Search

Efforts to monitor the emissions and absorptions of atmospheric carbon dioxide (CO2) over the globe and to understand their varying regional patterns with greater accuracy have intensified in recent years. This study evaluates the performance of a new model coupling, ICO, built around the Laboratoire de Météorologie Dynamique atmospheric general circulation model (LMDZ) for simulating CO2 transport. ICO utilizes the new icosahedral hydrostatic dynamical core called Dynamico running on an unstructured grid, which enables potential improvements in spatial resolution at the Equator while removing artificial distortions and numerical filters at the poles. Comparisons with a reference configuration using a structured latitude-longitude grid reveal that ICO well captures seasonal variations in CO2 concentrations at surface stations. While not significantly enhancing the capture of complex seasonal patterns, ICO maintains comparable accuracy. Both configurations exhibit similar vertical CO2 concentration profiles and display a consistent bias in the lower stratosphere relative to observational data. ICO demonstrates advantages in computational efficiency and storage, thanks to its reduced cell count per level and a homogeneous grid structure. It holds promise for future developments, including with the LMDZ offline model and associated inversion system, which contribute to the Copernicus Atmosphere Monitoring Service. Overall, the ICO configuration showcases the efficacy of utilizing an unstructured grid for the physics, and the capability of Dynamico in accurately simulating CO2 transport. This study emphasizes the importance of advanced modeling approaches and high-resolution innovative grids in enhancing our understanding of the global carbon cycle and refining climate models. [ABSTRACT FROM AUTHOR]

Carbonyl sulfide (COS) is an atmospheric trace gas of interest for C cycle research because COS uptake by continental vegetation is strongly related to terrestrial gross primary productivity (GPP), the largest and most uncertain flux in atmospheric CO 2 budgets. However, to use atmospheric COS as an additional tracer of GPP, an accurate quantification of COS exchange by soils is also needed. At present, the atmospheric COS budget is unbalanced globally, with total COS flux estimates from oxic and anoxic soils that vary between -409 and -89 GgS yr -1. This uncertainty hampers the use of atmospheric COS concentrations to constrain GPP estimates through atmospheric transport inversions. In this study we implemented a mechanistic soil COS model in the ORCHIDEE (Organising Carbon and Hydrology In Dynamic Ecosystems) land surface model to simulate COS fluxes in oxic and anoxic soils. Evaluation of the model against flux measurements at seven sites yields a mean root mean square deviation of 1.6 pmol m -2 s -1 , instead of 2 pmol m -2 s -1 when using a previous empirical approach that links soil COS uptake to soil heterotrophic respiration. However, soil COS model evaluation is still limited by the scarcity of observation sites and long-term measurement periods, with all sites located in a latitudinal band between 39 and 62 ∘ N and no observations during wintertime in this study. The new model predicts that, globally and over the 2009–2016 period, oxic soils act as a net uptake of -126 GgS yr -1 and anoxic soils are a source of + 96 GgS yr -1 , leading to a global net soil sink of only -30 GgS yr -1 , i.e. much smaller than previous estimates. The small magnitude of the soil fluxes suggests that the error in the COS budget is dominated by the much larger fluxes from plants, oceans, and industrial activities. The predicted spatial distribution of soil COS fluxes, with large emissions from oxic (up to 68.2 pmol COS m -2 s -1) and anoxic (up to 36.8 pmol COS m -2 s -1) soils in the tropics, especially in India and in the Sahel region, marginally improves the latitudinal gradient of atmospheric COS concentrations, after transport by the LMDZ (Laboratoire de Météorologie Dynamique) atmospheric transport model. The impact of different soil COS flux representations on the latitudinal gradient of the atmospheric COS concentrations is strongest in the Northern Hemisphere. We also implemented spatiotemporal variations in near-ground atmospheric COS concentrations in the modelling of biospheric COS fluxes, which helped reduce the imbalance of the atmospheric COS budget by lowering soil COS uptake by 10 % and plant COS uptake by 8 % globally (with a revised mean vegetation budget of -576 GgS yr -1 over 2009–2016). Sensitivity analyses highlighted the different parameters to which each soil COS flux model is the most responsive, selected in a parameter optimization framework. Having both vegetation and soil COS fluxes modelled within ORCHIDEE opens the way for using observed ecosystem COS fluxes and larger-scale atmospheric COS mixing ratios to improve the simulated GPP, through data assimilation techniques. [ABSTRACT FROM AUTHOR]

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 successMost global inversion systems underestimate dormant‐season frontal and vertical CO2 differencesInversion systems appear to explain more of the model‐data differences in CO2 weather metrics than CO2 data sources [ABSTRACT FROM AUTHOR]

Carbonyl sulfide (COS) is an atmospheric trace gas of interest for C cycle research because COS uptake by continental vegetation is strongly related to terrestrial gross primary productivity (GPP), the largest and most uncertain flux in atmospheric CO2 budgets. However, to use atmospheric COS budgets as an additional tracer of GPP, an accurate quantification of COS exchange by soils is also needed. At present, the atmospheric COS budget is unbalanced globally, with total COS flux estimates from oxic and anoxic soils that vary between -409 and -104 GgS yr-1. This uncertainty hampers the use of atmospheric COS concentrations to constrain GPP estimates through atmospheric transport inversions. In this study we implemented a mechanistic soil COS model in the ORCHIDEE land surface model to simulate COS fluxes in oxic and anoxic soils. Evaluation of the model against flux measurements at 7 sites yields a mean root mean square deviation of 1.6 pmol m-2 s-1, instead of 2 pmol m-2 s-1 when using a previous empirical approach that links soil COS uptake to soil heterotrophic respiration. The new model predicts that, globally and over the 2009-2016 period, oxic soils act as a net uptake of -126 GgS yr-1, and anoxic soils are a source of +96 GgS yr-1, leading to a global net soil sink of only -30 GgS yr-1, i.e., much smaller than previous estimates. The small magnitude of the soil fluxes suggests that the error in the COS budget is dominated by the much larger fluxes from plants, oceans, and industrial activities. The predicted spatial distribution of soil COS fluxes, with large emissions in the tropics from oxic (up to 68.2 pmol COS m-2 s-1) and anoxic (up to 36.8 pmol COS m-2 s-1) soils, marginally improves the latitudinal gradient of atmospheric COS concentrations, after transport by the LMDZ atmospheric transport model. The impact of different soil COS flux representations on the latitudinal gradient of the atmospheric COS concentrations is strongest in the northern hemisphere. We also implemented spatio-temporal variations of near-ground atmospheric COS concentrations in the modelling of biospheric COS fluxes, which helped reduce the imbalance of the atmospheric COS budget by lowering COS uptake by soils and vegetation globally (-10% for soil, and -8% for vegetation with a revised mean estimate of -576 GgS yr-1 over 2009-2016). Sensitivity analyses highlighted the different parameters to which each soil COS flux model is the most responsive, selected in a parameter optimization framework. Having both vegetation and soil COS fluxes modelled within ORCHIDEE opens the way for using observed ecosystem COS fluxes and larger scale atmospheric COS mixing ratios to improve the simulated GPP, through data assimilation techniques. [ABSTRACT FROM AUTHOR]

We study an ensemble of six multi-year global Bayesian carbon dioxide (CO2) atmospheric inversions that vary in terms of assimilated observations (either column retrievals from one of two satellites or surface air sample measurements) and transport model. The time series of inferred annual fluxes are first compared with each other at various spatial scales. We then objectively evaluate the small inversion ensemble based on a large dataset of accurate aircraft measurements in the free troposphere over the globe, which are independent of all assimilated data. The measured variables are connected with the inferred fluxes through mass-conserving transport in the global atmosphere and are part of the inversion results. Large-scale annual fluxes estimated from the bias-corrected land retrievals of the second Orbiting Carbon Observatory (OCO-2) differ greatly from the prior fluxes, but are similar to the fluxes estimated from the surface network within the uncertainty of these surface-based estimates. The OCO-2-based and surface-based inversions have similar performance when projected in the space of the aircraft data, but the relative strengths and weaknesses of the two flux estimates vary within the northern and tropical parts of the continents. The verification data also suggest that the more complex and more recent transport model does not improve the inversion skill. In contrast, the inversion using bias-corrected retrievals from the Greenhouse Gases Observing Satellite (GOSAT) or, to a larger extent, a non-Bayesian inversion that simply adjusts a recent bottom-up flux estimate with the annual growth rate diagnosed from marine surface measurements both estimate much different fluxes and fit the aircraft data less. Our study highlights a way to rate global atmospheric inversions. Without any general claim regarding the usefulness of all OCO-2 retrieval datasets vs. all GOSAT retrieval datasets, it still suggests that some satellite retrievals can now provide inversion results that are, despite their uncertainty, comparable with respect to credibility to traditional inversions using the accurate but sparse surface network and that are therefore complementary for studies of the global carbon budget. [ABSTRACT FROM AUTHOR]