07 Jan 2021

07 Jan 2021

Review status: this preprint is currently under review for the journal ACP.

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

Sabour Baray1, Daniel J. Jacob2, Joannes D. Massakkers3, Jian-Xiong Sheng4, Melissa P. Sulprizio2, Dylan B. A. Jones5, A. Anthony Bloom6, and Robert McLaren1 Sabour Baray et al.
  • 1Centre for Atmospheric Chemistry, York University, Toronto, Canada
  • 2Harvard University, Cambridge, MA, USA
  • 3SRON Netherlands Institute for Space Research, Utrecht, The Netherlands
  • 4Massachusetts Institute of Technology, Cambridge, MA, USA
  • 5University of Toronto, Toronto, Canada
  • 6Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

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.

Sabour Baray et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2020-1195', Julia Marshall, 29 Mar 2021
  • RC2: 'Comment on acp-2020-1195', Anonymous Referee #2, 01 Apr 2021

Sabour Baray et al.

Sabour Baray et al.


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Short summary
We use 2010–2015 surface and satellite observations to disentangle methane from anthropogenic and natural sources in Canada. Using a chemical transport model (GEOS-Chem), the mismatch between modelled and observed methane concentrations can be used to infer emissions according to Bayesian statistics. Compared to prior knowledge, we show higher anthropogenic emissions attributed to energy and/or agriculture in Western Canada, and lower natural emissions from Boreal wetlands.