Evaluating consistency between total column CO<sub>2</sub> retrievals from OCO-2 and the in situ network over North America: implications for carbon flux estimation

Abstract

Abstract. Feedbacks between the climate system and the carbon cycle represent a key source of uncertainty in model projections of Earth's climate, in part due to our inability to directly measure large-scale biosphere–atmosphere carbon fluxes. In situ measurements of the CO2 mole fraction from surface flasks, towers, and aircraft are used in inverse models to infer fluxes, but measurement networks remain sparse, with limited or no coverage over large parts of the planet. Satellite retrievals of total column CO2 (XCO2), such as those from NASA's Orbiting Carbon Observatory-2 (OCO-2), can potentially provide unprecedented global information about CO2 spatiotemporal variability. However, for use in inverse modeling, data need to be extremely stable, highly precise, and unbiased to distinguish abundance changes emanating from surface fluxes from those associated with variability in weather. Systematic errors in XCO2 have been identified and, while bias correction algorithms are applied globally, inconsistencies persist at regional and smaller scales that may complicate or confound flux estimation. To evaluate XCO2 retrievals and assess potential biases, we compare OCO-2 v10 retrievals with in situ data-constrained XCO2 simulations over North America estimated using surface fluxes and boundary conditions optimized with observations that are rigorously calibrated relative to the World Meteorological Organization X2007 CO2 scale. Systematic errors in simulated atmospheric transport are independently evaluated using unassimilated aircraft and AirCore profiles. We find that the global OCO-2 v10 bias correction shifts the distribution of retrievals closer to the simulated XCO2, as intended. Comparisons between bias-corrected and simulated XCO2 reveal differences that vary seasonally. Importantly, the difference between simulations and retrievals is of the same magnitude as the imprint of recent surface flux in the total column. This work demonstrates that systematic errors in OCO-2 v10 retrievals of XCO2 over land can be large enough to confound reliable surface flux estimation and that further improvements in retrieval and bias correction techniques are essential. Finally, we show that independent observations, especially vertical profile data, such as those from the National Oceanic and Atmospheric Administration aircraft and AirCore programs are critical for evaluating errors in both satellite retrievals and carbon cycle models.

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

“…Despite improvements in the retrieval algorithm, systematic biases over regional and finer scales in the latest version (v10) XCO 2 can be large enough to impede surface flux estimation. Rastogi et al (2021) compared bias-corrected v10 XCO 2 retrievals with in situ data-constrained simulated XCO 2 over North America. They found differences between the retrieved and simulated quantities on local scales (tens of kilometers) of the same magnitude as the imprint of surface fluxes in the total column and were able to attribute these differences to persisting fine-scale systematic errors in XCO 2 .…”

A global surface CO2 flux dataset (2015–2022) inferred from OCO-2 retrievals using the GONGGA inversion system

“…11). Apart from RMSE and bias, we further present the random error here, which was calculated as the standard deviation of the differences between simulated and observed CO 2 concentrations (Rastogi et al, 2021). The monthly simulations closely agreed with the observations.…”

Biome-scale temperature sensitivity of ecosystem respiration revealed by atmospheric CO2 observations

“…Sensitivities of CO 2 mixing ratio measurements to surface fluxes, also known as transport footprints, were produced from high-resolution (10 km for temperate North America and 40 km for tropical and high-latitude North America) WRF–STILT (Weather Research and Forecasting – Stochastic Time Inverted Lagrangian Transport) model runs 106 and post aggregated to a 1° × 1° resolution as part of the CarbonTracker–Lagrange project ( https://gml.noaa.gov/ccgg/carbontracker-lagrange/ ). The potential transport error is demonstrably minor relative to the spread in TBM flux estimates 107 , 108 .…”