scholarly journals Limitations of the Radon Tracer Method (RTM) to estimate regional Greenhouse Gases (GHG) emissions – a case study for methane in Heidelberg

2021 ◽  
Author(s):  
Ingeborg Levin ◽  
Ute Karstens ◽  
Samuel Hammer ◽  
Julian DellaColetta ◽  
Fabian Maier ◽  
...  
Keyword(s):  
Greenhouse Gases ◽  
Catchment Area ◽  
Ghg Emissions ◽  
Point Sources ◽  
Atmospheric Methane ◽  
Tracer Method ◽  
Night Time ◽  
Ch4 Emissions ◽  
Annual Variations ◽  
Atmospheric Observations

Abstract. Correlations of night-time atmospheric methane (CH4) and 222Radon (222Rn) observations in Heidelberg, Germany, were evaluated with the Radon Tracer Method (RTM) to estimate the trend of annual CH4 emissions from 1996–2020 in the catchment area of the station. After an initial 30 % decrease of emissions from 1996 to 2004, no further systematic trend but small inter-annual variations were observed thereafter. This is in accordance with the trend of emissions until 2010 reported by the EDGARv6.0 inventory for the surroundings of Heidelberg. We show that the reliability of total CH4 emission estimates with the RTM critically depends on the accuracy and representativeness of the 222Rn exhalation rate from soils in the catchment area of the site. Simply using 222Rn fluxes as estimated by Karstens et al. (2015) could lead to biases in the estimated greenhouse gases (GHG) fluxes as large as a factor of two. RTM-based GHG flux estimates also depend on the parameters chosen for the night-time correlations of CH4 and 222Rn, such as the night-time period for regressions as well as the R2 cut-off value for the goodness of the fit. Quantitative comparison of total RTM-based top-down with bottom-up emission inventories requires representative high-resolution footprint modelling, particularly in polluted areas where CH4 emissions show large heterogeneity. Even then, RTM-based estimates are likely biased low if point sources play a significant role in the station/observation footprint as their emissions are not captured by the RTM method. Long-term representative 222Rn flux observations in the catchment area of a station are indispensable in order to apply the RTM method for reliable quantitative flux estimations of GHG emissions from atmospheric observations.

scholarly journals Limitations of the radon tracer method (RTM) to estimate regional greenhouse gas (GHG) emissions – a case study for methane in Heidelberg

2021 ◽  
Vol 21 (23) ◽  
pp. 17907-17926
Author(s):  
Ingeborg Levin ◽  
Ute Karstens ◽  
Samuel Hammer ◽  
Julian DellaColetta ◽  
Fabian Maier ◽  
...  
Keyword(s):  
Greenhouse Gas ◽  
Inversion Layer ◽  
Ghg Emissions ◽  
Point Sources ◽  
Atmospheric Methane ◽  
Tracer Method ◽  
Top Down ◽  
Bottom Up ◽  
Ch4 Emissions ◽  
Annual Variations

Abstract. Correlations of nighttime atmospheric methane (CH4) and 222radon (222Rn) observations in Heidelberg, Germany, were evaluated with the radon tracer method (RTM) to estimate the trend of annual nocturnal CH4 emissions from 1996–2020 in the footprint of the station. After an initial 30 % decrease in emissions from 1996 to 2004, there was no further systematic trend but small inter-annual variations were observed thereafter. This is in accordance with the trend of total emissions until 2010 reported by the EDGARv6.0 inventory for the surroundings of Heidelberg and provides a fully independent top-down verification of the bottom-up inventory changes. We show that the reliability of total nocturnal CH4 emission estimates with the RTM critically depends on the accuracy and representativeness of the 222Rn exhalation rates estimated from soils in the footprint of the site. Simply using 222Rn fluxes as estimated by Karstens et al. (2015) could lead to biases in the estimated greenhouse gas (GHG) fluxes as large as a factor of 2. RTM-based GHG flux estimates also depend on the parameters chosen for the nighttime correlations of CH4 and 222Rn, such as the nighttime period for regressions and the R2 cut-off value for the goodness of the fit. Quantitative comparison of total RTM-based top-down flux estimates with bottom-up emission inventories requires representative high-resolution footprint modelling, particularly in polluted areas where CH4 emissions show large heterogeneity. Even then, RTM-based estimates are likely biased low if point sources play a significant role in the station footprint as their emissions may not be fully captured by the RTM method, for example, if stack emissions are injected above the top of the nocturnal inversion layer or if point-source emissions from the surface are not well mixed into the footprint of the measurement site. Long-term representative 222Rn flux observations in the footprint of a station are indispensable in order to apply the RTM method for reliable quantitative flux estimations of GHG emissions from atmospheric observations.

scholarly journals Diagnostic methods for atmospheric inversions of long-lived greenhouse gases

2016 ◽  
Author(s):  
Anna M. Michalak ◽  
Nina A. Randazzo ◽  
Frédéric Chevallier
Keyword(s):  
Inverse Problems ◽  
Greenhouse Gases ◽  
Atmospheric Chemistry ◽  
Transport Model ◽  
Ghg Emissions ◽  
Surface Fluxes ◽  
Diagnostic Methods ◽  
Clear Understanding ◽  
Atmospheric Observations ◽  
Atmospheric Inversions

Abstract. The ability to predict the trajectory of climate change requires a clear understanding of the emissions and uptake (a.k.a. surface fluxes) of long-lived greenhouse gases (GHGs). Furthermore, the development of climate policies is driving a need to constrain the budgets of anthropogenic GHG emissions. Inverse problems that couple atmospheric observations of GHG concentrations with an atmospheric chemistry and transport model have increasingly been used to gain insights into surface fluxes. Given the inherent technical challenges associated with their solution, it is imperative that objective approaches exist for the evaluation of such inverse problems. Because direct observation of fluxes at compatible spatiotemporal scales is rarely possible, diagnostics tools must rely on indirect measures. Here we review diagnostics that have been implemented in recent studies, and discuss their use in informing adjustments to model setup. We group the diagnostics along a continuum starting with those that are most closely related to the scientific question being targeted, and ending with those most closely tied to the statistical and computational setup of the inversion. We thus begin with diagnostics based on assessments against independent information (e.g., unused atmospheric observations, large-scale scientific constraints), followed by statistical diagnostics of inversion results, diagnostics based on sensitivity tests and analyses of robustness (e.g., tests focusing on the chemistry and transport model, the atmospheric observations, or the statistical and computational framework), and close with the use of synthetic data experiments (a.k.a. observing system simulation experiments (OSSEs)). We find that existing diagnostics provide a crucial toolbox for evaluating and improving flux estimates, but, not surprisingly, cannot overcome the fundamental challenges associated with limited atmospheric observations or the lack of direct flux measurements at compatible scales. As atmospheric inversions are increasingly expected to contribute to national reporting of GHG emissions, the need for developing and implementing robust and transparent evaluation approaches will only grow.

scholarly journals Analysis of atmospheric CH<sub>4</sub> in Canadian Arctic and estimation of the regional CH<sub>4</sub> fluxes

2018 ◽  
Author(s):  
Misa Ishizawa ◽  
Douglas Chan ◽  
Doug Worthy ◽  
Elton Chan ◽  
Felix Vogel ◽  
...  
Keyword(s):  
Biomass Burning ◽  
Canadian Arctic ◽  
Weather Conditions ◽  
Temporal Variations ◽  
The Arctic ◽  
Atmospheric Methane ◽  
Ch4 Emissions ◽  
Annual Variations ◽  
Source Regions ◽  
Long Term Trends

Abstract. The Canadian Arctic has the potential for enhanced atmospheric methane (CH4) source regions as a response to the ongoing global warming. Current bottom-up and top-down estimates of the regional CH4 flux range widely. This study analyses the recent observations of atmospheric CH4 from five arctic monitoring sites and presents estimates of the regional CH4 fluxes for 2012–2015. The observational data reveal sizeable synoptic summertime enhancements in the atmospheric CH4 that are clearly distinguishable from background variations, which indicate strong regional fluxes (mainly wetland and biomass burning CH4 emissions) around Behchoko and Inuvik in the western Canadian Arctic. Multiple regional Bayesian inversion modelling systems are applied to estimate fluxes for the entire Canadian Arctic and show relatively robust results in amplitude and temporal variations even across different transport models, prior fluxes and sub-region masking. The estimated mean total CH4 annual flux for the Canadian Arctic is 1.8 ± 0.6 Tg CH4 yr−1. The flux estimate in this study is partitioned into biomass burning, 0.3 ± 0.1 Tg CH4 yr−1, and the remaining natural (wetland) flux 1.5 ± 0.5 Tg CH4 yr−1. The estimated summertime natural CH4 fluxes show clear inter-annual variability that is positively correlated with surface temperature anomalies. This indicates that the hot summer weather conditions stimulate the wetland CH4 emissions. More data and analysis are required to statistically characterise the dependence of regional CH4 fluxes on climate in the Arctic. These Arctic measurement sites should help quantify the inter-annual variations and long-term trends in CH4 emissions in the Canadian Arctic.

scholarly journals Diagnostic methods for atmospheric inversions of long-lived greenhouse gases

2017 ◽  
Vol 17 (12) ◽  
pp. 7405-7421 ◽  
Author(s):  
Anna M. Michalak ◽  
Nina A. Randazzo ◽  
Frédéric Chevallier
Keyword(s):  
Inverse Problems ◽  
Greenhouse Gases ◽  
Atmospheric Chemistry ◽  
Transport Model ◽  
Ghg Emissions ◽  
Surface Fluxes ◽  
Diagnostic Methods ◽  
Clear Understanding ◽  
Atmospheric Observations ◽  
Atmospheric Inversions

Abstract. The ability to predict the trajectory of climate change requires a clear understanding of the emissions and uptake (i.e., surface fluxes) of long-lived greenhouse gases (GHGs). Furthermore, the development of climate policies is driving a need to constrain the budgets of anthropogenic GHG emissions. Inverse problems that couple atmospheric observations of GHG concentrations with an atmospheric chemistry and transport model have increasingly been used to gain insights into surface fluxes. Given the inherent technical challenges associated with their solution, it is imperative that objective approaches exist for the evaluation of such inverse problems. Because direct observation of fluxes at compatible spatiotemporal scales is rarely possible, diagnostics tools must rely on indirect measures. Here we review diagnostics that have been implemented in recent studies and discuss their use in informing adjustments to model setup. We group the diagnostics along a continuum starting with those that are most closely related to the scientific question being targeted, and ending with those most closely tied to the statistical and computational setup of the inversion. We thus begin with diagnostics based on assessments against independent information (e.g., unused atmospheric observations, large-scale scientific constraints), followed by statistical diagnostics of inversion results, diagnostics based on sensitivity tests, and analyses of robustness (e.g., tests focusing on the chemistry and transport model, the atmospheric observations, or the statistical and computational framework), and close with the use of synthetic data experiments (i.e., observing system simulation experiments, OSSEs). We find that existing diagnostics provide a crucial toolbox for evaluating and improving flux estimates but, not surprisingly, cannot overcome the fundamental challenges associated with limited atmospheric observations or the lack of direct flux measurements at compatible scales. As atmospheric inversions are increasingly expected to contribute to national reporting of GHG emissions, the need for developing and implementing robust and transparent evaluation approaches will only grow.

Sources of Nitrogen and Phosphorus in the Catchment Area of Neusiedlersee/Fertö, Austria/Hungary

1993 ◽  
Vol 28 (3-5) ◽  
pp. 101-110 ◽  
Author(s):  
W. v. d. Emde ◽  
H. Fleckseder ◽  
N. Matsché ◽  
F. Plahl-Wabnegg ◽  
G. Spatzierer ◽  
...  
Keyword(s):  
Point Source ◽  
Catchment Area ◽  
Management Practice ◽  
Water Erosion ◽  
Nonpoint Source ◽  
Point Sources ◽  
Source Control ◽  
Nitrogen And Phosphorus ◽  
Catchment Areas ◽  
The One

Neusiedlersee (in German) / Fertö tó (in Hungarian) is a shallow lake at the Austro-Hungarian border. In the late 1970s, the question arose what to do in order to protect the lake against eutrophication. A preliminary report established the need for point-source control as well as gave first estimates for non-point source inputs. The proposed point-source control was quickly implemented, non-point sources were - among other topics - studied in detail in the period 1982 - 1986. The preliminary work had shown, based on integrated sampling and data from literature, that the aeolic input outweighed the one via water erosion (work was for totP only). In contrast to this, the 1982 - 1986 study showed that (a) water erosion by far dominates over aeolic inputs and (b) the size of nonpoint-source inputs was assessed for the largest catchment area in pronounced detail, whereas additional estimates were undertaken for smaller additional catchment areas. The methods as well as the results are presented in the following. The paper concludes with some remarks on the present management practice of nonpoint-source inputs.

scholarly journals A nature-based negative emissions technology able to remove atmospheric methane and other greenhouse gases

2021 ◽  
Author(s):  
Tingzhen Ming ◽  
Renaud de Richter ◽  
Franz Dietrich Oeste ◽  
Robert Tulip ◽  
Sylvain Caillol
Keyword(s):  
Greenhouse Gases ◽  
Atmospheric Methane ◽  
Negative Emissions

scholarly journals Spatial scale-dependent land–atmospheric methane exchanges in the northern high latitudes from 1993 to 2004

2014 ◽  
Vol 11 (7) ◽  
pp. 1693-1704 ◽  
Author(s):  
X. Zhu ◽  
Q. Zhuang ◽  
X. Lu ◽  
L. Song
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Scale ◽  
Water Table ◽  
Spatial Scales ◽  
Grid Cell ◽  
High Latitudes ◽  
Atmospheric Methane ◽  
Ch4 Emissions ◽  
Biogeochemical Models ◽  
Macro Scale

Abstract. Effects of various spatial scales of water table dynamics on land–atmospheric methane (CH4) exchanges have not yet been assessed for large regions. Here we used a coupled hydrology–biogeochemistry model to quantify daily CH4 exchanges over the pan-Arctic from 1993 to 2004 at two spatial scales of 100 km and 5 km. The effects of sub-grid spatial variability of the water table depth (WTD) on CH4 emissions were examined with a TOPMODEL-based parameterization scheme for the northern high latitudes. We found that both WTD and CH4 emissions are better simulated at a 5 km spatial resolution. By considering the spatial heterogeneity of WTD, net regional CH4 emissions at a 5 km resolution are 38.1–55.4 Tg CH4 yr−1 from 1993 to 2004, which are on average 42% larger than those simulated at a 100 km resolution using a grid-cell-mean WTD scheme. The difference in annual CH4 emissions is attributed to the increased emitting area and enhanced flux density with finer resolution for WTD. Further, the inclusion of sub-grid WTD spatial heterogeneity also influences the inter-annual variability of CH4 emissions. Soil temperature plays an important role in the 100 km estimates, while the 5 km estimates are mainly influenced by WTD. This study suggests that previous macro-scale biogeochemical models using a grid-cell-mean WTD scheme might have underestimated the regional CH4 emissions. The spatial scale-dependent effects of WTD should be considered in future quantification of regional CH4 emissions.

scholarly journals On the role of trend and variability in the hydroxyl radical (OH) in the global methane budget

2020 ◽  
Vol 20 (21) ◽  
pp. 13011-13022
Author(s):  
Yuanhong Zhao ◽  
Marielle Saunois ◽  
Philippe Bousquet ◽  
Xin Lin ◽  
Antoine Berchet ◽  
...  
Keyword(s):  
Hydroxyl Radical ◽  
El Niño ◽  
Climate Model ◽  
El Nino ◽  
Interannual Variations ◽  
Atmospheric Methane ◽  
Tropical Regions ◽  
Ch4 Emissions ◽  
Methane Budget

Abstract. Decadal trends and interannual variations in the hydroxyl radical (OH), while poorly constrained at present, are critical for understanding the observed evolution of atmospheric methane (CH4). Through analyzing the OH fields simulated by the model ensemble of the Chemistry–Climate Model Initiative (CCMI), we find (1) the negative OH anomalies during the El Niño years mainly corresponding to the enhanced carbon monoxide (CO) emissions from biomass burning and (2) a positive OH trend during 1980–2010 dominated by the elevated primary production and the reduced loss of OH due to decreasing CO after 2000. Both two-box model inversions and variational 4D inversions suggest that ignoring the negative anomaly of OH during the El Niño years leads to a large overestimation of the increase in global CH4 emissions by up to 10 ± 3 Tg yr−1 to match the observed CH4 increase over these years. Not accounting for the increasing OH trends given by the CCMI models leads to an underestimation of the CH4 emission increase by 23 ± 9 Tg yr−1 from 1986 to 2010. The variational-inversion-estimated CH4 emissions show that the tropical regions contribute most to the uncertainties related to OH. This study highlights the significant impact of climate and chemical feedbacks related to OH on the top-down estimates of the global CH4 budget.

scholarly journals Evaluation of anthropogenic CH<sub>4</sub> emissions over China using bottom-up inventories

2020 ◽  
Author(s):  
Xiaohui Lin ◽  
Wen Zhang ◽  
Monica Crippa ◽  
Shushi Peng ◽  
Pengfei Han ◽  
...  
Keyword(s):  
Waste Treatment ◽  
Comprehensive Evaluation ◽  
Energy Sector ◽  
Atmospheric Methane ◽  
Activity Data ◽  
Bottom Up ◽  
Average Growth ◽  
Ch4 Emissions ◽  
Source Contributions ◽  
Change Mitigation

Abstract. Atmospheric methane (CH4) is a potent greenhouse gas that is strongly influenced by several human activities. China, as one of the major agricultural and energy production countries, e.g., rice cultivation, ruminant feeding and coal production, contributes considerably to the global anthropogenic CH4 emissions. Understanding the characteristics of China's CH4 emissions is necessary for interpreting source contributions and for further climate change mitigation. However, the scarcity of data from some sources or years and spatially explicit information pose great challenges to completing an analysis of CH4 emissions. This study provides a comprehensive evaluation of China's anthropogenic CH4 emissions by synthesizing most of the currently available data (12 inventories). The results show that anthropogenic CH4 emissions differ widely among inventories, with values ranging from 41.9–57.5 Tg CH4 yr−1 in 2010. The discrepancy primarily resulted from the energy sector (27.3–60.0 % of total emissions), followed by the agricultural (26.9–50.8 %), and waste treatment (8.1–21.2 %) sectors. Temporally, emissions among inventories stabilized in the 1990s, but increased significantly thereafter, with annual average growth rates (AAGRs) of 1.8–3.9 % during 2000–2010, but slower AAGRs of 0.5–2.2 % during 2011–2015. Spatially, the growth of CH4 emissions could be attributed mostly to an increase in emissions from the energy sector (mainly from coal mining) in the northern and central inland regions, followed by waste treatment in the southern and eastern regions. The availability of detailed activity data for sectors or subsectors and the use of region-specific emission factors play important roles in understanding source contributions, and reducing the uncertainty of bottom-up inventories.

scholarly journals Quantifying methane point sources from fine-scale (GHGSat) satellite observations of atmospheric methane plumes

2018 ◽  
Author(s):  
Daniel J. Varon ◽  
Daniel J. Jacob ◽  
Jason McKeever ◽  
Dylan Jervis ◽  
Berke O. A. Durak ◽  
...  
Keyword(s):  
Wind Speed ◽  
Methane Emissions ◽  
Point Sources ◽  
Small Scale ◽  
Atmospheric Methane ◽  
Flux Method ◽  
Pixel Resolution ◽  
Cross Sectional ◽  
Source Rate ◽  
The Cross

Abstract. Anthropogenic methane emissions originate from a large number of relatively small point sources. The planned GHGSat satellite fleet aims to quantify emissions from individual point sources by measuring methane column plumes over selected ~ 10 × 10 km2 domains with ≤ 50 × 50 m2 pixel resolution and 1–5 % measurement precision. Here we develop algorithms for retrieving point source rates from such measurements. We simulate a large ensemble of instantaneous methane column plumes at 50 × 50 m2 pixel resolution for a range of atmospheric conditions using the Weather Research and Forecasting model (WRF) in large eddy simulation (LES) mode and adding instrument noise. We show that standard methods to infer source rates by Gaussian plume inversion or source pixel mass balance are prone to large errors because the turbulence cannot be properly parameterized on the small scale of instantaneous methane plumes. The integrated mass enhancement (IME) method, which relates total plume mass to source rate, and the cross-sectional flux method, which infers source rate from fluxes across plume transects, are better adapted to the problem. We show that the IME method with local measurements of the 10-m wind speed can infer source rates with error of 0.07–0.17 t h−1 + 5–12 % depending on instrument precision (1–5 %). The cross-sectional flux method has slightly larger errors (0.07–0.26 t h−1 + 8–12 %) but a simpler physical basis. For comparison, point sources larger than 0.5 t h−1 contribute more than 75 % of methane emissions reported to the U.S. Greenhouse Gas Reporting Program. Additional error applies if local wind speed measurements are not available, and may dominate the overall error at low wind speeds. Low winds are beneficial for source detection but not for source quantification.

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