Advanced

Modeled Microbial Dynamics Explain the Apparent Temperature Sensitivity of Wetland Methane Emissions

Chadburn, Sarah E. ; Aalto, Tuula ; Aurela, Mika ; Baldocchi, Dennis ; Biasi, Christina ; Boike, Julia ; Burke, Eleanor J. ; Comyn-Platt, Edward ; Dolman, A. Johannes and Duran-Rojas, Carolina , et al. (2020) In Global Biogeochemical Cycles 34(11).
Abstract

Methane emissions from natural wetlands tend to increase with temperature and therefore may lead to a positive feedback under future climate change. However, their temperature response includes confounding factors and appears to differ on different time scales. Observed methane emissions depend strongly on temperature on a seasonal basis, but if the annual mean emissions are compared between sites, there is only a small temperature effect. We hypothesize that microbial dynamics are a major driver of the seasonal cycle and that they can explain this apparent discrepancy. We introduce a relatively simple model of methanogenic growth and dormancy into a wetland methane scheme that is used in an Earth system model. We show that this... (More)

Methane emissions from natural wetlands tend to increase with temperature and therefore may lead to a positive feedback under future climate change. However, their temperature response includes confounding factors and appears to differ on different time scales. Observed methane emissions depend strongly on temperature on a seasonal basis, but if the annual mean emissions are compared between sites, there is only a small temperature effect. We hypothesize that microbial dynamics are a major driver of the seasonal cycle and that they can explain this apparent discrepancy. We introduce a relatively simple model of methanogenic growth and dormancy into a wetland methane scheme that is used in an Earth system model. We show that this addition is sufficient to reproduce the observed seasonal dynamics of methane emissions in fully saturated wetland sites, at the same time as reproducing the annual mean emissions. We find that a more complex scheme used in recent Earth system models does not add predictive power. The sites used span a range of climatic conditions, with the majority in high latitudes. The difference in apparent temperature sensitivity seasonally versus spatially cannot be recreated by the non-microbial schemes tested. We therefore conclude that microbial dynamics are a strong candidate to be driving the seasonal cycle of wetland methane emissions. We quantify longer-term temperature sensitivity using this scheme and show that it gives approximately a 12% increase in emissions per degree of warming globally. This is in addition to any hydrological changes, which could also impact future methane emissions.

(Less)
Please use this url to cite or link to this publication:
author
; ; ; ; ; ; ; ; and , et al. (More)
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; and (Less)
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
global modeling, methane, methanogens, microbial modeling, wetland methane
in
Global Biogeochemical Cycles
volume
34
issue
11
article number
e2020GB006678
publisher
American Geophysical Union (AGU)
external identifiers
  • scopus:85096439087
ISSN
0886-6236
DOI
10.1029/2020GB006678
language
English
LU publication?
yes
id
12ac5c56-5d8b-410b-8781-b7217592ad22
date added to LUP
2020-12-01 11:30:02
date last changed
2020-12-08 04:55:10
@article{12ac5c56-5d8b-410b-8781-b7217592ad22,
  abstract     = {<p>Methane emissions from natural wetlands tend to increase with temperature and therefore may lead to a positive feedback under future climate change. However, their temperature response includes confounding factors and appears to differ on different time scales. Observed methane emissions depend strongly on temperature on a seasonal basis, but if the annual mean emissions are compared between sites, there is only a small temperature effect. We hypothesize that microbial dynamics are a major driver of the seasonal cycle and that they can explain this apparent discrepancy. We introduce a relatively simple model of methanogenic growth and dormancy into a wetland methane scheme that is used in an Earth system model. We show that this addition is sufficient to reproduce the observed seasonal dynamics of methane emissions in fully saturated wetland sites, at the same time as reproducing the annual mean emissions. We find that a more complex scheme used in recent Earth system models does not add predictive power. The sites used span a range of climatic conditions, with the majority in high latitudes. The difference in apparent temperature sensitivity seasonally versus spatially cannot be recreated by the non-microbial schemes tested. We therefore conclude that microbial dynamics are a strong candidate to be driving the seasonal cycle of wetland methane emissions. We quantify longer-term temperature sensitivity using this scheme and show that it gives approximately a 12% increase in emissions per degree of warming globally. This is in addition to any hydrological changes, which could also impact future methane emissions.</p>},
  author       = {Chadburn, Sarah E. and Aalto, Tuula and Aurela, Mika and Baldocchi, Dennis and Biasi, Christina and Boike, Julia and Burke, Eleanor J. and Comyn-Platt, Edward and Dolman, A. Johannes and Duran-Rojas, Carolina and Fan, Yuanchao and Friborg, Thomas and Gao, Yao and Gedney, Nicola and Göckede, Mathias and Hayman, Garry D. and Holl, David and Hugelius, Gustaf and Kutzbach, Lars and Lee, Hanna and Lohila, Annalea and Parmentier, Frans Jan W. and Sachs, Torsten and Shurpali, Narasinha J. and Westermann, Sebastian},
  issn         = {0886-6236},
  language     = {eng},
  number       = {11},
  publisher    = {American Geophysical Union (AGU)},
  series       = {Global Biogeochemical Cycles},
  title        = {Modeled Microbial Dynamics Explain the Apparent Temperature Sensitivity of Wetland Methane Emissions},
  url          = {http://dx.doi.org/10.1029/2020GB006678},
  doi          = {10.1029/2020GB006678},
  volume       = {34},
  year         = {2020},
}