Can a global model reproduce observed trends in summertime surface ozone levels?

Koumoutsaris, Symeon; Bey, I.

doi:10.5194/acp-12-6983-2012

Cited by 21 publications

(17 citation statements)

References 68 publications

(88 reference statements)

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“…An approach to overcome this complication is to compare long-term ozone observations with model hindcasts forced (or nudged) with observed meteorology. This better isolates model-observation disagreements, and could potentially suggest model improvements (e.g., Pozzoli et al, 2011;Koumoutsaris and Bey, 2012;BrownSteiner et al, 2015;Lin et al, 2015b;Strode et al, 2015;Tilmes et al, 2016;Lin et al, 2017). Overall, such hindcast simulations capture observed decreases in summertime surface ozone in the populated regions of North America and Europe during 1990-2010, but have difficulties simulating the ozone increases measured at remote baseline sites.…”

Section: State Of Knowledgementioning

confidence: 99%

“…CTMs and SD-CCMs (and nudged CCMs) are often used for performing process-oriented analysis, including interpretation of short-term field measurements (e.g., Law et al, 1998;Liang et al, 2007;Zhang et al, 2008;Telford et al, 2010;Lin et al, 2012a;Wespes et al, 2012) and understanding the causes of ozone variability and long-term trends in observational records, by isolating the roles of emissions and meteorology (Koumoutsaris and Bey, 2012;Lin et al, 2014Lin et al, , 2015Lin et al, , 2017Strode et al, 2015). These models are also used to make chemical forecasts as part of flight planning for field missions (e.g., Fast et al, 2007).…”

Section: Atmospheric Chemistry In Offline Global Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Tropospheric Ozone Assessment Report: Assessment of global-scale model performance for global and regional ozone distributions, variability, and trends

Young

Naik

Fiore

et al. 2018

Elementa: Science of the Anthropocene

264

274

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The goal of the Tropospheric Ozone Assessment Report (TOAR) is to provide the research community with an up-to-date scientific assessment of tropospheric ozone, from the surface to the tropopause. While a suite of observations provides significant information on the spatial and temporal distribution of tropospheric ozone, observational gaps make it necessary to use global atmospheric chemistry models to synthesize our understanding of the processes and variables that control tropospheric ozone abundance and its variability. Models facilitate the interpretation of the observations and allow us to make projections of future tropospheric ozone and trace gas distributions for different anthropogenic or natural perturbations. This paper assesses the skill of current-generation global atmospheric chemistry models in simulating the observed present-day tropospheric ozone distribution, variability, and trends. Drawing upon the results of recent international multi-model intercomparisons and using a range of model evaluation techniques, we demonstrate that global chemistry models are broadly skillful in capturing the spatio-temporal variations of tropospheric ozone over the seasonal cycle, for extreme pollution episodes, and changes over interannual to decadal periods. However, models are consistently biased high in the northern hemisphere and biased low in the southern hemisphere, throughout the depth of the troposphere, and are unable to replicate particular metrics that define the longer term trends in tropospheric ozone as derived from some background sites. When the models compare unfavorably against observations, we discuss the potential causes of model biases and propose directions for future developments, including improved evaluations that may be able to better diagnose the root cause of the model-observation disparity. Overall, model results should be approached critically, including determining whether the model performance is acceptable for the problem being addressed, whether biases can be tolerated or corrected, whether the model is appropriately constituted, and whether there is a way to satisfactorily quantify the uncertainty.

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Section: State Of Knowledgementioning

confidence: 99%

Section: Atmospheric Chemistry In Offline Global Modelsmentioning

confidence: 99%

Tropospheric Ozone Assessment Report: Assessment of global-scale model performance for global and regional ozone distributions, variability, and trends

Young

Naik

Fiore

et al. 2018

Elementa: Science of the Anthropocene

264

274

View full text Add to dashboard Cite

show abstract

“…However, due to the large computational cost, very few studies have examined the decadal trend in air pollution over large regions such as northern hemisphere. Koumoutsaris and Bey (2012) evaluated the global model performance of O 3 trends simulation (1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005) through comparison with long-term observed records from EMEP, the World Data Centre for Greenhouse Gases (WD-CGG, http://ds.data.jma.go.jp/gmd/wdcgg/) and the Clean Air Status and Trends Network (US-CASTNET, http://epa. gov/castnet/).…”

Section: Introductionmentioning

confidence: 99%

Observations and modeling of air quality trends over 1990–2010 across the Northern Hemisphere: China, the United States and Europe

et al. 2015

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Abstract.Trends in air quality across the Northern Hemisphere over a 21-year period were simulated using the Community Multiscale Air Quality (CMAQ) multiscale chemical transport model driven by meteorology from Weather Research and Forecasting (WRF) simulations and internally consistent historical emission inventories obtained from EDGAR. Thorough comparison with several ground observation networks mostly over Europe and North America was conducted to evaluate the model performance as well as the ability of CMAQ to reproduce the observed trends in air quality over the past 2 decades in three regions: eastern China, the continental United States and Europe.The model successfully reproduced the observed decreasing trends in SO 2 , NO 2 , 8 h O 3 maxima, SO 2− 4 and elemental carbon (EC) in the US and Europe. However, the model fails to reproduce the decreasing trends in NO − 3 in the US, potentially pointing to uncertainties of NH 3 emissions. The model failed to capture the 6-year trends of SO 2 and NO 2 in CN-API (China -Air Pollution Index) from 2005 to 2010, but reproduced the observed pattern of O 3 trends shown in three World Data Centre for Greenhouse Gases (WDCGG) sites over eastern Asia. Due to the coarse spatial resolution employed in these calculations, predicted SO 2 and NO 2 concentrations are underestimated relative to all urban networks, i.e., US-AQS (US -Air Quality System; normalized mean bias (NMB) = −38 % and −48 %), EU-AIRBASE (European Air quality data Base; NMB = −18 and −54 %) and CN-API (NMB = −36 and −68 %). Conversely, at the rural network EU-EMEP (European Monitoring and Evaluation Programme), SO 2 is overestimated (NMB from 4 to 150 %) while NO 2 is simulated well (NMB within ±15 %) in all seasons. Correlations between simulated and observed O 3 wintertime daily 8 h maxima (DM8) are poor compared to other seasons for all networks. Better correlation between simulated and observed SO 2− 4 was found compared to that for SO 2 . Underestimation of summer SO 2− 4 in the US may be associated with the uncertainty in precipitation and associated wet scavenging representation in the model. The model exhibits worse performance for NO − 3 predictions, particularly in summer, due to high uncertainties in the gas/particle partitioning of NO − 3 as well as seasonal variations of NH 3 emissions. There are high correlations (R > 0.5) between observed and simulated EC, although the model underestimates the EC concentration by 65 % due to the coarse grid resolution as well as uncertainties in the PM speciation profile associated with EC emissions.The almost linear response seen in the trajectory of modeled O 3 changes in eastern China over the past 2 decades suggests that control strategies that focus on combined control of NO x and volatile organic compound (VOC) emissions with a ratio of 0.46 may provide the most effective means for O 3 reductions for the region devoid of nonlinear response potentially associated with NO x or VOC limitation resulting from alternate strategies. The response of O 3 is ...

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“…It is well established that O 3 formation depends on both temperature (e.g., Weaver et al, 2009) and humidity and changes in these climate variables must be considered when evaluating trends. For example, Bloomer et al (2010) Zhang et al, 2008), (2) free-running chemistryclimate models (CCMs) that generate their own weather, but are driven with historical emissions (Cooper et al, 2014;Lamarque et al, 2010;Parrish et al, 2014), and (3) multi-decadal hindcast simulations driven with observed meteorology and historical emissions (Brown-Steiner et al, 2015;Koumoutsaris and Bey, 2012;Lin et al, 2015b;Lin et al, 2014;Lin et al, 2017;Strode et al, 2015;Xing et al, 2015). The O 3 trends derived from observations are higher than those from CTMs with constant meteorology, and from free-running CCMs by a factor of two at some sites (e.g., Parrish et al, 2014).…”

Section: Interannual Variability and Trends In Baseline And Usb Omentioning

confidence: 99%

Scientific assessment of background ozone over the U.S.: Implications for air quality management

Jaffe

Cooper

Fiore

et al. 2018

Elementa: Science of the Anthropocene

114

149

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Ozone (O3) is a key air pollutant that is produced from precursor emissions and has adverse impacts on human health and ecosystems. In the U.S., the Clean Air Act (CAA) regulates O3levels to protect public health and welfare, but unraveling the origins of surface O3is complicated by the presence of contributions from multiple sources including background sources like stratospheric transport, wildfires, biogenic precursors, and international anthropogenic pollution, in addition to U.S. anthropogenic sources. In this report, we consider more than 100 published studies and assess current knowledge on the spatial and temporal distribution, trends, and sources of background O3over the continental U.S., and evaluate how it influences attainment of the air quality standards. We conclude that spring and summer seasonal mean U.S. background O3(USB O3), or O3formed from natural sources plus anthropogenic sources in countries outside the U.S., is greatest at high elevation locations in the western U.S., with monthly mean maximum daily 8-hour average (MDA8) mole fractions approaching 50 parts per billion (ppb) and annual 4thhighest MDA8s exceeding 60 ppb, at some locations. At lower elevation sites, e.g., along the West and East Coasts, seasonal mean MDA8 USB O3is in the range of 20–40 ppb, with generally smaller contributions on the highest O3days. The uncertainty in U.S. background O3is around ±10 ppb for seasonal mean values and higher for individual days. Noncontrollable O3sources, such as stratospheric intrusions or precursors from wildfires, can make significant contributions to O3on some days, but it is challenging to quantify accurately these contributions. We recommend enhanced routine observations, focused field studies, process-oriented modeling studies, and greater emphasis on the complex photochemistry in smoke plumes as key steps to reduce the uncertainty associated with background O3in the U.S.

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Can a global model reproduce observed trends in summertime surface ozone levels?

Cited by 21 publications

References 68 publications

Tropospheric Ozone Assessment Report: Assessment of global-scale model performance for global and regional ozone distributions, variability, and trends

Tropospheric Ozone Assessment Report: Assessment of global-scale model performance for global and regional ozone distributions, variability, and trends

Observations and modeling of air quality trends over 1990–2010 across the Northern Hemisphere: China, the United States and Europe

Scientific assessment of background ozone over the U.S.: Implications for air quality management

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