Anthropogenic Aerosols - Model Projections

The atmospheric brown cloud that has been associated with South Asia, is made up of a number of aerosols. A significant part of the brown cloud is formed of black carbon (Ramanathan and Crutzen, 2003). Black carbon is primarily produced by soot from fossil fuel combustion and biomass burning (Collins et al., 2002). Thus, it is no surprise that black carbon may have an impact on the Indian monsoon.  Meehl etal., (2008) have completed a six-member ensemble of 20^th Century simulations, using a variety of black carbon scenarios. The CCSM3 model (see Collins et al., 2006) produced the following outputs. Increased black carbon increases lower-tropospheric heating over South Asia and reduces the amount of solar radiation reaching the surface during the dry season. Surface temperatures are subsequently reduced in the Bay of Bengal, Arabian Sea, and over India. Prior to the monsoon in March-April-May precipitation increases, which is followed by reduced precipitation during the active monsoon period.

Similar results were produced by Collier and Zhang (2009) using the NCAR Community Atmosphere Model, CAM3. The high-resolution model is run for a 16 month period to understand the implications of increased aerosols (soil dust, black and organic carbons and sulphate). Like Meehl et al., (2008) the model suggests a decrease in surface temperatures and consequently increases in precipitation during the pre-monsoon months. Furthermore, the presence of aerosols induces tropospheric shortwave heating over central India, which decreases convection and precipitation, and increases evaporation. Increased evaporation during pre-monsoon months weakens the near-surface cyclonic circulation, and consequently reduces precipitation during the active monsoon months. The spatial distribution of changes in precipitation remains uncertain, which therefore requires assistance from observational data.

The two studies highlighted here present different results from previous observational research. The observational data provides specific areal changes of precipitation, whereas the models mainly focus on total precipitation. The models fail to identify areas of intense precipitation and so more research is required to understand why the models fail to project that yet the observations have witnessed it.