Uncertainty Quantification of Global Net Methane Emissions From Terrestrial Ecosystems Using a Mechanistically Based Biogeochemistry Model

Licheng Liu, Qianlai Zhuang, Youmi Oh, Narasinha J. Shurpali, Seungbum Kim, Ben Poulter

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

Quantification of methane (CH4) emissions from wetlands and its sinks from uplands is still fraught with large uncertainties. Here, a methane biogeochemistry model was revised, parameterized, and verified for various wetland ecosystems across the globe. The model was then extrapolated to the global scale to quantify the uncertainty induced from four different types of uncertainty sources including parameterization, wetland type distribution, wetland area distribution, and meteorological input. We found that global wetland emissions are 212 ± 62 and 212 ± 32 Tg CH4 year−1 (1Tg = 1012 g) due to uncertain parameters and wetland type distribution, respectively, during 2000–2012. Using two wetland distribution data sets and three sets of climate data, the model simulations indicated that the global wetland emissions range from 186 to 212 CH4 year−1 for the same period. The parameters were the most significant uncertainty source. After combining the global methane consumption in the range of −34 to −46 Tg CH4 year−1, we estimated that the global net land methane emissions are 149–176 Tg CH4 year−1 due to uncertain wetland distribution and meteorological input. Spatially, the northeast United States and Amazon were two hotspots of methane emission, while consumption hotspots were in the Eastern United States and eastern China. During 1950–2016, both wetland emissions and upland consumption increased during El Niño events and decreased during La Niña events. This study highlights the need for more in situ methane flux data, more accurate wetland type, and area distribution information to better constrain the model uncertainty.

Original languageEnglish (US)
Article numbere2019JG005428
JournalJournal of Geophysical Research: Biogeosciences
Volume125
Issue number6
DOIs
StatePublished - Jun 1 2020

Bibliographical note

Funding Information:
This study is supported by NASA (NNX17AK20G), the Department of Energy (DESC0008092 and DE‐SC0007007), and the NSF Division of Information and Intelligent Systems (NSF‐1028291). The supercomputing is provided by the Rosen Center for Advanced Computing at Purdue University. We are also grateful to the University of Tuscia (dep. DIBAF), Italy, and their affiliated members for their help and the use of their field data.

Funding Information:
This study is supported by NASA (NNX17AK20G), the Department of Energy (DESC0008092 and DE-SC0007007), and the NSF Division of Information and Intelligent Systems (NSF-1028291). The supercomputing is provided by the Rosen Center for Advanced Computing at Purdue University. We are also grateful to the University of Tuscia (dep. DIBAF), Italy, and their affiliated members for their help and the use of their field data.

Publisher Copyright:
©2020. American Geophysical Union. All Rights Reserved.

Keywords

  • biogeochemistry modeling
  • global modeling
  • uncertainty analysis
  • wetland methane emission

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