Examining CO₂ Model Observation Residuals Using ACT-America Data

Atmospheric (Formula presented.) inversion typically relies on the specification of prior flux and atmospheric model transport errors, which have large uncertainties. Here, we used ACT-America airborne observations to compare (Formula presented.) model observation mismatch in the eastern U.S. and during four climatological seasons for the mesoscale WRF(-Chem) and global scale CarbonTracker/TM5 (CT) models. Models used identical surface carbon fluxes, and CT was used as (Formula presented.) boundary condition for WRF. Both models showed reasonable agreement with observations, and (Formula presented.) residuals follow near symmetric peaked (i.e., non-Gaussian) distribution with near-zero bias of both models (CT: (Formula presented.) ppm; WRF: (Formula presented.) ppm). We also found large magnitude residuals at the tails of the distribution that contribute considerably to overall bias. Atmospheric boundary-layer biases (1–10 ppm) were much larger than free tropospheric biases (0.5–1 ppm) and were of same magnitude as model-model differences, whereas free tropospheric biases were mostly governed by (Formula presented.) background conditions. Results revealed systematic differences in atmospheric transport, most pronounced in the warm and cold sectors of synoptic systems, highlighting the importance of transport for (Formula presented.) residuals. While CT could reproduce the principal (Formula presented.) dynamics associated with synoptic systems, WRF showed a clearer distinction for (Formula presented.) differences across fronts. Variograms were used to quantify spatial correlation of residuals and showed characteristic residual length scales of approximately 100–300 km. Our findings suggest that inclusion of synoptic weather-dependent and non-Gaussian error structure may benefit inversion systems.