Observational constraints on the distribution, seasonality, and environmental predictors of North American boreal methane emissions

Miller, Scot M.; Worthy, Doug; Michalak, A. M.; Wofsy, Steven C.; Kort, E. A.; Havice, Talya C.; Andrews, A. E.; Dlugokencky, Edward J.; Kaplan, Jed O.; Levi, Patricia J.; Tian, Hanqin; Zhang, Bowen

doi:10.1002/2013gb004580

Cited by 51 publications

(95 citation statements)

References 76 publications

(103 reference statements)

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“…Observations at the NOAA towers consist of daily (occasionally weekly) flasks, and observations at the Environment Canada sites are continuous measurements. As in Miller et al (2014), we use afternoon averages of these continuous data. In addition to these towers, we utilize observations from 17 regular NOAA aircraft monitoring locations in the US and Canada (Fig.…”

Section: Data and Atmospheric Modelmentioning

confidence: 99%

“…Observations above that height are usually representative of the free troposphere with limited sensitivity to surface fluxes. These observations and the associated model runs (described below) are the same as those used in Miller et al (2013) and Miller et al (2014).…”

Section: Data and Atmospheric Modelmentioning

confidence: 99%

“…We multiply the footprints by a flux model and add this product to an estimate of the "background" concentration -the CH 4 concentration of air entering the North American regional domain. We estimate this background concentration using CH 4 observations collected near or over the Pacific Ocean and in the high Arctic, a setup described in detail by Miller et al (2013) and Miller et al (2014). The resulting modeled concentrations can be compared directly against atmospheric CH 4 observations.…”

Section: Data and Atmospheric Modelmentioning

confidence: 99%

“…The resulting modeled concentrations can be compared directly against atmospheric CH 4 observations. The observations, WRF-STILT runs, background concentrations, and uncertainties in the modeling framework are described in greater detail in the Supplement, Miller et al (2013), andMiller et al (2014).…”

Section: Data and Atmospheric Modelmentioning

confidence: 99%

“…These measurements usually encompass fluxes from a relatively small area, and fluxes can often vary greatly with landscape heterogeneity at these spatial scales (Waddington and Roulet, 1996;Hendriks et al, 2010). CH 4 data collected in the atmosphere observe the cumulative effect of CH 4 fluxes across a broader region (e.g., Winderlich et al, 2010;Pickett-Heaps et al, 2011;Bruhwiler et al, 2014;Miller et al, 2014). Hence, atmospheric data can provide a unique tool for evaluating existing CH 4 flux estimates across different countries or continents.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of wetland methane emissions across North America using atmospheric data and inverse modeling

et al. 2016

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Abstract. Existing estimates of methane (CH 4 ) fluxes from North American wetlands vary widely in both magnitude and distribution. In light of these differences, this study uses atmospheric CH 4 observations from the US and Canada to analyze seven different bottom-up, wetland CH 4 estimates reported in a recent model comparison project. We first use synthetic data to explore whether wetland CH 4 fluxes are detectable at atmospheric observation sites. We find that the observation network can detect aggregate wetland fluxes from both eastern and western Canada but generally not from the US. Based upon these results, we then use real data and inverse modeling results to analyze the magnitude, seasonality, and spatial distribution of each model estimate. The magnitude of Canadian fluxes in many models is larger than indicated by atmospheric observations. Many models predict a seasonality that is narrower than implied by inverse modeling results, possibly indicating an oversensitivity to air or soil temperatures. The LPJ-Bern and SDGVM models have a geographic distribution that is most consistent with atmospheric observations, depending upon the region and season. These models utilize land cover maps or dynamic modeling to estimate wetland coverage while most other models rely primarily on remote sensing inundation data.

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Section: Data and Atmospheric Modelmentioning

confidence: 99%

Section: Data and Atmospheric Modelmentioning

confidence: 99%

Section: Data and Atmospheric Modelmentioning

confidence: 99%

Section: Data and Atmospheric Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of wetland methane emissions across North America using atmospheric data and inverse modeling

et al. 2016

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Mapping of North American methane emissions with high spatial resolution by inversion of SCIAMACHY satellite data

Wecht

Jacob

Frankenberg

et al. 2014

J. Geophys. Res. Atmos.

174

268

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We estimate methane emissions from North America with high spatial resolution by inversion of Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) satellite observations using the Goddard Earth Observing System Chemistry (GEOS-Chem) chemical transport model and its adjoint. The inversion focuses on summer 2004 when data from the Intercontinental Chemical Transport Experiment-North America (INTEX-A) aircraft campaign over the eastern U.S. are available to validate the SCIAMACHY retrievals and evaluate the inversion. From the INTEX-A data we identify and correct a water vapor-dependent bias in the SCIAMACHY data. We conduct an initial inversion of emissions on the horizontal grid of GEOS-Chem (1/2°× 2/3°) to identify correction tendencies relative to the Emission Database for Global Atmospheric Research (EDGAR) v4.2 emission inventory used as a priori. We then cluster these grid cells with a hierarchical algorithm to extract the maximum information from the SCIAMACHY observations. A 1000 cluster ensemble can be adequately constrained, providing~100 km resolution across North America. and Environmental Protection Agency (EPA) inventories, respectively. We find that U.S. livestock emissions are 40% greater than in these two inventories. No such discrepancy is apparent for overall U.S. oil and gas emissions, although this may reflect some compensation between overestimate of emissions from storage/distribution and underestimate from production. We find that U.S. livestock emissions are 70% greater than the oil and gas emissions, in contrast to the EDGAR v4.2 and EPA inventories where these two sources are of comparable magnitude.

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Methane emissions from global rice fields: Magnitude, spatiotemporal patterns, and environmental controls

Zhang

Tian

Ren

et al. 2016

Global Biogeochemical Cycles

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107

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Given the importance of the potential positive feedback between methane (CH4) emissions and climate change, it is critical to accurately estimate the magnitude and spatiotemporal patterns of CH4 emissions from global rice fields and better understand the underlying determinants governing the emissions. Here we used a coupled biogeochemical model in combination with satellite‐derived contemporary inundation area to quantify the magnitude and spatiotemporal variation of CH4 emissions from global rice fields and attribute the environmental controls of CH4 emissions during 1901–2010. Our study estimated that CH4 emissions from global rice fields varied from 18.3 ± 0.1 Tg CH4/yr (Avg. ±1 SD) under intermittent irrigation to 38.8 ± 1.0 Tg CH4/yr under continuous flooding in the 2000s, indicating that the magnitude of CH4 emissions from global rice fields is largely dependent on different water schemes. Over the past 110 years, our simulated results showed that global CH4 emissions from rice cultivation increased by 85%. The expansion of rice fields was the dominant factor for the increasing trends of CH4 emissions, followed by elevated CO2 concentration, and nitrogen fertilizer use. On the contrary, climate variability had reduced the cumulative CH4 emissions for most of the years over the study period. Our results imply that CH4 emissions from global rice fields could be reduced through optimizing irrigation practices. Therefore, the future magnitude of CH4 emissions from rice fields will be determined by the human demand for rice production as well as the implementation of optimized water management practices.

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Observational constraints on the distribution, seasonality, and environmental predictors of North American boreal methane emissions

Cited by 51 publications

References 76 publications

Evaluation of wetland methane emissions across North America using atmospheric data and inverse modeling

Evaluation of wetland methane emissions across North America using atmospheric data and inverse modeling

Mapping of North American methane emissions with high spatial resolution by inversion of SCIAMACHY satellite data

Methane emissions from global rice fields: Magnitude, spatiotemporal patterns, and environmental controls

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