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Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions
- Chang, Kuang-Yu;
- Riley, William J;
- Knox, Sara H;
- Jackson, Robert B;
- McNicol, Gavin;
- Poulter, Benjamin;
- Aurela, Mika;
- Baldocchi, Dennis;
- Bansal, Sheel;
- Bohrer, Gil;
- Campbell, David I;
- Cescatti, Alessandro;
- Chu, Housen;
- Delwiche, Kyle B;
- Desai, Ankur R;
- Euskirchen, Eugenie;
- Friborg, Thomas;
- Goeckede, Mathias;
- Helbig, Manuel;
- Hemes, Kyle S;
- Hirano, Takashi;
- Iwata, Hiroki;
- Kang, Minseok;
- Keenan, Trevor;
- Krauss, Ken W;
- Lohila, Annalea;
- Mammarella, Ivan;
- Mitra, Bhaskar;
- Miyata, Akira;
- Nilsson, Mats B;
- Noormets, Asko;
- Oechel, Walter C;
- Papale, Dario;
- Peichl, Matthias;
- Reba, Michele L;
- Rinne, Janne;
- Runkle, Benjamin RK;
- Ryu, Youngryel;
- Sachs, Torsten;
- Schäfer, Karina VR;
- Schmid, Hans Peter;
- Shurpali, Narasinha;
- Sonnentag, Oliver;
- Tang, Angela CI;
- Torn, Margaret S;
- Trotta, Carlo;
- Tuittila, Eeva-Stiina;
- Ueyama, Masahito;
- Vargas, Rodrigo;
- Vesala, Timo;
- Windham-Myers, Lisamarie;
- Zhang, Zhen;
- Zona, Donatella
- et al.
Abstract
Wetland methane (CH4) emissions ([Formula: see text]) are important in global carbon budgets and climate change assessments. Currently, [Formula: see text] projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent [Formula: see text] temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that [Formula: see text] are often controlled by factors beyond temperature. Here, we evaluate the relationship between [Formula: see text] and temperature using observations from the FLUXNET-CH4 database. Measurements collected across the globe show substantial seasonal hysteresis between [Formula: see text] and temperature, suggesting larger [Formula: see text] sensitivity to temperature later in the frost-free season (about 77% of site-years). Results derived from a machine-learning model and several regression models highlight the importance of representing the large spatial and temporal variability within site-years and ecosystem types. Mechanistic advancements in biogeochemical model parameterization and detailed measurements in factors modulating CH4 production are thus needed to improve global CH4 budget assessments.
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