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Quantifying PM2.5-meteorology sensitivities in a global climate model

机译:在全球气候模型中量化PM2.5气象敏感性

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摘要

Climate change can influence fine particulate matter concentrations (PM2.5) through changes in air pollution meteorology. Knowledge of the extent to which climate change can exacerbate or alleviate air pollution in the future is needed for robust climate and air pollution policy decision-making. To examine the influence of climate on PM2.5, we use the Geophysical Fluid Dynamics Laboratory Coupled Model version 3 (GFDL CM3), a fully-coupled chemistry-climate model, combined with future emissions and concentrations provided by the four Representative Concentration Pathways (RCPs). For each of the RCPs, we conduct future simulations in which emissions of aerosols and their precursors are held at 2005 levels while other climate forcing agents evolve in time, such that only climate (and thus meteorology) can influence PM2.5 surface concentrations. We find, a small increase in global, annual mean PM2.5 of about 0.21 mu g m(-3) (5%) for RCP8.5, a scenario with maximum warming. Changes in global mean PM2.5 are at a maximum in the fall and are mainly controlled by sulfate followed by organic aerosol with minimal influence of black carbon. RCP2.6 is the only scenario that projects a decrease in global PM2.5 with future climate changes, albeit only by 0.06 mu g m(-3) (1.5%) by the end of the 21st century. Regional and local changes in PM2.5 are larger, reaching upwards of 2 mu g m(-3) for polluted (eastern China) and dusty (western Africa) locations on an annually averaged basis in RCP8.5. Using multiple linear regression, we find that future PM2.5 concentrations are most sensitive to local temperature, followed by surface wind and precipitation. PM2.5 concentrations are robustly positively associated with temperature, while negatively related with precipitation and wind speed. Present-day (2006-2015) modeled sensitivities of PM2.5 to meteorological variables are evaluated against observations and found to agree reasonably well with observed sensitivities (within 10-50% over the eastern United States for several variables), although the modeled PM2.5 is less sensitive to precipitation than in the observations due to weaker convective scavenging. We conclude that the hypothesized "climate penalty" of future increases in PM2.5 is relatively minor on a global scale compared to the influence of emissions on PM2.5 concentrations. (C) 2016 Elsevier Ltd. All rights reserved.
机译:气候变化可通过空气污染气象学的变化影响细颗粒物浓度(PM2.5)。强有力的气候和空气污染政策决策需要了解气候变化在将来会加剧或减轻空气污染的程度。要检查气候对PM2.5的影响,我们使用地球物理流体动力学实验室耦合模型版本3(GFDL CM3),这是一种完全耦合的化学-气候模型,结合了四个代表性浓度路径( RCP)。对于每个RCP,我们进行未来的模拟,将气溶胶及其前体的排放保持在2005年的水平,同时其他气候强迫因子会随时间变化,因此只有气候(以及气象学)才能影响PM2.5表面浓度。我们发现,RCP8.5的全球年平均PM2.5略有增加,约为0.21μg m(-3)(5%),这是增温最大的情况。全球平均PM2.5的变化在秋季最大,主要受硫酸盐控制,其次是有机气溶胶,对黑碳的影响最小。 RCP2.6是唯一预测随着未来气候变化全球PM2.5下降的情景,尽管到21世纪末仅下降0.06μg m(-3)(1.5%)。在RCP8.5中,PM2.5的区域和局部变化较大,在受污染的(中国东部)和尘土飞扬的(西非)地区,每年平均超过2μg m(-3)。使用多元线性回归,我们发现未来的PM2.5浓度对局部温度最敏感,其次是地表风和降水。 PM2.5浓度与温度呈正相关,而与降水量和风速呈负相关。尽管对PM2进行了建模,但目前(2006-2015年)对PM2.5对气象变量的敏感性进行了建模,并与观测值进行了比较,发现其与观测到的敏感性(在美国东部的几个变量之间在10-50%之内)相当吻合。 .5由于对流清除较弱,因此对降水的敏感性低于观测结果。我们得出结论,与排放量对PM2.5浓度的影响相比,在全球范围内,假设的PM2.5未来增加的“气候损失”相对较小。 (C)2016 Elsevier Ltd.保留所有权利。

著录项

  • 来源
    《Atmospheric environment》 |2016年第10期|43-56|共14页
  • 作者单位

    Columbia Univ, Lamont Doherty Earth Observ, Palisades, NY USA|Princeton Univ, Program Sci Technol & Envrionm Policy, Woodrow Wilson Sch Publ & Int Affairs, Princeton, NJ 08544 USA;

    NOAA, Geophys Fluid Dynam Lab, Princeton, NJ USA;

    NOAA, Geophys Fluid Dynam Lab, Princeton, NJ USA;

    Chinese Univ Hong Kong, Earth Syst Sci Programme, Hong Kong, Hong Kong, Peoples R China|Chinese Univ Hong Kong, Grad Div Earth & Atmospher Sci, Hong Kong, Hong Kong, Peoples R China;

    Columbia Univ, Lamont Doherty Earth Observ, Palisades, NY USA|Columbia Univ, Dept Earth & Environm Sci, New York, NY USA;

    Princeton Univ, Program Sci Technol & Envrionm Policy, Woodrow Wilson Sch Publ & Int Affairs, Princeton, NJ 08544 USA|Princeton Univ, Dept Civil & Environm Engn, Princeton, NJ 08544 USA;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    PM2.5; Climate change; Climate model; Sensitivity;

    机译:PM2.5;气候变化;气候模式;敏感性;

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