Recent studies have suggested that brown carbon (BrC), an absorbing component in organic aerosol, has strong absorption in the near-ultraviolet wavelengths, and contributes to regional and global radiative forcing (RF). However, the inclusion of BrC in global climate models leads to significant uncertainties in estimated RF, mainly attributed to uncertain BrC properties and relevant BrC parameters assigned in the model. In this study, we modified the bulk aerosol optical scheme (BAOS) in Community Atmospheric Model version 5.3 by including BrC absorption and evaluated the performance of the modified BAOS by comparing the simulated aerosol absorption with 2-year surface observational data in two Asian cities, Kanpur, India and Nanjing, China. The mean relative errors in the simulated total aerosol absorption (B-abs) and absorption Angstrom exponent in modified BAOS are around 35% in Kanpur and even below 20% in Nanjing. Our results show that the inclusion of BrC remedies the underestimated total aerosol absorption by 20% and 14% on average at Kanpur and Nanjing, respectively, exhibiting a better agreement with ground-based observations of aerosol absorption at both sites. We also conducted a series of sensitivity experiments to quantify the uncertainties caused by varying parameters related to BrC. The model simulations suggest that the imaginary refractive index of BrC is the most significant factor contributing to the uncertainties in aerosol optical properties calculated in BAOS at the Kanpur site. While in the Nanjing site, both particle size distribution and mixing state have dominant impacts on the calculated aerosol optical properties.

Uncertainties in the climate response to a doubling of atmospheric CO2 concentrations are quantified in a perturbed land surface parameter experiment. The ensemble of 108 members is constructed by systematically perturbing five poorly constrained land surface parameters of global climate model individually and in all possible combinations. The land surface parameters induce small uncertainties at global scale, substantial uncertainties at regional and seasonal scale and very large uncertainties in the tails of the distribution, the climate extremes. Climate sensitivity varies across the ensemble mainly due to the perturbation of the snow albedo parameterization, which controls the snow albedo feedback strength. The uncertainty range in the global response is small relative to perturbed physics experiments focusing on atmospheric parameters. However, land surface parameters are revealed to control the response not only of the mean but also of the variability of temperature. Major uncertainties are identified in the response of climate extremes to a doubling of CO2. During winter the response both of temperature mean and daily variability relates to fractional snow cover. Cold extremes over high latitudes warm disproportionately in ensemble members with strong snow albedo feedback and large snow cover reduction. Reduced snow cover leads to more winter warming and stronger variability decrease. As a result uncertainties in mean and variability response line up, with some members showing weak and others very strong warming of the cold tail of the distribution, depending on the snow albedo parametrization. The uncertainty across the ensemble regionally exceeds the CMIP3 multi-model range. Regarding summer hot extremes, the uncertainties are larger than for mean summer warming but smaller than in multi-model experiments. The summer precipitation response to a doubling of CO2 is not robust over many regions. Land surface parameter perturbations and natural variability alter the sign of the response even over subtropical regions.