Today, I’m going to give the first of a series of reports on the Feedback Session at Erice: I’ll try to do a post on each of the presentations – Lindzen, Choi, Kininmonth and Paltridge. I’ll also try to do reports on the presentations by Essex and Swanson in a different session.
I’ll do a quick overview today. However, I’d prefer that people don’t jump the gun on discussing themes until materials from the specific presenters are provided. To try to forestall that, in addition to an overview, I’ll provide a brief profile of the first part of YS Choi’s presentation, in which he provided some quite startling results on clouds from recent satellite data.
In addition to taking notes on the presentations, I spent a lot of time at lunches, dinners and breaks, talking to each of the speakers about their issues, and probably learned as much or more in these sessions as the presentations themselves. In addition, I’ve had considerable follow-up correspondence with Choi, who promptly and cordially provided supplementary information and scripts.
Overview
In no particular order. And once again, I plan to post on each presentation so please reserve comments on other presentations until its turn.
William Kininmonth’s presentation was similar to his Heartland presentation. His thesis is that GCMs are systemically under-estimating the proportion of incoming radiation in the tropics to evaporation and that this makes the models increase surface temperature too much, resulting in a too high climate sensitivity.
Garth Paltridge argued for lower-than-IPCC feedback on a different basis. He observed that high-feedback models required a moistening trend in the upper tropical troposphere. As he had observed in a recent paper together with Albert Arking, he noted that such a trend could not be observed in available radiosonde data. He noted that inhomogeneities in the radiosonde record were substantial and large enough that a reasonable observer could choose not to place a lot of weight of this point, but nonetheless that’s what was on the table.
Lindzen presented the results of Lindzen and Choi (GRL 2009) on ERBE, amplified by some new results using CERES data. This analysis argues that the sign of the feedback evidenced by ERBE/CERES data is opposite to the sign from corresponding analyses of GCMs.
Choi is a Korean post-doc at MIT, studying under Lindzen. His website here has a lengthy list of publications for a young man and, interestingly, even a few patents. The second part of Choi’s presentation was on aerosols, in which he presented new analyses of the most recent satellite data, showing that aerosol properties were different from those commonly attributed to them.
Choi on Clouds
The first part of Choi’s presentation was on the properties of clouds as shown by the most recent MODIS satellite results. One could not help returning from Choi’s presentations with much increased awareness of the major differences between old and new satellite data – 21st century satellites are providing a lot more data and a lot better data than 1980 satellites – a point that people sometimes lose sight of when presented with “homogenized” series.
Here is an astonishing table from Choi’s presentation on global cloud cover from MODIS as compared to earlier estimates. As you see, the most modern data shows global cloud cover at nearly 78%, as compared to estimates of 51% from the early NIMBUS satellite and 61% from ISCCP2.
The bulk of the increase is in thin clouds, not picked up in the coarser analyses. Choi showed that a time series of ISCCP2 measurements showed decreasing cloud cover, thus the MODIS difference was related to more accurate measurement and not to trends in cloud cover.
I’ve noted to CA readers on earlier occasions that Bony et al 2006 had reported that the major difference between high-sensitivity and low-sensitivity models related to their handling of thin marine boundary layer clouds and that it appeared that all GCMs under-produced thin clouds (and over-produced “thick” clouds). Since the impact of thin -vs-thick clouds is highly non-linear, such differences impacted overall GCM production.
Choi mentioned that this problem with clouds has been known in the remote sensing community for a while, but it doesn’t seem to have been assimilated by the climate modeling community thus far. As I understand it (and I do not claim equal expertise in these comments as comments on proxies), the analyses cited in AR4 all used older versions of cloud data. So if there was under-production of clouds using the older ISCCP2 data, the situation is obviously “worse than we thought” using modern MODIS data.
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27 Comments
I’m anticipating that cloud taxonomy is going to be important to understanding this aspect of climate model sensitivity. Do you know of a reference (not a meteorological picture book) explaining the cloud types and their physical characteristics that are important to modeling?
Re: Gary (#1), Well, at the most basic level, what I know is that most cloud types (nimbus, cumulus) act primarily on shortwave, and cool by their presence by reflecting sunlight. Cirrus are the only except to the general rule that I’m aware of. They act primarily on the infrared, and warm the atmosphere more than they cool it.
Steve-interesting indeed that Choi showed how dramatically improved our ability to measure clouds as become, leading to revelations that the Earth is cloudier than thought. That must have a BIG impact on the radiative budget calcs, no?
Re: Andrew (#3),
I seem to recall discussions a couple of years ago (maybe longer) concerning a reduction in surface insolation as measured by evaporative pans or something like that. That might be a result of some additional reflection from clouds which wasn’t allowed for. Of course it could also be that the increase in long-wave interactions would cause the opposite affect. Though if this were already accounted for via humidity it might still be a negative feedback to the presumed situation.
Was anything said about haze and so on since there is a foggy distinction in meaning between cloud and incompletely clear air?
Steve,
“the situation” you speak of are the analyses cited in AR4, is that correct?
I think I’ve never heard so loud
The quiet message in a cloud.
===================
Steve
has Choi made those time series on cloudiness available online?
[Excuse the bad pun…] I think I’m pro-Choi.
Even if cirrus clouds absorb IR, isn’t the fact that they are much higher than the ground significant? Specifically, wouldn’t about half the IR be radiated into space as it does not have to penetrate the bulk of the atmosphere on the way out?
If you take a look at a recent study which looks at the effects of cloud cover things are not so rosy. It turns out that in models which give reasonable results for the observed cloud cover behavior (there is only one, HadGEM1) the derived climate sensitivity is 4.4C. This is not good.
It seems rather odd that all of the people who scream about uncertainties in the models (and there are lots of them), it always seems as though the uncertainties always lead to a lower value of S. This is a silly thing to assume. Some of the uncertainties will lead to a lower value, and some will lead to a higher value. While more research needs to be done on cloud feedback, to assume that it leads to a lower value of S is SILLY.
Steve: One of the issues that I raised in framing questions for the Erice session was the sorts of feedbacks/model issues that could lead to major under-estimation of model sensitivity as well as major over-estimation. Invitations to Erice were issued to people who might have presented this point of view, but unfortunately they declined the invitaiton.
As I’ve observed in connection with the HS when people say – if the Stick is wrong, the situation is much worse than we think. My answer is always – then we should find out and govern ourselves accordingly. And we should give no thanks to people whose obstruction of data and code has delayed identification of their errors.
Re: Rattus Norvegicus (#10), If you want to focus on ten year old papers….Well, anyway, let’s leave the snark aside. The recent sophisticated model results of:
Wyant, M. C., M., Khairoutdinov, and C. S. Bretherton (2006), Climate sensitivity and cloud response of a GCM with a superparameterization. Geophysical Research Letters, 33, L06714
Conclude that very advanced modeling of cloud behavior may well reveal negative cloud feedback.
Sure, uncertainty can be two sided, but rather than assume that it is either even or leaning one way or the other, it makes a bit more sense to look at reality and see what she is telling us.
Cirrus clouds are composed of ice particles as opposed to water droplets for other clouds. There’s a peak in the albedo for ice particles in the thermal IR at about 30 micrometers that isn’t there for water droplets. So cirrus clouds do reflect IR more than water droplet clouds.
I don’t see what the big deal is about whoop-de-doo newer MODIS cloud measurements. After all, 78% cloudiness is just an offset from 62% cloudiness. We’re all interested in the effects of differential CO2 levels, here. So, calculate the anomaly separating two GCM test runs and get good solid scientific data on the climatological effect of differential CO2. The actual level of cloudiness just plain “doesn’t matter.” Jeez, how often does this basic point need to be made?
🙂
Higher cloud cover doesn’t mean the energy balance changes. We know the albedo of the planet by measurement of things like reflected light from the new moon and satellite measurements. What it must mean is that the albedo of clouds in the solar spectrum is lower than thought so that clouds could well retain more IR from the surface than they reflect sunlight away.
No. No. The fog, the dew, the cloud!
Meant lightly – did you sing verses like this at night at Erice?
“Now, lay you still, you silly young fool,
And don’t you feel afraid,
For if you want to work with me,
You got to learn your trade.”
I learned her all that summertime,
And all the winter, too.
And truth to tell, she learned that well,
She saved us from the foggy, foggy dew.
Pat Frank: wrote: quote [] 78% cloudiness is just an offset from 62% cloudiness. We’re all interested in the effects of differential CO2 levels, here. So, calculate the anomaly separating two GCM test runs and get good solid scientific data on the climatological effect of differential CO2. The actual level of cloudiness just plain “doesn’t matter.” [] unquote.
Quite. Our host covers this point:
quote Choi showed that a time series of ISCCP2 measurements showed decreasing cloud cover, thus the MODIS difference was related to more accurate measurement and not to trends in cloud cover. unquote
Palle/s work shows large albedo swings, presumably related to clouds. Cloud research is the key. Google VOCALS.
JF
Cloud Cover over the South Pole from Visual Observations, Satellite Retrievals, and
Surface-Based Infrared Radiation Measurements, Town, Warren, Walden 2006, Figure 4: shows “The seasonal cycle of monthly mean Cvis, Cpyr, and CPAERI for 2001. The algorithm to determine CPAERI is given in Town et al. (2005). The timing of the CPAERI point during March is explained in Town et al. (2005). The discrepancy between CPAERI, Cvis, and Cpyr during February is explained in the text.(CPAERI data for February was missing).
Cvis are “visual observations of cloud cover from 1957 to 2004 taken by the South Pole Meteorological Office (SPMO)…,” Cpyr are “routine surface-based pyrgeometer measurements made by the Earth System Research Laboratory (ESRL)-Global Monitoring Division (GMD) of (NOAA),” and CPAERI are “spectral infrared radiances during 2001 as part of the South Pole Atmospheric Radiation and Cloud Lidar Experiment (SPARCLE).”
It is interesting how different the measurements of cloud cover are.
Clouds always matter as the gatekeepers for insolation. Since that’s what generates LW radiation from the surface, it’s unphysical to expect increased LW power from increased cloudiness. Nature does not accumulate power locally, it disperses it via perpetually increasing entropy.
All that the sharp discrepancy in South Pole cloud cover amounts shows is that visual observations are difficult to make in the long austral winter night.
At the present time, my group is looking at the cloud cover data that we are getting from the Lunar Orbiters and the Apollo missions. While this is all visible light data, it should allow differentation of the major cloud types. A very rough version of this will be presented as a poster session at AGU this year. We are drawing no conclusions, just gathering the data for the community. The clouds do look interesting though, especially from the mid sixties.
Did I read recently that a mere 1% increase in global cloud cover would negate any CO2 effects ?. Is this valid?
Are the minutes of this conference, any of the papers presented there, or any reports of sessions Steve might not have time to write about available online? I couldn’t find them with Google, but they may have been buried under other Erice conferences and other works by the authors mentioned because I didn’t use the optimum combination of keywords.
Or maybe I should wait a few weeks and try again.
An indirect indication of cloudiness is by the amount of incoming radiation measured at different places all over the world. That led to the “global dimming / brightening” discussions of some years ago. While that was generally attributed to (human) aerosols, a change in cloudiness is more likely, as e.g. the increase of industrialisation in China and the brightening there over the past decade are not compatible with the influence of aerosols (neither is a similar dimming/brightening in Australia or the Antarctic data compatible with far less aerosols in the SH)…
A good oversight of incoming radiation is in the supplemental material of the Science article of Wild e.a. From dimming to brightening
Re: Ferdinand Engelbeen (#23), Aren’t the effects of different kinds of aerosols very, er, different? Could it be that China’s pollution is having the opposite effect of other pollutants? They seem to be emitting quite a lot of black carbon, for instance.
(By the way, love those unitless qualitative graphics. Thanks NYT!)
Re: Andrew (#26),
The effect on incoming radiation of black carbon (absorbing) and sulfate (reflecting) measured at the surface is the same, but their total effect on the radiation balance is opposite. Black carbon absorbes light in the lower troposphere, adding in balance to the temperature increase (despite less radiation reaching the surface) and helps to melt the Himalayan glaciers due to surface deposit, while sulfate aerosols are the largest negative forcing, according to the climate modellers. In total, the IPCC/models give far more weight to sulfate aerosols than to black carbon, see:
http://en.wikipedia.org/wiki/File:Radiative-forcings.svg and the modelled effect 1750-2000 from the IPCC TAR here.
But that seems quite discutable, as the influence of aerosols as a whole is clearly overestimated, both in amount (models overestimate the ratio anthro/natural, see Heald e.a.) as in effect (there is no measurable effect of the huge reduction of SO2 emissions in Europe, see here, even the overall sign (cooling or warming) is discutable…
What I wonder in the NYT graphs is the large influence of black carbon from cooking in China, I thought that that was mainly a problem in India, not in China…
Did Choi discuss average cloud cover in the 12 year interval from 1988 to 2000 that is missing from the box ?
The link suggests he has made useful contributions to hyperspectral satellite data interpretation, so could you give us some idea of how, on a channel by channel basis, the results he presented translate into optical depth time series ?
David Weisman: I hope the Ettore Majorana Center will issue a volume of the complete proceedings of this Erice conference, as was the case with the last two I attended. Debate was vigorous at both,so one hopes we will learn more here of the reception afforded Dick and Steve’s presentations by other participants like Mike MacCracken.
With regard to ISCCP’s data:
Evan, A.T., A.K. Heidinger and D.J. Vimont, 2007. Arguments against a physical long-term trend in global ISCCP cloud amounts. Geophysical Research Letters, 34:LO4701.
#15 Julian Flood
Palle et al on the Earthshine project observe an increase in cloud from late 1998 and the maintainence of elevated levels since. This would seem to be consistent with the difference between MODIS and earlier satellite observations, though the magnitude of the difference may well be mostly due to improved technology as Choi notes.
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