Characterizing Average Seasonal, Synoptic, and Finer Variability in Orbiting Carbon Observatory-2 XCO₂ Across North America and Adjacent Ocean Basins

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. Plain Language Summary The National Aeronautics and Space Administration's Orbiting Carbon Observatory-2 satellite collects measurements of atmosphere total column-mean CO2 (XCO2), providing a constraint on surface carbon fluxes. Fluxes of carbon into Earth's surface by the ocean and land biosphere (uptake) counteract the rising levels of atmospheric CO2 caused by increased anthropogenic emissions. To use XCO2 for flux estimation in inverse models, variability in the data must be attributed to either gradients in surface carbon fluxes, atmospheric transport, or retrieval errors. We decompose OCO-2 XCO2 variability over North America and adjacent ocean into seasonal, synoptic (order of days, thousands of kilometers) and finer scales to uncover the relative influences of these processes on XCO2. Spatial patterns in seasonal and synoptic-scale XCO2 variability follow large-scale atmospheric circulation and reflect the mean north-south distribution of biosphere carbon uptake in the Northern Hemisphere rather than underlying local surface flux variability. On finer scales, geostatistical analysis shows that patterns in XCO2 variability are driven by correlated retrieval errors, obscuring the influence of transport and error. We compute new estimates of XCO2 uncertainty for inverse model studies that assimilate the data and discuss the impact of errors over different land and ocean regions.