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A semi-empirical approach based on surface-renewal theory for estimating the friction velocity is tested for measurements taken in the inertial sublayer. For unstable cases, the input requirements are the mean wind speed and the high-frequency trace (10 or 20 Hz) of the air or sonic temperature. The method has been extended to traces of water vapour (H2O) and carbon dioxide (CO2) concentrations. For stable cases, the stability parameter must also be considered. The method’s performance, taking the direct friction velocity measured by sonic anemometry as a reference, was tested over a growing cotton field that included bare soil with some crop residues at the beginning of the season. In general, the proposed friction-velocity estimates are reliable. For unstable cases, the method shows the potential to outperform the wind logarithmic-law computation. Discarding cases with low wind speeds (e.g., <0.3 m s−1 and mean wind shear<1 Hz), the proposed approach may be recommended as an alternative method to estimating the friction velocity. There is the potential, based on the input requirements, that the proposed formulation may offer significant advantages in the estimation of the friction velocity in some marine environments. 

Abstract Wetland methane (CH 4 ) emissions ( $${F}_{{{CH}}_{4}}$$ F C H 4 ) are important in global carbon budgets and climate change assessments. Currently, $${F}_{{{CH}}_{4}}$$ F C H 4 projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent $${F}_{{{CH}}_{4}}$$ F C H 4 temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that $${F}_{{{CH}}_{4}}$$ F C H 4 are often controlled by factors beyond temperature. Here, we evaluate the relationship between $${F}_{{{CH}}_{4}}$$ F C H 4 and temperature using observations from the FLUXNET-CH 4 database. Measurements collected across the globe show substantial seasonal hysteresis between $${F}_{{{CH}}_{4}}$$ F C H 4 and temperature, suggesting larger $${F}_{{{CH}}_{4}}$$ F C H 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 CH 4 production are thus needed to improve global CH 4 budget assessments.