Evaluation of modeled surface ozone biases as a function of cloud cover fraction

Kim, Hyun Cheol; Lee, P.; Ngan, Fong; Tang, Youhua; Yoo, Hyelim; Pan, Li

doi:10.5194/gmd-8-2959-2015

Cited by 11 publications

(6 citation statements)

References 29 publications

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“…This is because of the weaker response in the model to cloud cover and rain (4 ppb relative to clear-sky) than observed (7 ppb and 11 ppb respectively). Kim et al (2015a) previously observed a 1 ppb decrease in ozone per 10 % increase in cloud cover over the 25 contiguous United States, and found that their model response to cloud (from the NOAA National Air Quality Forecast) was approximately half that, a similar bias to our model. We conducted a model sensitivity study with the low cloud fraction adjusted to the mean observed value of 32 % from the ASOS observations.…”

Section: Relationship To Cloud Cover and Precipitationsupporting

confidence: 80%

Resolving ozone vertical gradients in air quality models

Travis

Jacob

Keller

et al. 2017

Preprint

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Abstract. Models severely overestimate surface ozone in the Southeast US during summertime and this overestimation has implications for the design of air quality regulations. We use the GEOS-Chem model to interpret ozone observations from aircraft 15 (SEAC 4 RS), ozonesondes (SEACIONS), and surface sites (CASTNET) in August-September 2013. After correcting for a 30-50 % NOx emission overestimate in the US EPA National Emission Inventory, we find that the model is unbiased relative to aircraft observations below 1 km. However, surface observations of maximum daily 8-h average (MDA8) ozone are still biased high in the model (averaging 48 ± 9 ppb) compared to observations (40 ± 9 ppb). The low tail in the observations (MDA8 ozone < 25 ppb) is associated with rain and is not captured by the model. The model bias decreases by 3 ppb when accounting for the subgrid 20 vertical gradient between the lowest model level (centered 60 m above ground) and the measurement altitude (10 m). The model underestimates low cloud cover, but this underestimate is insufficient to explain the remaining surface ozone bias because the response of model ozone to cloud cover is weaker than observed. Midday ozonesondes at Huntsville, Alabama show mean decreases in ozone from 1 km to the surface of 4 ppb under clear-sky and 7 ppb under low cloud, whereas the model decreases by only 1 ppb under both conditions. By contrast, potential temperature below 1 km is well-mixed in both the observations and the 25 model. The observations thus imply a strong asymmetry between top-down and bottom-up mixing that is missing from GEOSChem and appears to be insufficiently represented in current air quality models. A sensitivity simulation reducing top-down eddy diffusion and removing top-down non-local vertical transport of ozone can reproduce the observed ozone gradients in the mixed layer.

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Section: Relationship To Cloud Cover and Precipitationsupporting

confidence: 80%

Resolving ozone vertical gradients in air quality models

Travis

Jacob

Keller

et al. 2017

Preprint

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“…Differences in these average profiles can have many causes: temperature and O 3 profiles, spectral data for both J -O1D and J -NO2, ways of integrating over wavelength, surface albedo conditions, treatment of Rayleigh scattering, basic radiative transfer methods, SZA, and, of course, clouds. In typical comparisons we try to control these differences by specifying as many conditions as possible, but here we want to compare the "natural" J s used in their full-scale simulations (e.g., Lamarque et al, 2013) and thus leave each model to its native atmospheres, spectral data, algorithms, and approximations.…”

Section: Measuring and Modeling J Values Under Realistic Cloudy Skiesmentioning

confidence: 99%

Cloud impacts on photochemistry: building a climatology of photolysis rates from the Atmospheric Tomography mission

et al. 2018

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Abstract. Measurements from actinic flux spectroradiometers on board the NASA DC-8 during the Atmospheric Tomography (ATom) mission provide an extensive set of statistics on how clouds alter photolysis rates (J values) throughout the remote Pacific and Atlantic Ocean basins. J values control tropospheric ozone and methane abundances, and thus clouds have been included for more than three decades in tropospheric chemistry modeling. ATom made four profiling circumnavigations of the troposphere capturing each of the seasons during 2016–2018. This work examines J values from the Pacific Ocean flights of the first deployment, but publishes the complete Atom-1 data set (29 July to 23 August 2016). We compare the observed J values (every 3 s along flight track) with those calculated by nine global chemistry–climate/transport models (globally gridded, hourly, for a mid-August day). To compare these disparate data sets, we build a commensurate statistical picture of the impact of clouds on J values using the ratio of J-cloudy (standard, sometimes cloudy conditions) to J-clear (artificially cleared of clouds). The range of modeled cloud effects is inconsistently large but they fall into two distinct classes: (1) models with large cloud effects showing mostly enhanced J values aloft and or diminished at the surface and (2) models with small effects having nearly clear-sky J values much of the time. The ATom-1 measurements generally favor large cloud effects but are not precise or robust enough to point out the best cloud-modeling approach. The models here have resolutions of 50–200 km and thus reduce the occurrence of clear sky when averaging over grid cells. In situ measurements also average scattered sunlight over a mixed cloud field, but only out to scales of tens of kilometers. A primary uncertainty remains in the role of clouds in chemistry, in particular, how models average over cloud fields, and how such averages can simulate measurements.

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“…Evaluation of cloud fields is challenging, based in part on the need to define so-called "cloudiness". Kim et al (2015) found no consensus regarding the physical interpretation of clouds from ground observation, chemistry models, and satellite measurements. However, cloud fraction might have an important impact on photochemical reactions, such as the production of surface ozone.…”

Section: Model Evaluationmentioning

confidence: 99%

Regional contributions to particulate matter concentration in the Seoul metropolitan area, South Korea: seasonal variation and sensitivity to meteorology and emissions inventory

et al. 2017

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Abstract. The impact of regional emissions (e.g., domestic and international) on surface particulate matter (PM) concentrations in the Seoul metropolitan area (SMA), South Korea, and its sensitivities to meteorology and emissions inventories are quantitatively estimated for 2014 using regional air quality modeling systems. Located on the downwind side of strong sources of anthropogenic emissions, South Korea bears the full impact of the regional transport of pollutants and their precursors. However, the impact of foreign emissions sources has not yet been fully documented. We utilized two regional air quality simulation systems: (1) a Weather Research and Forecasting and Community Multi-Scale Air Quality (CMAQ) system and (2) a United Kingdom Met Office Unified Model and CMAQ system. The following combinations of emissions inventories are used: the Intercontinental Chemical Transport Experiment-Phase B, the Intercomparison Study for Asia 2010, and the National Institute of Environment Research Clean Air Policy Support System. Partial contributions of domestic and foreign emissions are estimated using a brute force approach, adjusting South Korean emissions to 50 %. Results show that foreign emissions contributed ∼ 60 % of SMA surface PM concentration in 2014. Estimated contributions display clear seasonal variation, with foreign emissions having a higher impact during the cold season (fall to spring), reaching ∼ 70 % in March, and making lower contributions in the summer, ∼ 45 % in September. We also found that simulated surface PM concentration is sensitive to meteorology, but estimated contributions are mostly consistent. Regional contributions are also found to be sensitive to the choice of emissions inventories.

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Evaluation of modeled surface ozone biases as a function of cloud cover fraction

Cited by 11 publications

References 29 publications

Resolving ozone vertical gradients in air quality models

Resolving ozone vertical gradients in air quality models

Cloud impacts on photochemistry: building a climatology of photolysis rates from the Atmospheric Tomography mission

Regional contributions to particulate matter concentration in the Seoul metropolitan area, South Korea: seasonal variation and sensitivity to meteorology and emissions inventory

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