times more high clouds than on the real planet. c does not represent the cloudiness of planet earth so his equation is misleading. But again, you have to read the text. It’s an attempt to explain the greenhouse effect.

            To repeat. The real clouds block on average 44 W/m2 of the SW insolation and sends 27 W/m2 LW downwards. So yes, the net result is 17 W/m2 cooling.

            Yes, 20 years of data is too short, but the best we have as far as I know. I don’t think it’s a good option to forget about it an check again in 50 years or so.

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