The effects of global change upon United States air quality

Gonzalez-Abraham, R.; Chung, S. H.; Avise, J.; Lamb, Brian; Salathé, Eric P.; Nolte, Christopher G.; Loughlin, Daniel H.; Wiedinmyer, Christine; Duhl, T.; Zhang, Yang; Streets, D. G.

doi:10.5194/acp-15-12645-2015

Cited by 30 publications

(19 citation statements)

References 81 publications

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“…Because no reliable observations of nighttime cloud fraction exist, we focus on daytime measurements. On average, increased cloud fraction is associated with cooler surface air temperatures, but the relationship between cloud fraction and temperature can also have a strong seasonal cycle and vary by region (Groisman et al, 2000;Sun et al, 2000). Figure 8 shows the slopes of monthly mean cloud fraction (> 680 hPa) and surface temperature in summer from 2004 to 2012 over the southeast in daytime.…”

Section: Impact Of 2000-2050 Climate Changes On Pm 25 From Statisticmentioning

confidence: 99%

Influence of 2000–2050 climate change on particulate matter in the United States: results from a new statistical model

2017

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Abstract. We use a statistical model to investigate the effect of 2000–2050 climate change on fine particulate matter (PM2. 5) air quality across the contiguous United States. By applying observed relationships of PM2. 5 and meteorology to the IPCC Coupled Model Intercomparision Project Phase 5 (CMIP5) archives, we bypass some of the uncertainties inherent in chemistry-climate models. Our approach uses both the relationships between PM2. 5 and local meteorology as well as the synoptic circulation patterns, defined as the singular value decomposition (SVD) pattern of the spatial correlations between PM2. 5 and meteorological variables in the surrounding region. Using an ensemble of 19 global climate models (GCMs) under the RCP4.5 scenario, we project an increase of 0.4–1.4 µg m−3 in annual mean PM2. 5 in the eastern US and a decrease of 0.3–1.2 µg m−3 in the Intermountain West by the 2050s, assuming present-day anthropogenic sources of PM2. 5. Mean summertime PM2. 5 increases as much as 2–3 µg m−3 in the eastern United States due to faster oxidation rates and greater mass of organic aerosols from biogenic emissions. Mean wintertime PM2. 5 decreases by 0.3–3 µg m−3 over most regions in the United States, likely due to the volatilization of ammonium nitrate. Our approach provides an efficient method to calculate the potential climate penalty on air quality across a range of models and scenarios. We find that current atmospheric chemistry models may underestimate or even fail to capture the strongly positive sensitivity of monthly mean PM2. 5 to temperature in the eastern United States in summer, and they may underestimate future changes in PM2. 5 in a warmer climate. In GEOS-Chem, the underestimate in monthly mean PM2. 5–temperature relationship in the east in summer is likely caused by overly strong negative sensitivity of monthly mean low cloud fraction to temperature in the assimilated meteorology ( ∼  −0.04 K−1) compared to the weak sensitivity implied by satellite observations (±0.01 K−1). The strong negative dependence of low cloud cover on temperature in turn causes the modeled rates of sulfate aqueous oxidation to diminish too rapidly as temperatures rise, leading to the underestimate of sulfate–temperature slopes, especially in the south. Our work underscores the importance of evaluating the sensitivity of PM2. 5 to its key controlling meteorological variables in climate-chemistry models on multiple timescales before they are applied to project future air quality.

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Section: Impact Of 2000-2050 Climate Changes On Pm 25 From Statisticmentioning

confidence: 99%

Influence of 2000–2050 climate change on particulate matter in the United States: results from a new statistical model

2017

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“…Numerous studies using regional CTMs that have considered both changing climate and changing emissions on future air pollutant concentrations have found that changes in emissions dominate (Nolte et al, 2008;Kelly et al, 2012;Colette et al, 2013;Trail et al, 2014;Day and Pandis, 2015;Gonzalez-Abraham et al, 2015;He et al, 2016). Modeled pollutant concentrations are highly sensitive to lateral chemical boundary conditions (e.g., Tang et al, 2007;Katragkou et al, 2010;Schere et al, 2012), and different assumptions regarding changes in long-range transport have been shown to have a significant impact on future pollutant levels (Nolte et al, 2008;Colette et al, 2013;Pfister et al, 2014;Gonzalez-Abraham et al, 2015;He et al, 2016;Zhang et al, 2016). Several previous studies have also highlighted the importance of rising levels of methane for ozone chemistry (Fiore et al, 2002;West and Fiore, 2005;Nolte et al, 2008).…”

Section: Chemical Transport Modelmentioning

confidence: 99%

“…Studies using global climate model (GCM) data to drive global or regional chemical transport models (CTMs) have found that climate change yields meteorological conditions that are more conducive to forming high O 3 , exacerbating summertime O 3 over polluted continental regions (Mickley et al, 2004;Leung and Gustafson Jr., 2005;Stevenson et al, 2006;Katragkou et al, 2011;Horton et al, 2012;Gao et al, 2013). Modeling studies conducted using mid-21st century climate data project up to 2-8 ppb increases in summer average ozone levels in the US, depending on climate change scenario and time period (e.g., Wu et al, 2008;Nolte et al, 2008;Weaver et al, 2009;Kelly et al, 2012;Trail et al, 2014;Pfister et al, 2014;Gonzalez-Abraham et al, 2015;Fann et al, 2015;He et al, 2016;Dionisio et al, 2017). This deterioration of air quality due to climate change is known as the "climate penalty" (Wu et al, 2008;Rasmussen et al, 2013) and could potentially offset some of the improvement in air quality that would otherwise occur due to reductions in ozone precursor emissions.…”

Section: Introductionmentioning

confidence: 99%

The potential effects of climate change on air quality across the conterminous US at 2030 under three Representative Concentration Pathways

et al. 2018

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Abstract. The potential impacts of climate change on regional ozone (O3) and fine particulate (PM2.5) air quality in the United States (US) are investigated by linking global climate simulations with regional-scale meteorological and chemical transport models. Regional climate at 2000 and at 2030 under three Representative Concentration Pathways (RCPs) is simulated by using the Weather Research and Forecasting (WRF) model to downscale 11-year time slices from the Community Earth System Model (CESM). The downscaled meteorology is then used with the Community Multiscale Air Quality (CMAQ) model to simulate air quality during each of these 11-year periods. The analysis isolates the future air quality differences arising from climate-driven changes in meteorological parameters and specific natural emissions sources that are strongly influenced by meteorology. Other factors that will affect future air quality, such as anthropogenic air pollutant emissions and chemical boundary conditions, are unchanged across the simulations. The regional climate fields represent historical daily maximum and daily minimum temperatures well, with mean biases of less than 2 K for most regions of the US and most seasons of the year and good representation of variability. Precipitation in the central and eastern US is well simulated for the historical period, with seasonal and annual biases generally less than 25 %, with positive biases exceeding 25 % in the western US throughout the year and in part of the eastern US during summer. Maximum daily 8 h ozone (MDA8 O3) is projected to increase during summer and autumn in the central and eastern US. The increase in summer mean MDA8 O3 is largest under RCP8.5, exceeding 4 ppb in some locations, with smaller seasonal mean increases of up to 2 ppb simulated during autumn and changes during spring generally less than 1 ppb. Increases are magnified at the upper end of the O3 distribution, particularly where projected increases in temperature are greater. Annual average PM2.5 concentration changes range from −1.0 to 1.0 µg m−3. Organic PM2.5 concentrations increase during summer and autumn due to increased biogenic emissions. Aerosol nitrate decreases during winter, accompanied by lesser decreases in ammonium and sulfate, due to warmer temperatures causing increased partitioning to the gas phase. Among meteorological factors examined to account for modeled changes in pollution, temperature and isoprene emissions are found to have the largest changes and the greatest impact on O3 concentrations.

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“…Land use changes can affect air quality simulations through changes in emissions of biogenic volatile organic compounds, soil NO x , dust, smoke, and dry deposition of pollutants (Heald & Spracklen, 2015). For instance, future land use changes are predicted to increase PM 2.5 by 2050 in many parts of the United States due to increased biogenic emissions and SOA formation (Gonzalez‐Abraham et al, 2015). Uncertainties in dry deposition parameterization are estimated to introduce uncertainty of 5–15% in CMAQ PM 2.5 simulations (Saylor et al, 2019) or up to 40% in anthropogenic and 52% on biogenic organic aerosols over the CONUS in WRF‐Chem (Hodzic et al, 2014; Knote et al, 2015).…”

Section: Summary and Discussionmentioning

confidence: 99%

A Novel Ensemble Design for Probabilistic Predictions of Fine Particulate Matter Over the Contiguous United States (CONUS)

et al. 2020

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This study examines the benefit of using a dynamical ensemble for 48 hr deterministic and probabilistic predictions of near‐surface fine particulate matter (PM2.5) over the contiguous United States (CONUS). Our ensemble design captures three key sources of uncertainties in PM2.5 modeling including meteorology, emissions, and secondary organic aerosol (SOA) formation. Twenty‐four ensemble members were simulated using the Community Multiscale Air Quality (CMAQ) model during January, April, July, and October 2016. The raw ensemble mean performed better than most of the ensemble members but underestimated the observed PM2.5 over the CONUS with the largest underestimation over the western CONUS owing to negative PM2.5 bias in nearly all the members. To improve the ensemble performance, we calibrated the raw ensemble using model output statistics (MOS) and variance deficit methods. The calibrated ensemble captured the diurnal and day‐to‐day variability in observed PM2.5 very well and exhibited almost zero mean bias. The mean bias in the calibrated ensemble was reduced by 90–100% in the western CONUS and by 40–100% in other parts of the CONUS, compared to the raw ensemble in all months. The corresponding reduction in root‐mean‐square error (RMSE) was 13–40%. The calibrated ensemble also showed 30% improvement in the RMSE and spread matching compared to the raw ensemble. We have also shown that a nine‐member ensemble based on combinations of three meteorological and three anthropogenic emission scenarios can be used as a smaller subset of the full ensemble when sufficient computational resources are not available in the operational setting.

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The effects of global change upon United States air quality

Cited by 30 publications

References 81 publications

Influence of 2000–2050 climate change on particulate matter in the United States: results from a new statistical model

Influence of 2000–2050 climate change on particulate matter in the United States: results from a new statistical model

The potential effects of climate change on air quality across the conterminous US at 2030 under three Representative Concentration Pathways

A Novel Ensemble Design for Probabilistic Predictions of Fine Particulate Matter Over the Contiguous United States (CONUS)

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