The increasing severity of droughts/floods and worsening air quality from increasing aerosols in Asia monsoon regions are the two gravest threats facing over 60% of the world population living in Asian monsoon regions. These dual threats have fueled a large body of research in the last decade on the roles of aerosols in impacting Asian monsoon weather and climate. This paper provides a comprehensive review of studies on Asian aerosols, monsoons, and their interactions. The Asian monsoon region is a primary source of emissions of diverse species of aerosols from both anthropogenic and natural origins. The distributions of aerosol loading are strongly influenced by distinct weather and climatic regimes, which are, in turn, modulated by aerosol effects. On a continental scale, aerosols reduce surface insolation and weaken the land-ocean thermal contrast, thus inhibiting the development of monsoons. Locally, aerosol radiative effects alter the thermodynamic stability and convective potential of the lower atmosphere leading to reduced temperatures, increased atmospheric stability, and weakened wind and atmospheric circulations. The atmospheric thermodynamic state, which determines the formation of clouds, convection, and precipitation, may also be altered by aerosols serving as cloud condensation nuclei or ice nuclei. Absorbing aerosols such as black carbon and desert dust in Asian monsoon regions may also induce dynamical feedback processes, leading to a strengthening of the early monsoon and affecting the subsequent evolution of the monsoon. Many mechanisms have been put forth regarding how aerosols modulate the amplitude, frequency, intensity, and phase of different monsoon climate variables. A wide range of theoretical, observational, and modeling findings on the Asian monsoon, aerosols, and their interactions are synthesized. A new paradigm is proposed on investigating aerosol-monsoon interactions, in which natural aerosols such as desert dust, black carbon from biomass burning, and biogenic aerosols from vegetation are considered integral components of an intrinsic aerosol-monsoon climate system, subject to external forcing of global warming, anthropogenic aerosols, and land use and change. Future research on aerosol-monsoon interactions calls for an integrated approach and international collaborations based on long-term sustained observations, process measurements, and improved models, as well as using observations to constrain model simulations and projections.

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[1] We estimate seasonal variations in methane (CH 4 ) emissions from central California from December 2007 through November 2008 by comparing CH 4 mixing ratios measured at a tall tower with transport model predictions based on a global 1 a priori CH 4 emissions map (EDGAR32) and a 10 km seasonally varying California-specific map, calibrated to statewide by CH 4 emission totals. Atmospheric particle trajectories and surface footprints are computed using the Weather Research and Forecasting and Stochastic Time-Inverted Lagrangian Transport models. Uncertainties due to wind velocity and boundary layer mixing depth are evaluated using measurements from radar wind profilers. CH 4 signals calculated using the EDGAR32 emission model are larger than those based on the California-specific model and in better agreement with measurements. However, Bayesian inverse analyses using the California-specific and EDGAR32 maps yield comparable annually averaged posterior CH 4 emissions totaling 1.55 AE 0.24 times and 1.84 AE 0.27 times larger than the California-specific prior emissions, respectively, for a region of central California within approximately 150 km of the tower. If these results are applicable across California, state total CH 4 emissions would account for approximately 9% of state total greenhouse gas emissions. Spatial resolution of emissions within the region near the tower reveal seasonality expected from several biogenic sources, but correlations in the posterior errors on emissions from both prior models indicate that the tower footprints do not resolve spatial structure of emissions. This suggests that including additional towers in a measurement network will improve the regional specificity of the posterior estimates.

[1] Methane mixing ratios measured at a tall tower are compared to model predictions to estimate surface emissions of CH 4 in Central California for October-December 2007 using an inverse technique. Predicted CH 4 mixing ratios are calculated based on spatially resolved a priori CH 4 emissions and simulated atmospheric trajectories. The atmospheric trajectories, along with surface footprints, are computed using the Weather Research and Forecast (WRF) coupled to the Stochastic Time-Inverted Lagrangian Transport (STILT) model. An uncertainty analysis is performed to provide quantitative uncertainties in estimated CH 4 emissions. Three inverse model estimates of CH 4 emissions are reported. First, linear regressions of modeled and measured CH 4 mixing ratios obtain slopes of 0.73 ± 0.11 and 1.09 ± 0.14 using California-specific and Edgar 3.2 emission maps, respectively, suggesting that actual CH 4 emissions were about 37 ± 21% higher than California-specific inventory estimates. Second, a Bayesian ''source'' analysis suggests that livestock emissions are 63 ± 22% higher than the a priori estimates. Third, a Bayesian ''region'' analysis is carried out for CH 4 emissions from 13 subregions, which shows that inventory CH 4 emissions from the Central Valley are underestimated and uncertainties in CH 4 emissions are reduced for subregions near the tower site, yielding best estimates of flux from those regions consistent with ''source'' analysis results. The uncertainty reductions for regions near the tower indicate that a regional network of measurements will be necessary to provide accurate estimates of surface CH 4 emissions for multiple regions.