People send me stuff. Lance Wallace writes:
Anthony, this short “Perspectives” report in Science seems to me to be worthy of a posting in WUWT. Not only is it a very clear indication of crucial problems with the GCMs, it appears in Science magazine, for years a dogged defender of the faith. I’m including the article (paywalled of course) because I think your readers will be blown away by the figure if you can run it.
The authors ran some extremely simplified CMIP5 GCMs, looking only at how they treated water (precipitation, cloud formation), and found extreme differences from one model to the next, as is evident from the figure.
In the final section titled Back to Basics, they make clear that the problem is a fundamental one of not understanding the coupling between water and general circulation. They specifically state it would be better to go towards numerical weather prediction rather than continue to expand the coverage of the GCMs.
By the way, they picked just two aspects–clouds and precipitation–to concentrate on, but they mention a few others, such as sensitivity and arctic amplification of temperature change. Then there are also aerosols, energy balance, and ocean circulation. I could see more examples of models simplified down to each of these aspects in turn and compared to see how they perform. – Lance Wallace
Science 31 May 2013:
Vol. 340 no. 6136 pp. 1053-1054
What Are Climate Models Missing?
Fifty years ago, Joseph Smagorinsky published a landmark paper (1) describing numerical experiments using the primitive equations (a set of fluid equations that describe global atmospheric flows). In so doing, he introduced what later became known as a General Circulation Model (GCM). GCMs have come to provide a compelling framework for coupling the atmospheric circulation to a great variety of processes. Although early GCMs could only consider a small subset of these processes, it was widely appreciated that a more comprehensive treatment was necessary to adequately represent the drivers of the circulation. But how comprehensive this treatment must be was unclear and, as Smagorinsky realized (2), could only be determined through numerical experimentation. These types of experiments have since shown that an adequate description of basic processes like cloud formation, moist convection, and mixing is what climate models miss most.
Full text at http://www.sciencemag.org/content/340/6136/1053.summary (paywalled)
The figure from the article shows how four different models have wide variances on clouds and precipitation.
Clouds and water are central to our global atmospheric processes, and clearly, these models aren’t doing much better than dartboards at figuring out what the real atmospheric score is.
With wide variances like that, no wonder climate models can’t model reality, from Dr. Roy Spencer’s recent post: STILL Epic Fail: 73 Climate Models vs. Measurements, Running 5-Year Means