Characterization of Regional‐Scale CO₂ Transport Uncertainties in an Ensemble with Flow‐Dependent Transport Errors

Abstract

Inference of CO2 surface fluxes using atmospheric CO2 observations in atmospheric inversions depends critically on accurate representation of atmospheric transport. Here we characterize regional‐scale CO2 transport uncertainties due to uncertainties in meteorological fields using a mesoscale atmospheric model and an ensemble of simulations with flow‐dependent transport errors. During a 1‐month summer period over North America, transport uncertainties yield an ensemble spread in instantaneous CO2 at 100 m above ground level comparable to the CO2 uncertainties resulting from 48% relative uncertainty in 3‐hourly natural CO2 fluxes. Temporal averaging reduces transport uncertainties but increases the influence of CO2 uncertainties from the lateral boundaries. The influence of CO2 background uncertainties is especially large for column‐averaged CO2. These results suggest that transport errors and CO2 background errors limit regional atmospheric inversions at two distinct timescales and that the error characteristics of transport and background errors should guide the design of regional inversion systems.

Representing Model Uncertainty for Global Atmospheric CO<sub>2</sub> Flux Inversions Using ECMWF-IFS-46R1

“…The spatial and temporal variability of errors and resulting signal-to-noise ratios are influenced by neighbouring hotspots, local orography and meteorological variability. Our findings, on a global scale, agree well with the regional study of Chen et al (2019).…”

Observations of Greenhouse Gas Changes Across Summer Frontal Boundaries in the Eastern United States

“…Model evaluation should focus on both the GHG flux and atmospheric transport properties of the model. This modeling work is underway within the ACT project (e.g., Chen, Zhang, Lauvaux, et al, ; Chen, Zhang, Zhang, et al, ; Feng, Lauvaux, Davis, et al, ; Feng, Lauvaux, Keller, et al, ; Schuh et al, ).…”

Diagnosing spatial error structures in CO<sub>2</sub> mole fractions and XCO<sub>2</sub> column mole fractions from atmospheric transport

“…The increased density in existing tower networks and the availability of fine-scale satellite retrievals raised concerns about spatial and temporal structures in transport model errors (Rayner and O'Brien, 2001;Lauvaux et al, 2009;Miller et al, 2015). The proximity of the measurements (e.g., a couple of kilometers between OCO-2 retrievals) means that spatial correlations in model errors are significant and can 1 http://www.esrl.noaa.gov/gmd/ccgg/trends/, last access: 29 August 2019. no longer be ignored (Chevallier, 2007). This issue becomes critical to greenhouse gas inversion problems when applied to urban scales (Lauvaux et al, 2016) but remains poorly studied to date.…”