Unlike natural ecosystems, agricultural ecosystems are unique ecosystems in which artificial factors play a significant role. The material cycling within an agricultural ecosystem is influenced by factors such as agricultural activities, weather, and soil conditions. Understanding the material cycling and energy flow in these ecosystems is important to cope with climate change. In this study, we measured energy and carbon dioxide flux using the eddy covariance method to assess material cycling in rice paddy and soybean field ecosystems with similar weather conditions but different vegetation. Additionally, growth surveys were conducted every two weeks to analyze crop development. During the summer, the weather and soil conditions in rice paddy and soybean field were comparable, resulting in similar levels of latent heat flux for both ecosystems. In July 2020, despite the rainy season, the water use efficiency(WUE) of rice paddy was higher than that of other periods, influenced by vegetation and weather conditions. WUE during the summer resembled that of the cropping period, indicating a potential impact on overall crop grain weight.

How to cite: Kang, M., Shim, K., Kim, Y., Hur, J., Jo, S., Kim, E., and Hong, S.: Study on energy and CO2 flux in a monsoon temperate rice paddy and soybean field in Korea, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14255, https://doi.org/10.5194/egusphere-egu24-14255, 2024.

The global Stocktake, a fundamental component of the Paris Agreement tracking progress on national mitigation actions, collects the Nationally Determined Contributions (NDCs) generated through the means of annual national inventories. Greenhouse gases (GHG) inventories are prone to uncertainties, especially when considering sub-national scales, sub-annual frequencies, or the natural component of GHG budgets, lacking verification and transparency. Atmospheric observations assimilated through the inverse approach can help constrain both the natural and anthropogenic components of national carbon budgets. Here, we aim at quantifying the carbon dioxide (CO₂) fluxes over continental France by combining atmospheric greenhouse gas concentrations from the ICOS (Integrated Carbon Observation System) measurement network and a high-resolution inversion system.

We present an assessment of the observational constraint from the current ICOS network. We also determine the optimal locations and number of additional stations to monitor CO₂ fluxes from human activities and different ecosystems. The CO₂ concentration measurements influenced by surface CO₂ fluxes are analyzed using a Lagrangian Particle Dispersion (LPDM) model. LPDM is run backward in time with meteorological inputs from the Weather Research Forescating (WRF) model, at 3km resolution over continental France. We infer the origin of the CO₂ using the TNO high-resolution fossil fuel inventory and biogenic CO₂ fluxes produced by the Vegetation Photosynthesis Respiration Model (VPRM). The VPRM model simulates both the CO₂ uptake from photosynthesis and the release from respiration using meteorological re-analysis products and surface remote sensing data.

We start by evaluating the improved model performances at high resolution compared to low resolution simulations. Then we analyze the influence of biogenic and fossil fuel sources at each tower of the ICOS network, and finally we explore which areas are constrained by atmospheric stations using different criteria: by ecosystem type, by land cover, and in terms of net carbon fluxes and fossil fuel emissions. We discuss here how our future inversion system could help constrain the regional distribution of CO₂ fluxes, sub-annual variations at seasonal and monthly timescales to track current climate change impacts (forest fires, droughts), and the effects of emission mitigation policies.

How to cite: D'Angeli, C., Lauvaux, T., Matajira Rueda, D., Abdallah, C., Bazzi, H., Ciais, P., Lopez, M., Ramonet, M., and Rivier, L.: Optimal network designs of in situ atmospheric CO2 stations over continental France, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13160, https://doi.org/10.5194/egusphere-egu24-13160, 2024.

Atmospheric transport model (ATM) uncertainty continues to be a significant constraining factor in making confident top-down (inverse model based) GHG emission estimates. Despite its importance, accurately gauging model uncertainty and capturing its temporal fluctuations remains a challenge. Inversion frameworks typically involve an empirical selection of data to be assimilated whereby only the data from periods where the ATM has the lowest uncertainties are used for the inversion.  There are numerous data filtering methods, that often depend on modelled parameters (mixing height, wind speed, potential temperature), which could result in data selection bias.

To address this, we present analysis of radon measurements, a natural radioactive noble gas with simple and well-constrained source and sink. Radon’s unique characteristics make it an ideal tracer to study the transport and mixing of air and thus has potential to act as an independent metric to evaluate ATM performance. A new approach involves utilising measured and modelled radon (calculated using the Met Office Numerical Atmospheric Modelling Environment (NAME) dispersion model and radon flux map) to classify the ATM output uncertainty as either high (poor performance) or low (the best performance). This approach could be universally applied to any location measuring radon from a single inlet height and in conjunction with any other dispersion modelling scenarios.  

To evaluate the effectiveness of the radon selection method, we assess the methane (CH₄) emissions across the UK using four tall tower sites (part of the Deriving Emissions linked to Climate Change - DECC network): Heathfield, Ridge Hill, Tacolneston and Weybourne. The CH₄ emissions are estimated by the Met Office’s inversion modelling system – Inversion Technique for Emission Modelling (InTEM). We will compare how emissions sensitivity varies between our radon-based approach and the current selection method, which relies on model parameters and the vertical gradient of CH₄ measurements. This comparative analysis aims to demonstrate the potential advantages of using radon as a tool for improving the accuracy of ATM performance assessments in GHG emission estimates.

How to cite: Kikaj, D., Saba, M., Manning, A., Andrews, P., Chung, E., Foster, G., Wenger, A., O’Doherty, S., Rigby, M., Rennick, C., Pitt, J., and Arnold, T.: Analysis of atmospheric radon for uncertainty evaluation in regional-scale greenhouse gas emissions estimation , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18698, https://doi.org/10.5194/egusphere-egu24-18698, 2024.

Top-down constraints of CO₂ emissions from coal-fired power plants are critical to improving the accuracy of CO₂ emission inventory and designing carbon reduction strategies. Different top-down models based on satellite observation have been proposed in previous studies, but discrepancies between these models and the underlying drivers are rarely explored, limiting the confidence of their application to monitor point-source CO₂ emissions from satellite. Here, we apply three top-down models to estimate CO₂ emissions from individual coal-fired power plants in the United States (US) and China in 2014-2021 from Orbiting Carbon Observatory 2 (OCO-2) satellite observations. The first one applies the Gaussian plume model to optimize emissions by fitting modeled CO₂ enhancement to the observation. The second and third methods apply the same inversion framework, but with WRF-Chem and WRF-FLEXPART as forward models, respectively. We evaluate consistency between the three methods in estimating emissions of 10 power plants in the US, using daily reported values from the US Environmental Protection Agency (EPA) as a benchmark, and then apply the three methods to quantify emissions of 13 power plants in China. Results show that the WRF-Chem and WRF-FLEXPART based inversion results are more consistent with the EPA reported emission rates compared to the Gaussian plume model method, with correlation coefficients of 0.76 and 0.89 and mean biases of 4.06 and 3.22 ktCO₂/d relative to EPA reports at 10 power plants, respectively. This is because application of high-resolution three-dimensional wind fields better captures the shape of observed plumes, compared to the Gaussian plume model which relies on wind field at a single point, and thus the Gaussian plume model has difficulty to optimize emissions under inhomogeneous wind fields or when observations are far away from the power plant. In general, using the WRF-FLEXPART model as the forward model in the inverse analysis shows the best consistency with the EPA’s reports, likely due to its capability to simulate narrow-shape plumes in the absence of numerical diffusion which is inherent in Eulerian model such as WRF-Chem. Emissions estimated by the three top-town methods show a moderate consistency at 13 coal-fired power plant cases in China, with 8 of 13 cases showing differences of less than 30% between at least two methods. However, large differences emerge when wind fields are inhomogeneous and number of available observations is limited. Using different meteorological wind fields and OCO-2 data versions can also bring substantial differences to the posterior emissions for all three approaches. We find that the posterior CO₂ emissions, though only reflecting instantaneous emission rates at satellite overpass time, are not proportional to the reported capacities of these power plants, indicating that attributing CO₂ emissions simply based on the capacity of power plants in some bottom-up approaches may have significant discrepancies. Our study exposes the capability and limitation of different top-down approaches in quantifying point-source CO₂ emissions, advancing their application for better serving increasing constellations of point-source imagers in the future.

How to cite: He, C., Lu, X., and Fan, S.: Revisiting the quantification of power plant CO2 emissions in the United States and China from satellite: a comparative study using three top-down approaches., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1269, https://doi.org/10.5194/egusphere-egu24-1269, 2024.

In this study, we present an ICON-ART (ICOsahedral Non-hydrostatic Aerosols and Reactive Trace gases) model based sectoral differentiation of CH₄ concentration in terms of a feasibility study. ICON-ART is an extension of the numerical weather prediction model ICON used by DWD. The physical parameterizations and numerical methods of ICON used in ICON-ART, which simulated the interactions between trace subtances and the state of the atmosphere.

The motivation for the sectoral differentiation based on the model is directed towards the comparison of the field measurements, with the assumption that the modeled simulations could represent a response signal of how each sector contributes to the measured concentration on the Integrated Carbon Observation System (ICOS) stations of interest.

The CH₄ concentrations for various economic sectors of Europe and of Germany are simulated using ICON-ART model. In order to compare the model results and against measurements  from the Integrated Carbon Observation System (ICOS) stations, the model equivalents have been extracted at the locations of the ICOS monitoring stations. We test our experimenal setup in a feasibility study, which shows benefits of using the ICON-ART model to comprehend emissions from various sectors.

How to cite: Mamtimin, B., Rösch, T., Ellerhoff, B., Jiménez de la Cuesta Otero, D., and Kaiser-Weiss, A. K.: Sectoral differentiation of CH4 footprints by using the ICON-ART model – A feasibility study, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10050, https://doi.org/10.5194/egusphere-egu24-10050, 2024.

A greenhouse gas satellite data assimilation system is currently being developed for the ICOsahedral Nonhydrostatic (ICON) - Aerosols and Reactive Trace gases (ART) - Limited Area Mode (LAM) model at the German Weather Service. This work is part of the modelling module of the Integrated Greenhouse Gas Monitoring System project (ITMS-M). A first step towards the satellite data assimilation system is the derivation of vertical columns of methane from ICON-ART-LAM simulations that can be compared to column retrievals of CH₄ from satellite sensors such as the TROPOspheric Monitoring Instrument (TROPOMI) on board of the Copernicus Sentinel-5 Precursor satellite. As the ICON-ART-LAM simulations are limited to about 20 km altitude, vertical columns cannot directly be derived from the model output alone.

In this presentation, the potential of adding CH₄ concentrations from the Copernicus Atmosphere Monitoring Service (CAMS) egg4 greenhouse gas reanalysis and CAMS inversion optimized products above the ICON-ART-LAM upper boundary is evaluated for the time period May-June 2018 and a domain covering Europe (6.5 x 6.5 km² horizontal grid spacing). The vertical profiles of ICON-ART-LAM are investigated for consistency with the CAMS simulations and ICON-ART-LAM vertical columns derived from the model output will be compared against CH₄ vertical columns from TROPOMI. For the latter, the satellite orbit and the sensitivity of the satellite sensor towards retrieving CH₄ in different layers of the atmosphere are considered.

How to cite: Blechschmidt, A.-M., Mamtimin, B., Rösch, T., and Kaiser-Weiss, A.: Evaluating ICON-ART-LAM vertical profiles and columns of CH4 for May-June 2018 over Europe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10496, https://doi.org/10.5194/egusphere-egu24-10496, 2024.

Boreal wetlands are components of the terrestrial carbon cycle, acting as significant natural sources of methane (CH4) in circumpolar regions. With accelerated Arctic warming, emissions from these ecosystems become hard to predict with high uncertainties on future hydrological regimes, wetland/permafrost extent, and organic matter decomposition rates, subsequently affecting CH4 emissions. Validation of accurate quantification methods for these emissions is therefore pivotal in order to better understand and manage potential climate feedbacks.

In this context, the MAGIC2021 international large-scale field campaign's airborne measurements provide key empirical data to assess CH4 emissions from boreal wetlands in Fennoscandia. Led by CNRS and CNES, the campaign took place in August 2021 and involved 70 scientists from 14 international research teams. More than twenty instruments were deployed, onboard research aircraft (in-situ and lidars), as well as on stratospheric balloons (AirCores) and on the ground (EM27/SUN). In particular, obtaining CH4 concentrations from aircraft flights within the boundary layer allowed to directly capture the signatures of wetland emissions, offering a robust dataset for model validation.

Our study employs two Lagrangian models, FLEXPART driven by ERA5 data and WRF-LPDM, to estimate wetland CH4 fluxes from these measurements. The use of these distinct Lagrangian approaches allows for cross-validation of results, enhancing the reliability of our findings. The derived fluxes are compared with outputs from two bottom-up emission models, WetCHARTs and JSBACH-HIMMELI, which simulate wetland CH4 dynamics at different scales and resolutions. This comparative analysis not only benchmarks the performance of these models against observational data but also sheds light on discrepancies in modelled bottom-up fluxes that can guide future improvements.

Contributions of this study to the session include:

• A high resolution assessment of boreal wetland CH4 emissions and atmospheric distribution, using state-of-the-art airborne observational techniques.
• Integration of multiple Lagrangian modelling frameworks to validate and corroborate CH4 flux estimates.
• A critical evaluation of bottom-up models WetCHARTs and JSBACH-HIMMELI against empirical data, advancing our understanding of model uncertainties and informing on possible enhancements in wetland CH4 emission.

This research aims to further improve our understanding of methane emission processes from boreal wetlands, which helps improve predictions about these important ecosystems. The outcomes contribute to a more accurate global methane budget and underscore the importance of synergistic observational and modelling strategies in environmental science.

How to cite: Langot, F., Crevoisier, C., Lauvaux, T., Abdallah, C., Berchet, A., Gottschaldt, K.-D., Fiehn, A., Pernin, J., Guedj, A., Ponthieu, T., Roiger, A., Wittig, S., Saunois, M., and Lin, X.: Evaluating Boreal Wetland Methane Emissions in Fennoscandia using MAGIC2021 airborne measurements and Atmospheric Modelling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11312, https://doi.org/10.5194/egusphere-egu24-11312, 2024.

Atmospheric methane contributes to approximately 20-30% of the current global radiative forcing by greenhouse gases. Despite the potential for a 39% reduction in emissions from the oil and gas sector at no net cost, the lack of dependable emission data hinders governments from implementing timely and impactful mitigation actions aligned with the Global Methane Pledge. Existing regulations rely on national methane emission inventories, significantly underestimating methane sources across various emission sectors as revealed by recent studies. The primary cause of this discrepancy is the exclusion of super-emitters in these inventories. Super-emitters, characterized by high emission rates, collectively account for an average of 40% of total methane emissions. To implement effective regulations for reducing methane emissions, a novel, reliable, and accurate inventory methodology is needed. We propose here a framework for an innovative dynamic and intelligent inventory based on artificial intelligence tools.  The dynamic component involves the collection and automatic association, over time, of methane plume detections from satellite source points with the oil and gas infrastructures at their origin. The intelligent part of the inventory enables automatic statistical and forecasting analyses contributing to the definition of multi-level emission profiles in near real-time, spanning country, region, basin, operator, site, and infrastructure levels. The proposed framework is divided into two main parts, the first part focusing on instantiated detection of potentially methane-emitting infrastructures, without recourse to fixed inventories of oil and gas (O&G) infrastructures. As the landscape of O&G infrastructures is constantly evolving, the use of an emission inventory produced at time t can quickly become inaccurate. The principle of snapshot instantiation is essential for building up an up-to-date inventory of infrastructures especially in the context of quasi-continuous monitoring. This first part is based on the use of object detection algorithms to automatically detect and recognize O&G infrastrucutres for each methane plume detection with an accuracy of over 94%. The second part of the framework consists in matching the infrastructure closest to that of the detected plume, using the K-nearest-neighbor algorithm. Carried out successively in time, this method allows to build up a time series of the rate and frequency of methane emissions by O&G infrastructures which form the basis for methane emissions spatio-temporal analysis and forecasting. To show how this framework can be used, we present a study case that consists in estimating a methane emissions inventory for compressors, tanks and wells in the Permian Basin (USA).

How to cite: Guisiano, J. E., Lauvaux, T., Tzompa Sosa, Z., Moulines, É., and Sublime, J.: Artificial intelligence for dynamic and intelligent methane inventory , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19625, https://doi.org/10.5194/egusphere-egu24-19625, 2024.