Substantial Hysteresis in Emergent Temperature Sensitivity of Global Wetland CH4 Emissions
Methane (CH4) is a strong greenhouse gas that accelerates climate change and offsets mitigation efforts. In this study, we analyzed CH4-temperature relationships measured across 48 global wetland and rice paddy sites to disentangle factors controlling CH4 emissions to the atmosphere. The results demonstrate the large spatial and temporal variability of CH4-temperature relationships and imply the need to incorporate substrate and microbial dynamics into next-generation CH4 models. Collectively, this study presents an important and overlooked ecosystem property and highlights uncertainty in current CH4 model parameterization and thereby climate change projections.
Wetland methane emissions contribute to global warming and are oversimplified in carbon-climate models. Here we use eddy covariance measurements from 48 global sites to illustrate seasonal hysteresis in methane-temperature relationships and suggest the importance of microbial processes. Our findings challenge a key assumption embedded in most existing carbon-climate models and demonstrate wetland methane emissions cannot be estimated by fixed temperature relations due to the large variability in site- and time-specific methane-temperature relationships.
Wetland methane (CH4) emissions (F_(〖CH〗_4 )) are important in global carbon budgets and climate change assessments. Currently, F_(〖CH〗_4 ) projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent F_(〖CH〗_4 ) temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that F_(〖CH〗_4 ) are often controlled by factors beyond temperature. Here, we evaluate the relationship between F_(〖CH〗_4 ) and temperature using observations from the FLUXNET-CH4 database. Measurements collected across the globe show substantial seasonal hysteresis between F_(〖CH〗_4 ) and temperature, suggesting larger F_(〖CH〗_4 ) 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.