Lane et al. (2000), for example, evaluated the sensitivities of the cloud-radiation parameterizations utilized in contemporary GCMs to changes in vertical model resolution, varying the latter from 16 to 60 layers in increments of four and comparing the results to observed values.  This effort revealed that cloud fraction varied by approximately 10% over the range of resolutions tested, which corresponded to about 20% of the observed cloud cover fraction.  Similarly, outgoing longwave radiation varied by 10 to 20 Wm^-2 as model vertical resolution was varied, amounting to approximately 5 to 10% of observed values, while incoming solar radiation experienced similar significant variations across the range of resolutions tested.  What is more, the model results did not converge, even at a resolution of 60 layers.

In an analysis of the multiple roles played by cloud microphysical processes in determining tropical climate, Grabowski (2000) found much the same thing, noting there were serious problems related to the degree to which computer models failed to correctly incorporate cloud microphysics.  These observations led him to conclude that "it is unlikely that traditional convection parameterizations can be used to address this fundamental question in an effective way."  He also became convinced that "classical convection parameterizations do not include realistic elements of cloud physics and they represent interactions among cloud physics, radiative processes, and surface processes within a very limited scope."  Consequently, he but stated the obvious when he concluded that "model results must be treated as qualitative rather than quantitative."

Reaching rather similar conclusions were Gordon et al. (2000), who determined that many GCMs of the late 1990s tended to under predict the presence of subtropical marine stratocumulus clouds, and that they failed to simulate the seasonal cycle of the clouds.  These deficiencies are extremely important, because these particular clouds exert a major cooling influence on the surface temperatures of the sea below them.  In the situation investigated Gorden and his colleagues, for example, the removal of the low clouds, as occurred in the normal application of their model, led to sea surface temperature increases on the order of 5.5°C.

Further condemnation of turn-of-the-century model treatments of clouds came from Harries (2000), who wrote that our knowledge of high cirrus clouds is very poor and that "we could easily have uncertainties of many tens of Wm^-2 in our description of the radiative effect of such clouds, and how these properties may change under climate forcing."  This problem is particularly noteworthy in light of the fact that the radiative effect of a doubling of the air's CO2 content is only on the order of low single-digit Wm^-2.  It is, therefore, truly an understatement to say, as Harries did, that "uncertainties as large as, or larger than, the doubled CO2 forcing could easily exist in our modeling of future climate trends, due to uncertainties in the feedback processes."

Moving into the 21st century, Lindzen et al. (2001) analyzed cloud cover and sea surface temperature (SST) data over a large portion of the Pacific Ocean, finding a strong inverse relationship between upper-level cloud area and mean SST, such that the area of cirrus cloud coverage normalized by a measure of the area of cumulus coverage decreased by about 22% for each degree C increase in cloudy region SST.  Essentially, as the researchers described it, "the cloudy-moist region appears to act as an infrared adaptive iris that opens up and closes down the regions free of upper-level clouds, which more effectively permit infrared cooling, in such a manner as to resist changes in tropical surface temperature."  The sensitivity of this negative feedback was calculated by Lindzen et al. to be substantial.  In fact, they estimated it would "more than cancel all the positive feedbacks in the more sensitive current climate models" that were being used to predict the consequences of projected increases in atmospheric CO2 concentration.  And, as one might suppose, evidence of this potential impediment to global warming was nowhere to be seen then, and is nowhere to be seen now, even in today's most advanced GCMs.

Clearly, this challenge to climatic political correctness could not go uncontested; and Hartmann and Michelsen (2002) quickly claimed that the correlation noted by Lindzen et al. resulted from variations in subtropical clouds that are not physically connected to deep convection near the equator, and that it was thus "unreasonable to interpret these changes as evidence that deep tropical convective anvils contract in response to SST increases."  Fu et al. (2002) also chipped away at the adaptive infrared iris concept, arguing that "the contribution of tropical high clouds to the feedback process would be small since the radiative forcing over the tropical high cloud region is near zero and not strongly positive," while also claiming to show that water vapor and low cloud effects were overestimated by Lindzen et al. by at least 60% and 33%, respectively."  As a result, they obtained a feedback factor in the range of -0.15 to -0.51, compared to Lindzen et al.'s much larger negative feedback factor of -0.45 to -1.03.

In a contemporaneously published reply to this critique, Chou et al. (2002) stated that Fu et al.'s approach of specifying longwave emission and cloud albedos "appears to be inappropriate for studying the iris effect," and that since "thin cirrus are widespread in the tropics and ... low boundary clouds are optically thick, the cloud albedo calculated by [Fu et al.] is too large for cirrus clouds and too small for boundary layer clouds," so that "the near-zero contrast in cloud albedos derived by [Fu et al.] has the effect of underestimating the iris effect."  In the end, however, Chou et al. agreed that Lindzen et al. "may indeed have overestimated the iris effect somewhat, though hardly by as much as that suggested by [Fu et al.]."

Although there has thus been some convergence in the two extreme views of the subject, the debate over the reality and/or magnitude of the adaptive infrared iris effect continues apace; and when some of the meteorological community's best minds continue to clash over the nature and magnitude of the phenomenon, it is amazing that climate alarmists continue to clamor for actions to reduce anthropogenic CO2 emissions at almost all costs, as if the issue were settled when it clearly is not.

This situation is illustrative of the importance of the advice given two years earlier by Grassel (2000), who in a review of the then-current status of the climate modeling enterprise noted that changes in many climate-related phenomena, including cloud optical and precipitation properties caused by changes in the spectrum of cloud condensation nuclei, were insufficiently well known to provide useful insights into future conditions.  His advice in the light of this knowledge gap was that "we must continuously evaluate and improve the GCMs we use," although he was forced to acknowledge that contemporary climate model results were already being "used by many decision-makers, including governments."

This state of affairs has continued to the present day and is very disturbing, as national and international policy is being made on the basis of vastly imperfect mathematical representations of a whole host of physical, chemical and biological phenomena, many of which involve clouds.  Although some may think that what we currently know about the subject is sufficient for predictive purposes, a host of questions posed by Grassl - for which we still lack definitive answers - demonstrates that this assumption is erroneous.

As but a single example, Charlson et al. (1987) described a negative feedback process that links biologically-produced dimethyl sulfide (DMS) in the oceans with climate.  The basic tenant of this hypothesis derives from the fact that the global radiation balance is significantly influenced by the albedo of marine stratus clouds, and that the albedo of these clouds is a function of cloud droplet concentration, which is dependent upon the availability of condensation nuclei that have their origin in the flux of DMS from the world's oceans to the atmosphere.

Acknowledging that the roles played by DMS oxidation products within the context described above are indeed "diverse and complex" and in many instances "not well understood," Ayers and Gillett (2000) summarized empirical evidence supporting Charlson et al.'s hypothesis that was derived from data collected at Cape Grim, Tasmania, and from reports of other pertinent studies in the peer-reviewed scientific literature.  According to their findings, the "major links in the feedback chain proposed by Charlson et al. (1987) have a sound physical basis," and there is "compelling observational evidence to suggest that DMS and its atmospheric products participate significantly in processes of climate regulation and reactive atmospheric chemistry in the remote marine boundary layer of the Southern Hemisphere."

The empirical evidence analyzed by Ayers and Gillett (see also, in this regard, Dimethyl Sulfide in our Subject Index) highlights an important suite of negative feedback processes that act in opposition to model-predicted CO2-induced global warming over the world's oceans; and these processes are not fully incorporated into even the very best of the current crop of climate models, nor are analogous phenomena that occur over land included in them, such as those discussed by Idso (1990).

Further to this point, O'Dowd et al. (2004) measured size-resolved physical and chemical properties of aerosols found in northeast Atlantic marine air arriving at the Mace Head Atmospheric Research station on the west coast of Ireland during phytoplanktonic blooms at various times of the year.  In doing so, they found that in the winter, when biological activity was at its lowest, the organic fraction of the submicrometer aerosol mass was about 15%.  During the spring through autumn, however, when biological activity was high, they found that "the organic fraction dominates and contributes 63% to the submicrometer aerosol mass (about 45% is water-insoluble and about 18% water-soluble)."  Based on these findings, they performed model simulations that indicated that the marine-derived organic matter "can enhance the cloud droplet concentration by 15% to more than 100% and is therefore an important component of the aerosol-cloud-climate feedback system involving marine biota."

As for the significance of their findings, O'Dowd et al. state that their data "completely change the picture of what influences marine cloud condensation nuclei given that water-soluble organic carbon, water-insoluble organic carbon and surface-active properties, all of which influence the cloud condensation nuclei activation potential, are typically not parameterized in current climate models," or as they say in another place in their paper, "an important source of organic matter from the ocean is omitted from current climate-modeling predictions and should be taken into account."  We agree, as we have long championed the idea that both marine and terrestrial biology can influence climate via a number of important negative feedback phenomena involving atmospheric aerosols (see, for example, Feedback Factors (Biophysical) in our Subject Index).

Another perspective on the cloud-climate conundrum is provided by Randall et al. (2003), who state at the outset of their review of the subject that "the representation of cloud processes in global atmospheric models has been recognized for decades as the source of much of the uncertainty surrounding predictions of climate variability."  They report, however, that "despite the best efforts of [the climate modeling] community ... the problem remains largely unsolved."  What is more, they say that "at the current rate of progress, cloud parameterization deficiencies will continue to plague us for many more decades into the future."

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.  For starters, they state 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 also don't know all that much about what comes down, as they report that "downdrafts are either not parameterized or crudely parameterized in large-scale models."

With respect to stratiform clouds, the situation is no better, as their parameterizations are described by Randall et al. 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 at the time of their study, in fact, they had to report that the concept of detrainment was "somewhat murky," and that the conditions that trigger detrainment were "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. additionally 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 this point home, they say "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 just 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, according to Randall et al., 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 they 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."  Nevertheless, 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."

A few more decades, however, 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 up front that it is truly needed in order to obtain a definitive answer to the question of CO2-induced "anthropogenic climate change."  And admitting such, let's not do anything rash in the interim, like totally reorganizing the way the world produces and uses energy, in an expensive and likely futile attempt to alter the course of future climate.

So it comes down to this: 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 how it works 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.  The cloud parameterization problem by itself is so complex that no one can validly claim that humanity's continued utilization of fossil-fuel energy will result in massive counter-productive climatic changes.  There is absolutely no justification for that conclusion in reliable theoretical models, simply because there are none.

That the basis for this conclusion is robust, and cannot be said to rest on the less-than-enthusiastic remarks of a handful of exasperated climate modelers, we report the results of two additional studies of the subject that were published subsequent to the analysis of Randall et al., and which therefore could have readily refuted their assessment of the situation if they felt that such was appropriate.

In the first of these studies, which was conducted by seventeen other climate modelers, Siebesma et al. (2004) report that "simulations with nine large-scale models [were] carried out for June/July/August 1998 and the quality of the results [was] assessed along a cross-section in the subtropical and tropical North Pacific ranging from (235°E, 35°N) to (187.5°E, 1°S)," in order to "document the performance quality of state-of-the-art GCMs in modeling the first-order characteristics of subtropical and tropical cloud systems."  The main conclusions of this study, according to Siebesma et al., were that "(1) almost all models strongly underpredicted both cloud cover and cloud amount in the stratocumulus regions while (2) the situation is opposite in the trade-wind region and the tropics where cloud cover and cloud amount are overpredicted by most models."  In fact, they report that "these deficiencies result in an overprediction of the downwelling surface short-wave radiation of typically 60 W m^-2 in the stratocumulus regimes and a similar underprediction of 60 W m^-2 in the trade-wind regions and in the intertropical convergence zone (ITCZ)," which discrepancies are to be compared with a radiative forcing of only a couple of W m^-2 for a 300-ppm increase in the atmosphere's CO2 concentration.  In addition, they state that "similar biases for the short-wave radiation were found at the top of the atmosphere, while discrepancies in the outgoing long-wave radiation are most pronounced in the ITCZ."

The seventeen scientists, who hail from nine different countries, also state that "the representation of clouds in general-circulation models remains one of the most important as yet unresolved [our italics] issues in atmospheric modeling."  This is partially due, they continue, "to the overwhelming variety of clouds observed in the atmosphere, but even more so due to the large number of physical processes governing cloud formation and evolution as well as the great complexity of their interactions."  Hence, they conclude that through repeated critical evaluations of the type they conducted, "the scientific community will be forced to develop further physically sound parameterizations that ultimately [our italics] result in models that are capable of simulating our climate system with increasing realism."  Until that time (indeed, until climate simulations can be done, not with increasing realism, but with true realism), we suggest that it is not wise to put much credence in what these admittedly inadequate state-of-the-art GCMs suggest about the future; and to actually mandate drastic reductions in fossil-fuel energy use on the basis of what these models currently suggest can only be described as downright foolish.

In perhaps the most recent effort to assess the status of state-of-the-art climate models in simulating cloud-related processes, Zhang et al. (2005) compared basic cloud climatologies derived from ten atmospheric GCMs with satellite measurements obtained from the International Satellite Cloud Climatology Project (ISCCP) and the Clouds and Earth's Radiant Energy System (CERES) program.  ISCCP data were available from 1983 to 2001, while data from the CERES program were available for the winter months of 2001 and 2002 and for the summer months of 2000 and 2001.  The purpose of their analysis was two-fold: (1) to assess the current status of climate models in simulating clouds so that future progress can be measured more objectively, and (2) to reveal serious deficiencies in the models so as to improve them.

The work of the twenty additional climate modelers involved in this exercise reveals a huge list of major model imperfections.  First, Zhang et al. report a four-fold difference in high clouds among the models, and that the majority of the models only simulated 30-40% of the observed middle clouds, with some models simulating less than a quarter of observed middle clouds.  For low clouds, they report that half the models underestimated them, such that the grand mean of low clouds from all models was only 70-80% of what was observed.  Furthermore, when stratified in optical thickness ranges, the majority of the models simulated optically thick clouds more than twice as frequently as was found to be the case in the satellite observations, while the grand mean of all models simulated about 80% of optically intermediate clouds and 60% of optically thin clouds.  And in the case of individual cloud types, the group of researchers reports that "differences of seasonal amplitudes among the models and satellite measurements can reach several hundred percent."

As a result of these and other observations, Zhang et al. conclude that "much more needs to be done to fully understand the physical causes of model cloud biases presented here and to improve the models."  We agree, especially since the deficiencies they discovered have relevance to model predictions of CO2-induced global warming.  Until these major problems can be overcome, it is an affront to all logic to call for reductions in fossil-fuel usage on the basis of such poorly performing climate models.

In conclusion, there is absolutely no question but that the set of problems that currently restricts our ability to properly model a whole suite of cloud-related processes likewise restricts our ability to simulate future climate with any degree of confidence in the accuracy of the results.  Consequently, it must be acknowledged that the model-inspired specter of catastrophic CO2-induced global warming that looms on the horizon is but a paper tiger, totally clawless and devoid of teeth.  That it is being used for powerful political purposes may be true, but that it has any roots in reality is demonstrably false.

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