To address these uncertainties, researchers from the MIT Joint Program on the Science and Policy of Global Change set out to see if they could better model aerosol emissions and distribution from urban regions. To accurately model urban areas, factors such as the amount and distribution of emissions, the meteorological and geographical properties of the region, and the chemical and physical processing of emissions over time would need to be considered on spatial and temporal scales much smaller than global models. Previously, modelers have attempted to account for urban aerosol emissions by using a correction factor, which diluted total aerosol emissions across global model grid cells. This dilution method, however, does not capture the heterogeneity of urban and non-urban areas within each grid cell.

A polynomial chaos expansion and the probabilistic collocation method have been used to develop the metamodel, and its coefficients, so that it is applicable under a broad range of present-day and future conditions. The inputs upon which this metamodel have been formed are based on a combination of physical properties (average temperature, diurnal temperature range, date, and latitude), anthropogenic properties (patterns and amounts of emissions), and the nature of the surrounding environment (background concentrations of species).

The metamodel development involved using probability distribution functions (PDFs) of the inputs to run a detailed parent chemical and physical model, the Comprehensive Air Quality Model with Extensions (CAMx), thousands of times. Outputs from these runs were used in turn to both determine the coefficients of and test the precision of the metamodel, as compared with the detailed parent model.

It was determined that the deviations between the metamodel and the parent mode for many important species (O₃, CO, NO_x, and black carbon (BC)) were found to have a weighted RMS [root mean square] error less than 10% in all cases, with many of the specific cases having a weighted RMS error less than 1%. Some of the other important species (VOCs, PAN, OC, and sulfate aerosol) usually have their weighted RMS error less than 10% as well, except for a small number of cases. In these cases, the complexity and non-linearity of the physical, chemical, and meteorological processing is too large for the third order metamodel to give an accurate fit.

...sensitivity tests have been performed, to observe the response of the 16 metamodels (4 different meteorologies and 4 different urban types) to a broad set of potential inputs. These results were compared with observations of ozone, CO, formaldehyde, BC, and PM₁₀ from a few well observed urban areas, and in most of the cases, the output distributions were found to be within ranges of the observations.

—Cohen and Prinn

Cohen and Prinn used meteorological and emissions data from 16 representative urban areas. The urban processing model examined seven different types of aerosols of different sizes and composition, and modeled a total of 251 urban areas, including 91 from China, 36 from India, 50 from developed nations (Australia, Canada, EU, Japan, Singapore, South Korea, US) and 74 from developing nations. The urban processing model was then included into a larger global model that simulates atmospheric chemistry and transport at regional to global scales.

Not only are we the first group to successfully incorporate an urban-scale chemical processing model into a 3-dimensional global model, but our results resolve important processes which the rest of the modeling community still neglects to include.

—Dr. Jason Cohen, lead author

The study found that the urban processing model predicted a lower concentration of atmospheric aerosols than the dilution method, particularly in the Northern Hemisphere and in the summer season. In addition, the urban processing model showed increased concentrations of primary aerosols, such as black carbon and organic carbon, and decreased concentrations of secondary aerosols, like sulfates. Thus excluding the urban processing model could lead to an overestimation of some aerosols and an underestimation of others.

The reason these biases exist in the dilution method is that urban areas tend to be more efficient at oxidizing and removing substances like black carbon and organic carbon from the atmosphere—not taking this into consideration leads to an overestimation of the concentration of these species. Because these aerosol species are oxidized, generation of the secondary aerosol species actually increase in urban areas—not taking this into consideration leads to an underestimation of the concentration of those species.

Aerosols tend to cause negative radiative forcing. In other words, they have an overall “cooling effect” on the global climate. But using the urban processing method instead of the dilution method demonstrated an overall smaller concentration of aerosols in the atmosphere. Thus the detailed urban processing model predicts significantly less negative aerosol radiative forcing (less cooling) than the dilution method.

We are continuing this effort, looking at the long-term climate effects of using detailed urban processing, such as how average surface temperature, precipitation, and cloud cover will be impacted. We hope that as we continue to look into the impacts of this new methodology and continue to learn more about the mistakes that the dilution simplification have led to, that others in the climate modeling community will adopt and use our new standard.

—Jason Cohen

Resources

• Cohen, J.B., and R.G. Prinn (2011) Development of a fast, urban chemistry metamodel for inclusion in global models. Atmospheric Chemistry and Physics, 11(15): 7629-7656 doi: 10.5194/acp-11-7629-2011