| Aerosols perturb the Earth's energy balance by absorbing and scattering solar radiation. Radiative effects of aerosols, especially absorbing aerosols, are still highly uncertain, part of which comes from model capabilities of simulating global aerosol distributions. Results from the IPCC model intercomparison workshop indicate that the variation of modeled annual mean global burdens is about a factor of 2.5 for sulfate aerosols, black carbon, organic carbon, dust and sea salt. Compared with observations, the model predicted sulfate seasonal cycle is not well characterized, although modeled surface aerosol concentrations are more consistent with observations on an annual mean basis. The comparison with observations is more scattered for black carbon and organic carbon than for sulfate aerosols. There is a large variation in model predicted aerosol vertical distributions. The modeled aerosol concentrations in the mid-latitude upper troposphere vary by a factor of 10 for all aerosol types.; Both the direct and the indirect radiative effects of carbonaceous aerosols depend on the aerosol vertical distribution. Simulations were carried out using the coupled CCM1/GRANTOUR model. The sensitivity study shows that as the height of the aerosol layer increases, so will the TOA forcing and the atmospheric absorption, while the surface forcing remains almost unchanged. Both fossil fuel and biomass burning carbonaceous aerosols warm the mid-troposphere relative to the surface. The change of the temperature lapse rate and clouds depends on the aerosol vertical distribution. Fossil fuel soot causes a decrease of low-level clouds, while biomass smoke causes an increase of low-level clouds and a decrease of clouds in the midtroposphere. However, if biomass smoke is injected near the surface, low-level clouds are reduced.; The last part of this work presents results of the inverse modeling of biomass smoke emissions. The IMPACT model with DAO meteorology data in 1997 are utilized to obtain aerosol spatial and temporal distributions. Then a radiative transfer model is applied to generate the modeled AI. A Bayesian inverse technique is applied to optimize the difference between the modeled AI and the EP TOMS AI in the same period by regulating monthly a priori biomass smoke emissions. The modeled AI with a posteriori emissions generally is better agreement with the EP TOMS AI. (Abstract shortened by UMI.)... |
