As more countries commit to emissions reductions by midcentury to curb anthropogenic climate change, decarbonization of the electricity sector becomes a first-order task in reaching this goal. Renewables, particularly wind and solar power, will be predominant components of this transition. How availability of the wind and solar resource will change in the future in response to regional climate changes is an important and underdiscussed topic of the decarbonization process. Here, we study changes in potential for wind power in China and India, evaluating prospectively until the year 2060. To do this, we study a downscaled, high-resolution multimodel ensemble of CMIP5 models under high and low emissions scenarios. While there is some intermodel variability, we find that spatial changes are generally consistent across models, with decreases of up to 965 (a 1% change) and 186 TWh (a 2% change) in annual electricity generation potential for China and India, respectively. Compensating for the declining resource are weakened seasonal and diurnal variabilities, allowing for easier large-scale wind power integration. We conclude that while the ensemble indicates available wind resource over China and India will decline slightly in the future, there remains enormous potential for significant wind power expansion, which must play a major role in carbon neutral aspirations.

The South Asian summer monsoon supplies over 80 % of India's precipitation. Industrialization over the past few decades has resulted in severe aerosol pollution in India. Understanding monsoonal sensitivity to aerosol emissions in general circulation models (GCMs) could improve predictability of observed future precipitation changes. The aims here are (1) to assess the role of aerosols on India's monsoon precipitation and (2) to determine the roles of local and regional emissions. For (1), we study the Precipitation Driver Response Model Intercomparison Project experiments. We find that the precipitation response to changes in black carbon is highly uncertain with a large intermodel spread due in part to model differences in simulating changes in cloud vertical profiles. Effects from sulfate are clearer; increased sulfate reduces Indian precipitation, a consistency through all of the models studied here. For (2), we study bespoke simulations, with reduced Chinese and/or Indian emissions in three GCMs. A significant increase in precipitation (up to ~ 20 %) is found only when both countries' sulfur emissions are regulated, which has been driven in large part by dynamic shifts in the location of convective regions in India. These changes have the potential to restore a portion of the precipitation losses induced by sulfate forcing over the last few decades.

Hydroxymethanesulfonate (HMS) has recently been identified as an abundant organosulfur compound in aerosols during winter haze episodes in northern China. It has also been detected in other regions although the concentrations are low. Because of the sparse field measurements, the global significance of HMS and its spatial and seasonal patterns remain unclear. Here, we modify and add to the implementation of HMS chemistry in the GEOS-Chem chemical transport model and conduct multiple global simulations. The model accounts for cloud entrainment and gas–aqueous mass transfer within the rate expressions for heterogeneous sulfur chemistry. Our simulations can generally reproduce quantitative HMS observations from Beijing and show that East Asia has the highest HMS concentration, followed by Europe and North America. The simulated HMS shows a seasonal pattern with higher values in the colder period. Photochemical oxidizing capacity affects the competition of formaldehyde with oxidants (such as ozone and hydrogen peroxide) for sulfur dioxide and is a key factor influencing the seasonality of HMS. The highest average HMS concentration (1–3 µg m−3) and HMS ∕ sulfate molar ratio (0.1–0.2) are found in northern China in winter. The simulations suggest that aqueous clouds act as the major medium for HMS chemistry while aerosol liquid water may play a role if its rate constant for HMS formation is greatly enhanced compared to cloud water.

Deep decarbonization pathways (DDPs) can be cost-effective for carbon mitigation, but they also have environmental co-benefits and economic impacts that cannot be ignored. Despite many empirical studies on the co-benefits of NDCs at the national or sectoral level, there is lack of integrated assessment on DDPs for their energy, economic, and environmental impact. This is due to the limitations of bottom-up and top-down models when used alone. This paper aims to fill this gap and link the bottom-up MAPLE model with a top-down CGE model to evaluate China's DDPs' comprehensive impacts. First, results show that carbon dioxide emissions can be observed to peak in or before 2030, and non-fossil energy consumption in 2030 is around 27%, which is well above the NDC target of 20%. Second, significant environmental co-benefits can be expected: 7.1 million tons of SO2, 3.96 million tons of NOx, and 1.02 million tons of PM2.5 will be reduced in the DDP scenario compared to the reference scenario. The health co-benefits demonstrated with the model-linking approach is around 678 billion RMB, and we observe that the linked model results are more in accordance with the conclusions of existing studies. Third, after linking, we find the real GDP loss from deep decarbonization is reduced from 0.92% to 0.54% in 2030. If the environmental co-benefits are considered, the GDP loss is further offset by 0.39%. The primary innovation of this study is to give a full picture of DDPs' impact, considering both environmental co-benefits and economic losses. We aim to provide positive evidence that developing countries can achieve targets higher than stated in the NDCs through DDP efforts, which will have clear environmental co-benefits to offset the economic losses.