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Atmospheric Particles and Climate: can we Evaluate the Impact of man's Activities?
Published online by Cambridge University Press: 20 January 2017
Abstract
The equilibrium temperature of the Earth is maintained by a balance between the unreflected part of the incoming solar energy, which is absorbed by the Earth-atmosphere system, and the outgoing long-wave radiation escaping from the Earth to space. It has long been suspected that suspended atmospheric particles (aerosols) might affect this balance, primarily by affecting the albedo or reflectivity of the Earth, thereby altering the amount of solar energy absorbed by the Earth. In light of some recent evidence suggesting the existence of an increase in atmospheric particle concentrations (presumably related to man's activities), the need for development of adequate numerical models to study this problem is apparent. Recent numerical models studying the effect of particles on climate are often based on multiple scattering radiative transfer calculations, and use global averages for particle concentrations and optical properties. By contrasting certain existing models, some major problems in modeling studies that attempt to answer the question of the effects of increased atmospheric particles on climate can be illustrated. It will also be apparent that another uncertainty in the results of such studies arises from a lack of adequate observed input data on the geographic and vertical distributions of particle concentrations and their optical properties. Furthermore, a model that could realistically simulate the impact of increasing atmospheric particle concentration on climate must eventually include the simultaneous coupled effects of all the important atmospheric processes, such as fluid motions and cloud microphysics, in addition to the radiative transfer effects. Current modeling studies already do predict that increases in particle concentrations could have a significant effect on climate. Now, it remains for us to develop the kinds of refined models needed to verify or deny these predictions.
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