Global modelling studies of hydrogen and its isotopomers using STOCHEM-CRI:Likely radiative forcing consequences of a future hydrogen economy

A global chemistry-transport model has been employed to describe the global sources and sinks of hydrogen (H2) and its isotopomer (HD). The model is able to satisfactorily describe the observed tropospheric distributions of H2 and HD and deliver budgets and turnovers which agree with literature studies. We than go on to quantify the methane and ozone responses to emission pulses of hydrogen and their likely radiative forcing consequences. These radiative forcing consequences have been expressed on a 1 Tg basis and integrated over a hundred-year time horizon. When compared to the consequences of a 1 Tg emission pulse of carbon dioxide, 1 Tg of hydrogen causes 5 ± 1 times as much time-integrated radiative forcing over a hundred-year time horizon. That is to say, hydrogen has a global warming potential (GWP) of 5 ± 1 over a hundred-year time horizon. The global warming consequences of a hydrogen-based low-carbon energy system therefore depend critically on the hydrogen leakage rate. If the leakage of hydrogen from all stages in the production, distribution, storage and utilisation of hydrogen is efficiently curtailed, then hydrogen-based energy systems appear to be an attractive proposition in providing a future replacement for fossil-fuel based energy systems

The Potential of the Geostationary Carbon Cycle Observatory (GeoCarb) to Provide Multi-scale Constraints on the Carbon Cycle in the Americas

The second NASA Earth Venture Mission, Geostationary Carbon Cycle Observatory (GeoCarb), will provide measurements of atmospheric carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), and solar-induced fluorescence (SIF) from Geostationary Orbit (GEO). The GeoCarb mission will deliver daily maps of column concentrations of CO2, CH4, and CO over the observed landmasses in the Americas at a spatial resolution of roughly 10 × 10 km. Persistent measurements of CO2, CH4, CO, and SIF will contribute significantly to resolving carbon emissions and illuminating biotic processes at urban to continental scales, which will allow the improvement of modeled biogeochemical processes in Earth System Models as well as monitor the response of the biosphere to disturbance. This is essential to improve understanding of the Carbon-Climate connection. In this paper, we introduce the instrument and the GeoCarb Mission, and we demonstrate the potential scientific contribution of the mission through a series of CO2 and CH4 simulation experiments. We find that GeoCarb will be able to constrain emissions at urban to continental spatial scales on weekly to annual time scales. The GeoCarb mission particularly builds upon the Orbiting Carbon Obserevatory-2 (OCO-2), which is flying in Low Earth Orbit

Multiscale applications of two online-coupled meteorology-chemistry models during recent field campaigns in Australia, Part I: Model description and WRF/Chem-ROMS evaluation using surface and satellite data and sensitivity to spatial grid resolutions

Air pollution and associated human exposure are important research areas in Greater Sydney, Australia. Several field campaigns were conducted to characterize the pollution sources and their impacts on ambient air quality including the Sydney Particle Study Stages 1 and 2 (SPS1 and SPS2), and the Measurements of Urban, Marine, and Biogenic Air (MUMBA). In this work, the Weather Research and Forecasting model with chemistry (WRF/Chem) and the coupled WRF/Chem with the Regional Ocean Model System (ROMS) (WRF/Chem-ROMS) are applied during these field campaigns to assess the models\u27 capability in reproducing atmospheric observations. The model simulations are performed over quadruple-nested domains at grid resolutions of 81-, 27-, 9-, and 3-km over Australia, an area in southeastern Australia, an area in New South Wales, and the Greater Sydney area, respectively. A comprehensive model evaluation is conducted using surface observations from these field campaigns, satellite retrievals, and other data. This paper evaluates the performance of WRF/Chem-ROMS and its sensitivity to spatial grid resolutions. The model generally performs well at 3-, 9-, and 27-km resolutions for sea-surface temperature and boundary layer meteorology in terms of performance statistics, seasonality, and daily variation. Moderate biases occur for temperature at 2-m and wind speed at 10-m in the mornings and evenings due to the inaccurate representation of the nocturnal boundary layer and surface heat fluxes. Larger underpredictions occur for total precipitation due to the limitations of the cloud microphysics scheme or cumulus parameterization. The model performs well at 3-, 9-, and 27-km resolutions for surface O 3 in terms of statistics, spatial distributions, and diurnal and daily variations. The model underpredicts PM 2.5 and PM 10 during SPS1 and MUMBA but overpredicts PM 2.5 and underpredicts PM 10 during SPS2. These biases are attributed to inaccurate meteorology, precursor emissions, insufficient SO2 conversion to sulfate, inadequate dispersion at finer grid resolutions, and underprediction in secondary organic aerosol. The model gives moderate biases for net shortwave radiation and cloud condensation nuclei but large biases for other radiative and cloud variables. The performance of aerosol optical depth and latent/sensible heat flux varies for different simulation periods. Among all variables evaluated, wind speed at 10-m, precipitation, surface concentrations of CO, NO, NO 2 , SO 2 , O 3 , PM 2.5 , and PM 10 , aerosol optical depth, cloud optical thickness, cloud condensation nuclei, and column NO 2 show moderate-to-strong sensitivity to spatial grid resolutions. The use of finer grid resolutions (3- or 9-km) can generally improve the performance for those variables. While the performance for most of these variables is consistent with that over the U.S. and East Asia, several differences along with future work are identified to pinpoint reasons for such differences

Hot summers: Effect of extreme temperatures on ozone in Sydney, Australia

2018 by the authors. Poor air quality is often associated with hot weather, but the quantitative attribution of high temperatures on air quality remains unclear. In this study, the effect of elevated temperatures on air quality is investigated in Greater Sydney using January 2013, a period of extreme heat during which temperatures at times exceeded 40 °C, as a case study. Using observations from 17 measurement sites and the Weather Research and Forecasting Chemistry (WRF-Chem) model, we analyse the effect of elevated temperatures on ozone in Sydney by running a number of sensitivity studies in which: (1) the model is run with biogenic emissions generated by MEGAN and separately run with monthly average Model of Emissions of Gases and Aerosols from Nature ( MEGAN) biogenic emissions (for January 2013); (2) the model results from the standard run are compared with those in which average temperatures (for January 2013) are only applied to the chemistry; (3) the model is run using both averaged biogenic emissions and temperatures; and (4 and 5) the model is run with half and zero biogenic emissions. The results show that the impact on simulated ozone through the effect of temperature on reaction rates is similar to the impact via the effect of temperature on biogenic emissions and the relative impacts are largely additive when compared to the run in which both are averaged. When averaged across 17 sites in Greater Sydney, the differences between ozone simulated under standard and averaged model conditions are as high as 16 ppbv. Removing biogenic emissions in the model has the effect of removing all simulated ozone episodes during extreme heat periods, highlighting the important role of biogenic emissions in Australia, where Eucalypts are a key biogenic source

Evaluation of regional air quality models over Sydney and Australia: Part 1-Meteorological model comparison

The ability of meteorological models to accurately characterise regional meteorology plays a crucial role in the performance of photochemical simulations of air pollution. As part of the research funded by the Australian government\u27s Department of the Environment Clean Air and Urban Landscape hub, this study set out to complete an intercomparison of air quality models over the Sydney region. This intercomparison would test existing modelling capabilities, identify any problems and provide the necessary validation of models in the region. The first component of the intercomparison study was to assess the ability of the models to reproduce meteorological observations, since it is a significant driver of air quality. To evaluate the meteorological component of these air quality modelling systems, seven different simulations based on varying configurations of inputs, integrations and physical parameterizations of two meteorological models (the Weather Research and Forecasting (WRF) and Conformal Cubic Atmospheric Model (CCAM)) were examined. The modelling was conducted for three periods coinciding with comprehensive air quality measurement campaigns (the Sydney Particle Studies (SPS) 1 and 2 and the Measurement of Urban, Marine and Biogenic Air (MUMBA)). The analysis focuses on meteorological variables (temperature, mixing ratio of water, wind (via wind speed and zonal wind components), precipitation and planetary boundary layer height), that are relevant to air quality. The surface meteorology simulations were evaluated against observations from seven Bureau of Meteorology (BoM) Automatic Weather Stations through composite diurnal plots, Taylor plots and paired mean bias plots. Simulated vertical profiles of temperature, mixing ratio of water and wind (via wind speed and zonal wind components) were assessed through comparison with radiosonde data from the Sydney Airport BoM site. The statistical comparisons with observations identified systematic overestimations of wind speeds that were more pronounced overnight. The temperature was well simulated, with biases generally between ±2 °C and the largest biases seen overnight (up to 4 °C). The models tend to have a drier lower atmosphere than observed, implying that better representations of soil moisture and surface moisture fluxes would improve the subsequent air quality simulations. On average the models captured local-scale meteorological features, like the sea breeze, which is a critical feature driving ozone formation in the Sydney Basin. The overall performance and model biases were generally within the recommended benchmark values (e.g., ±1 °C mean bias in temperature, ±1 g/kg mean bias of water vapour mixing ratio and ±1.5 m s-1 mean bias of wind speed) except at either end of the scale, where the bias tends to be larger. The model biases reported here are similar to those seen in other model intercomparisons

Skill-testing chemical transport models across contrasting atmospheric mixing states using radon-222

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 ( \u3c 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 \u27natural complicating factors\u27 (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

Hot Summers: Effect of Extreme Temperatures on Ozone in Sydney, Australia

Poor air quality is often associated with hot weather, but the quantitative attribution of high temperatures on air quality remains unclear. In this study, the effect of elevated temperatures on air quality is investigated in Greater Sydney using January 2013, a period of extreme heat during which temperatures at times exceeded 40 °C, as a case study. Using observations from 17 measurement sites and the Weather Research and Forecasting Chemistry (WRF-Chem) model, we analyse the effect of elevated temperatures on ozone in Sydney by running a number of sensitivity studies in which: (1) the model is run with biogenic emissions generated by MEGAN and separately run with monthly average Model of Emissions of Gases and Aerosols from Nature ( MEGAN) biogenic emissions (for January 2013); (2) the model results from the standard run are compared with those in which average temperatures (for January 2013) are only applied to the chemistry; (3) the model is run using both averaged biogenic emissions and temperatures; and (4 and 5) the model is run with half and zero biogenic emissions. The results show that the impact on simulated ozone through the effect of temperature on reaction rates is similar to the impact via the effect of temperature on biogenic emissions and the relative impacts are largely additive when compared to the run in which both are averaged. When averaged across 17 sites in Greater Sydney, the differences between ozone simulated under standard and averaged model conditions are as high as 16 ppbv. Removing biogenic emissions in the model has the effect of removing all simulated ozone episodes during extreme heat periods, highlighting the important role of biogenic emissions in Australia, where Eucalypts are a key biogenic source