The present paper reviews existing knowledge with regard to Organic Aerosol (OA) of importance for global climate modelling and defines critical gaps needed to reduce the involved uncertainties. All pieces required for the representation of OA in a global climate model are sketched out with special attention to Secondary Organic Aerosol (SOA): The emission estimates of primary carbonaceous particles and SOA precursor gases are summarized. The up-to-date understanding of the chemical formation and transformation of condensable organic material is outlined. Knowledge on the hygroscopicity of OA and measurements of optical properties of the organic aerosol constituents are summarized. The mechanisms of interactions of OA with clouds and dry and wet removal processes parameterisations in global models are outlined. This information is synthesized to provide a continuous analysis of the flow from the emitted material to the atmosphere up to the point of the climate impact of the produced organic aerosol. The sources of uncertainties at each step of this process are highlighted as areas that require further studies.

/pdf/organic-aerosol-and-global-climate-modelling-a-review-4ojfzx6x50.pdf

Organic aerosol and global climate modelling: a review

This paper describes the development and evaluation of a new Model for Simulating Aerosol Interactions and Chemistry (MOSAIC), with a special focus on addressing the long-standing issues associated with solving the dynamic partitioning of semi-volatile inorganic gases (HNO3, HCl, and NH3) to size-distributed atmospheric aerosol particles. The coupled ordinary differential equations (ODE) for dynamic gas-particle mass transfer are extremely stiff, and the available numerical techniques are either too expensive or produce oscillatory and/or inaccurate steady-state solutions. These limitations are overcome in MOSAIC, which couples an accurate and computationally efficient thermodynamic module [Zaveri et al., 2005a,b] with a new dynamic gas-particle partitioning module described here. The algorithm involves time-split integrations of non-volatile and semi-volatile species, and a new concept of “dynamic pH” and an adaptive time-stepping scheme hold the key to smooth, accurate, and efficient solutions over the entire relative humidity range. MOSAIC is found to be in excellent agreement with a benchmark version of the model that uses LSODES (a Gear solver) for rigorously integrating the stiff ODEs. The steady-state MOSAIC results for monodisperse aerosol test cases are also in excellent agreement with those obtained with the benchmark equilibrium model AIM. MOSAIC is also evaluated within a 3-D model, andmore » the average CPU speed is estimated to be over 100 times faster than the dynamic aerosol model MADM [Pilinis et al., 2000]. These results suggest that MOSAIC is highly attractive for use in 3-D aerosol and air quality models in which both accuracy and efficiency are critically important.« less

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2007JD008782

Model for Simulating Aerosol Interactions and Chemistry (MOSAIC)

[1] A new fully coupled meteorology-chemistry-aerosol model is used to simulate the urban- to regional-scale variations in trace gases, particulates, and aerosol direct radiative forcing in the vicinity of Houston over a 5 day summer period. Model performance is evaluated using a wide range of meteorological, chemistry, and particulate measurements obtained during the 2000 Texas Air Quality Study. The predicted trace gas and particulate distributions were qualitatively similar to the surface and aircraft measurements with considerable spatial variations resulting from urban, power plant, and industrial sources of primary pollutants. Sulfate, organic carbon, and other inorganics were the largest constituents of the predicted particulates. The predicted shortwave radiation was 30 to 40 W m−2 closer to the observations when the aerosol optical properties were incorporated into the shortwave radiation scheme; however, the predicted hourly aerosol radiative forcing was still underestimated by 10 to 50 W m−2. The predicted aerosol radiative forcing was larger over Houston and the industrial ship channel than over the rural areas, consistent with surface measurements. The differences between the observed and simulated aerosol radiative forcing resulted from transport errors, relative humidity errors in the upper convective boundary layer that affect aerosol water content, secondary organic aerosols that were not yet included in the model, and uncertainties in the primary particulate emission rates. The current model was run in a predictive mode and demonstrates the challenges of accurately simulating all of the meteorological, chemical, and aerosol parameters over urban to regional scales that can affect aerosol radiative forcing.

Evolution of ozone, particulates, and aerosol direct radiative forcing in the vicinity of Houston using a fully coupled meteorology-chemistry-aerosol model

[1] An initial evaluation of the Models-3 Community Multiscale Air Quality (CMAQ) model aerosol component reveals CMAQ's varying ability to simulate observed visibility indices and aerosol species concentrations. The visibility evaluation, using National Weather Service observations from 139 airports for 11–15 July 1995, shows that CMAQ reasonably captured the general spatial and temporal patterns of visibility degradation, including major gradients, maxima and minima. However, CMAQ's two visibility prediction methods, Mie theory approximation and mass reconstruction, both underpredict visibility degradation (i.e., overpredict visibility). The mean bias, normalized mean bias (NMB), mean error and normalized mean error (NME) for the Mie calculations are −5.9 dv, −21.7%, 7.0 dv and 25.4%, respectively. For the reconstruction simulations, these statistics are −9.8 dv, −35.5%, 10.0 dv and 36.2%, respectively. Most simulated values (∼90% Mie and ∼85% reconstruction) fall within a factor of two of the observations, although r2 = 0.25 (Mie) and r2 = 0.24 (reconstruction). The speciated aerosol evaluation uses observations of sulfate, nitrate, PM2.5, PM10 and organic carbon obtained from 18 stations of the Interagency Monitoring of Protected Visual Environments (IMPROVE) network in June 1995. This evaluation reveals that, with the exception of sulfate (mean bias: 0.15 μg/m3, NMB: 3.1%), the model consistently underpredicts aerosol concentrations of nitrate (−0.10 μg/m3, −33.1%), PM2.5 (−3.9 μg/m3, −30.1%), PM10 (−5.66 μg/m3, −29.2%) and organic carbon (−0.78 μg/m3, −33.7%). Sulfate was simulated best by the model (r2 = 0.63, mean error = 1.75 μg/m3, NME = 36.2%), followed by PM2.5 (0.55, 5.00 μg/m3, 38.5%), organic carbon (0.25, 0.94 μg/m3, 40.6%), PM10 (0.13, 9.85 μg/m3, 50.8%) and nitrate (0.01, 0.33 μg/m3, 104.3%). Except for nitrate, 75–80% of simulated concentrations fall within a factor of two of the IMPROVE observations.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2001JD001409

Fredrick W Lurmann

Papers

Modelling urban and regional aerosols—I. model development