Radiation Feedbacks and the Credibility of Atmospheric Models

This workshop was devoted to the topic of climate-radiation feedbacks and the credibility of atmospheric models. Presentations and discussions considered topics such as the role of clouds in the energy balance of the Earth. Much discussion was given to how to represent clouds in climate models and what can be learned from observations and field campaigns such as the DOE’s ARM program.

Keywords: models and modeling; climate variability and change, clouds

Overview & Relevance:
Cloud-radiation interactions have long ranked as one of the most critical areas in global change research. In particular, when climate models are intercompared, cloud-radiation parameterizations are responsible for most of the global-mean differences in sensitivity to greenhouse gas increases. The uncertainty in model responses is directly due to a lack of fundamental understanding of the physical processes involved. A major research effort is underway worldwide in response to this challenge. As one prominent example, the Atmospheric Radiation Measurement (ARM) Program, the flagship global change effort of the U. S. Department of Energy, has been undertaken in response to this pressing scientific need. Furthermore, closely related research areas, such as the role of atmospheric aerosols in climate, are also beginning to receive the attention they deserve. We felt that the time was ripe to devote a two-week AGCI summer session to this general area, with a format designed to encourage a thorough examination of the key scientific issues, something that is impossible at a typical research meeting which lasts a few days and is made up of many short talks.

Content:
The Aspen Global Change Institute (AGCI) devoted its first of three 1994 summer science sessions to the topic of climate-radiation feedbacks and the credibility of atmospheric models. The topic was picked because of its scientific importance and timeliness.
Each participants gave a talk on a topic of their choice. Typically, the topic was on some aspect of their own recent research related to the general subject area of cloud-radiation interactions and closely allied fields. We suggested to each invitee that it would be especially interesting to hear about new work, work in progress, and thoughts on important directions for future work. During the afternoons, several specialized discussion groups formed around topics which the participants themselves selected. In addition, we held impromptu tutorials on GCMs and on fractals and multi-fractals, in which experts in these areas provided background material for the research talks. We also held wrap-up sessions at the end of each of the two weeks, in which we tried to summarize our progress and identify key issues for further attention.

When good scientists are brought together for two weeks in a pleasant and unstructured environment with few distractions, then worthwhile scientific interactions occur spontaneously. Every working scientist knows that new ideas and new research collaborations often spring from such meetings, and they were among the most valuable products of this AGCI session. Our goal was simply that at the end of the two weeks, the participants should feel that the experience had been intellectually worthwhile and that the seeds of some promising research had been sown. This goal was certainly met at this AGCI session.
Presentations and discussions encompassed the following topics:
1. Cloud Dynamics and Microphysics
2. Atmosphere-Surface Interactions
3. Parameterizations
4. Radiative Transfer Developments and Investigations
5. Multifractal and Stochastic Cloud Analysis and Modeling
6. Observations

Cloud Dynamics and Microphysics
For many years, virtually all general circulation models (GCMs) treatments of clouds were based on simple algorithms relating cloud amount to relative humidity. Such parameterizations usually produced positive global-average cloud-radiation feedbacks in numerical experiments simulating greenhouse-induced climate change. For example, in a typical integration performed with a GCM developed a decade or two ago, a climate warming due to increased atmospheric carbon dioxide concentrations would lead to increased average cloud heights and/or decreased average cloud amounts. It is easy to understand qualitatively why such feedbacks were positive. First, higher clouds are colder and so less effective infrared emitters, and they generally have lower albedos than lower clouds, so the cloud-height feedback was positive (i. e., the change in clouds produced by the warming tended to amplify the warming). Second, average model clouds, like average real clouds, contribute more strongly to the planetary albedo than to the planetary greenhouse effect (the shortwave cloud forcing is larger than the longwave cloud forcing by about 20 Watts per square meter (Wm-2). Hence, a reduction in cloud amount reduces the shortwave effect more than the longwave effect of clouds. Thus, the cloud amount feedback is also positive.
Climate models are now more numerous and more complicated, however, and model responses to increased greenhouse gas concentrations are more varied. GCMs today attempt to take into account a broader range of physical processes involved in cloud- radiation feedbacks. The climate modeling community now realizes clearly that cloud feedback processes are not limited to macrophysical cloud properties, such as cloud amount and cloud altitude. In recent years, many GCMs have begun to include cloud parameterizations which include explicit treatments of cloud physics. Several talks were concerned with the connections between climate and the microphysical aspects of clouds.

Atmosphere-Surface Interactions
Recent years have seen renewed interest in the simple question of which physical processes are responsible for the observed large-scale upper bound of about 304K on sea surface temperature (SST). In a sense, this question itself is an indicator of our lack of understanding of fundamental properties of the climate system, especially of the role of clouds. Many other such questions, seemingly simple in form but impossible to answer conclusively, could be posed. For example, why is the global cloud cover about 60%, and why is the planetary albedo about 30%? Because we cannot account theoretically for these observed properties of the present climate, we are at a loss to explain convincingly how they might change in some future climate, such as one modified by increased greenhouse gas concentrations.

Parameterizations
One way to define the awkward term parameterization is simply as an algorithm uniquely relating the statistical effect of small-scale processes on large-scale fields, with the critical restriction that the algorithm must be an explicit function of the large-scale fields themselves. The common GCM expedient of making cloud amount dependent on relative humidity illustrates the nature of the parameterization problem. Relative humidity is calculable as an explicitly predicted model variable on the GCM grid scale. Cloud amount has substantial subgrid variability, however, and there is no obvious way to relate cloud amount to relative humidity based on first principles. In general, a sufficiently moist but subsaturated GCM grid volume will contain some clouds, and a saturated one will presumably be overcast, but there is no evident route to specifying a universal and deterministic relationship between cloud cover and relative humidity.

Not only are we uncertain how much of the observed variability of clouds can be related to relative humidity, we are also unable to say with any confidence whether other large-scale variables, such as vertical velocity, need to be invoked. Indeed, the fundamental question of parameterizability, the determination of the extent to which parameterization is possible in principle, is unanswered. Thus, a great variety of ad-hoc formulas have been devised, justifiable only empirically to the extent that they are justifiable at all.

Radiative Transfer Developments and Investigations
One of the most important recent developments in radiative transfer involves three-dimensional radiative transfer computations through complex distributions of liquid water (or ice). New formalisms are slowly emerging to facilitate radiative transfer computations in three dimensions, but a main thrust of research activities is in the use of Monte-Carlo models (a direct simulation of the physical processes involved in radiative transfer in which the path of a photon is described by probability functions) and of approximate radiative transfer methods.

Many of the proposed stochastic approaches presented have substantial appeal for the development of new GCM parameterizations of radiation transfer through inhomogeneous clouds. The most important aspects of the spatial variability of clouds for radiative transfer (e.g., photon path probability distributions, multifractal parameters) could be characterized from observations (e.g., cloud probes, millimeter-wave radars, microwave radiometers) and then expressed with a few key parameters that GCMs predict (e.g., cloud fraction). It seems likely that fast radiative transfer methods based on these parameters could be developed to include the effects of cloud inhomogeneity in cloud radiation GCM parameterizations.

Multifractal and Stochastic Cloud Analysis and Modeling
Clouds have long been described as plane parallel infinite layers of liquid water or ice despite the fact that such clouds can never be found in nature. However, almost all measurements of cloud liquid water content (LWC) from aircraft show intermittent dry patches embedded within clouds. While different LWC records appear quite distinct in terms of the amount of variability, their power spectra are generally quite similar over a wide range of scales. The analysis of satellite observations of clouds in the visible or infrared spectral bands show similar scaling characteristics. These observations taken together suggest that, although clouds have a complex structure, that structure can generally be described by probability distributions fully characterized by a small number of parameters (i. e., three) derived from multifractal theory.