scholarly journals On the use of14CO2as a tracer for fossil fuel CO2: Quantifying uncertainties using an atmospheric transport model

2009 ◽  
Vol 114 (D22) ◽  
Author(s):  
Jocelyn Turnbull ◽  
Peter Rayner ◽  
John Miller ◽  
Tobias Naegler ◽  
Philippe Ciais ◽  
...  
Keyword(s):  
Fossil Fuel ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Atmospheric Transport Model

scholarly journals Spatiotemporal patterns of the fossil-fuel CO<sub>2</sub> signal in central Europe: Results from a high-resolution atmospheric transport model

2017 ◽  
Author(s):  
Yu Liu ◽  
Nicolas Gruber ◽  
Dominik Brunner
Keyword(s):  
High Resolution ◽  
Central Europe ◽  
Western Europe ◽  
Fossil Fuel ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Temporal Variations ◽  
Atmospheric Co2 ◽  
Fuel Component ◽  
Atmospheric Transport Model

Abstract. The emission of CO2 from the burning of fossil fuel is a prime determinant of variations in atmospheric CO2. Here, we simulate this fossil fuel signal together with the natural and background components with a regional high-resolution atmospheric transport model for central and southern Europe considering separately the emissions from different sectors and countries on the basis of emission inventories and hourly emission time functions. The simulated variations in atmospheric CO2 agree very well with observation-based estimates, although the observed variance is slightly underestimated, particularly for the fossil fuel component. Despite relatively rapid atmospheric mixing, the simulated fossil fuel signal reveals distinct annual mean structures deep into the troposphere reflecting the spatially dense aggregation of most emissions. The fossil fuel signal accounts for more than half of the total (fossil fuel + biospheric + background) temporal variations in atmospheric CO2 in most areas of northern and western central Europe, with the largest variations occurring on diurnal timescales owing to the combination of diurnal variations in emissions and atmospheric mixing/transport out of the surface layer. Their co-variance leads to a fossil-fuel diurnal rectifier effect with a magnitude as large as 9 ppm compared to a case with time-constant emissions. The spatial pattern of CO2 from the different sectors largely reflects the distribution and relative magnitude of the corresponding emissions, with power plant emissions leaving the most distinguished mark. An exception is southern and western Europe, where the emissions from the transportation sector dominate the fossil fuel signal. Most of the fossil fuel CO2 remains within the country responsible for the emission, although in smaller countries, up to 80 % of the fossil fuel signal can come from abroad. A fossil fuel emission reduction of 30 % is clearly detectable for a surface-based observing system for atmospheric CO2, while it is beyond the edge of detectability for the current generation of satellites with the exception of a few hotspot sites. Changes in variability in atmospheric CO2 might open an additional door for the monitoring and verification of changes in fossil fuel emissions, primarily for surface based systems.

scholarly journals Spatiotemporal patterns of the fossil-fuel CO<sub>2</sub> signal in central Europe: results from a high-resolution atmospheric transport model

2017 ◽  
Vol 17 (22) ◽  
pp. 14145-14169 ◽  
Author(s):  
Yu Liu ◽  
Nicolas Gruber ◽  
Dominik Brunner
Keyword(s):  
High Resolution ◽  
Central Europe ◽  
Fossil Fuel ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Temporal Variations ◽  
Atmospheric Co2 ◽  
Fuel Component ◽  
Atmospheric Transport Model ◽  
Fossil Fuel Emissions

Abstract. The emission of CO2 from the burning of fossil fuel is a prime determinant of variations in atmospheric CO2. Here, we simulate this fossil-fuel signal together with the natural and background components with a regional high-resolution atmospheric transport model for central and southern Europe considering separately the emissions from different sectors and countries on the basis of emission inventories and hourly emission time functions. The simulated variations in atmospheric CO2 agree very well with observation-based estimates, although the observed variance is slightly underestimated, particularly for the fossil-fuel component. Despite relatively rapid atmospheric mixing, the simulated fossil-fuel signal reveals distinct annual mean structures deep into the troposphere, reflecting the spatially dense aggregation of most emissions. The fossil-fuel signal accounts for more than half of the total (fossil fuel + biospheric + background) temporal variations in atmospheric CO2 in most areas of northern and western central Europe, with the largest variations occurring on diurnal timescales owing to the combination of diurnal variations in emissions and atmospheric mixing and transport out of the surface layer. The covariance of the fossil-fuel emissions and atmospheric transport on diurnal timescales leads to a diurnal fossil-fuel rectifier effect of up to 9 ppm compared to a case with time-constant emissions. The spatial pattern of CO2 from the different sectors largely reflects the distribution and relative magnitude of the corresponding emissions, with power plant emissions leaving the most distinguished mark. An exception is southern and western Europe, where the emissions from the transportation sector dominate the fossil-fuel signal. Most of the fossil-fuel CO2 remains within the country responsible for the emission, although in smaller countries up to 80 % of the fossil-fuel signal can come from abroad. A fossil-fuel emission reduction of 30 % is clearly detectable for a surface-based observing system for atmospheric CO2, while it is beyond the edge of detectability for the current generation of satellites with the exception of a few hotspot sites. Changes in variability in atmospheric CO2 might open an additional door for the monitoring and verification of changes in fossil-fuel emissions, primarily for surface-based systems.

scholarly journals Inverse modelling of CF<sub>4</sub> and NF<sub>3</sub> emissions in East Asia

2018 ◽  
Vol 18 (18) ◽  
pp. 13305-13320 ◽  
Author(s):  
Tim Arnold ◽  
Alistair J. Manning ◽  
Jooil Kim ◽  
Shanlan Li ◽  
Helen Webster ◽  
...  
Keyword(s):  
South Korea ◽  
East Asia ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Atmospheric Gases ◽  
Atmospheric Measurements ◽  
Small Uncertainty ◽  
Measurement Location ◽  
Atmospheric Transport Model ◽  
Good Agreement

Abstract. Decadal trends in the atmospheric abundances of carbon tetrafluoride (CF4) and nitrogen trifluoride (NF3) have been well characterised and have provided a time series of global total emissions. Information on locations of emissions contributing to the global total, however, is currently poor. We use a unique set of measurements between 2008 and 2015 from the Gosan station, Jeju Island, South Korea (part of the Advanced Global Atmospheric Gases Experiment network), together with an atmospheric transport model, to make spatially disaggregated emission estimates of these gases in East Asia. Due to the poor availability of good prior information for this study, our emission estimates are largely influenced by the atmospheric measurements. Notably, we are able to highlight emission hotspots of NF3 and CF4 in South Korea due to the measurement location. We calculate emissions of CF4 to be quite constant between the years 2008 and 2015 for both China and South Korea, with 2015 emissions calculated at 4.3±2.7 and 0.36±0.11 Gg yr−1, respectively. Emission estimates of NF3 from South Korea could be made with relatively small uncertainty at 0.6±0.07 Gg yr−1 in 2015, which equates to ∼1.6 % of the country's CO2 emissions. We also apply our method to calculate emissions of CHF3 (HFC-23) between 2008 and 2012, for which our results find good agreement with other studies and which helps support our choice in methodology for CF4 and NF3.

scholarly journals A European-wide <sup>222</sup>Radon and <sup>222</sup>Radon progeny comparison study

2016 ◽  
Author(s):  
Dominik Schmithüsen ◽  
Scott Chambers ◽  
Bernd Fischer ◽  
Stefan Gilge ◽  
Juha Hatakka ◽  
...  
Keyword(s):  
Transport Model ◽  
Atmospheric Transport ◽  
Ground Level ◽  
Data Sets ◽  
Correction Factors ◽  
Comparison Study ◽  
Data Set ◽  
Parallel Data ◽  
Linear Regressions ◽  
Atmospheric Transport Model

Abstract. A European-wide 222Radon/222Radon progeny comparison study has been conducted in order to determine correction factors that could be applied to existing atmospheric 222Radon data sets for quantitative use of this tracer in atmospheric transport model validation. Two compact and easy-to-transport Heidelberg Radon Monitors (HRM) were moved around to run for at least one month at each of the nine European measurement stations that were included in the comparison. Linear regressions between parallel data sets were calculated, yielding correction factors relative to the HRM ranging from 0.68 to 1.45. A calibration bias between ANSTO (Australian Nuclear Science and Technology Organisation) two-filter radon monitors and the HRM of ANSTO/HRM = 1.11 ± 0.05 was found. For continental stations, which use one-filter systems, preliminary 214Po/222Rn disequilibrium values were estimated to lie between 0.8 at mountain stations (e.g. Schauinsland) and 0.9 at non-mountain sites for sampling heights around 20 to 30 m above ground level. Respective corrections need to be applied to obtain a consistent European 222Radon data set for further applications.

scholarly journals Global to local impacts on atmospheric CO2 caused by COVID-19 lockdown

2021 ◽  
Author(s):  
Ning Zeng
Keyword(s):  
Fossil Fuel ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Carbon Sink ◽  
Co2 Concentration ◽  
Atmospheric Co2 ◽  
Monitoring Systems ◽  
Carbon Cycle Model ◽  
Safe Level ◽  
Small Change

&lt;p&gt;&lt;span&gt;The world-wide lockdown in response to the COVID-19 pandemic in year 2020 led to economic slowdown and large reduction of fossil fuel CO2 emissions 1,2, but it is unclear how much it would reduce atmospheric CO2 concentration, the main driver of climate change, and whether it can be observed. We estimated that a 7.9% reduction in emissions for 4 months would result in a 0.25 ppm decrease in the Northern Hemisphere CO2, an increment that is within the capability of current CO2 analyzers, but is a few times smaller than natural CO2 variabilities caused by weather and the biosphere such as El Nino. We used a state-of-the-art atmospheric transport model to simulate CO2, driven by a new daily fossil fuel emissions dataset and hourly biospheric fluxes from a carbon cycle model forced with observed climate variability. Our results show a 0.13 ppm decrease in atmospheric column CO2 anomaly averaged over 50S-50N for the period February-April 2020 relative to a 10-year climatology. A similar decrease was observed by the carbon satellite GOSAT3. Using model sensitivity experiments, we further found that COVID, the biosphere and weather contributed 54%, 23%, and 23% respectively. In May 2020, the CO2 anomaly continued to decrease and was 0.36 ppm below climatology, mostly due to the COVID reduction and a biosphere that turned from a relative carbon source to carbon sink, while weather impact fluctuated. This seemingly small change stands out as the largest sub-annual anomaly in the last 10 years. Measurements from global ground stations were analyzed. At city scale, on-road CO2 enhancement measured in Beijing shows reduction of 20-30 ppm, consistent with drastically reduced traffic during the lockdown, while station data suggest that the expected COVID signal of 5-10 ppm was swamped by weather-driven variability on multi-day time scales. The ability of our current carbon monitoring systems in detecting the small and short-lasting COVID signal on the background of fossil fuel CO2 accumulated over the last two centuries is encouraging. The COVID-19 pandemic is an unintended experiment whose impact suggests that to keep atmospheric CO2 at a climate-safe level will require sustained effort of similar magnitude and improved accuracy and expanded spatiotemporal coverage of our monitoring systems.&lt;/span&gt;&lt;/p&gt;

scholarly journals Characterizing uncertainties in atmospheric inversions of fossil fuel CO<sub>2</sub> emissions in California

2019 ◽  
Vol 19 (5) ◽  
pp. 2991-3006 ◽  
Author(s):  
Kieran Brophy ◽  
Heather Graven ◽  
Alistair J. Manning ◽  
Emily White ◽  
Tim Arnold ◽  
...  
Keyword(s):  
Fossil Fuel ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Temporal Distribution ◽  
Potential Effect ◽  
Spatial And Temporal Distribution ◽  
Transport Models ◽  
Total State ◽  
Pseudo Data ◽  
Atmospheric Inversions

Abstract. Atmospheric inverse modelling has become an increasingly useful tool for evaluating emissions of greenhouse gases including methane, nitrous oxide, and synthetic gases such as hydrofluorocarbons (HFCs). Atmospheric inversions for emissions of CO2 from fossil fuel combustion (ffCO2) are currently being developed. The aim of this paper is to investigate potential errors and uncertainties related to the spatial and temporal prior representation of emissions and modelled atmospheric transport for the inversion of ffCO2 emissions in the US state of California. We perform simulation experiments based on a network of ground-based observations of CO2 concentration and radiocarbon in CO2 (a tracer of ffCO2), combining prior (bottom-up) emission models and transport models currently used in many atmospheric studies. The potential effect of errors in the spatial and temporal distribution of prior emission estimates is investigated in experiments by using perturbed versions of the emission estimates used to create the pseudo-data. The potential effect of transport error was investigated by using three different atmospheric transport models for the prior and pseudo-data simulations. We find that the magnitude of biases in posterior total state emissions arising from errors in the spatial and temporal distribution in prior emissions in these experiments are 1 %–15 % of posterior total state emissions and are generally smaller than the 2σ uncertainty in posterior emissions. Transport error in these experiments introduces biases of −10 % to +6 % into posterior total state emissions. Our results indicate that uncertainties in posterior total state ffCO2 estimates arising from the choice of prior emissions or atmospheric transport model are on the order of 15 % or less for the ground-based network in California we consider. We highlight the need for temporal variations to be included in prior emissions and for continuing efforts to evaluate and improve the representation of atmospheric transport for regional ffCO2 inversions.

scholarly journals The Canadian atmospheric transport model for simulating greenhouse gas evolution on regional scales: GEM-MACH-GHG v.137-reg

2019 ◽  
Author(s):  
Jinwoong Kim ◽  
Saroja Polavarapu ◽  
Douglas Chan ◽  
Michael Neish
Keyword(s):  
Greenhouse Gas ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Forecast Error ◽  
Regional Scale ◽  
The United States ◽  
Regional Model ◽  
Boreal Summer ◽  
Chemical Transport Model ◽  
Atmospheric Transport Model

Abstract. In this study, we present the development of a regional atmospheric transport model for greenhouse gas (GHG) simulation based on an operational weather forecast model and a chemical transport model at Environment and Climate Change Canada (ECCC), with the goal of improving our understanding of the high spatio-temporal resolution interaction between the atmosphere and surface GHG fluxes over Canada and the United States. The regional model uses 10 km × 10 km horizontal grid spacing and 80 vertical levels spanning the ground to 0.1 hPa. The lateral boundary conditions of meteorology and tracers are provided by the global transport model used for GHG simulation at ECCC. The performance of the regional model and added benefit of the regional model over our lower resolution global models is investigated in terms of modelled CO2 concentration and meteorological forecast quality for multiple seasons in 2015. We find that our regional model has the capability to simulate high spatial (horizontal and vertical) and temporal scales of atmospheric CO2 concentrations, based on comparisons to surface and aircraft observations. In addition, reduced bias and standard deviation of forecast error in boreal summer are obtained by the regional model. Better representation of model topography in the regional model reduces transport and representation errors significantly compared to the global model, especially in regions of complex topography, as revealed by the more precise and detailed structure of the CO2 diurnal cycle produced at observation sites and in model space. The new regional model will form the basis of a flux inversion system that estimates regional scale fluxes of GHGs over Canada.

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