Numerical Modeling of Climate-Chemistry Connections: Recent Developments and Future Challenges

Dameris, Martin; Jöckel, Patrick

doi:10.3390/atmos4020132

Cited by 9 publications

(5 citation statements)

References 81 publications

(77 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The fidelity with which a model reproduces near-surface temperature is important for meteorological prediction, mixing parameterizations, and kinetics of chemical reactions [70]. It is therefore important to evaluate model meteorological skill prior to investigating air quality parameters.…”

Section: Evaluating Simulated Air Temperaturementioning

confidence: 99%

Skill-Testing Chemical Transport Models across Contrasting Atmospheric Mixing States Using Radon-222

Chambers

Guérette

Monk³

et al. 2019

Atmosphere

View full text Add to dashboard Cite

We propose a new technique to prepare statistically-robust benchmarking data for evaluating chemical transport model meteorology and air quality parameters within the urban boundary layer. The approach employs atmospheric class-typing, using nocturnal radon measurements to assign atmospheric mixing classes, and can be applied temporally (across the diurnal cycle), or spatially (to create angular distributions of pollutants as a top-down constraint on emissions inventories). In this study only a short (<1-month) campaign is used, but grouping of the relative mixing classes based on nocturnal mean radon concentrations can be adjusted according to dataset length (i.e., number of days per category), or desired range of within-class variability. Calculating hourly distributions of observed and simulated values across diurnal composites of each class-type helps to: (i) bridge the gap between scales of simulation and observation, (ii) represent the variability associated with spatial and temporal heterogeneity of sources and meteorology without being confused by it, and (iii) provide an objective way to group results over whole diurnal cycles that separates ‘natural complicating factors’ (synoptic non-stationarity, rainfall, mesoscale motions, extreme stability, etc.) from problems related to parameterizations, or between-model differences. We demonstrate the utility of this technique using output from a suite of seven contemporary regional forecast and chemical transport models. Meteorological model skill varied across the diurnal cycle for all models, with an additional dependence on the atmospheric mixing class that varied between models. From an air quality perspective, model skill regarding the duration and magnitude of morning and evening “rush hour” pollution events varied strongly as a function of mixing class. Model skill was typically the lowest when public exposure would have been the highest, which has important implications for assessing potential health risks in new and rapidly evolving urban regions, and also for prioritizing the areas of model improvement for future applications.

show abstract

Section: Evaluating Simulated Air Temperaturementioning

confidence: 99%

Skill-Testing Chemical Transport Models across Contrasting Atmospheric Mixing States Using Radon-222

Chambers

Guérette

Monk³

et al. 2019

Atmosphere

View full text Add to dashboard Cite

show abstract

“…Currently, global and hemispheric models are the primary tools available for investigating responses of Arctic air quality and climate to natural and anthropogenic influences. Chemistry-Climate Models (CCMs), and similar models, are based on physical principles and they reproduce important aspects of observed global and regional air pollution (Dameris and Jöckel, 2013;AMAP, 2015). In recent years, many global climate models have been extended into Earth System Models (ESMs), by including interactions between the physical climate system and ecosystems.…”

Section: Towards Increased Model Complexity -What Opportunities Are Omentioning

confidence: 99%

Arctic air pollution: Challenges and opportunities for the next decade

et al. 2016

View full text Add to dashboard Cite

The Arctic is a sentinel of global change. This region is influenced by multiple physical and socio-economic drivers and feedbacks, impacting both the natural and human environment. Air pollution is one such driver that impacts Arctic climate change, ecosystems and health but significant uncertainties still surround quantification of these effects. Arctic air pollution includes harmful trace gases (e.g. tropospheric ozone) and particles (e.g. black carbon, sulphate) and toxic substances (e.g. polycyclic aromatic hydrocarbons) that can be transported to the Arctic from emission sources located far outside the region, or emitted within the Arctic from activities including shipping, power production, and other industrial activities. This paper qualitatively summarizes the complex science issues motivating the creation of a new international initiative, PACES (air Pollution in the Arctic: Climate, Environment and Societies). Approaches for coordinated, international and interdisciplinary research on this topic are described with the goal to improve predictive capability via new understanding about sources, processes, feedbacks and impacts of Arctic air pollution. Overarching research actions are outlined, in which we describe our recommendations for 1) the development of trans-disciplinary approaches combining social and economic research with investigation of the chemical and physical aspects Arctic air pollution: Challenges and opportunities for the next decade of Arctic air pollution; 2) increasing the quality and quantity of observations in the Arctic using long-term monitoring and intensive field studies, both at the surface and throughout the troposphere; and 3) developing improved predictive capability across a range of spatial and temporal scales. Domain Editor-in-Chief

show abstract

“…Even so, atmospheric or atmosphereocean general circulation models (GCMs) are increasingly being transformed into chemistry-climate models (CCMs) with interactive representations of chemistry and aerosols (e.g. Zhang, 2008;Dameris and Jöckel, 2013;Lamarque et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Simulation of tropospheric chemistry and aerosols with the climate model EC-Earth

et al. 2014

View full text Add to dashboard Cite

Abstract. We have integrated the atmospheric chemistry and transport model TM5 into the global climate model EC-Earth version 2.4. We present an overview of the TM5 model and the two-way data exchange between TM5 and the IFS model from the European Centre for Medium-Range Weather Forecasts (ECMWF), the atmospheric general circulation model of EC-Earth. In this paper we evaluate the simulation of tropospheric chemistry and aerosols in a oneway coupled configuration. We have carried out a decadal simulation for present-day conditions and calculated chemical budgets and climatologies of tracer concentrations and aerosol optical depth. For comparison we have also performed offline simulations driven by meteorological fields from ECMWF's ERA-Interim reanalysis and output from the EC-Earth model itself. Compared to the offline simulations, the online-coupled system produces more efficient vertical mixing in the troposphere, which reflects an improvement of the treatment of cumulus convection. The chemistry in the EC-Earth simulations is affected by the fact that the current version of EC-Earth produces a cold bias with too dry air in large parts of the troposphere. Compared to the ERA-Interim driven simulation, the oxidizing capacity in EC-Earth is lower in the tropics and higher in the extratropics. The atmospheric lifetime of methane in EC-Earth is 9.4 years, which is 7 % longer than the lifetime obtained with ERA-Interim but remains well within the range reported in the literature. We further evaluate the model by comparing the simulated climatologies of surface radon-222 and carbon monoxide, tropospheric and surface ozone, and aerosol optical depth against observational data. The work presented in this study is the first step in the development of EC-Earth into an Earth system model with fully interactive atmospheric chemistry and aerosols.

show abstract

Numerical Modeling of Climate-Chemistry Connections: Recent Developments and Future Challenges

Cited by 9 publications

References 81 publications

Skill-Testing Chemical Transport Models across Contrasting Atmospheric Mixing States Using Radon-222

Skill-Testing Chemical Transport Models across Contrasting Atmospheric Mixing States Using Radon-222

Arctic air pollution: Challenges and opportunities for the next decade

Simulation of tropospheric chemistry and aerosols with the climate model EC-Earth

Contact Info

Product

Resources

About