Saharan dust storms have often been observed from space, but the full impact on the Earth's radiation balance has been difficult to assess, due to limited observations from the surface. We present the first simultaneous observations from space and from a comprehensive new mobile facility in Niamey, Niger, of a major dust storm in March 2006. The results indicate major perturbations to the radiation balance both at the top of the atmosphere and at the surface. Combining the satellite and surface data, we also estimate the impact on the radiation balance of the atmosphere itself. Using independent data from the mobile facility, we derive the optical properties of the dust and input these and other information into two radiation models to simulate the radiative fluxes. We show that the radiation models underestimate the observed absorption of solar radiation in the dusty atmosphere.

Using irradiance data from a Multi-Filter Rotating Shadowband Radiometer (MFRSR) and an actinic flux spectroradiometer (SR), we derive aerosol single scattering albedo, 0,λ , as a function of wavelength, λ. We find that in the near-UV spectral range (250 to 400 nm) 0,λ is much lower compared to 0,λ at 500 nm indicating enhanced absorption in the near-UV range. Absorption by elemental carbon, dust, or gas cannot account for this enhanced absorption leaving the organic carbon component of the aerosol (OA) as the most likely absorber. We use data from a surface deployed Aerodyne Aerosol Mass Spectrometer (AMS) along with the inferred 0,λ to estimate the Mass Absorption Cross section (MAC) for the organic aerosol. We find that the MAC is about 10.5 m 2 /g at 300 nm and falls close to zero at about 500 nm; values that are roughly consistent with other estimates of organic aerosol MAC. These MAC values can be considered as "radiatively correct", because when used in radiative transfer calculations, the calculated irradiances/actinic fluxes match those measured at the wavelengths considered here. For an illustrative case study described here, we estimate that the light absorption by the "brown" (organic) carbonaceous aerosol can add about 40% to the light absorption of black carbon in Mexico City. This contribution will vary depending on the relative abundance of organic aerosol relative to black carbon. Furthermore, our analysis indicates that organic aerosol would slow down photochemistry by selectively scavenging the light reaching the ground at those wavelengths that drive photochemical Correspondence to: J. C. Barnard (james.barnard@pnl.gov) reactions. Finally, satellite retrievals of trace gases that are used to infer emissions currently assume that the MAC of organic carbon is zero. For trace gases that are retrieved using wavelengths shorter then 420 nm (i.e. SO 2 , HCHO, halogenoxides, NO 2 ), the assumption of non-zero MAC values will induce an upward correction to the inferred emissions. This assumption will be particularly relevant in polluted urban atmospheres and areas of biomass burning where organic aerosols are particularly abundant.

An international Intercomparison of 3D Radiation Codes (I3RC) underscores the vast progress of recent years, but also highlights the challenges ahead for routine implementation in remote sensing and global climate modeling applications. Modeling atmospheric and oceanic processes is one of the most important methods of the earth sciences for understanding the interactions of the various components of the surface-atmosphere system and predicting future weather and climate states. Great leaps in the availability of computing power at continuously decreasing costs have led to widespread popularity of computer models for research and operational applications. As part of routine scientific work, output from models built for AFFILIATIONS: CAHALAN-NASA

Abstract. Much of the large uncertainty in estimates of anthropogenic aerosol effects on climate arises from the multiscale nature of the interactions between aerosols, clouds and dynamics, which are difficult to represent in conventional general circulation models (GCMs). In this study, we use a multi-scale aerosol-climate model that treats aerosols and clouds across multiple scales to study aerosol indirect effects. This multi-scale aerosol-climate model is an extension of a multi-scale modeling framework (MMF) model that embeds a cloud-resolving model (CRM) within each vertical column of a GCM grid. The extension allows a more physicallybased treatment of aerosol-cloud interactions in both stratiform and convective clouds on the global scale in a computationally feasible way. Simulated model fields, including liquid water path (LWP), ice water path, cloud fraction, shortwave and longwave cloud forcing, precipitation, water vapor, and cloud droplet number concentration are in reasonable agreement with observations. The new model performs quantitatively similar to the previous version of the MMF model in terms of simulated cloud fraction and precipitation. The simulated change in shortwave cloud forcing from anthropogenic aerosols is −0.77 W m −2 , which is less than half of that (−1.79 W m −2 ) calculated by the host GCM (NCAR CAM5) with traditional cloud parameterizations and is also at the low end of the estimates of other conventional global aerosol-climate models. The smaller forcing in the MMF model is attributed to a smaller (3.9 %) increase in LWP from preindustrial conditions (PI) to present day (PD) compared with 15.6 % increase in LWP in stratiform clouds in CAM5. The difference is caused by a much smaller response in LWP to a given perturbation in cloud condensation nuclei (CCN) concentrations from PI to PD in the MMF (about one-third of that in CAM5), and, to a lesser

Abstract. Anthropogenic aerosol effects on climate produce one of the largest uncertainties in estimates of radiative forcing of past and future climate change. Much of this uncertainty arises from the multi-scale nature of the interactions between aerosols, clouds and large-scale dynamics, which are difficult to represent in conventional general circulation models (GCMs). In this study, we develop a multi-scale aerosol-climate model that treats aerosols and clouds across different scales, and evaluate the model performance, with a focus on aerosol treatment. This new model is an extension of a multi-scale modeling framework (MMF) model that embeds a cloud-resolving model (CRM) within each grid column of a GCM. In this extension, the effects of clouds on aerosols are treated by using an explicit-cloud parameterized-pollutant (ECPP) approach that links aerosol and chemical processes on the large-scale grid with statistics of cloud properties and processes resolved by the CRM. A two-moment cloud microphysics scheme replaces the simple bulk microphysics scheme in the CRM, and a modal aerosol treatment is included in the GCM. With these extensions, this multi-scale aerosol-climate model allows the explicit simulation of aerosol and chemical processes in both stratiform and convective clouds on a global scale.Simulated aerosol budgets in this new model are in the ranges of other model studies. Simulated gas and aerosol concentrations are in reasonable agreement with observations (within a factor of 2 in most cases), although the model underestimates black carbon concentrations at the surface by a factor of 2-4. Simulated aerosol size distributions are in reasonable agreement with observations in the maCorrespondence to: M. Wang (minghuai.wang@pnl.gov) rine boundary layer and in the free troposphere, while the model underestimates the accumulation mode number concentrations near the surface, and overestimates the accumulation mode number concentrations in the middle and upper free troposphere by a factor of about 2. The overestimation of accumulation model number concentrations in the middle and upper free troposphere is consistent with large aerosol mass fraction above 5 km in the MMF model compared with other models. Simulated cloud condensation nuclei (CCN) concentrations are within the observational variations. Simulated aerosol optical depths (AOD) are in reasonable agreement with observations (within a factor of 2), and the spatial distribution of AOD is consistent with observations, while the model underestimates AOD over regions with strong fossil fuel and biomass burning emissions. Overall, this multiscale aerosol-climate model simulates aerosol fields as well as conventional aerosol models.