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CCSM4/WRF-CMAQ动力降尺度预估RCP8.5情景下京津冀地区空气质量的潜在变化
王堃,师华定,高佳佳,王晨龙,滑申冰,高庆先,等
作者单位E-mail
王堃 北京市劳动保护科学研究所 wkty@mail.bnu.edu.cn 
师华定 中国环境科学研究院 shihd@craes.org.cn 
高佳佳 北京市劳动保护科学研究所  
王晨龙 北京市劳动保护科学研究所  
滑申冰 中国电力科学研究院  
高庆先 中国环境科学研究院  
摘要:
目前,针对气候变化对区域空气质量影响的研究相对较少,且多采用统计降尺度方法对全球气候模式结果进行处理. 本研究采用WRF中尺度气象模式对CCSM4气候模式的CMIP5 RCP8.5情景预估结果进行动力降尺度处理,并为CMAQ空气质量模式提供气象场;在2012年清华大学MEIC大气污染物排放清单的基础上,选取2005年作为气候现状代表年、2049—2051年作为未来气候代表年,对京津冀地区典型月份(1月、4月、7月、10月)的气象及空气质量数值模拟结果进行对比,以此预估气候变化背景下京津冀地区空气质量潜在变化. 结果表明,在排放情况不变及RCP8.5情景下,未来代表年与现状代表年相比,京津冀地区以典型月份为代表的年均气象因素整体呈现温度升高,风速、湿度及大气边界层高度均降低,年均大气污染物整体呈现升高的趋势,其中,温度升高约0.8 ℃,风速降低约0.11 m/s,相对湿度降低约2 %,大气边界层高度降低约8 m,ρ(PM2.5)升高约2.4 μg/m3,ρ(SO2)升高约1.8 μg/m3,ρ(NOx)升高约1.0 μg/m3;此外,主要的气象条件(温度、风速、湿度、大气边界层高度)中,风速及大气边界层高度的降低可能是造成这些大气污染物浓度变化的主要气象因素,且风速及大气边界层高度的降低与PM2.5浓度降低的相关系数分别约为-0.44,-0.26. 研究显示,气候变化会对京津冀地区造成污染物浓度升高的潜在风险,同时由于现阶段缺乏可用于空气质量模式的未来排放情景数据、在线耦合模式日臻完善,在我国气候-空气质量的研究领域亟待进行更深层次的研究。
关键词:  CCSM4  CMAQ  动力降尺度  气候变化
DOI:
分类号:
基金项目:国家自然科学基金项目(21607008);国家环境保护公益性行业科研专项(201509001);中国环境科学研究院院属项目(2016YSKY-003)
Potential Variation of Beijing-Tianjin-Hebei Region’s Air Quality in RCP8.5 Scenarios by Dynamic Downscaling Method with CCSM4/WRF-CMAQ Model
Wang Kun,SHI HUA DING,GAOJIAJIA,王晨龙,滑申冰,高庆先,et al
Abstract:
Currently, there have few studies on the relationship between climate change and air quality, and most of which adopted the method of statistical downscaling. Here, we used dynamic downscaling method to prepare WRF initial and boundary conditions with CCSM4 output under the CMIP5 RCP8.5 scenarios and chose MEIC 2012 Data as CMAQ input inventory data in the Beijing-Tianjin-Hebei Region. In order to explore the impacts of meteorological conditions on air quality, the monthly variation characteristics of PBL height (PBLH), relative humidity (RH), wind velocity, temperature and air pollutant concentrations were analyzed and compared between forecast years (2049, 2050, 2051) and baseline year (2005) .The results indicated that in the case of constant emissions and RCP8.5 scenario, the meteorological condition would become increasingly detrimental to atmospheric pollutant dispersion. In the forecast years: Annual averaged temperature would increase about 0.8 ℃; Annual averaged wind speed, RH and PBLH would decrease about 0.11 m/s, 2 % and 8 m respectively; Annual averaged concentrations of PM2.5, SO2 and NOx would increase about 2.4 μg/m3,1.8 μg/m3 and 1.0 μg/m3 respectively. The decline in surface wind speed and lower PBLH should be the main influence factor of the increasing trend of atmospheric pollutants and the correlation coefficient between PM2.5 concentration and wind speed and PBLH was -0.44 and -0.26 respectively. Results presented that climate changes do affect the regional air quality but the impact intensity is still uncertain due to the lack of future emission scenarios that is used in air quality models, and the physical/chemical mechanisms among the mutual influence process between meteorological factors and pollutant dispersion also need to be studied further.
Key words:  CCSM4  CMAQ  dynamic downscaling  climate change