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                    Section 1: Publication
                                
                Publication Type
                Journal Article
                                
                Authorship
                Chang, K.-Y., Riley, W. J., Knox, S. H. et al. incl. Helbig, M., and Sonnentag, O. 
                                
                Title
                Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions
                                
                Year
                2021
                                
                Publication Outlet
                Nature Communications, 12, 2266
                                
                DOI
                
                                
                ISBN
                
                                
                ISSN
                
                                
                Citation
                
                    Chang, K.-Y., Riley, W. J., Knox, S. H. et al. incl. Helbig, M., and Sonnentag, O. (2021). Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions, Nature Communications, 12, 2266, 
https://doi.org/10.1038/s41467-021-22452-1
                Abstract
                
                    Wetland methane (CH4) emissions (FCH4) are important in global carbon budgets and climate change assessments. Currently, FCH4 projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent FCH4 temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that FCH4 are often controlled by factors beyond temperature. Here, we evaluate the relationship between FCH4 and temperature using observations from the FLUXNET-CH4 database. Measurements collected across the globe show substantial seasonal hysteresis between FCH4 and temperature, suggesting larger FCH4 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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