Multi‐Season Evaluation of CO2 Weather in OCO‐2 MIP Models. Issue 2 (10th January 2022)

• Record Type: Journal Article
• Authors: Zhang, Li; Davis, Kenneth J.; Schuh, Andrew E.; Jacobson, Andrew R.; Pal, Sandip; Cui, Yu Yan; Baker, David; Crowell, Sean; Chevallier, Frederic; Remaud, Marine; Liu, Junjie; Weir, Brad; Philip, Sajeev; Johnson, Matthew S.; Deng, Feng; Basu, Sourish
• Is Part Of: Journal of geophysical research. Volume 127:Issue 2(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-01-10
• Subjects: Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1029/2021JD035457
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: The ability of current global models to simulate the transport of CO2 by mid‐latitude, synoptic‐scale weather systems (i.e., CO2 weather) is important for inverse estimates of regional and global carbon budgets but remains unclear without comparisons to targeted measurements. Here, we evaluate ten models that participated in the Orbiting Carbon Observatory‐2 model intercomparison project (OCO‐2 MIP version 9) with intensive aircraft measurements collected from the Atmospheric Carbon Transport (ACT)‐America mission. We quantify model‐data differences in the spatial variability of CO2 mole fractions, mean winds, and boundary layer depths in 27 mid‐latitude cyclones spanning four seasons over the central and eastern United States. We find that the OCO‐2 MIP models are able to simulate observed CO2 frontal differences with varying degrees of success in summer and spring, and most underestimate frontal differences in winter and autumn. The models may underestimate the observed boundary layer‐to‐free troposphere CO2 differences in spring and autumn due to model errors in boundary layer height. Attribution of the causes of model biases in other seasons remains elusive. Transport errors, prior fluxes, and/or inversion algorithms appear to be the primary cause of these biases since model performance is not highly sensitive to the CO2 data used in the inversion. The metrics presented here provide new benchmarks regarding the ability of atmospheric inversion systems to reproduce the CO2 structure of mid‐latitude weather systems. Controlled experiments are needed to link these metrics more directly to the accuracy of regional or global flux estimates. Plain Language Summary: Global flux estimate systems use CO2 observations, atmospheric transport models, CO2 flux models (emissions and absorption), and mathematical optimization methods to estimate biosphere‐atmosphere CO2 exchange. Accurate representation of atmospheric transport is important for a reliable optimization of fluxes in these systems. We use intensive aircraft measurements of wind speed, boundary layer height, and horizontal and vertical differences of CO2 concentrations within 27 mid‐latitude cyclones collected by the Atmospheric Carbon Transport (ACT)‐America mission to evaluate the performance of ten global flux estimate systems from the Orbiting Carbon Observatory‐2 model intercomparison project (OCO‐2 MIP). We find the models can simulate observed horizontal CO2 differences between the warm and cold parts of cyclones with different degrees of success in summer and spring, but often underestimate the observed cross‐frontal and vertical differences in CO2 in winter and autumn. The models may underestimate the CO2 differences between the boundary layer and the free troposphere due to model errors in boundary layer height and surface fluxes. These weather‐oriented CO2 metrics provide benchmarks for testing simulations of the CO2 structure within cyclones. Future efforts are needed to link these metrics more directly to the accuracy of CO2 flux estimates. Key Points: Global inversion systems are able to simulate observed CO2 frontal differences but with varying degrees of success Most global inversion systems underestimate dormant‐season frontal and vertical CO2 differences Inversion systems appear to explain more of the model‐data differences in CO2 weather metrics than CO2 data sources