Spatial distributions and seasonal cycles of aerosol climate effects in India seen in a global climate–aerosol model
- 1Finnish Meteorological Institute, Helsinki, Finland
- 2Department of Physics, University of Helsinki, Finland
- 3Finnish Environment Institute (SYKE), Helsinki, Finland
- 4International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria
- 5Department of Physics, University of Eastern Finland, Kuopio, Finland
Abstract. Climate–aerosol interactions in India are studied by employing the global climate–aerosol model ECHAM5-HAM and the GAINS inventory for anthropogenic aerosol emissions. Model validation is done for black carbon surface concentrations in Mukteshwar and for features of the monsoon circulation. Seasonal cycles and spatial distributions of radiative forcing and the temperature and rainfall responses are presented for different model setups. While total aerosol radiative forcing is strongest in the summer, anthropogenic forcing is considerably stronger in winter than in summer. Local seasonal temperature anomalies caused by aerosols are mostly negative with some exceptions, e.g., parts of northern India in March–May. Rainfall increases due to the elevated heat pump (EHP) mechanism and decreases due to solar dimming mechanisms (SDMs) and the relative strengths of these effects during different seasons and for different model setups are studied. Aerosol light absorption does increase rainfall in northern India, but effects due to solar dimming and circulation work to cancel the increase. The total aerosol effect on rainfall is negative for northern India in the months of June–August, but during March–May the effect is positive for most model setups. These differences between responses in different seasons might help converge the ongoing debate on the EHPs and SDMs. Due to the complexity of the problem and known or potential sources for error and bias, the results should be interpreted cautiously as they are completely dependent on how realistic the model is. Aerosol–rainfall correlations and anticorrelations are shown not to be a reliable sole argument for deducing causality.