A31C-01 INVITED
Radiative Transfer in a Climate GCM: Or how to Optimize, Maximize, and Blend Computational Speed, Accuracy, Physical Realism, and Diagnostic Capability of Radiative Transfer Calculations
Climate modeling is a computational effort in deciphering a boundary value problem in physics. Absorbed solar radiation is the energy input to the Earth-atmosphere system, while thermal radiation emitted to space is the sink. Global energy balance, modulated by clouds, aerosols, absorbing gases, atmospheric and ocean heat transports, and their interacting heat capacities, produces the prevailing climate at the Earth's surface. Radiative heating and cooling determines the atmospheric temperature distributions which in turn provide the ultimate driving force behind atmospheric motions. The physics of radiative transfer is well understood, so that in principle, accurate heating and cooling rates could be calculated for any specified state of the atmosphere. The task of the GCM radiative transfer model is to perform these calculations fast enough to be practical in climate simulations, while retaining as much of the attainable accuracy as possible. For tractability, the physical realism of the model atmosphere is described in terms of idealized plane-parallel geometry. The GCM radiation model also provides diagnostic information by means of which the GCM performance can be evaluated against available observations. These objectives can be achieved using the correlated k-distribution for modeling thermal radiation, and the single gauss point doubling/adding for modeling solar radiation. Results show that CO2 is the principle non-condensable core-component of the terrestrial greenhouse, accounting for about 20 per cent of the total (33 K) terrestrial greenhouse effect, while the principal feedback components, water vapor and clouds, account for about 50 and 25 per cent, respectively. In the context of current climate, doubled CO2 by itself increases the terrestrial greenhouse by 1.2 K, while feedback contributions, due primarily to water vapor, add an additional 1.5 K.
A31C-02 INVITED
Virtues of Polarization in Remote Sensing of Atmospheres and Oceans
Polarization of skylight has been used for navigation by many insects and ocean organisms for millions of years. In fact, some marine organisms rely on polarization vision for their very existence. However, the use of polarization in remote sensing is just now becoming a powerful tool in many areas of science such as in the detection of cancerous skin lesions, bioaerosols (such as anthrax), hydrosols in the ocean, and plant diseases, just to mention a few. We will first introduce the Stokes parameter-Mueller matrix formalism and discuss ways to measure the elements of both. Several methods will be discussed to show how a combination of polarimetric quantities can be mapped to improve contrast by the human visual system when ordinary radiance measurements fail to do so. These ideas will be extended into the multiple scattering domain where we will introduce an "effective Mueller matrix" and see the benefits arising from it. One of the primary reasons for inclusion of polarization in the equation of transfer is that it is the only correct way to do radiative transfer. We will then conclude with areas of future research that will surely lead to even more striking applications.
A31C-03 INVITED
Recent Practical Applications of Radiative Transfer in Satellite Remote Sensing
Remote sensing of the atmosphere is highly dependent on the use of radiative transfer (RT) calculations by employing either basic detailed models or highly parameterized versions of results from those models. Although many of the physical parameters retrieved from passive satellite radiances, such as atmospheric soundings, have long had direct application in weather and climate studies, the continued development of new uses for RT in deriving new and enhanced parameters often goes unnoticed. This paper explores recent developments in satellite remote sensing that have direct application to problems related to air safety. Aircraft icing is one of the leading causes for air traffic accidents and its occurrence and impact can be minimized by identifying when and where icing conditions are likely to occur. Since icing requires the presence of supercooled water clouds and, often, large water droplets, icing conditions can be deduced from retrievals of cloud phase, optical depth, and effective droplet size. RT parameterizations have been used for many years to retrieve those parameters, but it has only been possible in recent years to determine them in near-real time so that they can be practically used to help warn air traffic about the probability of icing at a given location. Because air traffic is continuous over the course of the day, geostationary satellite data are the suitable means for monitoring aircraft icing conditions. Icing clouds also occur underneath upper-level cirrus clouds and can go undetected using traditional retrieval methods. By using a set of new two-layer cloud model parameterizations together with recently developed multilayered cloud detection techniques, it is possible to evaluate the low-level clouds in multilayered cloud conditions. These techniques and examples of their application using data from the Twelfth Geostationary Operational Environmental Satellite (GOES-12) in the NASA Langley Real-Time Cloud Analysis System will be presented along with validation of the retrieved results. Outstanding problems and future applications will be discussed. http://www- angler.larc.nasa.gov/satimage/products.html
A31C-04
A study of radiative properties of fractal soot aggregates using the superposition T-matrix method
We employ the numerically exact superposition T-matrix method to perform extensive computations of scattering and absorption properties of soot aggregates with different compactness and size. The fractal dimension is used to quantify the mass dispersion of the particles. The optical properties of soot aggregates versus fractal dimension are a complex function of the refractive index of the material, the number of monomers, and the monomer radius a. It is shown that for a smaller a, when the fractal dimension is smaller than 2, the absorption cross section tends to be a constant, but increases rapidly when the fractal dimension is greater than 2. With a sufficient number of monomers and sufficiently large complex refractive index and monomer size, a reduction of light absorption is observed as the fractal dimension increases. The scattering cross section, on the other hand, increases monotonously as fractals evolve from chain-like to more densely packed ones, which is a strong manifestation of the importance of multiple scattering.
A31C-05
Ice Cloud Optical Depth from MODIS Cirrus Reflectance
Here, the retrieval of ice cloud microphysical properties based on the 1.38-μm "cirrus detection" channel of the Moderate-resolution Imaging Spectroradiometer (MODIS) is detailed. The 1.38-μm channel is located in a strong water vapor absorption band, thus, under most circumstances, its measured reflectance is solely due to ice clouds. This band, in combination with a visible channel (here, 0.66-μm), can be used to derive the cirrus reflectance, a parameter included in the operational MODIS atmosphere product. A new method has been developed to derive ice cloud optical depth on a global scale from the cirrus reflectance using pre-calculated look-up tables. Forward radiative transfer calculations, used to construct the look-up library, are carried out using the discrete-ordinates radiative transfer method with the new bulk-scattering properties of ice clouds developed for the Collection 005 MODIS cloud product. This method is complimentary to the MODIS operational cloud retrieval algorithm for the case of ice clouds.
A31C-06
A 2-year climatology of Arctic clouds for Eureka, Canada prepared from High Spectral Resolution Lidar data.
Measurements show that Arctic is warming faster than the rest of the globe. Warming is also predicted by climate models. However, there is more disagreement between the predictions of individual models in the Arctic then at lower latitudes. Differences in cloud parametrization are the likely to be the main source of the model-to-model variations. Unfortunately, it is difficult to evaluate model predictions of Arctic cloudiness because of a lack of reliable cloud observations. The Canadian Network for the Detection of Atmospheric Change (CANDAC) and the NOAA Study of Environmental Arctic Change (SEARCH) have installed an instrumentation suite at Eureka(80 deg N, 86 deg W) in the Nunavut territory of Northern Canada. These instruments include the University of Wisconsin Arctic High Spectral Resolution Lidar(AHSRL) and the NOAA 8.6 mm wavelength cloud radar (MCR). Both instruments have operated nearly continuously since Sept 2005. This paper presents a record of cloud cover, cloud altitude and cloud phase derived from the lidar. It also presents comparisons between lidar, radar, and convention meteorological observations of cloudiness. It is shown that optically thin clouds are frequently observed at this site. As a result, the observed fractional cloud cover depends strongly on the optical depth threshold used to define the presence of cloud. The lidar data indicates that Eureka has fewer clouds than DOE Atmospheric Radiation Measurement (ARM) site in Barrow, Alaska and fewer clouds than were observed during the Surface Heat Budget of the Arctic Ocean(SHEBA) experiment. http://lidar.ssec.wisc.edu