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Lack of knowledge still remains on many processes leading to COS atmospheric fluxes, either natural such as the oceanic emissions or the vegetation and soil fluxes, or anthropogenic, from industrial activities and power generation. Moreover, COS atmospheric mixing ratio data are still too sparse to evaluate the estimations of these sources and sinks. This study assesses the anthropogenic and biogenic COS fluxes at the regional scale, in the footprint a measurement site in Western Europe, at a seasonal to diurnal time resolution over half a decade. The continuous time series of COS mixing ratios obtained at the monitoring site of Gif-sur-Yvette (in the Paris area) from August 2014 to December 2019 are compared to simulations with the Lagrangian model FLEXPART, transporting oceanic emissions, biogenic land fluxes from the process-model ORCHIDEE and anthropogenic emissions by two different inventories. The anthropogenic emission inventory based on reported industrial emissions and the characteristics of coal power plants in Europe is consistent with the observations. The flat temporal variability applied to anthropogenic fluxes due to lack of information on industrial and power-generation activity in viscose factories and coal-power plants and the potential mismatches in the representation of the plumes emitted from these hot-spots in the model are the main limitations of this inventory. We find that the net ecosystem COS uptake simulated by ORCHIDEE is underestimated in winter at night, which suggests improvements in the parameterization of the nighttime uptake by plants for COS. [ABSTRACT FROM AUTHOR]

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]

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]

Carbonyl Sulphide (COS), a trace gas showing striking similarity to CO2 in terms of biochemical diffusion pathway into leaves, has been recognized as a promising indicator of the plant gross primary production (GPP), the amount of carbon dioxide that is absorbed through photosynthesis by terrestrial ecosystems. However, large uncertainties about the other components of its atmospheric budget prevent us from directly relating the atmospheric COS measurements to GPP. The largest uncertainty comes from the closure of its atmospheric budget, with a source component missing. Here, we explore the benefit of assimilating both COS and CO2 measurements into the LMDz atmospheric transport model to obtain consistent information on GPP, plant respiration and COS budget. To this end, we develop an analytical inverse system that optimizes biospheric fluxes for the 15 plant functional types (PFTs) defined in the ORCHIDEE global land surface model. Plant uptake of COS is parameterized as a linear function of GPP of the leaf relative uptake (LRU), which is the ratio of COS to CO2 deposition velocities in plants. A possible scenario for the period 2008-2019 leads to a global biospheric sink of 800 GgS.yr-1, with higher absorption in the high latitudes and higher oceanic emissions between 400 and 600 GgS.yr-1 most of which is located in the tropics. As for the CO2 budget, the inverse system increases GPP in the high latitudes by a few GtC.yr-1 without modifying the respiration compared to the ORCHIDEE fluxes used as a prior. In contrast, in the tropics the system tends to weaken both respiration and GPP. The optimized components of the COS and CO2 have been evaluated against independent measurements over Northern America, the Pacific Ocean, at three sites in Japan and at one site in France. Overall, the posterior COS concentrations are in better agreement with the COS retrievals at 250 hPa from the MIPAS satellite and with airborne measurements made over North America and the Pacific Ocean. The system seems to have rightly corrected the underestimated GPP over the high latitudes. However, the change in seasonality of GPP in the tropics disagrees with Solar Induced Fluorescence (SIF) data. The decline in biospheric sink in the Amazon driven by the inversion also disagrees with MIPAS COS retrievals at 250 hPa, highlighting the lack of observational constraints in this region. Moreover, the comparison with the surface measurements in Japan and France suggests misplaced sources in the prior anthropogenic inventory, emphasizing the need for an improved inventory to better partition oceanic and continental sources in Asia and Europe. [ABSTRACT FROM AUTHOR]

Land surface modelers need measurable proxies to constrain the quantity of carbon dioxide (CO2) assimilated by continental plants through photosynthesis, known as Gross Primary Production (GPP). Carbonyl sulfide (COS), which is taken up by leaves through their stomates and then hydrolysed by photosynthetic enzymes, is a candidate GPP proxy. A former study with the ORCHIDEE land surface model used a fixed ratio of COS uptake to CO2 uptake normalized to respective ambient concentrations for each vegetation type (Leaf Relative Uptake, LRU). COS leaf fluxes were then computed from GPP, and the resulting concentrations were transported with an atmospheric model which included all other known COS fluxes as inputs. Modelled COS concentrations could then be compared to COS measurements from the NOAA air sampling tower network. The LRU approach is known to have limited accuracy since the LRU ratio changes with variables such as Photosynthetically Active Radiation (PAR): while CO2 uptake slows under low light, COS uptake is not light limited. However, the LRU approach has been popular for COS-GPP proxy studies because of its ease of application and apparent low contribution to uncertainty for regional scale applications. In this study we refined the COS-GPP relationship and implemented in ORCHIDEE a mechanistic model that describes COS uptake by continental vegetation. We compared the simulated COS fluxes against measured hourly COS fluxes at two sites, and studied the model behaviour and links with environmental drivers. We performed simulations at global scale, and estimated the global COS uptake by vegetation to be -756 Gg S yr-1, in the middle range of former studies (-490 to -1335 Gg S yr-1 ). Based on the mechanistic approach in ORCHIDEE, we derived new LRU values for the different vegetation types, ranging between 0.92 and 1.72, close to recently published averages for observed values of 1.21 for C4 and 1.68 for C3 plants. We transported the COS using the monthly vegetation COS fluxes derived from both the mechanistic and the LRU approaches, and evaluated the simulated COS concentrations at NOAA sites. Although the mechanistic approach was more appropriate when comparing to high-temporal-resolution COS flux measurements, both approaches gave similar results when transporting with monthly COS fluxes and evaluating COS concentrations at stations. In our study, uncertainties between these two approaches are of second importance as compared to the uncertainties in the COS global budget, which are currently a limiting factor to the potential of COS concentrations to constrain GPP simulated by land surface models on the global scale. [ABSTRACT FROM AUTHOR]

We study an ensemble of six multi-year global Bayesian 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, that are independent from 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 from the prior fluxes much, but are similar to the fluxes estimated from the surface network within the uncertainty of these surface-based estimates. The OCO-2- and surface-based inversions have similar performance when projected in the space of the aircraft data, but relative strengths and weaknesses of the two flux estimates vary within the Northern and Tropical parts of the continents. The verification data also suggests 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, estimate much different fluxes and fit the aircraft data less. Our study highlights a way to rate global atmospheric inversions. It suggests that some satellite retrievals can now provide inversion results that are, despite their uncertainty, comparable in 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]

The quality of the representation of greenhouse gas (GHG) transport in atmospheric General Circulation Models (GCMs) drives the potential of inverse systems to retrieve GHG surface fluxes to a large extent. In this work, the transport of CO2 is evaluated in the latest version of the LMDz GCM, developed for the Climate Model Intercomparison Project 6 (CMIP6) relative to the LMDz version developed for CMIP4. Several key changes have been implemented between the two versions; those include a more elaborate radiative scheme, new sub-grid scale parameterizations of convective and boundary layer processes, and a refined vertical resolution. We performed a set of simulations of LMDz with the different physical parameterizations, two different horizontal resolutions and different land surface schemes, in order to test the impact of those different configurations on the overall transport simulation. By modulating the intensity of vertical mixing, the physical parameterizations control the interhemispheric gradient and the amplitude of the seasonal cycle in the summer northern hemisphere, as emphasized by the comparison with observations at surface sites. However, the effect of the new parameterizations depends on the region considered, with a strong impact over South America (Brazil, Amazonian forest) but a smaller impact over Europe, Eastern Asia and North America. A finer horizontal resolution reduces the representation errors at observation sites near emission-hot spots or along the coastlines. In comparison, the sensitivities to the land surface model and to the increased vertical resolution are marginal. [ABSTRACT FROM AUTHOR]