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

Abstract

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.

Ocean-driven interannual variability in atmospheric CO2 quantified using OCO-2 observations and atmospheric transport simulations

“…In that analysis, we determined optimal spatiotemporal scales for aggregating the observations to detect IAV variability in light of other sources of error. We evaluated IAV signals against the TCCON ground-truth network, confirming that the IAV inferred from OCO-2 is robust given the small magnitude of IAV compared to other sources of variance (Mitchell et al, 2023). Further, the OCO-2 IAV timeseries show similar zonal patterns of OCO-2 XCO 2 IAV timeseries compared to GOSAT space-based observation and ground-based NOAA ESRL in situ data.…”

“…A possible explanation could be that the OCO-2 observations contain the imprint not only of ocean fluxes but also land and fossil emissions. The OCO-2 XCO 2 IAV amplitude, calculated as the standard deviation of the IAV timeseries, suggests that XCO 2 interannual variability over ocean basins is smaller than that over continents (around 0.4 ppm vs 1.2 ppm; Figure 8), although this may be due to larger error variance due to complex topography and land surface albedo variations (Guan et al, 2023;Mitchell et al, 2023). We calculate the correlation coefficient, slope, and fractional ratio between simulated XCO 2 IAV and OCO-2 XCO 2 IAV (Figure 9, Supplementary Figure S8) at the gridscale to explore where the imprint of the ocean might be detectible.…”

“…Given the small signature of ocean fluxes on atmospheric XCO 2 IAV, attributing ocean-induced IAV based on space-based observations will be challenging. Mitchell et al (2023) provide a detailed assessment of time and space scales of variation in OCO-2 XCO 2 over North American land and coastal ocean using a geostatistical approach. They identify synoptic scale variations as contributing up to 2 ppm 2 variance, mesoscale transport, and correlated error as contributing up to 1 ppm 2 variance, and random noise as up to 1 ppm.…”

Seasonal and Diurnal Variations in XCO2 Characteristics in China as Observed by OCO‐2/3 Satellites: Effects of Land Cover and Local Meteorology

“…The spatial and temporal variability of atmospheric CO 2 reflects a mixture of terrestrial emissions and uptake as well as atmospheric transport. Atmospheric CO 2 measurements can provide a top‐down constraint on the inference of carbon fluxes (Mitchell et al., 2023; L. Zhang et al., 2023a). Our investigation that using direct satellite‐based observations to study the XCO 2 variation in seasonal and diurnal cycles amplitude among different biomes gives some new insight into carbon cycle amplitude, to some extent, could also reduce the uncertainties caused by the new assumptions of source/sink inversion work (Kou et al., 2023) or machine learning methods (Sheng et al., 2023; Y. Wang, Yuan, et al., 2023; L. Zhang et al., 2022b).…”

“…The spatial and temporal variability of atmospheric CO 2 reflects a mixture of terrestrial emissions and uptake as well as atmospheric transport. Atmospheric CO 2 measurements can provide a top-down constraint on the inference of carbon fluxes (Mitchell et al, 2023;L. Zhang et al, 2023a).…”