Variations in atmosphere total column-mean CO2 (XCO2) collected by the National Aeronautics and Space Administration's Orbiting Carbon Observatory-2 satellite can be used to constrain surface carbon fluxes if the influence of atmospheric transport and observation errors on the data is known and accounted for. Due to sparse validation data, the portions of fine-scale variability in XCO2 driven by fluxes, transport, or retrieval errors remain uncertain, particularly over the ocean. To better understand these drivers, we characterize variability in OCO-2 Level 2 version 10 XCO2 from the seasonal scale, synoptic-scale (order of days, thousands of kilometers), and mesoscale (within-day, hundreds of kilometers) for 10 biomes over North America and adjacent ocean basins. Seasonal and synoptic variations in XCO2 reflect real geophysical drivers (transport and fluxes), following large-scale atmospheric circulation and the north-south distribution of biosphere carbon uptake. In contrast, geostatistical analysis of mesoscale and finer variability shows that real signals are obscured by systematic biases across the domain. Spatial correlations in along-track XCO2 are much shorter and spatially coherent variability is much larger in magnitude than can be attributed to fluxes or transport. We characterize random and coherent along-track XCO2 variability in addition to quantifying uncertainty in XCO2 aggregates across typical lengths used in inverse modeling. Even over the ocean, correlated errors decrease the independence and increase uncertainty in XCO2. We discuss the utility of computing geostatistical parameters and demonstrate their importance for XCO2 science applications spanning from data reprocessing and algorithm development to error estimation and carbon flux inference.
Keywords: OCO‐2; XCO2; carbon flux estimation; correlated error; mesoscale variability; synoptic variability.
© 2023. The Authors.