A framework for comparing remotely sensed and in-situ CO2 concentrations

India needs a high-resolution estimation of carbon sources and sinks to implement the country’s climate change action plans and mitigation strategy effectively. Current carbon estimates over the Indian region based on the “Bottom-up” approach suffer from significant uncertainty, which calls for more process-based models and atmospheric inverse modelling to obtain a more accurate budget. Inverse models constrain the carbon fluxes based on atmospheric observation of CO2 mole fractions. The unavailability of amble observations over the Indian domain critically impacts estimation accuracy. Fortunately, there are increasing efforts to improve the availability of CO2 observation over the domain. Along with the observations, the availability of a suitable transport model to simulate the CO2 distribution is essential to the accurate inverse estimation of carbon fluxes. The inability of coarse-resolution global models to simulate the fine-scale variability in CO2 distribution warrants developing a regional high-resolution modelling system. Here we evaluate the performance of a regional high-resolution modelling system which utilises meteorological fields from the Weather Research and Forecasting (WRF) model to simulate the CO2 transport over the Indian domain using a lagrangian particle dispersion model, Stochastic Time-Inverted Lagrangian Transport Model (STILT). Using lagrangian models enables us to study the CO2 distribution at very high resolution (even at sub-grid scale) with reduced cost. We use the vegetation photosynthesis and Respiration Model (VPRM), coupled with the modelling system, to simulate the biospheric fluxes. The anthropogenic and biomass burning fluxes are obtained from different available inventories. We use CO2 in-situ observations from different parts of the Indian domain, which utilises flask measurements and PICARRO CRDS instruments, to evaluate the modelling system. Our high-resolution modelling framework shows good skill in simulating the CO2 variability over the region. The results of the evaluation will be discussed in detail during the presentation.

There is an increasing concern over climate change and its environmental impacts. Effective greenhouse gases (GHG) monitoring and control strategies are of pivotal importance. Models such as STILT (Stochastic Time-Inverted Lagrangian Transport), statistical techniques, and experimental data analysis provide valuable tools for quantifying emissions and identifying greenhouse gas (GHG) tendencies. The Mediterranean basin is considered a global hotspot for air-quality and climate change: here, we combine experimental datasets of atmospheric methane (CH 4 ) and carbon dioxide (CO 2 ) with atmospheric transport models to present an atmospherically-based framework for monitoring GHG emissions. We applied methodologies, i.e., the Smoothed Minima (SM) and STILT, to extract background concentration data from the time series of atmospheric gases and identify measurements deemed representative of atmospheric background (GRD) levels. At the Lamezia Terme (Global Atmosphere Watch, GAW code: LMT), Capo Granitola (GAW code: CGR), and Lampedusa (GAW code: LMP) observation sites, GHG measurements were performed with specific calibration routines carried out using primary standards of calibration from the National Oceanic and Atmospheric Administration – Global Monitoring Laboratory (NOAA–GML), with secondary standards used to evaluate possible drifts and calibration factors stability. The first two are coastal stations and the third is an island station. At these sites, atmospheric CH 4 and CO 2 mole fractions can be evaluated at local and continental scales, in locations with specific Mediterranean climatic characteristics. This paper presents the variability of CH 4 and CO 2 in the central Mediterranean basin by analyzing hourly GHG concentrations over a 9-year period (2015–2023) for LMT, a 8-year period (2015–2022) for CGR, and a 19-year period (2006–2024) for CO 2 and 5-year period (2020–2024) for CH 4 at LMP. STILT provides 3-hourly results for methane and carbon dioxide concentrations that correlate well with surface measurements at LMT, CGR, and LMP. These analyses are aimed at relevant long-term datasets of GHG over southern Italy. This work would provide a useful contribution to comparing the observed concentrations of gases measured at three sites in the central Mediterranean with those predicted by models such as STILT. The results indicate good agreement between in situ measurements and modeling, and underline the importance of synergies between different institutions and methodologies. Compared to the CGR and LMP site, LMT has recorded higher levels of anthropogenic emissions in the area. Graphical Abstract Framework shows the variability of methane and carbon dioxide in the Mediterranean basin at three permanent World Meteorological Organization/Global Atmosphere Watch (WMO/GAW) stations in southern Italy: Lamezia Terme (GAW code: LMT), Capo Granitola (GAW code: CGR) and Lampedusa (GAW code: LMP). Accurate modeling of atmospheric transport is essential to address environmental concerns and to establish a quantitative link between observed gas distributions and surface emissions. In the present work, we used the Stochastic Time Inverted Lagrangian Transport (STILT) and the Smoothed Minima (SM) models. STILT simulates transport by following the time evolution of a particle ensemble, interpolating meteorological fields to the subgrid location of each particle. Both methods were used to extract background concentration data from the time series of atmospheric gases representative of the atmospheric background levels. The paper is an important contribution to the comparison between the observed and predicted by models concentrations of methane and carbon dioxide at three sites in the central Mediterranean. The STILT model datasets, validated at the three sites, show satisfactory results, with the exception of an overall underestimation in all comparisons (background and no-background). They demonstrate that the model can accurately estimate the CH 4 and CO 2 concentrations in the Mediterranean basin. Similar results are also obtained when comparing the SM and STILT background datasets. This last comparison indicates a good identification of the concentrations of the background gases by the models.

This paper describes the coupling between a mesoscale numerical weather prediction model, the Weather Research and Forecasting (WRF) model, and a Lagrangian Particle Dispersion Model, the Stochastic Time-Inverted Lagrangian Transport (STILT) model. The primary motivation for developing this coupled model has been to reduce transport errors in continental-scale top–down estimates of terrestrial greenhouse gas fluxes. Examples of the model’s application are shown here for backward trajectory computations originating at CO2 measurement sites in North America. Owing to its unique features, including meteorological realism and large support base, good mass conservation properties, and a realistic treatment of convection within STILT, the WRF–STILT model offers an attractive tool for a wide range of applications, including inverse flux estimates, flight planning, satellite validation, emergency response and source attribution, air quality, and planetary exploration.

This research addressed an important DOE science area: the role of natural disturbances in carbon cycling. Our project included both (a) modeling and field measurements and (b) strengthened ties between DOE national laboratories and universities which continue after the project was completed. Our focus was on understanding the consequences of drought and emissions (fire and urban) to atmospheric trace gas composition (carbon dioxide, carbon monoxide, and methane), including both concentration and stable isotope composition. Measurements of these atmospheric gases were used to infer the spatial and temporal patterns of both sources and sinks in the carbon cycle. As part of this effort, we maintained two long-term monitoring networks along a geographic gradient in Utah and Colorado that spans montane forests, urban regions, and oil/gas fields. In this research, we expanded on a well-accepted atmospheric model (STILT-WRF) as a tool to evaluate fluxes in CLM/CESM-related models. We used the STILT model to provide a strong linkage between point atmospheric measurements and the surface parameterizations/emissions that are part of the CLM/CESM models. Overall, this project benefited and supported the DOE Long Term Mission and Goals in four distinct ways: (a) testing of carbon cycle models, (b) testing and evaluating model mechanisms whereby factors in CLM influence trace gas composition in CESM, (c) acquiring high-quality, long-term data on concentrations of carbon dioxide in western USA ecosystems, and (d) by reducing uncertainties associated with the representation of climate-carbon feedbacks in Earth System models through the development of new methods for evaluating model performance. One postdoctoral associate (Henrique Duarte) and one Ph.D. student (Darek Malia) were trained with this support. Nine peer-reviewed publications and 14 poster presentations were produced with support from this project. This proposal addresses a specific science area in the RFP: the role of natural disturbances in carbon cycling. In particular, we will focus on the consequences of drought and fire at landscape to regional scales for atmospheric trace gas concentration and stable isotope composition (CO2, CO, and CH4). We seek to expand on the well accepted atmospheric Stochastic Time-Inverted Lagrangian Transport (STILT) model coupled to the Weather Research and Forecasting (WRF) model as a tool to evaluate stable isotope fluxes in CLM/CESM-related models through existing, long-term data sets and field campaigns where high-precision trace gas concentration and isotope measurements can be made along gradients and transects. Given the confounding impacts of anthropogenic trace gas emissions on landscape-to-regional atmospheric observations, we will also measure and parameterize isotopic characteristics of CO2, CO, and CH4 from anthropogenic or forest sources in order to allow us the opportunity to partition atmospheric trace gases into natural and anthropogenic sources.

<strong class="journal-contentHeaderColor">Abstract.</strong> Carbonyl sulfide (COS) is a promising tracer for the estimation of terrestrial ecosystem gross primary production (GPP). However, understanding its non-GPP related sources and sinks, e.g., anthropogenic sources and soil sources and sinks, is also critical to the success of the approach. Here we infer the regional sources and sinks of COS using continuous <em>in-situ</em> mole fraction profile measurements of COS along the 60-m tall Lutjewad tower (1 m a.s.l., 53&deg;24'N, 6&deg;21'E) in the Netherlands. To identify potential sources that caused the observed enhancements of COS mole fractions at Lutjewad, both discrete flask samples and in-situ measurements in the province of Groningen were made on a mobile van using a quantum cascade laser spectrometer (QCLS). We also simulated the COS mole fractions at Lutjewad using the Stochastic Time-Inverted Lagrangian Transport (STILT) model combined with emission inventories and plant uptake fluxes. We determined the nighttime COS fluxes to be -3.0 &plusmn; 2.6 pmol m^-2 s^-1 using the radon-tracer correlation approach and Lutjewad observations. Furthermore, we identified and quantified several COS sources, including biodigesters, sugar production facilities, and silicon carbide production facilities in the province of Groningen. Moreover, the simulation results show that the observed COS enhancements can be partially explained by known industrial sources of COS and CS₂, in particular from the Ruhr valley (51.5&deg; N, 7.2&deg; E) and Antwerp (51.2&deg; N, 4.4&deg; E) areas. The contribution of likely missing anthropogenic sources of COS and CS₂ in the inventory may be significant. The impact of the identified sources in the province of Groningen is estimated to be negligible to the observed COS enhancements. However, in specific conditions, these sources may influence the measurements in Lutjewad. These results are valuable for improving our understanding of the sources and sinks of COS, contributing to the use of COS as a tracer for GPP.

Abstract. Top–down estimates of the spatiotemporal variations in emissions and uptake of CO2 will benefit from the increasing measurement density brought by recent and future additions to the suite of in situ and remote CO2 measurement platforms. In particular, the planned NASA Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) satellite mission will provide greater coverage in cloudy regions, at high latitudes, and at night than passive satellite systems, as well as high precision and accuracy. In a novel approach to quantifying the ability of satellite column measurements to constrain CO2 fluxes, we use a portable library of footprints (surface influence functions) generated by the Stochastic Time-Inverted Lagrangian Transport (STILT) model in combination with the Weather Research and Forecasting (WRF) model in a regional Bayesian synthesis inversion. The regional Lagrangian particle dispersion model framework is well suited to make use of ASCENDS observations to constrain weekly fluxes in North America at a high resolution, in this case at 1° latitude × 1° longitude. We consider random measurement errors only, modeled as a function of the mission and instrument design specifications along with realistic atmospheric and surface conditions. We find that the ASCENDS observations could potentially reduce flux uncertainties substantially at biome and finer scales. At the grid scale and weekly resolution, the largest uncertainty reductions, on the order of 50%, occur where and when there is good coverage by observations with low measurement errors and the a priori uncertainties are large. Uncertainty reductions are smaller for a 1.57 μm candidate wavelength than for a 2.05 μm wavelength, and are smaller for the higher of the two measurement error levels that we consider (1.0 ppm vs. 0.5 ppm clear-sky error at Railroad Valley, Nevada). Uncertainty reductions at the annual biome scale range from ~40% to ~75% across our four instrument design cases and from ~65% to ~85% for the continent as a whole. Tests suggest that the quantitative results are moderately sensitive to assumptions regarding a priori uncertainties and boundary conditions. The a posteriori flux uncertainties we obtain, ranging from 0.01 to 0.06 Pg C yr−1 across the biomes, would meet requirements for improved understanding of long-term carbon sinks suggested by a previous study.

Methane and nitrous oxide are potent greenhouse gases whose atmospheric abundances have increased significantly in the past 200 years, together accounting for approximately half of the radiative forcing associated with increasing concentrations of carbon dioxide. In order to understand the factors causing increase of these gases globally, we need to determine their emission rates at regional to continental scales. We directly link atmospheric observations with surface emissions using a Lagrangian Particle Dispersion Model, and then determine emission rates by optimizing prior emissions estimates. We use measurements from NOAA's tall tower and aircraft program in 2004, The Stochastic Time-Inverted Lagrangian Transport model (STILT) driven by meteorological fields from a customized version of the Weather Research and Forecasting (WRF) model, and EDGAR32FT2000 and Global Emissions Inventory Activity (GEIA) as prior emission estimates. In the US and Canada, methane emission rates are found to be consistent with observations, while nitrous oxide emissions are significantly low, by a factor 2.5–3 in the peak emissions time period found to be February through May.

Estimating gross primary productivity (GPP) is important for simulating the subsequent carbon cycle elements and assessing the capacity of terrestrial ecosystems to support the sustainable development of human society. Light use efficiency (LUE) models were widely used to estimate GPP due to their concise model structures. However, quantifying LUEmax (maximum light use efficiency) and representing the responses of photosynthesis to environmental factors are still subject to large uncertainties, which lead to substantial errors in GPP simulations. In this study, we developed a hybrid model based on machine learning and a LUE model for GPP estimates. This hybrid model was built by targeting LUE with a machine learning approach, namely multi-layer perceptron (MLP), and then, estimating GPP within a LUE model framework with the MLP-based LUE and other required inputs. We trained the hybrid LUE (H-LUE) model and then, compared it against two conventional LUE models, the vegetation photosynthesis model (VPM) and vegetation photosynthesis and respiration model (VPRM), regarding GPP estimation, using tower-based daily-scale observations from 180 flux sites that cover nine different plant function types (PFTs). The results revealed better performance (R2 = 0.86 and RMSE = 1.79 gC m−2 d−1 on the test dataset) of the H-LUE model compared to the VPM and VPRM. Evaluations of the three models under four different extreme conditions consistently revealed better performance of the H-LUE model, indicating greater adaptability of the model to varied environments in the context of climate change. Furthermore, we also found that the H-LUE model can reasonably represent the responses of the LUE to meteorological variables. Our study revealed the reliable and robust performance of the developed hybrid LUE when simulating GPP across global biomes, providing references for developing better hybrid GPP models.

Diffuse radiation can promote the vegetation photosynthesis. Radiation use efficiency (RUE = GPP/PAR) simulated by Terrestrial Ecosystem Carbon Flux GPP Model (TEC), Vegetation Photosynthesis Model (VPM), Eddy Covariance Light Use Efficiency Model (EC‐LUE), and Diffuse Fraction‐Based Two‐Leaf Terrestrial Ecosystem Carbon Flux Model (DTEC) models were against the measured radiation‐use efficiency (RUE) from FLUXNET under different bins of diffuse photosynthetically active radiation fraction (FDIFFPAR) in this study. Our results showed that the observed RUE increased linearly with FDIFFPAR. The differences of RUE between the first bin and other bin (RUEbin) from two big‐leaf models were underestimated when the FDIFFPAR is higher than 0.5 except in evergreen broadleaf forests and RUEbin from the DTEC model was overestimated when the FDIFFPAR is higher than 0.7. The role of temperature and vapor pressure deficit in the diffuse radiation effects on vegetation photosynthesis was underestimated in EC‐LUE, TEC, VPM. Although many studies applied LUE models to assess the effects of diffuse radiation on vegetation photosynthesis, our results suggested that these models were needed to be improved.

Consistency between sun-induced chlorophyll fluorescence and gross primary production of vegetation in North America

Estimating biogenic CO 2 fluxes is essential to quantify urban anthropogenic emissions, yet urban vegetation heterogeneity presents a significant challenge to making accurate estimations. We have developed an hourly temporal, 10‐m spatial resolution biogenic CO 2 flux estimation framework based on the Vegetation Photosynthesis and Respiration Model (VPRM) and its variants (UrbanVPRM and VPRM‐modified). Unlike lower‐resolution models, our approach captures finer‐scale variability, particularly in fragmented urban green spaces like street trees and lawns. Results show that vegetation in Munich offsets 2.0%–2.8% of annual anthropogenic CO 2 emissions in the study domain, with tree‐covered areas as primary sinks and grasslands as net sources. During summer, daytime CO 2 uptake can match or exceed anthropogenic emissions. Evaluations employing city park field measurements and eddy covariance towers confirm strong performance of our models, while highlighting VPRM‐modified's advantage in grasslands and croplands, and UrbanVPRM's improvements in urban areas via impervious surface correction. These findings highlight the value of high‐resolution modeling in improving urban carbon flux assessments.

<strong class="journal-contentHeaderColor">Abstract.</strong> To address ambitious goals of carbon neutrality set at national and city scales, a number of atmospheric networks have been deployed to monitor greenhouse gas (GHG) concentrations in and around cities. To convert these measurements into estimates of emissions from cities, atmospheric models are used to simulate the transport of various tracer gases and help interpret these measurements. We set up a modelling framework using the Weather Research and Forecasting (WRF) model applied at a high spatial resolution (up to 400 m) to simulate the atmospheric transport of GHGs and interpret the observations provided by the Munich Urban Carbon Column Network (MUCCnet). Building on previous analyses using similar measurements performed within a campaign for the city of Berlin and its surroundings (Zhao et al., 2019), our modelling framework has been improved regarding the initialization of tagged tracers, model settings, and input data. To assess the model performance, we validate the modelled output against two local weather stations and observed column GHG concentrations provided by MUCCnet from 1 to 30 August 2018. The modelled wind matches well with the measurements from the weather stations, with wind speeds slightly overestimated. The measured slant column concentrations of GHGs and their variability are generally reproduced by the model, with a bias in CO₂ of around 3.7 ppm that can be attributed to the initial and boundary conditions used. The differential column method (DCM) has been applied to cancel out the influence from the background concentrations of CO₂. We optimize its application by selecting suitable days on which the assumption of the DCM holds true: a relatively uniform air mass travels over the city, passing from an upwind site to a downwind site. In particular, the Stochastic Time-Inverted Lagrangian Transport model (STILT) is used here and driven by our WRF modelled meteorological fields to obtain footprints (i.e., the potential areas of influence for signals observed at specific points), further used for interpreting measurement results. Combining these footprints with knowledge of local emission sources, we find evidence of CH₄ sources near Munich that are missing or underestimated in the emission inventory used. This demonstrates the potential of this data-model framework to constrain local sources and improve emission inventories.

The North-East Corridor (NEC) Testbed project is the 3rd of three NIST (National Institute of Standards and Technology) greenhouse gas emissions testbeds designed to advance greenhouse gas measurements capabilities. A design approach for a dense observing network combined with atmospheric inversion methodologies is described. The Advanced Research Weather Research and Forecasting Model with the Stochastic Time-Inverted Lagrangian Transport model were used to derive the sensitivity of hypothetical observations to surface greenhouse gas emissions (footprints). Unlike other network design algorithms, an iterative selection algorithm, based on a k-means clustering method, was applied to minimize the similarities between the temporal response of each site and maximize sensitivity to the urban emissions contribution. Once a network was selected, a synthetic inversion Bayesian Kalman filter was used to evaluate observing system performance. We present the performances of various measurement network configurations consisting of differing numbers of towers and tower locations. Results show that an overly spatially compact network has decreased spatial coverage, as the spatial information added per site is then suboptimal as to cover the largest possible area, whilst networks dispersed too broadly lose capabilities of constraining flux uncertainties. In addition, we explore the possibility of using a very high density network of lower cost and performance sensors characterized by larger uncertainties and temporal drift. Analysis convergence is faster with a large number of observing locations, reducing the response time of the filter. Larger uncertainties in the observations implies lower values of uncertainty reduction. On the other hand, the drift is a bias in nature, which is added to the observations and, therefore, biasing the retrieved fluxes.

Understanding of regional greenhouse gas emissions into the atmosphere is a prerequisite to mitigate climate change. In this study, we investigated the regional contributions of carbon dioxide (CO2) at the location of the high Alpine observatory Jungfraujoch ("JFJ", Switzerland, 3580 m a.s.l.). To this purpose, we combined receptor-oriented atmospheric transport simulations for CO2 concentration in the period of 2009–2017 with stable carbon isotope (δ13C-CO2) information. We applied two Lagrangian particle dispersion models driven by output from two different numerical weather prediction systems (FLEXPART-COSMO and STILT-ECMWF) in order to simulate CO2 concentration at JFJ based on regional CO2 fluxes, to estimate atmospheric δ13C-CO2, and to obtain model-based estimates of the mixed source signatures (δ13Cm). Anthropogenic fluxes were taken from a fuel type-specific version of the EDGAR v4.3 inventory and ecosystem fluxes were based on the Vegetation Photosynthesis and Respiration Model (VPRM). The simulations of CO2, δ13C-CO2 and δ13Cm were then compared to observations performed by quantum cascade laser absorption spectroscopy. Around 40 % of the regional CO2 variability above or below the large-scale background was captured by the models, and up to 35 % of the regional variability in δ13C-CO2. This is remarkable considering the complex Alpine topography, the low intensity of regional signals at JFJ, and the challenging measurements. Best agreement between simulations and observations in terms of short-term variability and intensity of the signals for CO2 and δ13C-CO2 was found between late autumn and early spring. The agreement was inferior in the early autumn periods and during summer. This may be associated with the atmospheric transport representation in the models. In addition, the net ecosystem exchange fluxes are a possible source of error, either through inaccuracies in their representation in VPRM for the (Alpine) vegetation or through a day (uptake) vs. night (respiration) transport discrimination to JFJ. Furthermore, the simulations suggest that JFJ is subject to relatively small regional anthropogenic contributions, due to its remote location (elevated and far from major anthropogenic sources), and the limited planetary boundary layer-influence during winter. Instead, the station is primarily exposed to summer-time ecosystem CO2 contributions, which are dominated by rather nearby sources (within 100 km). Even during winter, simulated gross ecosystem respiration accounted for approximately 50 % of all contributions to the CO2 concentrations above the largescale background. The model-based monthly mean δ13Cm ranged from −22 ‰ in winter to −28 ‰ in summer and reached the most depleted values of −35 ‰ at higher fractions of natural gas combustion, and the most enriched values of −17 to −12 ‰ when impacted by cement production emissions. Observation-based δ13Cm values derived by a moving Keeling-plot approach were in good agreement with the model-based estimates. They exhibited a larger scatter, while model-based estimates spread in a more narrow range. Overall, observation-based δ13Cm were limited to a smaller number of data points compared to model-based estimates owing to the stringent analysis prerequisites in combination with the low regional signal at JFJ.

Abstract. We develop PALM-CO2, a high-resolution urban carbon dioxide transport model with anthropogenic and biogenic carbon emissions. The model is based on an open-source urban flow large eddy simulation (LES) model, PALM, where we implemented a biogenic carbon emission module (Vegetation Photosynthesis and Respiration Model, VPRM) and customised output modules for carbon fluxes. PALM-CO2 is validated through a case study in London, comprising an 8 by 8 km2 domain covering the borough of Camden at a resolution of 10 m. Simulations are driven by reanalysis meteorological forcing and background CO2 concentrations, while the hourly anthropogenic emissions at 10-m resolution are explicitly derived in this study. Validation against eddy-covariance flux measurements inside the study region confirms that the model captures the diurnal variation of the turbulent transport and anthropogenic emissions. Additional validation against monthly biogenic flux diurnal profiles at a deciduous forest site in Czech Republic further confirms the biogenic flux module. The simulations reveal strong spatial heterogeneity in near-surface CO2 concentrations driven by building-induced turbulence, diurnal boundary layer evolution, and emission patterns. PALM-CO2 provides a high-resolution framework for investigating CO2 transport processes in complex urban and vegetated environments, providing improved quantification of urban emission sources.