The current state and future direction of Eulerian models in simulating the tropospheric chemistry and transport of trace species: a review

Peters, Leonard K.; Berkowitz, Carl M.; Carmichael, Gregory R.; Easter, Richard C.; Fairweather, Graeme; Ghan, Steven J.; Hales, J.M.; Leung, L. Ruby; Pennell, William R.; Potra, Florian A.; Saylor, Rick; Tsang, Tate T. H.

doi:10.1016/1352-2310(94)00235-d

Cited by 121 publications

(34 citation statements)

References 183 publications

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“…These focused on atmospheric profiles (Levy II, 1973), or on the hemispheric and global scales (Fishman and Crutzen, 1978;Fishman et al, 1979;Peters and Jouvanis, 1979;Logan et al, 1981). In the 1980s and early 1990s, tropospheric chemistry models became increasingly more sophisticated in their design, with greater chemical detail, improved parameterizations for atmospheric transport and removal processes, and better estimates for trace gas emissions (see Peters et al (1995) for a review of developments in tropospheric modeling up until this time). One key model result from these earlier studies was confirmation that in situ photochemical tropospheric ozone production was important at a global scale as well as in polluted urban centers, and that, globally, the net influx of ozone from the stratosphere was of secondary importance.…”

Section: Nomenclature Of Global Chemistry Models and International Asmentioning

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

265

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: Nomenclature Of Global Chemistry Models and International Asmentioning

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

265

274

View full text Add to dashboard Cite

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“…The first one relies on the modelling of the atmosphere, starting with the knowledge obtained from basic phenomena. A shortcoming of this kind of model is that the correct parametrization of the physical and chemical atmospheric processes is a very complex task; moreover, these models are often not tailored for real-time applications because of the large amount of input data and the large computational effort needed (see Reference [1] for a recent review). An alternative approach relies on the statistical analysis of time series data.…”

Section: Introductionmentioning

confidence: 99%

Real‐time forecasting of photosmog episodes: the Naples case study

Riccio

Barone

2001

Journal of Chemometrics

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SUMMARYIn this paper we analysed the ozone time series data collected by the local monitoring network in the Naples urban area (southern Italy) during the spring/summer period of 1996. Our aim was to identify a reliable and effective model that could be used for the real-time forecasting of photosmog episodes. We studied the applicability of seasonal autoregressive integrated moving average models with some exogenous variables (ARIMAX) to our case study. The choice of exogenous variables-temperature, [NO 2 ]/[NO] ratio and wind speed-was based on physical reasoning. The forecasting performance of all models was evaluated with data not used in model development, by means of an array of statistical indices: the comparison between observed and forecast means and standard deviations; intercept and slope of a least squares regression of forecast variable on observed variable; mean absolute and root mean square errors; and 95% confidence limits of forecast variable. The assessment of all models was also based on their tendency to forecast critical episodes. It was found that the model using information from the temperature data set to predict peak ozone levels gives satisfactory results, about 70% of critical episodes being correctly predicted by the 24 h ahead forecast function.

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“…These are representations of specific (often subgrid-scale) processes within the larger framework of the conservation equations of motion (Peters et al 1995). Parameterizations implemented in atmospheric models are based on theoretical and statistical relationships between measured quantities.…”

Section: Parameterizationsmentioning

confidence: 99%

A greenhouse-gas information system monitoring and validating emissions reporting and mitigation

Jonietz

Dimotakis²,

Roman³

et al. 2011

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Executive SummaryCurrent GHG-mitigating regimes, whether internationally agreed or self-imposed, rely on the aggregation of self-reported data, with limited checks for consistency and accuracy, for monitoring. As nations commit to more stringent GHG emissions-mitigation actions and as economic rewards or penalties are attached to emission levels, self-reported data will require independent confirmation that they are accurate and reliable, if they are to provide the basis for critical choices and actions that may be required.Supporting emissions-mitigation efforts and agreements, as well as monitoring energy-and fossil-fuel intensive national and global activities would be best achieved by a process of 1. monitoring of emissions and emission-mitigation actions, based, in part, on, 2. (self-) reporting of pertinent bottom-up inventory data, 3. verification that reported data derive from and are consistent with agreed-upon processes and procedures, and 4. validation that reported emissions and emissions-mitigation action data are correct, based on independent measurements (top-down) derived from a suite of sensors in space, air, land, and, possibly, sea, used to deduce and attribute anthropogenic emissions.These data would be assessed and used to deduce and attribute measured GHG concentrations to anthropogenic emissions, attributed geographically and, to the extent possible, by economic sector. The validation element is needed to provide independent assurance that emissions are in accord with reported values, and should be considered as an important addition to the accepted MRV process, leading to a MRV&V process.This study and report focus on attributes of a greenhouse-gas information system (GHGIS) needed to support MRV&V needs. These needs set the function of such a system apart from scientific/research monitoring of GHGs and carbon-cycle systems, and include (not exclusively): the need for a GHGIS that is operational, as required for decision-support; the need for a system that meets specifications derived from imposed requirements; the need for rigorous calibration, verification, and validation (CV&V) standards, processes, and records for all measurement and modeling/data-inversion data; the need to develop and adopt an uncertainty-quantification (UQ) regimen for all measurement and modeling data; and the requirement that GHGIS products can be subjected to third-party questioning and scientific scrutiny.This report examines and assesses presently available capabilities that could contribute to a future GHGIS. These capabilities include sensors and measurement technologies; data analysis and data uncertainty quantification (UQ) practices and methods; and model-based data-inversion practices, methods, and their associated UQ. The report further examines the need for traceable calibration, verification, and validation processes and attached metadata; differences between present science-/research-oriented needs and those that would be required for an operational GHGIS; the development, operation, and maintenance ...

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The current state and future direction of Eulerian models in simulating the tropospheric chemistry and transport of trace species: a review

Cited by 121 publications

References 183 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

Real‐time forecasting of photosmog episodes: the Naples case study

A greenhouse-gas information system monitoring and validating emissions reporting and mitigation

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