ECS > 2K?

I know Eli has already beaten me to it, but I thought I would also post the video of Andrew Dessler’s at the recent meeting on Earth’s Climate Sensitivity. It includes a talk by Bjorn Stevens called Some (not yet entirely convincing) reasons why 2K < ECS < 3.5K. This is a little ironic given that his recent paper on aerosol forcing is being used by some to argue that ECS is probably less than 2K.

Andrew Dessler’s talk is essentially also arguing that ECS > 2K. It’s quite interesting in that he’s trying to use short-term variability to estimate ECS. This has the advantage that the change in external forcing will be small, allowing it to be ignored. It does, however, require having some idea of how short-term estimates compare to longer-term estimates, which is obtained through comparing forced model runs, with unforced control runs. What he also did was to consider, initially, the feedbacks about which we have some confidence (water vapour, lapse rate, surface albedo) and showed that these alone would suggest an ECS of about 2K. There are then some reasons why cloud feedbacks are likely positive, which would then suggest that the ECS is probably greater than 2K. I don’t quite know the arguments for why cloud feedback is likely positive, so if anyone else does, it would be useful to get an idea of what they are.

Anwyay, that’s all I was going to say. The talk is quite good and clear, so just watch the video 🙂

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23 Responses to ECS > 2K?

  1. dana1981 says:

    Research by Dessler and others has suggested that at least the short-term cloud feedback is probably weakly positive.

  2. Dana,
    Thanks. I actually have read that before, but had forgotten.

  3. Andrew Dessler says:

    The argument about the cloud feedback is as follows: 1) Observations and the models agree on the magnitude of the cloud feedback in response to short-term interannual variability, which suggests that the models are doing something right (Dessler, A. E. (2010), A determination of the cloud feedback from climate variations over the past decade, Science, 330, 1523-1527, doi: 10.1126/science.1192546; Dessler, A. E. (2013), Observations of climate feedbacks over 2000-10 and comparisons to climate models, J. Climate, 26, 333-342, doi: 10.1175/jcli-d-11-00640.1). 2) There is a strong correlation in the models between the short-term and long-term cloud feedback, which gives us some reason to believe that the evidence of a positive short-term cloud feedback implies a positive long-term cloud feedback (plot of this in the talk). 3) While we don’t have an overarching theory of the cloud feedback, there are simple physical arguments why particular cloud types will generate positive cloud feedbacks (e.g., Zelinka, M., and D. L. Hartmann (2010), Why is longwave cloud feedback positive?, J. Geophys. Res., 116, D16117, doi: 10.1029/2010JD013817). Taken together, I conclude that it’s likely, or even very likely, that the long-term cloud feedback is positive I’ll just add that the IPCC AR/5 also concluded that the cloud feedback is “likely positive”.

  4. Andrew,
    Thanks, very useful. Dana’s skeptical science article was also very good. Any feedback from the meeting about Nic Lewis’s estimates. From Twitter I got the impression that the general view was that these energy balance estimates likely underestimate ECS by about 30%. Was that the general view from the meeting?

  5. Andrew Dessler says:

    I hesitate to make a statement about the “general view” of the meeting’s participants, but that’s certainly my view. There’s some disagreement about the exact cause of this … Gavin Schmidt argued that it’s due to different forcing efficacies (similar, I think, to the Kummer and Dessler paper https://www.youtube.com/watch?v=aqmw46Q4LdE), while Kyle Armour speculated about different SST patterns regulating feedback strength over time. This is what makes science fun!

  6. Thanks.

    This is what makes science fun!

    Indeed!

  7. Meow says:

    On the difference between climate sensitivity estimates from EBMs v. those from paleoclimate, GCMs, etc., isn’t one of the issues that EBMs treat feedbacks as if they act globally? But, in fact, every feedback really acts first locally, with some follow-on global effect. Thus, for example, if something increases T on some random square kilometer, the water-vapor feedback (to pick one) operates on that increase, not on what’s happening everywhere else on the planet.

  8. Lucifer says:

    The last step before making a weather forecast is to look out the window.
    Here is a climate window:

    This doesn’t reflect what energy may or may not be going into the oceans, of course.
    But the positive feedbacks ( water vapor, albedo ) are reputedly occuring.
    And the negative feedback ( lapse rate ) is evidently absent ( no hot spot ).
    Any discussion of predicted sensitivity should include rationale for why it might diverge from observed.

  9. Lucy, Why is your climate-fu so weak? You only go back to 1979. #WHUT is up with that?

    This is the way a regression is done:

    You model all the erratic movements going back to the start of the instrumental record, and thus get better estimates of the underlying global warming signal.
    .

  10. Meow,
    Yes, that is one issue that was highlighted by Shindell et al..

  11. BBD says:

    Lucifer

    And the negative feedback ( lapse rate ) is evidently absent ( no hot spot ).

    You’ve been told repeatedly now that the data do not support this assertion yet you keep repeating it. That makes you a liar in my book.

  12. BBD says:

    Lucifer

    Any discussion of predicted sensitivity should include rationale for why it might diverge from observed.

    It does. Why are you claiming that explanations are absent? Ignorance? Dishonesty?

    The suggested mechanisms behind the slowdown in the rate of surface warming include:

    – increase in the rate of ocean heat uptake (England et al 2014)

    – increased aerosol negative forcing (Ridley et al. 2014)

    – predominance of ENSO LN state (Banholzer & Donner 2014)

    – exceptional reduction in solar output during SC24 (SSN index)

    There is evidence that CMIP5 forcings are *corrected* to bring them into line with reality then modeled global average temperature comes into much closer agreement with observations (Schmidt et al. 2014).

    This would suggest that model physics and so emergent behaviours like model sensitivity are in fact reasonably accurate.

  13. BBD says:

    Lucifer

    It’s been obvious for some time that there are problems with UAH but the very specific nature of those problems is of very specific interest to misrepresenters in the ‘school of Christy’.

    Spencer and Christy are very rapidly running out of road. You will soon have to find some other misrepresentation to parrot persistently.

  14. Eli Rabett says:

    OK, so here is a question, or rather a ramble. We know that ozone is pretty constant in the tropics in spite of CFCs, we also know how water vapor and CO2 mixing ratios in the stratosphere have been increasing and stratospheric temperatures have been decreasing. We even have profiles and we know the mechanism. Somehow, somewhere, this should give an estimate of TCS or at least some of the lambdas.

    If you want to go down into the troposphere, we know the mixing profiles for CFCs and HFCs so we know the forcings for these guys. The feedbacks should be exactly the same as for CO2, so we should have a measure of the ECS from that.

    Just sayin’

  15. I suspect you will have to consider a finer scale energy balance model, to include “local” feedbacks and how they evolve in time and as the local conditions change. I’m not sure if this makes sense, but in oil resevoir models we use a coarse model to provide guidance and run a multitude of cases. The key is to have sensible connections between these coarse sectors. But maybe this climate zingy is too different, and we will probably have to wait 10 years. Meanwhile we should be beyond peak oil or getting close enough to it your RCP business as usual will be shot to hell.

  16. BBD says:

    Fernando

    See Armour et al. (2013) Time-Varying Climate Sensitivity from Regional Feedbacks.

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