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To effectively monitor the highly heterogeneous urban CO2 emissions using atmospheric observations, there is a need to deploy cost-effective CO2 sensors at multiple locations within the city with sufficient accuracy to capture the concentration gradients in urban environments. Its measurements could be used as input of an atmospheric inversion system for the quantification of emissions at the sub-city scale or separate specific sectors. Such quantification would offer valuable insights into the efficacy of local initiatives and could also identify unknown emission hotspots that require attention. Here we present the development and evaluation of a mid-cost CO2 instrument designed for continuous monitoring of atmospheric CO2 concentrations with a target accuracy of 1 ppm on hourly mean measurement. We assess the sensor sensitivity in relation to environmental factors such as humidity, pressure, temperature and CO2 signal, which leads to the development of an effective calibration algorithm. Since July 2020, eight mid-cost instruments have been installed within the city of Paris and its vicinity to provide continuous CO2 measurements, complementing the seven high-precision Cavity Ring-Down Spectroscopy (CRDS) stations that have been in operation since 2016. A data processing system, called CO2calqual, has been implemented to automatically handle data quality control, calibration and storage, which enables the management of extensive real-time CO2 measurements from the monitoring network. Colocation assessments with the high-precision instrument show that the accuracies of the eight mid-cost instruments are within the range of 1.0 to 2.4 ppm for hourly afternoon (12–17 UTC) measurements. The long-term stability issues require manual data checks and instrument maintenance. The analyses show that CO2 measurements can provide evidence for underestimations of CO2 emissions in the Paris region and a lack of several emission point sources in the emission inventory. Our study demonstrates promising prospects in integrating mid-cost measurements along with high precision data into the subsequent atmospheric inverse modeling to improve the accuracy of quantifying the fine-scale CO2 emissions in the Paris metropolitan area. [ABSTRACT FROM AUTHOR]

To effectively monitor the highly heterogeneous urban CO2 emissions using atmospheric observations, there is a need to deploy cost-effective CO2 sensors at multiple locations within the city with sufficient accuracy to capture the concentration gradients in urban environments. Its measurements could be used as input of an atmospheric inversion system for the quantification of emissions at the sub-city scale or separate specific sectors. Such quantification would offer valuable insights into the efficacy of local initiatives and could also identify unknown emission hotspots that require attention. Here we present the development and evaluation of a mid-cost CO2 instrument designed for continuous monitoring of atmospheric CO2 concentrations with a target accuracy of 1 ppm on hourly mean measurement. We assess the sensor sensitivity in relation to environmental factors such as humidity, pressure, temperature and CO2 signal, which leads to the development of an effective calibration algorithm. Since July 2020, eight mid-cost instruments have been installed within the city of Paris and its vicinity to provide continuous CO2 measurements, complementing the seven high-precision Cavity Ring-Down Spectroscopy (CRDS) stations that have been in operation since 2016. A data processing system, called CO2 calqual, has been implemented to automatically handle data quality control, calibration and storage, which enables the management of extensive real-time CO2 measurements from the monitoring network. Colocation assessments with the high-precision instrument show that the accuracies of the eight mid-cost instruments are within the range of 1.0 to 2.4 ppm for hourly afternoon (12–17 UTC) measurements. The long-term stability issues require manual data checks and instrument maintenance. The analyses show that CO2 measurements can provide evidence for underestimations of CO2 emissions in the Paris region and a lack of several emission point sources in the emission inventory. Our study demonstrates promising prospects in integrating mid-cost measurements along with high precision data into the subsequent atmospheric inverse modeling to improve the accuracy of quantifying the fine-scale CO2 emissions in the Paris metropolitan area. [ABSTRACT FROM AUTHOR]

Existing CO2 emissions reported by city inventories usually lag in real-time by a year or more and are prone to large uncertainties. This study responds to the growing need for timely and precise estimation of urban CO2 emissions to support present and future mitigation measures and policies. We focus on the Paris metropolitan area, the largest urban region in the European Union and the city with the densest atmospheric CO2 observation network in Europe. We performed long-term atmospheric inversions to quantify the citywide CO2 emissions, i.e., fossil fuel as well as biogenic sources and sinks, over 6 years (2016–2021) using a Bayesian inverse modeling system. Our inversion framework benefits from a novel near-real-time hourly fossil fuel CO2 emission inventory (Origins.earth) at 1 km spatial resolution. In addition to the mid-afternoon observations, we attempt to assimilate morning CO2 concentrations based on the ability of the Weather Research and Forecasting model with Chemistry (WRF-Chem) transport model to simulate atmospheric boundary layer dynamics constrained by observed layer heights. Our results show a long-term decreasing trend of around 2 % ± 0.6 % per year in annual CO2 emissions over the Paris region. The impact of the COVID-19 pandemic led to a 13 % ± 1 % reduction in annual fossil fuel CO2 emissions in 2020 with respect to 2019. Subsequently, annual emissions increased by 5.2 % ± 14.2 % from 32.6 ± 2.2 MtCO2 in 2020 to 34.3 ± 2.3 MtCO2 in 2021. Based on a combination of up-to-date inventories, high-resolution atmospheric modeling and high-precision observations, our current capacity can deliver near-real-time CO2 emission estimates at the city scale in less than a month, and the results agree within 10 % with independent estimates from multiple city-scale inventories. [ABSTRACT FROM AUTHOR]

Existing CO2 emissions reported by city inventories usually lag real-time by a year or more and are prone to large uncertainties. This study responds to the growing need for timely and precise estimation of urban CO2 emissions to support the present and future mitigation measures and policies. We focus on the Paris metropolitan area, the largest urban region in the European Union and the city with the densest atmospheric CO2 observation network in Europe. We performed long-term atmospheric inversions to quantify the citywide CO2 emissions, both fossil fuel and biogenic sources and sinks, over six years (2016–2021) using a Bayesian inverse modeling system. Our inversion framework benefits from a novel near-real-time hourly fossil fuel CO2 emission inventory (Origins.earth) at 1 km spatial resolution. In addition to the mid-afternoon observations, we attempt to assimilate morning CO2 concentrations based on the ability of the WRF-Chem transport model to simulate atmospheric boundary layer dynamics constrained by observed layer heights. Our results show a long-term decreasing trend by around 2 % per year in annual CO2 emissions over the Paris region. The impact of COVID-19 pandemic led to a 13 %±1 % reduction in annual fossil fuel CO2 emissions in 2020 with respect to 2019. Then, annual emissions increased by 5.2 % from 32.6±2.2 MtCO2 in 2020 to 34.3±2.3 MtCO2 in 2021. Based on a combination of up-to-date inventories, high-resolution atmospheric modeling, and high-precision observations, our current capacity could deliver near real-time CO2 emission estimates at city scale in less than a month, and the results agree within 10 % with independent estimates from multiple city-scale inventories. [ABSTRACT FROM AUTHOR]

Atmospheric inversions provide estimates of carbon dioxide (CO2) fluxes between the surface and atmosphere based on atmospheric CO2 concentration observations. The number of CO2 observations is projected to increase severalfold in the next decades from expanding in situ networks and next‐generation CO2‐observing satellites, providing both an opportunity and a challenge for inversions. This study introduces the TRACE Regional Atmosphere–Carbon Ensemble (TRACE) system, which employ an ensemble‐based simultaneous state and parameter estimation (ESSPE) approach to enable the assimilation of large volumes of observations for constraining CO2 flux parameters. TRACE uses an online full‐physics mesoscale atmospheric model and assimilates observations serially in a coupled atmosphere–carbon ensemble Kalman filter. The data assimilation system was tested in a series of observing system simulation experiments using in situ observations for a regional domain over North America in summer. Under ideal conditions with known prior flux parameter error covariances, TRACE reduced the error in domain‐integrated monthly CO2 fluxes by about 97% relative to the prior flux errors. In a more realistic scenario with unknown prior flux error statistics, the corresponding relative error reductions ranged from 80.6% to 88.5% depending on the specification of prior flux parameter error correlations. For regionally integrated fluxes on a spatial scale of 106 km2, the sum of absolute errors was reduced by 34.5%–50.9% relative to the prior flux errors. Moreover, TRACE produced posterior uncertainty estimates that were consistent with the true errors. These initial experiments show that the ESSPE approach in TRACE provides a promising method for advancing CO2 inversion techniques. Plain Language Summary: To gain a better understanding of the main drivers behind atmospheric carbon dioxide (CO2) variations and trends, it is essential to accurately quantify CO2 exchanges—also known as fluxes—between the atmosphere and other components of the Earth system. It is generally not possible to directly measure surface CO2 fluxes at regional scales; however, fluxes can be inferred from a network of atmospheric CO2 concentration observations combined with atmospheric modeling and inversion methods, which seek to find the most likely fluxes given prior knowledge and observational evidence. This paper presents a new regional inversion framework called TRACE Regional Atmosphere–Carbon Ensemble (TRACE) for deriving CO2 fluxes at high resolution in space and time. One of the major innovations in TRACE is the ensemble‐based dual state and parameter estimation approach, which makes it computationally feasible to ingest large volumes of observations into the system. A series of experiments were carried out in a domain over North America to test the new framework in a controlled setting. In the experiments, we used synthetic tower observations of atmospheric CO2 concentrations to constrain parameters controlling terrestrial biogenic and oceanic CO2 fluxes. The results show that TRACE is capable of accurately estimating both the magnitude and spatial pattern of regional CO2 fluxes. Key Points: An ensemble‐based simultaneous state and parameter estimation approach is introduced for regional CO2 flux inversionsThe new dual‐state TRACE Regional Atmosphere–Carbon Ensemble (TRACE) framework provides a flexible platform for conducting coupled atmosphere–carbon data assimilationControlled experiments show that TRACE is effective at constraining regional CO2 fluxes using in situ CO2 concentration observations [ABSTRACT FROM AUTHOR]

Atmospheric CO2 inversion typically relies on the specification of prior flux and atmospheric model transport errors, which have large uncertainties. Here, we used ACT‐America airborne observations to compare CO2 model observation mismatch in the eastern U.S. and during four climatological seasons for the mesoscale WRF(‐Chem) and global scale CarbonTracker/TM5 (CT) models. Models used identical surface carbon fluxes, and CT was used as CO2 boundary condition for WRF. Both models showed reasonable agreement with observations, and CO2 residuals follow near symmetric peaked (i.e., non‐Gaussian) distribution with near‐zero bias of both models (CT: −0.34±3.12 ppm; WRF: 0.82±4.37 ppm). We also found large magnitude residuals at the tails of the distribution that contribute considerably to overall bias. Atmospheric boundary‐layer biases (1–10 ppm) were much larger than free tropospheric biases (0.5–1 ppm) and were of same magnitude as model‐model differences, whereas free tropospheric biases were mostly governed by CO2 background conditions. Results revealed systematic differences in atmospheric transport, most pronounced in the warm and cold sectors of synoptic systems, highlighting the importance of transport for CO2 residuals. While CT could reproduce the principal CO2 dynamics associated with synoptic systems, WRF showed a clearer distinction for CO2 differences across fronts. Variograms were used to quantify spatial correlation of residuals and showed characteristic residual length scales of approximately 100–300 km. Our findings suggest that inclusion of synoptic weather‐dependent and non‐Gaussian error structure may benefit inversion systems. Key Points: CO2 observed by aircraft in Eastern U.S. compares well to models, but residuals are strongly non‐GaussianModel biases affect representation of cross‐frontal CO2 gradients governing CO2 transport in stormsInversion models may benefit from model‐data mismatch errors dependent upon synoptic sector [ABSTRACT FROM AUTHOR]

The top-down atmospheric inversion method that couples atmospheric CO 2 observations with an atmospheric transport model has been used extensively to quantify CO 2 emissions from cities. However, the potential of the method is limited by several sources of misfits between the measured and modeled CO 2 that are of different origins than the targeted CO 2 emissions. This study investigates the critical sources of errors that can compromise the estimates of the city-scale emissions and identifies the signal of emissions that has to be filtered when doing inversions. A set of 1-year forward simulations is carried out using the WRF-Chem model at a horizontal resolution of 1 km focusing on the Paris area with different anthropogenic emission inventories, physical parameterizations, and CO 2 boundary conditions. The simulated CO 2 concentrations are compared with in situ observations from six continuous monitoring stations located within Paris and its vicinity. Results highlight large nighttime model–data misfits, especially in winter within the city, which are attributed to large uncertainties in the diurnal profile of anthropogenic emissions as well as to errors in the vertical mixing near the surface in the WRF-Chem model. The nighttime biogenic respiration to the CO 2 concentration is a significant source of modeling errors during the growing season outside the city. When winds are from continental Europe and the CO 2 concentration of incoming air masses is influenced by remote emissions and large-scale biogenic fluxes, differences in the simulated CO 2 induced by the two different boundary conditions (CAMS and CarbonTracker) can be of up to 5 ppm. Nevertheless, our results demonstrate the potential of our optimal CO 2 atmospheric modeling system to be utilized in atmospheric inversions of CO 2 emissions over the Paris metropolitan area. We evaluated the model performances in terms of wind, vertical mixing, and CO 2 model–data mismatches, and we developed a filtering algorithm for outliers due to local contamination and unfavorable meteorological conditions. Analysis of model–data misfit indicates that future inversions at the mesoscale should only use afternoon urban CO 2 measurements in winter and suburban measurements in summer. Finally, we determined that errors related to CO 2 boundary conditions can be overcome by including distant background observations to constrain the boundary inflow or by assimilating CO 2 gradients of upwind–downwind stations rather than by assimilating absolute CO 2 concentrations. [ABSTRACT FROM AUTHOR]

Regional estimates of biogenic carbon fluxes over North America from both atmospheric inversions ("top‐down" approach) and terrestrial biosphere models ("bottom‐up") remain highly uncertain. We merge these approaches with an ensemble‐based, regional modeling system able to diagnose and quantify the causes of uncertainties in top‐down atmospheric estimates of the terrestrial sink over North America. Our ensemble approach quantifies and partitions the uncertainty stemming from atmospheric transport, the biosphere, and large‐scale CO2 boundary inflow (boundary conditions). We use meteorological data, CO2 fluxes, and CO2 mole fraction measurements to assure the reliability of the ensemble system. Our results show that all uncertainty components have clear seasonal variations. The biogenic flux component dominates modeled boundary layer CO2 uncertainty, ranging from 2.5 ppm in summer and winter to 1.5 ppm in fall and spring. Spatially, it remains highly uncertain in the U.S. Corn Belt regions. Transport uncertainty reaches a maximum of 2.5 ppm in the summer months and stays at 1.2 ppm for the rest of the year and is highly correlated with the biogenic CO2 fluxes. Boundary conditions play the smallest role in atmospheric boundary layer CO2 uncertainty with a magnitude smaller than 1 ppm. However, boundary conditions are the most important uncertainty component in column‐averaged CO2 (XCO2). The spatiotemporal variations of the uncertainties in modeled XCO2 are similar to those in atmospheric boundary layer CO2. Plain Language Summary: Uncertainty in future uptake of carbon dioxide (CO2) by terrestrial ecosystems drives divergent projections of future climate and uncertainty in prescriptions for climate mitigation. North American ecosystems are currently a significant sink of atmospheric CO2, but this sink is difficult to measure and thus understand. Terrestrial uptake and emission of CO2 from a continent such as North America can be inferred from atmospheric CO2 observations. This approach, however, requires an accurate understanding of all the sources of uncertainty in simulating atmospheric CO2. These include errors in the simulations of atmospheric transport, fossil fuel emissions of CO2, and transport of CO2 from the rest of the Earth's continents and oceans (boundary conditions), in addition to the uncertainty terrestrial CO2 uptake. Here we quantify the uncertainty in all of these components of an atmospheric CO2 simulation over North America using an ensemble of simulations. Observations of atmospheric winds and mixing, atmospheric CO2 mole fractions, and land‐atmosphere CO2 fluxes are used to ensure that the ensemble cover an appropriate range of uncertainty in each element of our simulation. Our results show that over North America, (i) terrestrial biosphere CO2 fluxes dominate the uncertainty in the atmospheric boundary layer CO2, (ii) atmospheric transport uncertainty is highly correlated with biosphere flux uncertainty in space and time, and (iii) inflow of CO2 from other continents and oceans plays the least important role in North American lower atmosphere CO2 but is the largest source of uncertainty in atmospheric column‐integrated CO2. Our study shows that atmospheric transport and continental boundary conditions are important limits to our ability to infer terrestrial ecosystem CO2 uptake using, respectively lower atmosphere and atmospheric column CO2 measurements. Key Points: Our calibrated ensemble system demonstrates that biogenic fluxes dominate the uncertainty in atmospheric boundary layer CO2Transport uncertainty is highly correlated with biogenic flux uncertainty in space and timeLarge‐scale CO2 boundary inflow dominates uncertainty in the atmospheric column‐integrated CO2 uncertainty [ABSTRACT FROM AUTHOR]

Emission inventories (EIs) are the fundamental tool to monitor compliance with greenhouse gas (GHG) emissions and emission reduction commitments. Inventory accounting guidelines provide the best practices to help EI compilers across different countries and regions make comparable, national emission estimates regardless of differences in data availability. However, there are a variety of sources of error and uncertainty that originate beyond what the inventory guidelines can define. Spatially explicit EIs, which are a key product for atmospheric modeling applications, are often developed for research purposes and there are no specific guidelines to achieve spatial emission estimates. The errors and uncertainties associated with the spatial estimates are unique to the approaches employed and are often difficult to assess. This study compares the global, high-resolution (1 km), fossil fuel, carbon dioxide (CO2), gridded EI Open-source Data Inventory for Anthropogenic CO2 (ODIAC) with the multi-resolution, spatially explicit bottom-up EI geoinformation technologies, spatio-temporal approaches, and full carbon account for improving the accuracy of GHG inventories (GESAPU) over the domain of Poland. By taking full advantage of the data granularity that bottom-up EI offers, this study characterized the potential biases in spatial disaggregation by emission sector (point and non-point emissions) across different scales (national, subnational/regional, and urban policy-relevant scales) and identified the root causes. While two EIs are in agreement in total and sectoral emissions (2.2% for the total emissions), the emission spatial patterns showed large differences (10~100% relative differences at 1 km) especially at the urban-rural transitioning areas (90–100%). We however found that the agreement of emissions over urban areas is surprisingly good compared with the estimates previously reported for US cities. This paper also discusses the use of spatially explicit EIs for climate mitigation applications beyond the common use in atmospheric modeling. We conclude with a discussion of current and future challenges of EIs in support of successful implementation of GHG emission monitoring and mitigation activity under the Paris Climate Agreement from the United Nations Framework Convention on Climate Change (UNFCCC) 21st Conference of the Parties (COP21). We highlight the importance of capacity building for EI development and coordinated research efforts of EI, atmospheric observations, and modeling to overcome the challenges. [ABSTRACT FROM AUTHOR]

Inference of CO2 surface fluxes using atmospheric CO2 observations in atmospheric inversions depends critically on accurate representation of atmospheric transport. Here we characterize regional‐scale CO2 transport uncertainties due to uncertainties in meteorological fields using a mesoscale atmospheric model and an ensemble of simulations with flow‐dependent transport errors. During a 1‐month summer period over North America, transport uncertainties yield an ensemble spread in instantaneous CO2 at 100 m above ground level comparable to the CO2 uncertainties resulting from 48% relative uncertainty in 3‐hourly natural CO2 fluxes. Temporal averaging reduces transport uncertainties but increases the influence of CO2 uncertainties from the lateral boundaries. The influence of CO2 background uncertainties is especially large for column‐averaged CO2. These results suggest that transport errors and CO2 background errors limit regional atmospheric inversions at two distinct timescales and that the error characteristics of transport and background errors should guide the design of regional inversion systems. Plain Language Summary: Accurate estimates of regional‐scale CO2 surface fluxes are essential to improve our understanding of the carbon cycle and to verify human CO2 emission inventories. CO2 surface fluxes can be inferred from atmospheric CO2 measurements through inversion methods, which use atmospheric transport models to relate CO2 concentration to fluxes. However, previous studies have shown that inversion results can be sensitive to errors in the simulated atmospheric transport. To better understand how to account for such transport errors, we characterize the uncertainties in simulated CO2 concentration due to uncertainties in atmospheric transport by running an ensemble of perturbed transport simulations in a regional atmospheric model. Our results show that CO2 uncertainties due to transport uncertainties are about half the magnitude as uncertainties due to erroneous CO2 surface fluxes while displaying similar spatial and temporal patterns. Transport uncertainties are reduced when CO2 is time averaged, but at the same time the influence of uncertainties in the CO2 background concentration is increased at longer timescales. Thus, the flux signals in regional inversions are degraded by transport errors and CO2 background errors at different timescales, and it is imperative to properly account for these errors to obtain reliable regional‐scale CO2 flux estimates. Key Points: We quantify CO2 transport uncertainties due to uncertainties in meteorological fields at the regional scale and submonthly timescalesAtmospheric CO2 estimates that are strongly influenced by CO2 surface fluxes tend to also have large transport uncertaintiesThe signal‐to‐noise ratio in in situ CO2 is limited by transport errors at subweekly timescales and CO2 background errors at longer scales [ABSTRACT FROM AUTHOR]

Atmospheric inversions have been used to assess biosphere–atmosphere CO2 surface exchanges at various scales, but variability among inverse flux estimates remains significant, especially at continental scales. Atmospheric transport errors are one of the main contributors to this variability. To characterize transport errors and their spatiotemporal structures, we present an objective method to generate a calibrated ensemble adjusted with meteorological measurements collected across a region, here the upper US Midwest in midsummer. Using multiple model configurations of the Weather Research and Forecasting (WRF) model, we show that a reduced number of simulations (less than 10 members) reproduces the transport error characteristics of a 45-member ensemble while minimizing the size of the ensemble. The large ensemble of 45 members was constructed using different physics parameterization (i.e., land surface models (LSMs), planetary boundary layer (PBL) schemes, cumulus parameterizations and microphysics parameterizations) and meteorological initial/boundary conditions. All the different models were coupled to CO2 fluxes and lateral boundary conditions from CarbonTracker to simulate CO2 mole fractions. Observed meteorological variables critical to inverse flux estimates, PBL wind speed, PBL wind direction and PBL height are used to calibrate our ensemble over the region. Two optimization techniques (i.e., simulated annealing and a genetic algorithm) are used for the selection of the optimal ensemble using the flatness of the rank histograms as the main criterion. We also choose model configurations that minimize the systematic errors (i.e., monthly biases) in the ensemble. We evaluate the impact of transport errors on atmospheric CO2 mole fraction to represent up to 40 % of the model–data mismatch (fraction of the total variance). We conclude that a carefully chosen subset of the physics ensemble can represent the uncertainties in the full ensemble, and that transport ensembles calibrated with relevant meteorological variables provide a promising path forward for improving the treatment of transport uncertainties in atmospheric inverse flux estimates. [ABSTRACT FROM AUTHOR]

Urban regions are responsible for emitting significant amounts of fossil fuel carbon dioxide (FFCO2), whose emissions at finer, city scales are more uncertain than those aggregated at the global scale. Carbon-observing satellites may provide independent top-down emission evaluations and compensate for the sparseness of surface CO2 observing networks, especially in urban areas. Although some previous studies have attempted to derive urban CO2 signals from satellite column-averaged CO2 data (XCO2) using simple statistical measures, less work has been carried out to link upwind emission sources to downwind atmospheric columns using atmospheric models. In addition to Eulerian atmospheric models that have been customized for emission estimates over specific cities, the Lagrangian modeling approach - in particular, the Lagrangian Particle Dispersion Model (LPDM) approach - has the potential to efficiently determine the sensitivity of downwind concentration changes to upwind sources. However, when applying LPDMs to interpret satellite XCO2, several issues - namely, uncertainties in anthropogenic XCO2 signals due to receptor configurations and errors in atmospheric transport and background XCO2 - have yet to be addressed. In this study, we present a modified version of the Stochastic Time-Inverted Lagrangian Transport (STILT) model, "X-STILT", for extracting urban XCO2 signals from NASA's Orbiting Carbon Observatory 2 (OCO-2) XCO2 data. X-STILT incorporates satellite profiles and provides comprehensive uncertainty estimates towards urban XCO2 enhancements for selected satellite soundings. Several methods to initialize receptors/particle setups and determine background XCO2 are presented and discussed via sensitivity analyses and comparisons. To illustrate X-STILT's utilities and applications, we examined five OCO-2 overpasses over Riyadh, Saudi Arabia, during a two-year time period and performed a simple scaling factor-based inverse analysis. As a result, the model is able to reproduce most observed XCO2 enhancements. Conservative error estimates show that the 68 % confidence limit of XCO2 uncertainties due to transport and emission uncertainties contribute to ~ 29 % and ~ 25 % of the averaged latitudinally-integrated urban signals, respectively, over five overpasses, given meteorological fields from the Global Data Assimilation System (GDAS). Additionally, a sizable negative "bias" of 0.56 ppm in background derived from a previous study employing simple statistics (daily median over a regional domain) leads to positive biases of 43 % in mean observed urban signal and 68 % in posterior scaling factor, from a simple inversion analysis. Based on these results, we foresee X-STILT serving as a tool for interpreting column measurements, estimating urban enhancement signals, and carrying out inverse modeling to improve quantification of urban emissions. [ABSTRACT FROM AUTHOR]

Most atmospheric models, including the Weather Research and Forecasting (WRF) model, use a spherical geographic coordinate system to internally represent input data and perform computations. However, most geographic information system (GIS) input data used by the models are based on a spheroid datum because it better represents the actual geometry of the earth. WRF and other atmospheric models use these GIS input layers as if they were in a spherical coordinate system without accounting for the difference in datum. When GIS layers are not properly reprojected, latitudinal errors of up to 21 km in the midlatitudes are introduced. Recent studies have suggested that for very high-resolution applications, the difference in datum in the GIS input data (e.g., terrain land use, orography) should be taken into account. However, the magnitude of errors introduced by the difference in coordinate systems remains unclear. This research quantifies the effect of using a spherical vs. a spheroid datum for the input GIS layers used by WRF to study greenhouse gas transport and dispersion in northeast Pennsylvania. [ABSTRACT FROM AUTHOR]

Numerical atmospheric models can generate simulations at very high spatial and temporal resolutions. Many of such models, including the Weather Research and Forecasting (WRF), assume a spherical geographic coordinate system to represent the data and for their computations. However, most if not all Geographic Information System (GIS) data use a spheroid datum because it best represents the surface of the earth. WRF and other numerical systems, simply assume that GIS layers can be input as if they were in a spherical coordinate system. The difference when reprojecting standard GIS layers onto a sphere can result in latitudinal errors of up to 21 km in the mid-latitudes. Recent studies have suggested that for very high resolution applications, there might be an impact when the GIS input data (e.g. terrain land use, orography) are not reprojected. These impacts introduced by the difference in coordinate systems remain unclear. This research investigates the role and importance of reprojecting GIS layers used by WRF as input by performing sensitivity studies of greenhouse gas transport and dispersion in Northeast Pennsylvania. [ABSTRACT FROM AUTHOR]

With the launch of the Observing Carbon Observatories (OCO-2 and OCO-3), we have entered a new era of greenhouse gas (GHG) data collection where sub-city scale data can be collected at varying times across the entire globe. An increasing focus on quantifying urban emissions of GHGs from policy makers has begun to spur new research into how best to use this unique and very high resolution data set. While, historically, this line of research has been the domain of limited domain mesoscale and regional models, an increasing understanding and respect for boundary inflow uncertainty and bias has led people to search for an alternative modeling framework that both characterizes high frequency variability of CO 2 in space and time as well as honor mass conservation requirements globally and be seamless with respect to boundaries. To monitor local anthropogenic emissions from space, the influence of atmospheric signals originating from outside the local area of interest needs to be quantified. In our first step towards building a comprehensive multi-scale CO 2 inversion system, we use free running simulations of the Ocean Land Atmosphere Model (OLAM), a variable-resolution general circulation model, to explore the signal-to-noise statistics of anthropogenic urban emissions of CO 2 versus the background inflow for approximately 40 of the largest cities across the globe. We show that signal-to-noise levels are much better in winter time than summer but also that the winter biological inflow is far from negligible, suggesting that the commonly held assumption that biology can be ignored in winter time urban emission estimates is probably incorrect. Simulated pressure-weighted column average CO 2 (XCO 2) is also used to evaluate the ability of fixed location XCO 2 measurements to provide background inflow estimates. Results show why Los Angeles, a heavily instrumented and studied urban center, is likely one of the easiest cities to observe globally from space, despite its relatively complex meteorology. Lastly, we discuss challenges and possible research paths forward to continue to advance the notion of multi-scale global CO 2 flux inversion systems capable of simultaneously optimizing local urban emissions and large-scale CO 2 transport patterns. • Variable resolution transport model. • High-res transport modeling minimizes systematic transport errors. • Natural variations in CO2 inflow into cities are significant. • Los Angeles is a unique urban emissions scenario. [ABSTRACT FROM AUTHOR]

Global greenhouse gases (GHG) gridded products have been and will remain an important tool to allow us to combine emissions estimates compiled as inventories with atmospheric modeling in order to objectively assess the impact of our emissions. However, the weakness of those emission products is the fact that subnational emissions are largely based on emission downscaling, and the emission changes seen in the gridded products are not necessarily accurately reflecting the impact of subnational emission drivers (e.g. local emission reduction action). Assessing the subnational downscaling errors is critical in order to provide a science-based emission monitoring support, especially at fine spatial scales of atmospheric modeling and satellite observations. With this in mind, we attempted to characterize and quantify the subnational disaggregation errors in a global downscaled emission dataset in hopes of formulating an error reduction/mitigation approach. This study focusses on the high-resolution, global fossil fuel carbon dioxide emission (FFCO2) gridded data product ODIAC. The ODIAC employs geospatial information such as point source emission estimates and geolocation as well as satellite-observed nightlights to achieve the global 1km emission distribution. Given the errors matter differently depending on applications of the gridded products, we took several different approaches to study the subnational disaggregation errors at different spatial scales. First, we compared ODIAC to the national scale multi-resolution emission dataset GESAPU over the domain of Poland in order to characterize spatial patterns of the disaggregation errors across different scales from urban to rural areas. Taking advantage of a high-resolution WRF simulation with Hestia urban emission estimates in Los Angeles, we attempted to quantify the disaggregation errors in ODIAC in the units of parts per millions (ppm), rather than emission units that are not directly measurable. We also examined the impacts of different FFCO2 configurations by including international bunker emissions, three dimensional aircraft emissions, and emissions in the form of reduced carbon species in a global flux inversion framework. [ABSTRACT FROM AUTHOR]