Greenbelt, MD 20771
A 0.5° C global warming should result from every 1% decrease in global albedo. It is therefore necessary to accurately quantify the cloud radiation interaction. Most radiation calculations are one- dimensional and attempt to deal with horizontal variability using a horizontally-averaged optical depth. This study presents detailed scale-by-scale statistical analysis of the cloud liquid water content (LWC) field. The aim is to use this information to provide radiation calculations with more adequate information about inhomogeneity in cloud fields. The radiation community needs to carefully specify the minimum requirements which GCMs must include in order to treat cloud-radiation interaction correctly. This may involve GCMs predicting not only mean cloud quantities but also cloud variability.
The radiative properties of clouds are typically treated using the plane parallel approximation but this is known to be inadequate for dealing with inhomogeneity in clouds. The next simplest description of cloud variability is provided by fractal analysis. Clouds have been shown to exhibit fractal structure over a large range of scales (30 meters to 1000 kilometers). Fractal clouds reflect about 5-10% less than their homogeneous equivalents (Figure 27.1).
There are a number of ways to measure cloud LWC. Aircraft have traditionally used hot wire probes (Johnson-Williams or King probes). The FSSP, which optically samples individual drops, is extensively used to obtain cloud droplet spectra. Large sample times are required to obtain adequate statistics, especially for higher order moments of droplet size such as LWC. A new optical probe, the Gerber probe, has a significantly larger sampling volume and promises to provide high quality LWC and effective radius measurements at much improved rates. There is also a possibility of using tomography to map the LWC field. From the surface, microwave radiometers provide a good estimate of cloud liquid water path.
Almost all records of LWC show intermittent dry patches (as distinct from equipment glitches). While different records appear quite distinct in terms of the amount of variability, the power spectra are generally very similar, with slopes between 1.3 and 1.7 over a wide range of scales. This naturally suggests a fractal approach to analysis. Interestingly, the ASTEX data shows an unexplained scale break around 60 meters (Figure 27.2).
Fractal analysis of the LWC record proceeds as follows: the Hurst exponent, H1, a measure of non-stationarity (or roughness), is obtained by structure function analysis. A second exponent, C1, which determines the intermittency of the data, is obtained through singular measure analysis. Different data records (FIRE, ASTEX, ARM, and differing synoptic regimes therein) can be represented by points on the (C1, H1) plane, the so-called "mean" multi-fractal plane (Figure 27.3). The scatter of points is large enough to suggest that a more complete multi-fractal description is required.
There are several methods which can be used to generate a fractal LWC field with both intermittency and non-stationarity. Among these are the Bounded Cascade model, and the Fractionally- Integrated Cascade. These LWC fields can be used to determine the dependence of cloud radiative properties on the fractal characteristics of the clouds, and how the independent pixel approximation (plane parallel at each pixel) can be improved, or corrected, to account for realistic cloud structure.
