So what's the problem?  "Clouds are complicated," Randall et al. declare, as they begin to describe what they call the "appalling complexity" of the cloud parameterization situation by stating that "our understanding of the interactions of the hot towers [of cumulus convection] with the global circulation is still in a fairly primitive state."  And not knowing all that much about what goes up, it's not surprising that we don't know all that much about what comes down.  At the present time, for example, Randall et al. report that "downdrafts are either not parameterized or crudely parameterized in large-scale models."

With respect to stratiform clouds, the situation is no better; current parameterizations are described in their review as "very rough caricatures of reality."  As for interactions between convective and stratiform clouds, forget about it ? which is pretty much what scientists themselves did during the 1970s and 80s, when Randall et al. report that "cumulus parameterizations were extensively tested against observations without even accounting for the effects of the attendant stratiform clouds."  Even now, in fact, they report that the concept of detrainment "is somewhat murky," and that the conditions that trigger detrainment "are imperfectly understood."  Hence, it should again come as no surprise that "at this time," as they put it, "no existing GCM includes a satisfactory parameterization of the effects of mesoscale cloud circulations."

Randall et al. also say that "the large-scale effects of microphysics, turbulence, and radiation should be parameterized as closely coupled processes acting in concert," but they report that only a few GCMs have even attempted to do so.  Why?  Because, as they continue, "the cloud parameterization problem is overwhelmingly complicated," and "cloud parameterization developers," as they call them, are still "struggling to identify the most important processes on the basis of woefully incomplete observations."  To drive the point home, they add that "there is little question why the cloud parameterization problem is taking a long time to solve: It is very, very hard."  In fact, the four scientists conclude that "a sober assessment suggests that with current approaches the cloud parameterization problem will not be 'solved' in any of our lifetimes."

With such a bleak assessment of where the climate-modeling community currently stands with respect to the single issue of cloud parameterization, it might be well to pause and ask ourselves how anyone could possibly feel confident about what even the best climate models of the day are predicting about CO2-induced global warming, where proper cloud responses are critical to reaching a correct conclusion.  The answer is so obvious it need not even be stated.

But wait!  There appears to be a glimmer of light at the end of the climate-modeling tunnel.  It's a long way off ? and it looks to be incredibly expensive ? but it's there.  And it beckons ever so enticingly.

The shining hope of the climate-modeling community of tomorrow resides, apparently, in something called "cloud system-resolving models" or CSRMs, which can be compared with single-column models or SCMs that can be "surgically extracted from their host GCMs."  These advanced models, as Randall et al. describe them, "have resolutions fine enough to represent individual cloud elements, and space-time domains large enough to encompass many clouds over many cloud lifetimes."  Of course, these improvements mean that "the computational cost of running a CSRM is hundreds or thousands of times greater than that of running an SCM."

In a few more decades, according to Randall et al., "it will become possible to use such global CSRMs to perform century-scale climate simulations, relevant to such problems as anthropogenic climate change."  But a few more decades is a little long to wait to address an issue that climate alarmists are prodding the world to confront now.  Hence, Randall et al. say that an approach that could be used very soon (to possibly determine whether or not there even is a problem) is to "run a CSRM as a 'superparameterization' inside a GCM," which configuration they call a "super-GCM."

Not wanting to be accused of impeding scientific progress, we say "go for it," but only with the proviso that if we are going to spend so much money on the project and devote so many scientific careers to it, let's admit that it is truly needed to obtain a definitive answer to the question of CO2-induced "anthropogenic climate change."  And admitting that, let's not do anything rash in the interim, like totally reorganizing the way the world produces and uses energy in an expensive and futile attempt to alter the course of future climate.

Either we know enough about how the world's climate system works, so that we don't need the postulated super-GCMs, or we don't know enough about it and we do need them.  We happen to believe with Randall et al. that our knowledge of many aspects of earth's climate system is sadly deficient.  So let's own up to that fact and openly admit that we currently have no rational basis for implementing programs designed to restrict anthropogenic CO2 emissions in an effort to save the world from CO2-induced global warming.  The cloud parameterization problem by itself is so complex that no one can validly claim our appetite for fossil-fuel energy has brought us to the verge of biospheric destruction.  In light of all the many good things CO2 does for plants -- and, therefore, the human and animal life that depend upon them for sustenance -- ill-founded actions designed to slow the rate of rise of the air's CO2 content could well end up jeopardizing the future well-being of the biosphere.

Reference
Randall, D., Khairoutdinov, M. Arakawa, A. and Grabowski, W.  2003.  Breaking the cloud parameterization deadlock.  Bulletin of the American Meteorological Society 84: 1547-1564.