Recent Advances in Simulating Cloud Albedo with GCMs. GABRIELE CANZI, MARIA ANGELELLA, RYAN DAVIS and ALBERT YAU ABSTRACT This review explores the recent advances and issues with simulating cloud albedo in global climate models (GCM). GCMs represent the most comprehensive description of all interactions between atmospheric processes, or feedback mechanisms. Because of the currently abbreviated understanding of feedback mechanisms, the precision of these climate models is limited. Recent advances in GCMs and cloud parameterization show that more is understood today about climate processes than ever before. This continued acquisition of knowledge will eventually lead to an accurate simulation of most microprocesses in the Earth's climate. Fig.1 Earth's view from satellite. Shows cloud cover and complex nature of clouds (NASA) 1. Introduction Albedo is described as the ratio of reflected to incident radiation. It can be defined by Equation 1 (1) and can be further expressed as a function of seven variables, (2)(Taylor et al. 2006). Where α is surface albedo, γ is fluxes and µ represents coefficients for absorption. The subscripts signify clear (clr), overcast (oc), and clouds (cld). Cloud albedo can then be defined by Equation 3. (3) Where the subscripts signify clear (clr), overcast (oc), and clouds (cld). Of the 0.31 total fraction of incoming radiation reflected by the Earth (Earth's albedo), 0.2 of that is from cloud albedo. Higher values of cloud albedo mean that the cloud reflects more solar radiation. FIG. 2. Percentage of reflected sun light in relation to the various surface conditions of the earth. This shows the wide variation of albedo from different sources. Groebe (2006) Cloud albedo varies depending on drop sizes, liquid water or ice content, thickness of the cloud, and the sun's zenith angle. Generally stratocumulus clouds, which are low and thick clouds, reflect most of the incoming solar radiation, while high thin clouds, also known as cirrus clouds, reflect less. Figure 2 outlines the variations of albedo from different sources. Judith Curry and Peter Webster’s textbook, Thermodynamics of Atmospheres and Oceans, explains that in order to understand and simulate climate accurately, “it is necessary to interpret the role of various physical processes in determining the magnitude of the climate response to a specific forcing.” (Curry 1998) These processes are changing the sensitivity of climate response and are categorized as feedback mechanisms. An example of a negative feedback mechanism would represent an increase in surface air temperature, which increases evaporation and the extent of cloud cover. Increased cloudcover reduces the solar radiation reaching the Earth's surface, thereby lowering the surface temperature. On the other hand a positive feedback mechanism example could be represented by an increase in surface air temperature, which would increase the water vapor present in the atmosphere. The concentration of water vapor in the atmosphere increases exponentially with temperature. Therefore, increases in temperature will yield increases in atmospheric water vapor. The increased water vapor will act as a greenhouse gas, leading to further warming. Understanding cloud albedo can lead to a more comprehensive understanding of these climate feedback mechanisms. Unlike water vapor, clouds impart an almost equal effect on the deposition of solar radiation primarily through the albedo effect. The thermal effects at the top of the atmosphere, are largely offset by the albedo effect. During the 1960s and 70s, the interrelation between albedo and emission from clouds was not well understood (Stephens et al. 2005). A lot of predictions from GCMs at the time did not match scientific observations. It was Twomey in 1977 that described the effects of aerosols concentrations and how they can lead to increases in cloud albedo using the assumption of a fixed water content. With higher aerosols concentrations, a larger number of smaller droplets will be formed, which reflect more sunlight than large drops and do not rain out easily. To better understand cloud albedo and resulting feedback, recent studies were selected as examples. These papers studied different GCMs, the Community Atmospheric Model, Version 3 (CAM3), Geophysical Fluid Dynamics Laboratory Atmospheric Model 2 (AM2), ECHAM5-HAM. They explained how cloud feedback, aerosols, and radiative forcing affect cloud albedo. All of these studies looked at how these factors are considered in trying to create a more precise GCM for the future. Figure 3 shows the evolution of GCMs from the 1970's into the present. As can be inferred from this diagram, the main difference between the models is the addition of more parameters with each subsequent model. The First Annual Report (FAR), Second annual report (SAR), Third annual report (TAR), and fourth annual report (AR4) were used as examples in the figure. As GCMs become more complex and accurate, to help improve climate predictions, it will impact government planning and policy for the future.2. GCM examples and the albedo effect a. Parameterizations In a recent paper by M. Zhang and C. Bretherton (2008), in which the CAM3 was accessed, several insights into the behavior of cloud feedbacks were ascertained. Because the understanding of cloud feedback is dependent on the comprehension of the physics, and parameterizations, which are methods of replacing processes that are too small-scale or complex to be physically represented in the model by a simplified process, within the clouds, the findings of this study are not only pertinent to our current textbook knowledge but they also increase our depth of knowledge of cloud feedbacks. The experiment consisted of a control experimental setup as shown in Figure 4 and a warm simulation of a two degree increase in both warm and cold sea surface temperatures (SST). The result of this experiment reinforces the theory that in order
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