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.

The upper-level jet stream impacts surface-level trace gas variability, yet the cause of this relationship remains unclear. We investigate the mechanism(s) responsible for the relationship using idealized tracers with different source regions within a chemical transport model. All tracers’ daily variabilities are correlated with the meridional position of the jet in the mid-latitudes, but tracers emitted south (north) of the jet increase (decrease) in the mid-latitudes when the jet is shifted poleward. The jet stream regulates the near-surface meridional wind, and this coupling together with the meridional tracer gradient robustly predicts where the jet stream and tracers are in and out of phase. Our study elucidates a major driver of trace gas variability and links it to the location of the jet stream and emissions. These results are useful for understanding changes in trace gas variability if the jet stream’s position or major emission source regions change in the future.

The carbon cycle displays strong sensitivity to short term variations in environmental conditions, and it is key to understand how these variations are linked with variations in CO2 fluxes. Previously, atmospheric observations of CO2 have been sparse in many regions of the globe, making it challenging to evaluate these relationships. However, the OCO-2 satellite, launched in July 2014, provides new insight into global CO2 fluxes, particularly in regions that were previously difficult to monitor. In this study, we combine OCO-2 observations with a geostatistical inverse model to explore data-driven relationships between inferred CO2 flux patterns and environmental drivers. We further use year 2016 as an initial case study to explore the applicability of the geostatistical approach to large satellite-based inverse problems. We estimate daily, global CO2 fluxes at the model grid scale and find that a combination of air temperature, daily precipitation, and photosynthetically active radiation (PAR) best describe patterns in CO2 fluxes in most biomes across the globe. PAR is an adept predictor of fluxes across mid-to-high latitudes, whereas a combined set of daily air temperature and precipitation shows strong explanatory power across tropical biomes. However, we are unable to quantify a larger number of relationships between environmental drivers and CO2 fluxes using OCO-2 due to the limited sensitivity of total column satellite observations to detailed surface processes. Overall, we estimate a global net biospheric flux of -1.73 ± 0.53 GtC in year 2016, in close agreement with recent inverse modeling studies using OCO-2 retrievals as observational constraints.

The daily variation of ground-level ozone (O3), a harmful pollutant, is positively correlated with air temperature (T) in many midlatitude land regions in the summer. The observed temporal regression slope between O3 and T is referred to as the “ozone-climate change penalty” and has been proposed as a way to predict the impact of future climate warming on O3 from observations. Here, we use two chemical transport models to show that the O3-T correlation is primarily due to the meridional advection of both fields, as opposed to direct temperature-dependent chemistry or emissions. Furthermore, the magnitude of the O3-T regression (dO3/dT) can be estimated by the ratio of the time-mean O3 and T meridional gradients. Consideration of expected changes in the meridional gradients of T and O3 due to climate change indicates that dO3/dT will likely change, and caution is needed when using the observed climate penalty to predict O3 changes.

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.