This is a discussion of clouds precisely because they are a very important part of the climate puzzle.
“Clearing Clouds of Uncertainty by Mark Zelinka, David Randall, Mark Webb and Steven Klein. Their commentary is really a summary of our recent understanding and – as illustrated by the figure on the right – they conclude that the evidence is converging on the cloud feedback likely being positive.”
From FAR 1990 positive but cloud feedback represented the largest source of uncertainty in climate sensitivity among atmospheric models.
To SAR Second Assessment Report; 1995), more climate models were predicting the mass of cloud liquid and ice, and generally finding negative cloud opacity feedbacks, albeit of widely differing strengths. The report concluded that it was not possible at that time to judge the sign of the net cloud feedback.
TAR 2001 …the sign of [the cloud] feedback remains unknown.
AR4 2007 it is not yet possible to assess which of the model estimates of cloud feedback is the most reliable
AR5 “The sign of the net radiative feedback due to all cloud types is…likely positive”
BUT Cloud opacity feedback “is highly uncertain”
“I don’t really see why I should be expected to post comments that are certain, but wrong.”
I repeated a fact, “Averaged globally and annually, clouds cause cooling ” from the article overview. I said in view of this,
“any increase in cloud cover should have a stronger negative than a positive effect.”
The current point of view says this is likely wrong but admit to high uncertainty still in areas like cloud opacity. There is a threefold variation in the global sensitivity parameter FAR 1990 due to
differences in cloud feedback.
This is a most important discussion which is not yet settled and needs open discussion.
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October 6, 2017 at 9:44 am
Jai John Mitchell says:
“The range of estimates of ECS on the low-end are dominated by observational estimates.”
Long time reader of your comments at ASIB.
Says it all really does it not?
Unreliable observational estimates are no match for real time paleological proxies and centennial-based feedbacks in models
The lower end range is not applicable as it only exists in an observational world.
Admire the effort and work that you have always put in to your comments and thanks for sharing those referrals above.
ECS range is around 3.0 at this site as befits IPCC, you can try to push it higher, I have failed to push it lower.
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October 6, 2017 at 10:21 am
Everett F Sargent says:
“Well RealClimate has another post on the Millar paper 2017/10/1-5oc-geophysically-impossible-or-not/”
Thanks Everett, I do not go to RealClimate as much as I should, partly as this site keeps well abreast of it which means there is no need generally. I do read a cross section of all of the others though Stoat is quiet for long periods and Tamino is dormant at the moment.
The choice of temperature data
“the conclusion that present day temperatures lie outside of the model distribution.”
remains a problem even if resorting to
” The anomaly between observations and the CMIP5 mean temperature response to cumulative emissions is halved by repeating the Millar analysis with the GISTEMP product instead of HadCRUT.”
The role of internal variability
“Both HadCRUT and GISTEMP suggest strongly negative index values for the period 2005-2014, suggesting a potential cold bias in the warming estimate due to natural variability of 0.1?C (with 5-95% values of 0.05-0.15?C).”
Which means it is 0.2 C potential natural variability shown in just a 10 year period. Interesting to contemplate what the actual range would be in a series of a 100 decades.
The low CMIP5 compatible emissions comment suggests fallibility.
” the combined evidence of the influence of natural variability on the unforced temperature estimate, the disagreement between different observational datasets on warming level, and the uncertainty introduced by an uncertain pre-industrial temperature baseline means that we can’t be confident as the Millar paper suggests on what the current level of warming is.”
and this gem on trust in models v observations ” Alternatively, we trust the cumulative emissions number and treat the models as full proxies for reality, as was done in AR5,”