How do Global Soot Models Measure Up?
Dark pollution particles popularly known as soot but also called black carbon probably contribute overall to global warming. Suspended in the atmosphere or deposited on snow, these particles absorb sunlight and warm the air or snow. Sources of black carbon include burning of wood and other biofuels, for domestic heating and cooking and in forest and field burning, and combustion of fossil fuels, especially coal and diesel.
Accurate modeling of soot is a critical component in climate models. However, particles are more challenging to model than greenhouse gases, and soot particles are particularly tricky to simulate correctly. Like other tiny atmospheric particles called aerosols, black carbon (BC) has a short lifetime in the atmosphere of about a week because it is removed by rain or snow. However, BC aging in the atmosphere increases its uptake into clouds and therefore its rainout, and its ability to absorb heat. In our study, we tested 17 recent global BC models from an aerosol model intercomparison group known as AeroCom against a variety of measurements.
The simplest test compared modeled and measured BC air concentrations. Figure 1 shows BC measurements and the average of the 17 models at the Earth's surface. The largest BC pollution occurs in Southeast Asia and Europe. Overall the models do a reasonably good job, although they typically underestimate BC in China. Note that many regions lack long-term measurements.
The second test compared model BC concentrations in the atmosphere above the surface with measurements made by aircraft. Figure 2 shows modeled and measured BC profiles over southern and northern North America. In the south (left panel) the observations show a sharp decrease of BC as the aircraft rose upward. Most models do not simulate the small BC amounts above the surface very well. In the Arctic (right panel), the observations show large amounts of BC at the surface that increases above the surface. Most models do not have as much BC as observed. Furthermore there is little agreement among models in this region. Overall the models seem to have too much BC above the surface over pollution sources but they also do not transport enough BC to remote regions like the Arctic.
The final test compared modeled and measured aerosol absorption, which indicates the amount of climate warming BC may cause. One measure of aerosol absorption in the atmospheric column is aerosol absorption optical depth (AAOD). AAOD is derived by AERONET, a ground-based network of instruments called sun photometers that are used to calculate how much aerosols block the sun's energy as it passes through the atmosphere, and also from an Aura satellite instrument known as OMI. Since dust particles are also absorbing, AAOD is most meaningful as a constraint for soot in polluted and biomass burning regions and are less useful in desert regions. Figure 3 shows AAOD from AERONET, OMI and from the average of the models. The models generally have almost enough absorption in North America and Europe, but not enough in most other places, including Southeast Asia, biomass-burning regions (South America and Africa) and in many remote locations. Overall the average of the AeroCom models underestimates BC absorption by about a factor of two.
We concluded from this study that most models have enough BC at ground level in polluted regions, too much in the atmosphere above source regions, but not enough in the Arctic where BC may play an important role in contributing to Arctic warming and ice/snow melt. The models' soot generally does not absorb enough sunlight and therefore these models would underestimate BC heating effects. This probably results from underestimating the absorbing properties of the particles rather than the amount (mass) of BC.
The next generation of aerosol models will include improved representation of mixing between BC and other chemical components and better handle the enhanced BC absorption from mixing. Improved information on particle size and source amount in some regions may also enhance model performance. Ongoing measurement networks, satellites and field campaigns continue to be critical to constrain and improve black carbon model development.
Koch, D., M. Schulz, S. Kinne, C. McNaughton, J.R. Spackman, T.C. Bond, Y. Balkanski, S. Bauer, T. Berntsen, O. Boucher, M. Chin, A. Clarke, N. De Luca, F. Dentener, T. Diehl, O. Dubovik, R. Easter, D.W. Fahey, J. Feichter, D. Fillmore, S. Freitag, S. Ghan, P. Ginoux, S. Gong, L. Horowitz, T. Iversen, A. Kirkevåg, Z. Klimont, Y. Kondo, M. Krol, X. Liu, R. Miller, V. Montanaro, N. Moteki, G. Myhre, J.E. Penner, Ja. Perlwitz, G. Pitari, S. Reddy, L. Sahu, H. Sakamoto, G. Schuster, J.P. Schwarz, Ø. Seland, P. Stier, N. Takegawa, T. Takemura, C. Textor, J.A. van Aardenne, and Y. Zhao, 2009: Evaluation of black carbon estimations in global aerosol models. Atmos. Chem. Phys., 9, 9001-9026.
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