Details:

Journal Title:
Biogeosciences
Personal Author:
Miller, Scot M. ; Commane, Roisin ; Melton, Joe R. ; Andrews, Arlyn E. ; Benmergui, Joshua ; ... More +
Miller, Scot M. ; Commane, Roisin ; Melton, Joe R. ; Andrews, Arlyn E. ; Benmergui, Joshua ; Dlugokencky, Edward J. ; Janssens-Maenhout, Greet ; Michalak, Anna M. ; Sweeney, Colm ; Worthy, Doug E. J. Less -
NOAA Program & Office:
OAR (Oceanic and Atmospheric Research) ; ESRL (Earth System Research Laboratory) ; GML (Global Monitoring Laboratory) ; CIRES (Cooperative Institute for Research in Environmental Sciences)
OAR (Oceanic and Atmospheric Research) ; ESRL (Earth System Research Laboratory) ; GML (Global Monitoring Laboratory) ; CIRES (Cooperative Institute for Research in Environmental Sciences) Less -
Description:
Existing estimates of methane (CH4) fluxes from North American wetlands vary widely in both magnitude and distribution. In light of these differences, this study uses atmospheric CH4 observations from the US and Canada to analyze seven different bottom-up, wetland CH4 estimates reported in a recent model comparison project. We first use synthetic data to explore whether wetland CH4 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.
Source:
Biogeosciences, 13(4), 1329-1339.
DOI:
https://doi.org/10.5194/bg-13-1329-2016
Document Type:
Journal Article
Rights Information:
CC BY
Compliance:
Submitted
Main Document Checksum:
[+]
urn:sha256:749422e42ceb775b01e3a8ae603a2093de3329bbc34dd11b1cee833a1adbe422
Download URL:
https://repository.library.noaa.gov/view/noaa/18311/noaa_18311_DS1.pdf
File Type:

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