US natural gas production increased by ˜43% between 2005 and 2015, but there is disagreement in the scientific literature on whether this growth led to increased methane emissions. In this study, we evaluate the possible contributions of emissions versus meteorology to an upward trend in US atmospheric methane observations during 2007-2015. We find that interannual variability (IAV) in meteorology yields an apparent upward trend in atmospheric methane across much of the US. We further find that IAV in atmospheric methane at several observation sites is correlated with IAV in local wind speed. Overall, our results show that US trends in atmospheric methane largely reflect variability in meteorology, and are unlikely to be a direct reflection of trends in emissions. The results of this study therefore lend support for the conclusion that there was little upward trend in US methane emissions during this time.

The US is one of the largest anthropogenic emitters of methane, behind only China and India (Saunois et al., 2020). Numerous recent studies indicate that US methane emissions are 48%-76% higher than estimated by the EPA Inventory of US Greenhouse Gas Emissions and Sinks (GHGI) (

Solar-induced fluorescence (SIF) shows enormous promise as a proxy for photosynthesis and as a tool for modeling variability in gross primary productivity (GPP) and net biosphere exchange (NBE). In this study, we explore the skill of SIF and other vegetation indicators in predicting variability in global atmospheric CO2 observations, and thus global variability in NBE. We do so using a four-year record of global CO2 observations from NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite and using a geostatistical inverse model. We find that existing SIF products closely correlate with space-time variability in atmospheric CO2 observations in the extra-tropics but show weaker explanatory power across the tropics. In the extra-tropics, all SIF products exhibit greater skill in explaining variability in atmospheric CO2 observations compared to an ensemble of process-based CO2 flux models and other vegetation indicators. Furthermore, we find that using SIF as a predictor variable in the geosatistical inverse model shifts the seasonal cycle of estimated NBE and yields an earlier end to the growing season relative to other vegetation indicators. In tropical biomes, by contrast, the seasonal cycles of SIF products and estimated NBE are out of phase, and existing respiration and biomass burning estimates do not reconcile this discrepancy. Overall, our results highlight several advantages and challenges of using SIF products to help predict global variability in GPP and NBE.

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