Greenhouse gas column observations from a portable spectrometer in Uganda

2021 ◽  
Author(s):  
Neil Humpage ◽  
Hartmut Boesch ◽  
William Okello ◽  
Florian Dietrich ◽  
Jia Chen ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Greenhouse Gas ◽  
East Africa ◽  
Satellite Data ◽  
Atmospheric Chemistry ◽  
Transport Model ◽  
Methane Emissions ◽  
Geographical Region ◽  
Natural Ecosystems ◽  
Tropical Africa

<p>The natural ecosystems of tropical Africa represent a significant store of carbon, and play an important but uncertain role in the atmospheric budgets of carbon dioxide and methane. Recent studies using satellite data have concluded that methane emissions from this geographical region have increased since 2010 as a result of increased wetland extent, accounting for a third of global methane growth (Lunt et al 2019), and that the tropical Africa region dominates net carbon emission across the tropics (Palmer et al 2019). The conclusions of such studies are based on the accuracy of various satellite datasets and atmospheric transport models, over a geographical region where there are few independent observations available to check the robustness and validity of these datasets.</p><p>Here we present the first ground-based observations of greenhouse gas (GHG) column concentrations over tropical East Africa, obtained using the University of Leicester EM27/SUN spectrometer during its deployment at the National Fisheries Resources Research Institute (NaFIRRI) in Jinja, Uganda. During the deployment we were able to operate the instrument remotely, using an automated weatherproof enclosure designed by the Technical University of Munich (Heinle and Chen 2018, Dietrich et al 2020). The instrument ran near-continuously for a three month period in early 2020, observing total atmospheric column concentrations of carbon dioxide and methane, along with other gases of interest including water vapour and carbon monoxide. We describe the data obtained during this period, processed using tools developed under the COCCON project (COllaborative Carbon Column Observing Network, Frey et al 2019), and demonstrate the value of performing GHG column measurements over tropical East Africa. We then evaluate the performance of CO<sub>2</sub> observations from OCO-2 and CH<sub>4</sub> from Sentinel 5P TROPOMI - datasets previously used in the studies of Palmer et al 2019 and Lunt et al 2019 respectively - and interpret the comparison with the ground-based observations in the light of data from the GEOS-Chem atmospheric chemistry transport model and the CAMS (Copernicus Atmospheric Monitoring Service) reanalyses.</p><p><strong>REFERENCES: </strong>Lunt, M. F., Palmer, P. I., Feng, L., Taylor, C. M., Boesch, H., and Parker, R. J.: An increase in methane emissions from tropical Africa between 2010 and 2016 inferred from satellite data, Atmos. Chem. Phys., 19, 14721–14740, https://doi.org/10.5194/acp-19-14721-2019, 2019.</p><p>Palmer, P.I., Feng, L., Baker, D., Chevallier, F., Boesch, H., and Somkuti, P.: Net carbon emissions from African biosphere dominate pan-tropical atmospheric CO2 signal. Nat Commun 10, 3344, https://doi.org/10.1038/s41467-019-11097-w, 2019.</p><p>Heinle, L. and Chen, J.: Automated enclosure and protection system for compact solar-tracking spectrometers, Atmos. Meas. Tech., 11, 2173–2185, https://doi.org/10.5194/amt-11-2173-2018, 2018.</p><p>Dietrich, F., Chen, J., Voggenreiter, B., Aigner, P., Nachtigall, N., and Reger, B.: Munich permanent urban greenhouse gas column observing network, Atmos. Meas. Tech. Discussions, 2020, 1–24, https://doi.org/10.5194/amt-2020-300, 2020.</p><p>Frey, M. et al.: Building the COllaborative Carbon Column Observing Network (COCCON): long-term stabilityand ensemble performance of the EM27/SUN Fourier transform spectrometer, Atmos. Meas. Tech., 12, 1513–1530, https://doi.org/10.5194/amt-12-1513-2019, 2019</p>

scholarly journals Sensitivity of the recent methane budget to LMDz sub-grid scale physical parameterizations

2015 ◽  
Vol 15 (8) ◽  
pp. 11853-11888
Author(s):  
R. Locatelli ◽  
P. Bousquet ◽  
M. Saunois ◽  
F. Chevallier ◽  
C. Cressot
Keyword(s):  
Satellite Data ◽  
Transport Model ◽  
Deep Convection ◽  
Chemical Transport ◽  
Global Scale ◽  
Methane Emissions ◽  
Inverse Modelling ◽  
Chemical Transport Model ◽  
Methane Budget ◽  
Physical Parameterizations

Abstract. With the densification of surface observing networks and the development of remote sensing of greenhouse gases from space, estimations of methane (CH4) sources and sinks by inverse modelling face new challenges. Indeed, the chemical transport model used to link the flux space with the mixing ratio space must be able to represent these different types of constraints for providing consistent flux estimations. Here we quantify the impact of sub-grid scale physical parameterization errors on the global methane budget inferred by inverse modelling using the same inversion set-up but different physical parameterizations within one chemical-transport model. Two different schemes for vertical diffusion, two others for deep convection, and one additional for thermals in the planetary boundary layer are tested. Different atmospheric methane datasets are used as constraints (surface observations or satellite retrievals). At the global scale, methane emissions differ, on average, from 4.1 Tg CH4 per year due to the use of different sub-grid scale parameterizations. Inversions using satellite total-column retrieved by GOSAT satellite are less impacted, at the global scale, by errors in physical parameterizations. Focusing on large-scale atmospheric transport, we show that inversions using the deep convection scheme of Emanuel (1991) derive smaller interhemispheric gradient in methane emissions. At regional scale, the use of different sub-grid scale parameterizations induces uncertainties ranging from 1.2 (2.7%) to 9.4% (14.2%) of methane emissions in Africa and Eurasia Boreal respectively when using only surface measurements from the background (extended) surface network. When using only satellite data, we show that the small biases found in inversions using GOSAT-CH4 data and a coarser version of the transport model were actually masking a poor representation of the stratosphere–troposphere gradient in the model. Improving the stratosphere–troposphere gradient reveals a larger bias in GOSAT-CH4 satellite data, which largely amplifies inconsistencies between surface and satellite inversions. A simple bias correction is proposed. The results of this work provide the level of confidence one can have for recent methane inversions relatively to physical parameterizations included in chemical-transport models.

Quantifying the contribution of regional methane emissions to the global methane budget between 2008 and 2018 using the TOMCAT chemical transport model

2021 ◽  
Author(s):  
Emily Dowd ◽  
Christopher Wilson ◽  
Martyn Chipperfield ◽  
Manuel Gloor
Keyword(s):  
Carbon Dioxide ◽  
Land Surface ◽  
Fossil Fuels ◽  
Fossil Fuel ◽  
Transport Model ◽  
Chemical Transport ◽  
Methane Emissions ◽  
Anthropogenic Sources ◽  
Chemical Transport Model ◽  
Methane Budget

<p>Methane (CH<sub>4</sub>) is the second most important atmospheric greenhouse gas after carbon dioxide. Global concentrations of CH<sub>4</sub> have been rising in the last decade and our understanding of what is driving the increase remains incomplete. Natural sources, such as wetlands, contribute to the uncertainty of the methane budget. However, anthropogenic sources, such as fossil fuels, present an opportunity to mitigate the human contribution to climate change on a relatively short timescale, since CH<sub>4</sub> has a much shorter lifetime than carbon dioxide. Therefore, it is important to know the relative contributions of these sources in different regions.</p><p>We have investigated the inter-annual variation (IAV) and rising trend of CH<sub>4</sub> concentrations using a global 3-D chemical transport model, TOMCAT. We independently tagged several regional natural and anthropogenic CH<sub>4</sub> tracers in TOMCAT to identify their contribution to the atmospheric CH<sub>4</sub> concentrations over the period 2009 – 2018. The tagged regions were selected based on the land surface types and the predominant flux sector within each region and include subcontinental regions, such as tropical South America, boreal regions and anthropogenic regions such as Europe. We used surface CH<sub>4</sub> fluxes derived from a previous TOMCAT-based atmospheric inversion study (Wilson et al., 2020). These atmospheric inversions were constrained by satellite and surface flask observations of CH<sub>4</sub>, giving optimised monthly estimates for fossil fuel and non-fossil fuel emissions on a 5.6° horizontal grid. During the study period, the total optimised CH<sub>4</sub> flux grew from 552 Tg/yr to 593 Tg/yr. This increase in emissions, particularly in the tropics, contributed to the increase in atmospheric CH<sub>4 </sub>concentrations and added to the imbalance in the CH<sub>4</sub> budget. We will use the results of the regional tagged tracers to quantify the contribution of regional methane emissions at surface observation sites, and to quantify the contributions of the natural and anthropogenic emissions from the tagged regions to the IAV and the rising methane concentrations.</p><p>Wilson, C., Chipperfield, M. P., Gloor, M., Parker, R. J., Boesch, H., McNorton, J., Gatti, L. V., Miller, J. B., Basso, L. S., and Monks, S. A.: Large and increasing methane emissions from Eastern Amazonia derived from satellite data, 2010–2018, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-1136, in review, 2020.</p>

scholarly journals Impact of transport model errors on the global and regional methane emissions estimated by inverse modelling

2013 ◽  
Vol 13 (19) ◽  
pp. 9917-9937 ◽  
Author(s):  
R. Locatelli ◽  
P. Bousquet ◽  
F. Chevallier ◽  
A. Fortems-Cheney ◽  
S. Szopa ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Transport Model ◽  
Initial Conditions ◽  
Spatial Scales ◽  
Global Scale ◽  
Methane Emissions ◽  
Inverse Modelling ◽  
Model Errors ◽  
Inverse Systems ◽  
The Impact

Abstract. A modelling experiment has been conceived to assess the impact of transport model errors on methane emissions estimated in an atmospheric inversion system. Synthetic methane observations, obtained from 10 different model outputs from the international TransCom-CH4 model inter-comparison exercise, are combined with a prior scenario of methane emissions and sinks, and integrated into the three-component PYVAR-LMDZ-SACS (PYthon VARiational-Laboratoire de Météorologie Dynamique model with Zooming capability-Simplified Atmospheric Chemistry System) inversion system to produce 10 different methane emission estimates at the global scale for the year 2005. The same methane sinks, emissions and initial conditions have been applied to produce the 10 synthetic observation datasets. The same inversion set-up (statistical errors, prior emissions, inverse procedure) is then applied to derive flux estimates by inverse modelling. Consequently, only differences in the modelling of atmospheric transport may cause differences in the estimated fluxes. In our framework, we show that transport model errors lead to a discrepancy of 27 Tg yr−1 at the global scale, representing 5% of total methane emissions. At continental and annual scales, transport model errors are proportionally larger than at the global scale, with errors ranging from 36 Tg yr−1 in North America to 7 Tg yr−1 in Boreal Eurasia (from 23 to 48%, respectively). At the model grid-scale, the spread of inverse estimates can reach 150% of the prior flux. Therefore, transport model errors contribute significantly to overall uncertainties in emission estimates by inverse modelling, especially when small spatial scales are examined. Sensitivity tests have been carried out to estimate the impact of the measurement network and the advantage of higher horizontal resolution in transport models. The large differences found between methane flux estimates inferred in these different configurations highly question the consistency of transport model errors in current inverse systems. Future inversions should include more accurately prescribed observation covariances matrices in order to limit the impact of transport model errors on estimated methane fluxes.

Foreign Direct Investment Dependence and the Neglected Greenhouse Gas: A Cross-National Analysis of Nitrous Oxide Emissions in Developing Countries, 1990–2014

2020 ◽  
pp. 073112142093773
Author(s):  
Steven Andrew Mejia
Keyword(s):  
Carbon Dioxide ◽  
Developing Countries ◽  
Nitrous Oxide ◽  
Foreign Direct Investment ◽  
Greenhouse Gas ◽  
Carbon Dioxide Emissions ◽  
Direct Investment ◽  
Methane Emissions ◽  
Nitrous Oxide Emissions ◽  
National Analysis

Scholars have long inquired the anthropogenic causes of greenhouse gas emissions. The majority of empirical work focuses on carbon dioxide and methane emissions, but limited attention is paid to nitrous oxide emissions. This is a crucial omission as nitrous oxide emissions are an extremely potent greenhouse gas and trigger ozone-depleting reactions upon reaching the atmosphere. Using a fixed effects panel regression of 106 developing countries, I estimate the effect of foreign direct investment dependence on nitrous oxide emissions. I find foreign capital dependency is positively associated with nitrous oxide emissions, supporting a refined ecostructural theory of foreign direct investment dependence. This analysis highlights the need for social scientists to consider the environmental impacts of the transnational organization of production beyond carbon dioxide emissions and methane emissions.

scholarly journals Aspects regarding the reduction of fugitive methane emissions from the valves

2021 ◽  
Vol 343 ◽  
pp. 09010
Author(s):  
Ioana Petre ◽  
Monica Emanuela Stoica
Keyword(s):  
Climate Change ◽  
Carbon Dioxide ◽  
Air Quality ◽  
Greenhouse Gas ◽  
European Commission ◽  
Fossil Fuels ◽  
Fossil Fuel ◽  
Energy System ◽  
Methane Emissions ◽  
Testing Procedures

Methane is the second strongest greenhouse gas contributing to climate change after carbon dioxide. Reducing methane emissions contributes to both slowing climate change and improving air quality. In order to reduce methane emissions from the energy sector, the European Commission has proposed the obligation to improve leak detection and disposal in fossil fuel infrastructure, as well as any other infrastructure that produces, transports or uses fossil fuels. Compressors and compressor stations are such a component of the energy system. The paper presents the testing procedures of the valves in the gas transmission pipes for the evaluation of external leaks and the proposed corrective actions to minimize them.

scholarly journals Quantifying methane emissions from Queensland's coal seam gas producing Surat Basin using inventory data and a regional Bayesian inversion

2020 ◽  
Vol 20 (23) ◽  
pp. 15487-15511
Author(s):  
Ashok K. Luhar ◽  
David M. Etheridge ◽  
Zoë M. Loh ◽  
Julie Noonan ◽  
Darren Spencer ◽  
...  
Keyword(s):  
Coal Seam ◽  
Greenhouse Gas ◽  
Transport Model ◽  
Methane Emissions ◽  
Inverse Model ◽  
Total Emission ◽  
Coal Seam Gas ◽  
Regional Transport ◽  
Bottom Up ◽  
Methane Concentrations

Abstract. Methane (CH4) is a potent greenhouse gas and a key precursor of tropospheric ozone, itself a powerful greenhouse gas and air pollutant. Methane emissions across Queensland's Surat Basin, Australia, result from a mix of activities, including the production and processing of coal seam gas (CSG). We measured methane concentrations over 1.5 years from two monitoring stations established 80 km apart on either side of the main CSG belt located within a study area of 350 km × 350 km. Using an inverse modelling approach coupled with a bottom-up inventory, we quantify methane emissions from this area. The inventory suggests that the total emission is 173.2 × 106 kg CH4 yr−1, with grazing cattle contributing about half of that, cattle feedlots ∼ 25 %, and CSG processing ∼ 8 %. Using the inventory emissions in a forward regional transport model indicates that the above sources are significant contributors to methane at both monitors. However, the model underestimates approximately the highest 15 % of the observed methane concentrations, suggesting underestimated or missing emissions. An efficient regional Bayesian inverse model is developed, incorporating an hourly source–receptor relationship based on a backward-in-time configuration of the forward regional transport model, a posterior sampling scheme, and the hourly methane observations and a derived methane background. The inferred emissions obtained from one of the inverse model setups that uses a Gaussian prior whose averages are identical to the gridded bottom-up inventory emissions across the domain with an uncertainty of 3 % of the averages best describes the observed methane. Having only two stations is not adequate at sampling distant source areas of the study domain, and this necessitates a small prior uncertainty. This inverse setup yields a total emission of (165.8 ± 8.5) × 106 kg CH4 yr−1, slightly smaller than the inventory total. However, in a subdomain covering the CSG development areas, the inferred emissions are (63.6 ± 4.7) × 106 kg CH4 yr−1, 33 % larger than those from the inventory. We also infer seasonal variation of methane emissions and examine its correlation with climatological rainfall in the area.

scholarly journals Greenhouse gas emissions from the water–air interface of a grassland river: a case study of the Xilin River

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xue Hao ◽  
Yu Ruihong ◽  
Zhang Zhuangzhuang ◽  
Qi Zhen ◽  
Lu Xixi ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Land Use ◽  
Nitrous Oxide ◽  
Greenhouse Gas Emissions ◽  
Greenhouse Gas ◽  
Ghg Emissions ◽  
Nitrous Oxide Emissions ◽  
Gas Emissions ◽  
Sand Land ◽  
Very High

AbstractGreenhouse gas (GHG) emissions from rivers and lakes have been shown to significantly contribute to global carbon and nitrogen cycling. In spatiotemporal-variable and human-impacted rivers in the grassland region, simultaneous carbon dioxide, methane and nitrous oxide emissions and their relationships under the different land use types are poorly documented. This research estimated greenhouse gas (CO2, CH4, N2O) emissions in the Xilin River of Inner Mongolia of China using direct measurements from 18 field campaigns under seven land use type (such as swamp, sand land, grassland, pond, reservoir, lake, waste water) conducted in 2018. The results showed that CO2 emissions were higher in June and August, mainly affected by pH and DO. Emissions of CH4 and N2O were higher in October, which were influenced by TN and TP. According to global warming potential, CO2 emissions accounted for 63.35% of the three GHG emissions, and CH4 and N2O emissions accounted for 35.98% and 0.66% in the Xilin river, respectively. Under the influence of different degrees of human-impact, the amount of CO2 emissions in the sand land type was very high, however, CH4 emissions and N2O emissions were very high in the artificial pond and the wastewater, respectively. For natural river, the greenhouse gas emissions from the reservoir and sand land were both low. The Xilin river was observed to be a source of carbon dioxide and methane, and the lake was a sink for nitrous oxide.

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