Clouds (Global Climate Models and their Limitations)

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Correctly parameterizing the influence of clouds on climate is an elusive goal that the creators of atmospheric general circulation models (GCMs) have yet to achieve. One reason for their lack of success has to do with model resolution on vertical and horizontal space scales. Lack of adequate resolution forces modelers to parameterize the ensemble large-scale effects of processes that occur on smaller scales than their models are capable of handling. This is particularly true of physical processes such as cloud formation and cloud-radiation interactions. Several studies suggest that older model parameterizations did not succeed in this regard (Groisman et al., 2000), and subsequent studies suggest they still are not succeeding.

Lane et al. (2000) 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 percent over the range of resolutions tested, which corresponded to about 20 percent 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 percent of observed values, while incoming solar radiation experienced similar significant variations across the range of resolutions tested. 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 says, “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 failed to simulate the seasonal cycle of 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 by Gordon and his colleagues, 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), previously cited in Section 1.1, 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.”

Moving into the twenty-first 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 percent 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.

Lindzen’s challenge to what had become climatic political correctness could not go uncontested, and Hartmann and Michelsen (2002) quickly claimed 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 percent and 33 percent, 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 opposing views of the subject, the debate over the reality and/or magnitude of the adaptive infrared iris effect continues. It is amazing that some political leaders proclaim the debate over global warming is “over” when some of the meteorological community’s best minds continue to clash over the nature and magnitude of a phenomenon that could entirely offset the effects of anthropogenic CO2 emissions.

Grassl (2000), in a review of the then-current status of the climate-modeling enterprise two years before the infrared iris effect debate emerged, 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.”

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. (See Section 2.3 for a more complete discussion.) The basic tenet of this hypothesis is 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 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). (See also, in this regard, Section 2.7 of this report.)

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 percent. During the spring through autumn, however, when biological activity was high, they found that “the organic fraction dominates and contributes 63 percent to the submicrometer aerosol mass (about 45 percent is water-insoluble and about 18 percent 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 percent to more than 100 percent 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.”

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, “at the current rate of progress, cloud parameterization deficiencies will continue to plague us for many more decades into the future.”

“Clouds are complicated,” Randall et al. declare, as they begin to describe what they call the “appalling complexity” of the cloud parameterization situation. 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 we also don’t know 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, during the 1970s and ‘80s, 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, they had to report that the concept of detrainment was “somewhat murky” and the conditions that trigger detrainment were “imperfectly understood.” “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.”

To show 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 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.

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 Wm-2 in the stratocumulus regimes and a similar underprediction of 60 Wm-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 Wm-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 17 scientists who wrote Siebesma et al., hailing from nine different countries, also found “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.”

In an 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 10 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 20 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 simulated only 30 to 40 percent 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 to 80 percent 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 percent of optically intermediate clouds and 60 percent 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.”

L’Ecuyer and Stephens (2007) used multi-sensor observations of visible, infrared, and microwave radiance obtained from the Tropical Rainfall Measuring Mission satellite for the period from January 1998 through December 1999, in order to evaluate the sensitivity of atmospheric heating—and the factors that modify it—to changes in east-west sea surface temperature gradients associated with the strong 1998 El Niño event in the tropical Pacific, as expressed by the simulations of nine general circulation models of the atmosphere that were utilized in the IPCC’s most recent Fourth Assessment Report. This protocol, in their words, “provides a natural example of a short-term climate change scenario in which clouds, precipitation, and regional energy budgets in the east and west Pacific are observed to respond to the eastward migration of warm sea surface temperatures.”

Results indicated that “a majority of the models examined do not reproduce the apparent westward transport of energy in the equatorial Pacific during the 1998 El Niño event.” They also found that “the intermodel variability in the responses of precipitation, total heating, and vertical motion is often larger than the intrinsic ENSO signal itself, implying an inherent lack of predictive capability in the ensemble with regard to the response of the mean zonal atmospheric circulation in the tropical Pacific to ENSO.” In addition, they reported that “many models also misrepresent the radiative impacts of clouds in both regions [the east and west Pacific], implying errors in total cloudiness, cloud thickness, and the relative frequency of occurrence of high and low clouds.” As a result of these much-less-than-adequate findings, the two researchers from Colorado State University’s Department of Atmospheric Science conclude that “deficiencies remain in the representation of relationships between radiation, clouds, and precipitation in current climate models,” and they say that these deficiencies “cannot be ignored when interpreting their predictions of future climate.”

In another recent paper, this one published in the Journal of the Atmospheric Sciences, Zhou et al. (2007) state that “clouds and precipitation play key roles in linking the earth’s energy cycle and water cycles,” noting that “the sensitivity of deep convective cloud systems and their associated precipitation efficiency in response to climate change are key factors in predicting the future climate.” They also report that cloud resolving models or CRMs “have become one of the primary tools to develop the physical parameterizations of moist and other subgrid-scale processes in global circulation and climate models,” and that CRMs could someday be used in place of traditional cloud parameterizations in such models.

In this regard, the authors note that “CRMs still need parameterizations on scales smaller than their grid resolutions and have many known and unknown deficiencies.” To help stimulate progress in these areas, the nine scientists compared the cloud and precipitation properties observed from the Clouds and the Earth’s Radiant Energy System (CERES) and Tropical Rainfall Measuring Mission (TRMM) instruments against simulations obtained from the three-dimensional Goddard Cumulus Ensemble (GCE) model during the South China Sea Monsoon Experiment (SCSMEX) field campaign of 18 May-18 June 1998.

The authors report that: (1) “the GCE rainfall spectrum includes a greater proportion of heavy rains than PR (Precipitation Radar) or TMI (TRMM Microwave Imager) observations”; (2) “the GCE model produces excessive condensed water loading in the column, especially the amount of graupel as indicated by both TMI and PR observations”; (3) “the model also cannot simulate the bright band and the sharp decrease of radar reflectivity above the freezing level in stratiform rain as seen from PR”; (4) “the model has much higher domain-averaged OLR (outgoing longwave radiation) due to smaller total cloud fraction”; (5) “the model has a more skewed distribution of OLR and effective cloud top than CERES observations, indicating that the model’s cloud field is insufficient in area extent”; (6) “the GCE is … not very efficient in stratiform rain conditions because of the large amounts of slowly falling snow and graupel that are simulated”; and finally, and in summation, (7) “large differences between model and observations exist in the rain spectrum and the vertical hydrometeor profiles that contribute to the associated cloud field.”

Even more recently, a study by Spencer and Braswell (2008) observed that “our understanding of how sensitive the climate system is to radiative perturbations has been limited by large uncertainties regarding how clouds and other elements of the climate system feed back to surface temperature change (e.g., Webster and Stephens, 1984; Cess et al., 1990; Senior and Mitchell, 1993; Stephens, 2005; Soden and Held, 2006; Spencer et al., 2007).” The two scientists from the Earth System Science Center at the University of Alabama in Huntsville, Alabama then point out that computer models typically assume that if the causes of internal sources of variability (X terms) are uncorrelated to surface temperature changes, then they will not affect the accuracy of regressions used to estimate the relationship between radiative flux changes and surface temperature (T). But “while it is true that the processes that cause the X terms are, by [Forster and Gregory (2006)] definition, uncorrelated to T, the response of T to those forcings cannot be uncorrelated to T – for the simple reason that it is a radiative forcing that causes changes in T [italics in the original].” They ask “to what degree could nonfeedback sources of radiative flux variability contaminate feedback estimates?”

Spencer and Braswell use a “very simple time-dependent model of temperature deviations away from an equilibrium state” to estimate the effects of “daily random fluctuations in an unknown nonfeedback radiative source term N, such as those one might expect from stochastic variations in low cloud cover.” Repeated runs of the model found the diagnosed feedback departed from the true, expected feedback value of the radiative forcing, with the difference increasing as the amount of nonfeedback radiative flux noise was increased. “It is significant,” the authors write, “that all model errors for runs consistent with satellite-observed variability are in the direction of positive feedback, raising the possibility that current observational estimates of cloud feedback are biased in the positive direction.” In other words, as the authors say in their abstract, “current observational diagnoses of cloud feedback – and possibly other feedbacks – could be significantly biased in the positive direction.”

In light of these findings, it is clear that CRMs still have a long way to go before they are ready to properly assess the roles of various types of clouds and forms of precipitation in the future evolution of earth’s climate in response to variations in anthropogenic and background forcings. This evaluation is not meant to denigrate the CRMs, it is merely done to indicate that the climate modeling enterprise is not yet at the stage where faith should be placed in what it currently suggests about earth’s climatic response to the ongoing rise in the air’s CO2 content.

The 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 nations of the world are confronting 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 the IPCC should admit it is truly needed in order to obtain a definitive answer to the question of CO2-induced “anthropogenic climate change.” In other words, the scientific debate over the causes and processes of global warming is still ongoing and there is no scientific case for governments to regulate greenhouse gas emissions in an expensive and likely futile attempt to alter the course of future climate.

We believe, with Randall et al., that our knowledge of many aspects of earth’s climate system is sadly deficient. Climate models currently do not provide a reliable scientific 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 no justification for that conclusion in reliable theoretical models.

Scafetta (2011) expanded on his own work demonstrating a link between well-known climate cycles of roughly 10, 20, and 60 years to celestial cycles resulting from the solar-lunar tidal oscillation and gravitational cycles related to the interaction between the sun and the largest planets, Jupiter and Saturn. In this recent paper, he reports that the IPCC Global Climate Models cannot capture this decadal and interdecadal variability: "Although these GCM simulations present some kind of red-noise variability supposed to simulate the multi-annual, decadal, and multidecadal natural variability, a simple visual comparison among the simulations and the temperature record gives a clear impression that the simulated variability has nothing to do with the observed temperature dynamics."

Scafetta demonstrates that natural variability is not solely the result of internal variations, but that external forcing also plays a role. External forcing modulates solar output, which in turn affects electrical activity in the upper atmosphere. This influences incoming cosmic ray fluxes, which have been linked to variability in cloudiness. These external cycles have similar periods to internal climate variations, showing that natural variations, therefore, likely account for more than half the climate variability since 1850. Additionally, Scafetta’s empirical model projected the cooling of the most recent decade, whereas the IPCC GCMs produced a quasi-monotonic warming of the climate from about the year 2000 on. Thus, he concludes that the IPCC erroneously ascribes all of the climate change to anthropogenic forcing.


Additional information on this topic, including reviews of newer publications as they become available, can be found at http://www.co2science.org/ subject/m/inadeqclouds.php.


References


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