气溶胶与气象要素之间通过辐射过程和云凝结核等机制产生双向反馈作用，对我国大气重污染过程和区域天气气候系统均构成重要影响。未来气候变化和污染物减排情景下中国地区气溶胶-气象相互作用将如何变化，是科学界和决策者关心的重要问题。针对这一问题，本研究构建了耦合包括全球气候模式、排放预测模型和区域大气化学传输模式在内的模型框架系统，分析了中国区域在当前（2014年）污染水平下的气溶胶与气象相互作用以及未来不同排放（2030年）和气候变化（2050年）情景下气溶胶-气象相互作用的响应机制和变化。本研究首先建立了由全球气候模式CESM、中国多尺度排放预测模型MEIC-Projection、区域在线大气化学传输模型WRF-Chem构成的模型系统，通过构建不同模式之间的数据接口，使得WRF-Chem能够对接MEIC-Projection提供的未来排放预测结果和CESM提供的未来全球气象场模拟结果。基于地面观测和卫星遥感观测的验证结果表明，该模型能够较好地模拟当前年份下我国大气气象要素特征及污染物浓度水平。其次，利用区域在线大气化学传输模型WRF-Chem模拟了中国地区2014年污染水平下的气溶胶-气象反馈过程，发现气溶胶辐射反馈（Aerosol Radiation Interactions，ARIs）ARIs可使得局地气温降低、湿度增高、风速下降、边界层降低、降水减少、干沉降速率降低，进一步导致PM2.5浓度升高，形成“自增强”的正反馈。该反馈在冬季最强，最高可使得PM2.5月均浓度升高超过30 μg/m3。而气溶胶云反馈（Aerosol Cloud Interactions，ACIs）使得降水减少，造成湿季PM2.5浓度上升，影响程度与ARIs效应相当，但对PM2.5全年平均浓度的影响不及ARIs效应。再次，通过模拟2030年不同排放情景下气溶胶-气象相互作用的变化，发现ARIs的反馈效应随着排放下降迅速降低，对PM2.5年均浓度的贡献可从基准情景下的3.6μg/m3下降至强化减排情景下的0.5μg/m3，而ACIs对PM2.5浓度的影响随排放变化不大，即在未来更为清洁的情景下，ACIs的相对重要性可能会提升。最后，通过CESM驱动WRF-Chem模拟得到未来RCP4.5情景下中国地区气溶胶-气象反馈过程对全球变暖的响应。发现全球增温情景下ARIs会使得中国大部分地区边界层发展进一步受阻且湿度进一步上升，从而使得PM2.5浓度进一步升高；而ACIs会导致未来中国地区产生更多的降水，从而增强对PM2.5的湿清除作用，使得空气更清洁。本研究揭示了未来不同气候变化及排放变化情景下气溶胶-气象相互作用对我国区域PM2.5浓度水平变化的影响特征，结果为
Aerosol interacts with meteorology via radation and cloud processes. This two-way feedback may effect not only air pollution episodes but also regional climate system as a whole. How aerosol-meteorological interactions may response to climate change in combination with emission control of pollutants in the future is a main concern by scientists and policy-makers. Based on this, a comprehensive model framework is developed including regional online atmospheric chemical transport model, general circulation model and future emission projection model, so as to provide technical system for analyzing mechanisms of aerosol-meteorology interactions in current pollution conditions (in the year of 2014), future emission pathways (in the year of 2030) and climate change background (in the year of 2050).The framework is set up by connecting global climate model CESM (Community Earth System Model), China’s local emission projection model MEIC-projection (Multi-scale Emission Inventory for China), regional on-line atmospheric chemical transport model WRF-Chem throughout multiple interfaces. Thus, WRF-Chem can be driven by both future emission projection datasets of MEIC-projections and future climate simulation results of CESM. The framework is thoroughly evaluated in simulating meteorological parameters and pollution levels when compared with both ground-based measurements and remote sensing dataset, and it is proved to be reliable for following studies. Then, simulation using regional chemical transport model WRF-Chem in the year of 2014 demonstrates that aerosol-radiation interaction (ARIs) can result in reductions of surface temperature, planetary boundary layer height, precipitation amounts and dry deposition velocity, as well as rises in surface relative humidity. These variances hence lead to regional surface PM2.5 concentration aggregation which can reach its maximum in winter at around 30 μg/m3 or above. Also, aerosol-cloud interactions (ACIs) are liable to decrease precipitation, yet it is only influential in wetter seasons. Considering year-round enhancement, ARIs take the lead when compared with ACIs. After that, simulations of aerosol-meteorological interactions driven by different future emission scenarios illustrate that ARIs may enhance less when emission in the future year of 2030 is cut down. Contribution of PM2.5 enhancements due to ARIs drops from 3.6 μg/m3 to 0.5 μg/m3 annually in 2030 baseline case and stringent emission reduction case respectively. That is to say, ACIs are getting more and more significant in future cleaner scenarios. Lastly, we used WRF-Chem driven by CESM to take global warming trend into account. In a hotter future of year 2050 under Representative Concentration Pathways (RCP 4.5), ACIs tend to provide stronger wash-out processes as sink of aerosols, while ARIs will worsen the air quality by further wetting the air and suppressing the development of the planetary boundary layer than ARIs’ current feedback mechanisms. The conclusions above unveil the characteristics of ARIs and ACIs’ responses to different future emission scenarios and climate change background and its impact on regional particulate matter concentrations, which gives directions on China’s targeted future emission reduction management.
