Robert McLaren

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.

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.

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.

The atmospheric lifetimes of pollutants determine their impacts on human health, ecosystems and climate and yet pollutant lifetimes due to dry deposition over large regions have not been determined from measurements. Here, a new methodology based on aircraft observations is used to determine the lifetimes of oxidized sulfur and nitrogen due to dry deposition over (3–6) × 10³ km² of boreal forest in Canada. Dry deposition fluxes decreased exponentially with distance, resulting in lifetimes of 2.2–26 hours. Fluxes were 2–14 and 1–18 times higher than model estimates for oxidized sulfur and nitrogen, respectively, indicating dry deposition velocities which were 1.2–5.4 times higher than those computed for models. A Monte-Carlo analysis with five commonly used inferential dry deposition algorithms indicates that such model underestimates of dry deposition velocity are typical. These findings indicate that deposition to vegetation surfaces are likely under-estimated in regional and global chemical transport models regardless of the model algorithm used. The model-observation gaps may be reduced if surface pH, and quasi-laminar and aerodynamic resistances in algorithms are optimized as shown in the Monte-Carlo analysis. Assessing the air quality and climate impacts of atmospheric pollutants on regional and global scales requires improved measurement-based understanding of atmospheric lifetimes of these pollutants.

The atmospheric lifetimes of pollutants determine their impacts on human health, ecosystems and climate and yet pollutant lifetimes due to dry deposition over large regions have not been determined from measurements. Here, a new methodology based on aircraft observations is used to determine the lifetimes of oxidized sulfur and nitrogen due to dry deposition over (3–6) × 10³ km² of boreal forest in Canada. Dry deposition fluxes decreased exponentially with distance, resulting in lifetimes of 2.2–26 hours. Fluxes were 2–14 and 1–18 times higher than model estimates for oxidized sulfur and nitrogen, respectively, indicating dry deposition velocities which were 1.2–5.4 times higher than those computed for models. A Monte-Carlo analysis with five commonly used inferential dry deposition algorithms indicates that such model underestimates of dry deposition velocity are typical. These findings indicate that deposition to vegetation surfaces are likely under-estimated in regional and global chemical transport models regardless of the model algorithm used. The model-observation gaps may be reduced if surface pH, and quasi-laminar and aerodynamic resistances in algorithms are optimized as shown in the Monte-Carlo analysis. Assessing the air quality and climate impacts of atmospheric pollutants on regional and global scales requires improved measurement-based understanding of atmospheric lifetimes of these pollutants.

The atmospheric lifetimes of pollutants determine their impacts on human health, ecosystems and climate and yet pollutant lifetimes due to dry deposition over large regions have not been determined from measurements. Here, a new methodology based on aircraft observations is used to determine the lifetimes of oxidized sulfur and nitrogen due to dry deposition over (3–6) × 10³ km² of boreal forest in Canada. Dry deposition fluxes decreased exponentially with distance, resulting in lifetimes of 2.2–26 hours. Fluxes were 2–14 and 1–18 times higher than model estimates for oxidized sulfur and nitrogen, respectively, indicating dry deposition velocities which were 1.2–5.4 times higher than those computed for models. A Monte-Carlo analysis with five commonly used inferential dry deposition algorithms indicates that such model underestimates of dry deposition velocity are typical. These findings indicate that deposition to vegetation surfaces are likely under-estimated in regional and global chemical transport models regardless of the model algorithm used. The model-observation gaps may be reduced if surface pH, and quasi-laminar and aerodynamic resistances in algorithms are optimized as shown in the Monte-Carlo analysis. Assessing the air quality and climate impacts of atmospheric pollutants on regional and global scales requires improved measurement-based understanding of atmospheric lifetimes of these pollutants.

The atmospheric lifetimes of pollutants determine their impacts on human health, ecosystems and climate and yet pollutant lifetimes due to dry deposition over large regions have not been determined from measurements. Here, a new methodology based on aircraft observations is used to determine the lifetimes of oxidized sulfur and nitrogen due to dry deposition over (3–6) × 10³ km² of boreal forest in Canada. Dry deposition fluxes decreased exponentially with distance, resulting in lifetimes of 2.2–26 hours. Fluxes were 2–14 and 1–18 times higher than model estimates for oxidized sulfur and nitrogen, respectively, indicating dry deposition velocities which were 1.2–5.4 times higher than those computed for models. A Monte-Carlo analysis with five commonly used inferential dry deposition algorithms indicates that such model underestimates of dry deposition velocity are typical. These findings indicate that deposition to vegetation surfaces are likely under-estimated in regional and global chemical transport models regardless of the model algorithm used. The model-observation gaps may be reduced if surface pH, and quasi-laminar and aerodynamic resistances in algorithms are optimized as shown in the Monte-Carlo analysis. Assessing the air quality and climate impacts of atmospheric pollutants on regional and global scales requires improved measurement-based understanding of atmospheric lifetimes of these pollutants.