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.
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.