Cloud feedback still largest source of uncertainty in GCMâ€™s
What did they say? Clouds a positive feedback?
Last week, a new article by Brian Soden of the University of Miami and Isaac Held of GFDL entitled â€œAn Assessment of Climate Feedbacks in Couopled Ocean-Atmosphere Modelsâ€ was published in the Journal of Climate. Among their findings were that the representation of clouds and their associated feedbacks within GCM’s still presents one of the largest sources of uncertainty in current predictions of climate sensitivity. In this paper, Soden and Held compare the most predominant feedback mechanisms in a group of state-of-the-art atmosphere-ocean GCMâ€™s (GFDL, GISS, CCSM3, HADCM3, etc.). Although in many ways this study is a confirmation of what is already known (that clouds are still a thorn in the side of modelers), there were several interesting take home points I found from this article.
1. This study was one of the first (or at least, one of few) to incorporate a methodology for calculating feedbacks in a consistent manner for each of the various models. Previous work has largely focused on gathering feedback estimates from disparate parts of the scientific literature. Soden and Held find that
â€œ…the range of feedback strengths computed here is smaller [than previous studies] for all feedbacks except clouds.â€
and provide some evidence that reduction in feedback strength difference between models is due to inconsistencies in previous studies, not an improvement of model physics.
2. The standard deviation of cloud feedback strength among the various GCMâ€™s is about 4 times greater than the next largest standard deviation, which is the lapse rate/water vapor feedback (the motivation for combining the lapse rate and water vapor feedbacks is explained in the article. In short, the lapse rate feedback affects the water vapor feedback, so combining them is more appropriate).
3. Cloud feedbacks are positive in all models !!!??? Okâ€¦ so Iâ€™m embarrassed to admit this, but before reading this article I didnâ€™t know that clouds represented a positive feedback in climate models. Is there anyone else out there that was under the impression that clouds were a negative feedback? Any student who has ever taken a class in weather/climate has learned about the cloud-albedo feedback, where the presence of bright (high albedo) clouds reduces the TOA radiative forcing by reflecting sunlight back to space. I have always been under the impression that because of this, clouds are a negative feedback (i.e., warmer Earth -> more clouds -> cooling effect). So why does this simplistic explanation fail? Well, it must be wrong. To get a positive feedback, either warmer Earth -> LESS clouds -> warming effect, or warmer Earth -> more clouds -> warming effect. There is no evidence of which Iâ€™m aware that cloud coverage/amount will decrease in a warmed climate. I would be glad to see evidence in the peer-reviewed literature either way, if anyone would like to comment. So if it is the latter of these two methods for getting a positive cloud feedback that is correct, how do we reconcile this with the cloud-albedo effect? I assume that the answer lies primarily in the fact that clouds have a warming effect at night by trapping outgoing longwave radiation (OLR). Because the cloud feedback is a diurnal (and annual and geographical, for that matter) average, it is possible that other effects outweigh the cloud-albedo effect to produce a positive cloud feedback. Nonetheless, from an educational standpoint, I am a little stunned by why this is not made more clear in undergraduate/graduate classes. This was not clear at all to me as a Ph.D. candidate in the subject, and very certainly is not made clear (and likely often mistaught!) in the undergraduate classes. Perhaps it needs to be emphasized in educational settings that the NET cloud feedback is positive, even though some of the contributing feedbacks are strongly negative.
4. Finally, at the end of the article Soden and Held speculate that intermodel differences in low cloud coverage account for most of the discrepancies in cloud feedback, although they note that their methodology is unable to identify the source of these discrepancies. As someone who studies cirrus clouds, Iâ€™ve always liked to think that it is my type of clouds that are the most important to understand in order to improve GCM predictions of climate sensitivity :) â€¦ The speculation of Soden and Held, which cites a 2005 GRL paper by Bony and Dufresne and another by Webb et al. (in press, Climate Dyn.), is that it is in fact marine boundary layer clouds. Oh wellâ€¦ perhaps the authors or some other group will care to take this study a step further and quantify the portion of the discrepancy that can be attributed to various cloud types, or overage/amount. If cirrus donâ€™t end up being the most important, I can always fall back on the old line that they are most important per weight