scholarly journals Estimating 2010–2015 Anthropogenic and Natural Methane Emissions in Canada using ECCC Surface and GOSAT Satellite Observations

2021 ◽  
Author(s):  
Sabour Baray ◽  
Daniel J. Jacob ◽  
Joannes D. Massakkers ◽  
Jian-Xiong Sheng ◽  
Melissa P. Sulprizio ◽  
...  

Abstract. Methane emissions in Canada have both anthropogenic and natural sources. Anthropogenic emissions are estimated to be 4.1 Tg a−1 from 2010–2015 in the Canadian Greenhouse Gas Inventory. Natural emissions, which are mostly due to Boreal wetlands, are the largest methane source in Canada and highly uncertain, on the order of ~20 Tg a−1 in biosphere process models. Top-down constraints on Canadian methane emissions using atmospheric observations have been limited by the sparse coverage of both surface and satellite observations. Aircraft studies over the last several years have provided snapshot emissions that have been conflicting with inventory estimates. Here we use surface data from the Environment and Climate Change Canada (ECCC) in situ network and space borne data from the Greenhouse Gases Observing Satellite (GOSAT) to determine 2010–2015 anthropogenic and natural methane emissions in Canada in a Bayesian inverse modelling framework. We use GEOS-Chem to simulate anthropogenic emissions comparable to the Canadian inventory and wetlands emissions using an ensemble of WetCHARTS v1.0 scenarios in addition to other minor natural sources. We conduct a comparative analysis of the monthly natural emissions and yearly anthropogenic emissions optimized by surface and satellite data independently. Mean 2010–2015 posterior emissions using ECCC surface data are 6.0 ± 0.4 Tg a−1 for total anthropogenic and 10.5 ± 1.9 Tg a−1 for total natural emissions, where the error intervals represent the 1-σ spread in yearly posterior results. These results agree with our posterior using GOSAT data of 6.5 ± 0.7 Tg a−1 for total anthropogenic and 11.7 ± 1.2 Tg a−1 for total natural emissions. The seasonal pattern of posterior natural emissions using either dataset shows slower to start emissions in the spring and a less intense peak in the summer compared to the mean of WetCHARTS scenarios. We combine ECCC and GOSAT data to evaluate capabilities for sectoral and provincial level inversions and identify limitations. We estimate Energy + Agriculture emissions to be 5.1 ± 1.0 Tg a−1 which is 59 % higher than the National GHG Inventory. We attribute 39 % higher anthropogenic emissions to Western Canada than the prior. Natural emissions are lower across Canada with large downscaling in the Hudson Bay Lowlands. Inversion results are verified against independent aircraft data in Saskatchewan and surface data in Quebec which show better agreement with posterior emissions. This study shows a readjustment of the Canadian methane budget is necessary to better match atmospheric observations with higher anthropogenic emissions partially offset by lower natural emissions.

2021 ◽  
Vol 21 (23) ◽  
pp. 18101-18121
Author(s):  
Sabour Baray ◽  
Daniel J. Jacob ◽  
Joannes D. Maasakkers ◽  
Jian-Xiong Sheng ◽  
Melissa P. Sulprizio ◽  
...  

Abstract. Methane emissions in Canada have both anthropogenic and natural sources. Anthropogenic emissions are estimated to be 4.1 Tg a−1 from 2010–2015 in the National Inventory Report submitted to the United Nation's Framework Convention on Climate Change (UNFCCC). Natural emissions, which are mostly due to boreal wetlands, are the largest methane source in Canada and highly uncertain, on the order of ∼ 20 Tg a−1 in biosphere process models. Aircraft studies over the last several years have provided “snapshot” emissions that conflict with inventory estimates. Here we use surface data from the Environment and Climate Change Canada (ECCC) in situ network and space-borne data from the Greenhouse Gases Observing Satellite (GOSAT) to determine 2010–2015 anthropogenic and natural methane emissions in Canada in a Bayesian inverse modelling framework. We use GEOS-Chem to simulate anthropogenic emissions comparable to the National Inventory and wetlands emissions using an ensemble of WetCHARTS v1.0 scenarios in addition to other minor natural sources. We conduct a comparative analysis of the monthly natural emissions and yearly anthropogenic emissions optimized by surface and satellite data independently. Mean 2010–2015 posterior emissions using ECCC surface data are 6.0 ± 0.4 Tg a−1 for total anthropogenic and 11.6 ± 1.2 Tg a−1 for total natural emissions. These results agree with our posterior emissions of 6.5 ± 0.7 Tg a−1 for total anthropogenic and 11.7 ± 1.2 Tg a−1 for total natural emissions using GOSAT data. The seasonal pattern of posterior natural emissions using either dataset shows slower to start emissions in the spring and a less intense peak in the summer compared to the mean of WetCHARTS scenarios. We combine ECCC and GOSAT data to characterize limitations towards sectoral and provincial-level inversions. We estimate energy + agriculture emissions to be 5.1 ± 1.0 Tg a−1, which is 59 % higher than the national inventory. We attribute 39 % higher anthropogenic emissions to Western Canada than the prior. Natural emissions are lower across Canada. Inversion results are verified against independent aircraft data and surface data, which show better agreement with posterior emissions. This study shows a readjustment of the Canadian methane budget is necessary to better match atmospheric observations with lower natural emissions partially offset by higher anthropogenic emissions.


2016 ◽  
Vol 16 (23) ◽  
pp. 14891-14908 ◽  
Author(s):  
Nicola J. Warwick ◽  
Michelle L. Cain ◽  
Rebecca Fisher ◽  
James L. France ◽  
David Lowry ◽  
...  

Abstract. We present a global methane modelling study assessing the sensitivity of Arctic atmospheric CH4 mole fractions, δ13C-CH4 and δD-CH4 to uncertainties in Arctic methane sources. Model simulations include methane tracers tagged by source and isotopic composition and are compared with atmospheric data at four northern high-latitude measurement sites. We find the model's ability to capture the magnitude and phase of observed seasonal cycles of CH4 mixing ratios, δ13C-CH4 and δD-CH4 at northern high latitudes is much improved using a later spring kick-off and autumn decline in northern high-latitude wetland emissions than predicted by most process models. Results from our model simulations indicate that recent predictions of large methane emissions from thawing submarine permafrost in the East Siberian Arctic Shelf region could only be reconciled with global-scale atmospheric observations by making large adjustments to high-latitude anthropogenic or wetland emission inventories.


2016 ◽  
Author(s):  
Nicola J. Warwick ◽  
Michelle L. Cain ◽  
Rebecca Fisher ◽  
James L. France ◽  
David Lowry ◽  
...  

Abstract. We present a global methane modelling study assessing the sensitivity of Arctic atmospheric CH4 mole fractions, δ13C-CH4 and δD-CH4 to uncertainties in Arctic methane sources. Model simulations include methane tracers coloured by source and isotopic composition and are compared with atmospheric data at four high northern latitude measurement sites. We find the model's ability to capture the magnitude and phase of observed seasonal cycles of CH4 mixing ratios, δ13C-CH4 and δD-CH4 in high northern latitudes is much improved using a later spring kick-off and autumn decline in high northern latitude wetland emissions than predicted by most process models. Results from our model simulations indicate that recent predictions of large methane emissions from thawing submarine permafrost in the East Siberian Arctic Shelf region could only be reconciled with global scale atmospheric observations by making large adjustments to high latitude emission inventories.


2020 ◽  
Vol 12 (3) ◽  
pp. 375 ◽  
Author(s):  
Rajesh Janardanan ◽  
Shamil Maksyutov ◽  
Aki Tsuruta ◽  
Fenjuan Wang ◽  
Yogesh K. Tiwari ◽  
...  

We employed a global high-resolution inverse model to optimize the CH4 emission using Greenhouse gas Observing Satellite (GOSAT) and surface observation data for a period from 2011–2017 for the two main source categories of anthropogenic and natural emissions. We used the Emission Database for Global Atmospheric Research (EDGAR v4.3.2) for anthropogenic methane emission and scaled them by country to match the national inventories reported to the United Nations Framework Convention on Climate Change (UNFCCC). Wetland and soil sink prior fluxes were simulated using the Vegetation Integrative Simulator of Trace gases (VISIT) model. Biomass burning prior fluxes were provided by the Global Fire Assimilation System (GFAS). We estimated a global total anthropogenic and natural methane emissions of 340.9 Tg CH4 yr−1 and 232.5 Tg CH4 yr−1, respectively. Country-scale analysis of the estimated anthropogenic emissions showed that all the top-emitting countries showed differences with their respective inventories to be within the uncertainty range of the inventories, confirming that the posterior anthropogenic emissions did not deviate from nationally reported values. Large countries, such as China, Russia, and the United States, had the mean estimated emission of 45.7 ± 8.6, 31.9 ± 7.8, and 29.8 ± 7.8 Tg CH4 yr−1, respectively. For natural wetland emissions, we estimated large emissions for Brazil (39.8 ± 12.4 Tg CH4 yr−1), the United States (25.9 ± 8.3 Tg CH4 yr−1), Russia (13.2 ± 9.3 Tg CH4 yr−1), India (12.3 ± 6.4 Tg CH4 yr−1), and Canada (12.2 ± 5.1 Tg CH4 yr−1). In both emission categories, the major emitting countries all had the model corrections to emissions within the uncertainty range of inventories. The advantages of the approach used in this study were: (1) use of high-resolution transport, useful for simulations near emission hotspots, (2) prior anthropogenic emissions adjusted to the UNFCCC reports, (3) combining surface and satellite observations, which improves the estimation of both natural and anthropogenic methane emissions over spatial scale of countries.


2020 ◽  
Author(s):  
Rajesh Janardanan ◽  
Shamil Maksyutov ◽  
Aki Tsuruta ◽  
Fenjuan Wang ◽  
Yogesh Tiwari ◽  
...  

<p>Here, we present the results of a global high-resolution inversion study of methane emissions and their analysis for the large emitting countries. We employ a global high-resolution inverse model to optimize CH<sub>4</sub> emissions using Greenhouse gas Observing Satellite (GOSAT) and surface observation data over the 2011-2017 period for the two main source categories of anthropogenic and natural emissions. As prior emissions, we used the Emission Database for Global Atmospheric Research (EDGAR v4.3.2) for anthropogenic methane emission, scaled by country to match the national emissions reported to the United Nations Framework Convention on Climate Change (UNFCCC). Wetland and soil sink prior fluxes were simulated using Vegetation Integrative Simulator of Trace gases (VISIT) model. Biomass burning prior fluxes were provided by the Global Fire Assimilation System (GFAS). We estimate a global total anthropogenic and natural methane emissions of 340.9 Tg CH<sub>4</sub> yr<sup>-1</sup> and 232.5 Tg CH<sub>4</sub> yr<sup>-1</sup>, respectively. This agrees with recent Global Carbon Project (GCP) estimates of 357 and 215 Tg CH<sub>4</sub> yr<sup>-1</sup>, respectively. Country-scale analysis of the estimated anthropogenic emissions shows that for all the top-emitting countries, differences with their respective nationally reported inventories are within the uncertainty range of the inventories. Large emitting countries such as China, Russia and the United States have mean estimated anthropogenic emission of 45.7±8.6, 31.9±7.8 and 29.8±7.8 Tg CH<sub>4</sub> yr<sup>-1 </sup>respectively. For natural emissions, we estimate large emissions for Brazil (39.8±12.4 Tg CH<sub>4</sub> yr<sup>-1</sup>), the United States (25.9±8.3 Tg CH<sub>4</sub> yr<sup>-1</sup>), Russia (13.2±9.3 Tg CH<sub>4</sub> yr<sup>-1</sup>), India (12.3±6.4 Tg CH<sub>4</sub> yr<sup>-1</sup>), and Canada (12.2±5.1 Tg CH<sub>4</sub> yr<sup>-1</sup>). In both emission categories, natural and anthropogenic, the major emitting countries all had model corrections to their emissions that were within the uncertainty range of the inventories and the inverse model uncertainty. As a special case, we evaluate anthropogenic emissions estimated for India (24.2±5.3 Tg yr<sup>-1</sup>) with aircraft observation data over urban regions over India. On average, the optimized profiles showed a better match with the observations compared to the prior profile confirming improved estimates by the model for India.</p>


2020 ◽  
Author(s):  
Joannes D. Maasakkers ◽  
Daniel J. Jacob ◽  
Melissa P. Sulprizio ◽  
Tia R. Scarpelli ◽  
Hannah Nesser ◽  
...  

Abstract. We use 2010–2015 GOSAT satellite observations of atmospheric methane columns over North America in a high- resolution inversion of methane emissions, including contributions from different sectors and long-term trends. The inversion involves analytical solution to the Bayesian optimization problem for a Gaussian mixture model (GMM) of the emission field with up to 0.5° × 0.625° resolution in concentrated source regions. Analytical solution provides a closed-form characterization of the information content from the inversion and facilitates the construction of a large ensemble of solutions exploring the effect of different uncertainties and assumptions. Prior estimates for the inversion include a gridded version of the EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks (GHGI) and the WetCHARTS model ensemble for wetlands. Our best estimate for mean 2010–2015 US anthropogenic emissions is 30.6 (range: 29.4–31.3) Tg a-1, slightly higher than the gridded EPA inventory (28.7 (26.4–36.2) Tg a-1). The main discrepancy is for the oil and gas production sectors where we find higher emissions than the GHGI by 35 % and 22 % respectively. The most recent version of the EPA GHGI revises downward its estimate of emissions from oil production and we find that these are a factor 2 lower than our estimate. Our best estimate of US wetland emissions is 10.2 (5.6–11.1) Tg a-1, on the low end of the prior WetCHARTS inventory uncertainty range (14.2 (3.3–32.4) Tg a-1) and calling for better understanding of these emissions. We find an increasing trend in US anthropogenic emissions over 2010–2015 of 0.4 % a-1, lower than previous GOSAT-based estimates but opposite to the decrease reported by the EPA GHGI. Most of this increase appears driven by unconventional oil/gas production in the eastern US. We also find that oil/gas production emissions in Mexico are higher than in the nationally reported inventory, though there is evidence for a 2010–2015 decrease in emissions from offshore oil production.


2018 ◽  
Author(s):  
Jun Liu ◽  
Jeramy Dedrick ◽  
Lynn M. Russell ◽  
Gunnar I. Senum ◽  
Janek Uin ◽  
...  

Abstract. From November 2015 to December 2016, the ARM West Antarctic Radiation Experiment (AWARE) measured submicron aerosol properties near McMurdo Station at the southern tip of the Ross Island. Submicron organic mass (OM), particle number, and cloud condensation nuclei concentrations were higher in summer than other seasons. The measurements included a range of compositions and concentrations that likely reflected both local anthropogenic emissions and natural background sources. We isolated the natural organic components by separating a natural factor and a local combustion factor. The natural OM was 150 times higher in summer than in winter. The local anthropogenic emissions were not hygroscopic and had little contribution to the CCN concentrations. Natural sources that included marine sea spray and seabird emissions contributed 56 % of OM in the austral summer but only 3 % in the austral winter. The natural OM had high hydroxyl group fraction (55 %), 6 % alkane, and 6 % amine group mass, consistent with marine organic composition. In addition, the Fourier transform infrared (FTIR) spectra showed the natural sources of organic aerosol were characterized by amide group absorption, which may be from seabird populations. Carboxylic acid group contributions from natural sources were correlated to incoming solar radiation, indicating that some OM formed by secondary pathways.


2013 ◽  
Vol 10 (3) ◽  
pp. 1501-1516 ◽  
Author(s):  
J. P. Boisier ◽  
N. de Noblet-Ducoudré ◽  
P. Ciais

Abstract. Regional cooling resulting from increases in surface albedo has been identified in several studies as the main biogeophysical effect of past land use-induced land cover changes (LCC) on climate. However, the amplitude of this effect remains quite uncertain due to, among other factors, (a) uncertainties in the extent of historical LCC and, (b) differences in the way various models simulate surface albedo and more specifically its dependency on vegetation type and snow cover. We derived monthly albedo climatologies for croplands and four other land cover types from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations. We then reconstructed the changes in surface albedo between preindustrial times and present-day by combining these climatologies with the land cover maps of 1870 and 1992 used by seven land surface models (LSMs) in the context of the LUCID ("Land Use and Climate: identification of robust Impacts") intercomparison project. These reconstructions show surface albedo increases larger than 10% (absolute) in winter, and larger than 2% in summer between 1870 and 1992 over areas that experienced intense deforestation in the northern temperate regions. The historical surface albedo changes estimated with MODIS data were then compared to those simulated by the various climate models participating in LUCID. The inter-model mean albedo response to LCC shows a similar spatial and seasonal pattern to the one resulting from the MODIS-based reconstructions, that is, larger albedo increases in winter than in summer, driven by the presence of snow. However, individual models show significant differences between the simulated albedo changes and the corresponding reconstructions, despite the fact that land cover change maps are the same. Our analyses suggest that the primary reason for those discrepancies is how LSMs parameterize albedo. Another reason, of secondary importance, results from differences in their simulated snow extent. Our methodology is a useful tool not only to infer observations-based historical changes in land surface variables impacted by LCC, but also to point out deficiencies of the models. We therefore suggest that it could be more widely developed and used in conjunction with other tools in order to evaluate LSMs.


2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Subin Jose ◽  
Vijayakumar S. Nair ◽  
S. Suresh Babu

Abstract Atmospheric aerosols play an important role in the formation of warm clouds by acting as efficient cloud condensation nuclei (CCN) and their interactions are believed to cool the Earth-Atmosphere system (‘first indirect effect or Twomey effect’) in a highly uncertain manner compared to the other forcing agents. Here we demonstrate using long-term (2003–2016) satellite observations (NASA’s A-train satellite constellations) over the northern Indian Ocean, that enhanced aerosol loading (due to anthropogenic emissions) can reverse the first indirect effect significantly. In contrast to Twomey effect, a statistically significant increase in cloud effective radius (CER, µm) is observed with respect to an increase in aerosol loading for clouds having low liquid water path (LWP < 75 g m−2) and drier cloud tops. Probable physical mechanisms for this effect are the intense competition for available water vapour due to higher concentrations of anthropogenic aerosols and entrainment of dry air on cloud tops. For such clouds, cloud water content showed a negative response to cloud droplet number concentrations and the estimated intrinsic radiative effect suggest a warming at the Top of the Atmosphere. Although uncertainties exist in quantifying aerosol-cloud interactions (ACI) using satellite observations, present study indicates the physical existence of anti-Twomey effect over the northern Indian Ocean during south Asian outflow.


2017 ◽  
Vol 62 (25) ◽  
pp. 2941-2950 ◽  
Author(s):  
LiPing LEI ◽  
Hui ZHONG ◽  
ZhongHua HE ◽  
BoFeng CAI ◽  
ShaoYuan YANG ◽  
...  

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