FFCO2 Emissions Research Articles

What can we learn about effectiveness of carbon reduction policies from interannual variability of fossil fuel CO2 emissions in East Asia?

Although most countries have submitted their Nationally Determined Contributions (NDC), there is a lack of understanding what policies are effective in terms of carbon emission reduction under the announced pledges. We use East Asia as a case study to estimate the importance of national environmental policies in terms of reduction in fossil fuel carbon emissions (FFCO2). We show that the flagship policies of China, Japan, South Korea and Mongolia in the 2010s were generally beneficial in terms of slowing down FFCO2 growth rates. When flagship polices were enacted, annual FFCO2 growth rate has either slowed down by 1% (South Korea), 5% (Mongolia), 8% (China) or even resulted in a decline (Japan) comparing to prior periods. We find that the 12th Five-Year Plan (12th FYP) of China had the strongest footprint in FFCO2 emission dynamics across East Asia in 2010s. The recent slowest rate of FFCO2 growth across East Asia (2011–2015) temporally corresponds to the 12th FYP. This regional pattern of FFCO2 dynamics is driven by decrements in annual growth of FFCO2, coal use and cement production of China (all ˜8% per yer decrease) during the 12th FYP. Using compound periodical growth of FFCO2 emissions, we provide two baseline projections of emission distribution in East Asia, by assuming that all policies are enacted (policy-on) or not (policy-off) in the future. The projections show that policies were beneficial since policy-on scenario results in 24%, 80%, 166% less FFCO2 emissions than in policy-off scenario in East Asia by 2020, 2025 and 2030 respectively. This progress is yet insufficient for reaching NDC goals by 2030. Even in policy-on scenario in 2030, East Asian countries would either experience insufficient decline of FFCO2 like Japan (-13% of FFCO2 comparing to pledged -17%) or increase of FFCO2 like South Korea (11%) and Mongolia (4%) comparing to 2010 level. For China, due to lack of economy-independent goals, we were unable to assess NDC target compliance. We demonstrate that China will remain as the major FFCO2 emitter of EA in near future in any projection. For China, the highest emission cluster will remain at the Eastern Provinces with the strongest power generation demand. These provinces would be responsible for 43% and 52% of FFCO2 emissions in East Asia in policy-off and policy-on scenarios. We concluded that the current efforts of national flagship environmental policies are beneficial but not sufficient for reaching ambitious carbon reduction goals like Paris Agreement. This study once again underlined the necessity in the supranational framework that may control the carbon abatement goals in East Asia. Without the supranational framework, achievements in carbon emission reductions are strongly hindered by the socioeconomic environment and the regional (or sectoral) emphasis of carbon reduction activities within a national economy.

Characterizing uncertainties in atmospheric inversions of fossil fuel CO<sub>2</sub> emissions in California

Abstract. Atmospheric inverse modelling has become an increasingly useful tool for evaluating emissions of greenhouse gases including methane, nitrous oxide, and synthetic gases such as hydrofluorocarbons (HFCs). Atmospheric inversions for emissions of CO2 from fossil fuel combustion (ffCO2) are currently being developed. The aim of this paper is to investigate potential errors and uncertainties related to the spatial and temporal prior representation of emissions and modelled atmospheric transport for the inversion of ffCO2 emissions in the US state of California. We perform simulation experiments based on a network of ground-based observations of CO2 concentration and radiocarbon in CO2 (a tracer of ffCO2), combining prior (bottom-up) emission models and transport models currently used in many atmospheric studies. The potential effect of errors in the spatial and temporal distribution of prior emission estimates is investigated in experiments by using perturbed versions of the emission estimates used to create the pseudo-data. The potential effect of transport error was investigated by using three different atmospheric transport models for the prior and pseudo-data simulations. We find that the magnitude of biases in posterior total state emissions arising from errors in the spatial and temporal distribution in prior emissions in these experiments are 1 %–15 % of posterior total state emissions and are generally smaller than the 2σ uncertainty in posterior emissions. Transport error in these experiments introduces biases of −10 % to +6 % into posterior total state emissions. Our results indicate that uncertainties in posterior total state ffCO2 estimates arising from the choice of prior emissions or atmospheric transport model are on the order of 15 % or less for the ground-based network in California we consider. We highlight the need for temporal variations to be included in prior emissions and for continuing efforts to evaluate and improve the representation of atmospheric transport for regional ffCO2 inversions.

Atmospheric observation-based estimation of fossil fuel CO2 emissions from regions of central and southern California

Combustion of fossil fuel is the dominant source of greenhouse gas emissions to the atmosphere in California. Here, we describe radiocarbon (14CO2) measurements and atmospheric inverse modeling to estimate fossil fuel CO2 (ffCO2) emissions for 2009–2012 from a site in central California, and for June 2013–May 2014 from two sites in southern California. A priori predicted ffCO2 mixing ratios are computed based on regional atmospheric transport model (WRF-STILT) footprints and an hourly ffCO2 prior emission map (Vulcan 2.2). Regional inversions using observations from the central California site suggest that emissions from the San Francisco Bay Area (SFBA) are higher in winter and lower in summer. Taking all years together, the average of a total of fifteen 3-month inversions from 2009 to 2012 suggests ffCO2 emissions from SFBA were within 6 ± 35% of the a priori estimate for that region, where posterior emission uncertainties are reported as 95% confidence intervals. Results for four 3-month inversions using measurements in Los Angeles South Coast Air Basin (SoCAB) during June 2013–May 2014 suggest that emissions in SoCAB are within 13 ± 28% of the a priori estimate for that region, with marginal detection of any seasonality. While emissions from the SFBA and SoCAB urban regions (containing ~50% of prior emissions from California) are constrained by the observations, emissions from the remaining regions are less constrained, suggesting that additional observations will be valuable to more accurately estimate total ffCO2 emissions from California as a whole.

Assessing fossil fuel CO2 emissions in California using atmospheric observations and models

Analysis systems incorporating atmospheric observations could provide a powerful tool for validating fossil fuel CO2 (ffCO2) emissions reported for individual regions, provided that fossil fuel sources can be separated from other CO2 sources or sinks and atmospheric transport can be accurately accounted for. We quantified ffCO2 by measuring radiocarbon (14C) in CO2, an accurate fossil-carbon tracer, at nine observation sites in California for three months in 2014–15. There is strong agreement between the measurements and ffCO2 simulated using a high-resolution atmospheric model and a spatiotemporally-resolved fossil fuel flux estimate. Inverse estimates of total in-state ffCO2 emissions are consistent with the California Air Resources Board’s reported ffCO2 emissions, providing tentative validation of California’s reported ffCO2 emissions in 2014–15. Continuing this prototype analysis system could provide critical independent evaluation of reported ffCO2 emissions and emissions reductions in California, and the system could be expanded to other, more data-poor regions.

Potential of European <sup>14</sup>CO<sub>2</sub> observation network to estimate the fossil fuel CO<sub>2</sub> emissions via atmospheric inversions

Abstract. Combining measurements of atmospheric CO2 and its radiocarbon (14CO2) fraction and transport modeling in atmospheric inversions offers a way to derive improved estimates of CO2 emitted from fossil fuel (FFCO2). In this study, we solve for the monthly FFCO2 emission budgets at regional scale (i.e., the size of a medium-sized country in Europe) and investigate the performance of different observation networks and sampling strategies across Europe. The inversion system is built on the LMDZv4 global transport model at 3.75∘ × 2.5∘ resolution. We conduct Observing System Simulation Experiments (OSSEs) and use two types of diagnostics to assess the potential of the observation and inverse modeling frameworks. The first one relies on the theoretical computation of the uncertainty in the estimate of emissions from the inversion, known as “posterior uncertainty”, and on the uncertainty reduction compared to the uncertainty in the inventories of these emissions, which are used as a prior knowledge by the inversion (called “prior uncertainty”). The second one is based on comparisons of prior and posterior estimates of the emission to synthetic “true” emissions when these true emissions are used beforehand to generate the synthetic fossil fuel CO2 mixing ratio measurements that are assimilated in the inversion. With 17 stations currently measuring 14CO2 across Europe using 2-week integrated sampling, the uncertainty reduction for monthly FFCO2 emissions in a country where the network is rather dense like Germany, is larger than 30 %. With the 43 14CO2 measurement stations planned in Europe, the uncertainty reduction for monthly FFCO2 emissions is increased for the UK, France, Italy, eastern Europe and the Balkans, depending on the configuration of prior uncertainty. Further increasing the number of stations or the sampling frequency improves the uncertainty reduction (up to 40 to 70 %) in high emitting regions, but the performance of the inversion remains limited over low-emitting regions, even assuming a dense observation network covering the whole of Europe. This study also shows that both the theoretical uncertainty reduction (and resulting posterior uncertainty) from the inversion and the posterior estimate of emissions itself, for a given prior and “true” estimate of the emissions, are highly sensitive to the choice between two configurations of the prior uncertainty derived from the general estimate by inventory compilers or computations on existing inventories. In particular, when the configuration of the prior uncertainty statistics in the inversion system does not match the difference between these prior and true estimates, the posterior estimate of emissions deviates significantly from the truth. This highlights the difficulty of filtering the targeted signal in the model–data misfit for this specific inversion framework, the need to strongly rely on the prior uncertainty characterization for this and, consequently, the need for improved estimates of the uncertainties in current emission inventories for real applications with actual data. We apply the posterior uncertainty in annual emissions to the problem of detecting a trend of FFCO2, showing that increasing the monitoring period (e.g., more than 20 years) is more efficient than reducing uncertainty in annual emissions by adding stations. The coarse spatial resolution of the atmospheric transport model used in this OSSE (typical of models used for global inversions of natural CO2 fluxes) leads to large representation errors (related to the inability of the transport model to capture the spatial variability of the actual fluxes and mixing ratios at subgrid scales), which is a key limitation of our OSSE setup to improve the accuracy of the monitoring of FFCO2 emissions in European regions. Using a high-resolution transport model should improve the potential to retrieve FFCO2 emissions, and this needs to be investigated.

Large Uncertainties in Urban‐Scale Carbon Emissions

AbstractAccurate estimates of fossil fuel carbon dioxide (FFCO2) emissions are a critical component of local, regional, and global climate agreements. Current global inventories of FFCO2 emissions do not directly quantify emissions at local scales; instead, spatial proxies like population density, nighttime lights, and power plant databases are used to downscale emissions from national totals. We have developed a high‐resolution (hourly, 1 km2) bottom‐up Anthropogenic Carbon Emissions System (ACES) for FFCO2, based on local activity data for the year 2011 across the northeastern U.S. We compare ACES with three widely used global inventories, finding significant differences at regional (20%) and city scales (50–250%). At a spatial resolution of 0.1°, inventories differ by over 100% for half of the grid cells in the domain, with the largest differences in urban areas and oil and gas production regions. Given recent U.S. federal policy pull backs regarding greenhouse gas emissions reductions, inventories like ACES are crucial for U.S. actions, as the impetus for climate leadership has shifted to city and state governments. The development of a robust carbon monitoring system to track carbon fluxes is critical for emissions benchmarking and verification. We show that existing downscaled inventories are not suitable for urban emissions monitoring, as they do not consider important local activity patterns. The ACES methodology is designed for easy updating, making it suitable for emissions monitoring under most city, regional, and state greenhouse gas mitigation initiatives, in particular, for the small‐ and medium‐sized cities that lack the resources to regularly perform their own bottom‐up emissions inventories.

Estimation of observation errors for large-scale atmospheric inversion of CO2 emissions from fossil fuel combustion

National annual inventories of CO2 emitted during fossil fuel consumption (FFCO2) bear 5–10% uncertainties for developed countries, and are likely higher at intra annual scales or for developing countries. Given the current international efforts of mitigating actions, there is a need for independent verifications of these inventories. Atmospheric inversion assimilating atmospheric gradients of CO2 and radiocarbon measurements could provide an independent way of monitoring FFCO2 emissions. A strategy would be to deploy such measurements over continental scale networks and to conduct continental to global scale atmospheric inversions targeting the national and one-month scale budgets of the emissions. Uncertainties in the high-resolution distribution of the emissions could limit the skill for such a large-scale inversion framework. This study assesses the impact of such uncertainties on the potential for monitoring the emissions at large scale. In practice, it is more specifically dedicated to the derivation, typical quantification and analysis of critical sources of errors that affect the inversion of FFCO2 emissions when solving for them at a relatively coarse resolution with a coarse grid transport model. These errors include those due to the mismatch between the resolution of the transport model and the spatial variability of the actual fluxes and concentrations (i.e. the representation errors) and those due to the uncertainties in the spatial and temporal distribution of emissions at the transport model resolution when solving for the emissions at large scale (i.e. the aggregation errors). We show that the aggregation errors characterize the impact of the corresponding uncertainties on the potential for monitoring the emissions at large scale, even if solving for them at the transport model resolution. We propose a practical method to quantify these sources of errors, and compare them with the precision of FFCO2 measurements (i.e. the measurement errors) and the errors in the modelling of atmospheric transport (i.e. the transport errors). The results show that both the representation and measurement errors can be much larger than the aggregation errors. The magnitude of representation and aggregation errors is sensitive to sampling heights and temporal sampling integration time. The combination of these errors can reach up to about 50% of the typical signals, i.e. the atmospheric large-scale mean afternoon FFCO2 gradients between sites being assimilated by the inversion system. These errors have large temporal auto-correlation scales, but short spatial correlation scales. This indicates the need for accounting for these temporal auto-correlations in the atmospheric inversions and the need for dense networks to limit the impact of these errors on the inversion of FFCO2 emissions at large scale. More generally, comparisons of the representation and aggregation errors to the errors in simulated FFCO2 gradients due to uncertainties in current inventories suggest that the potential of inversions using global coarse-resolution models (with typical horizontal resolution of a couple of degrees) to retrieve FFCO2 emissions at sub-continental scale could be limited, and that meso-scale models with smaller representation errors would effectively increase the potential of inversions to constrain FFCO2 emission estimates.

Spatio-temporal Variations in on-road CO<sub>2</sub> Emissions in the Los Angeles Megacity

We quantify hourly on-road fossil fuel carbon dioxide (FFCO2) emissions at the road segment level for the Los Angeles (LA) megacity based on observed traffic data, and characterize emission patterns across space and time. This on-road FFCO2 emissions dataset for LA (from Hestia version 1.0), based on actual traffic volume, provides emissions per vehicle kilometer travelled (VKT)—an important metric for greenhouse gas (GHG) reductions. We further identify emissions hotpots that can help state and local policy makers plan the most effective GHG reduction strategies. On-road vehicle traffic accounts for half of the FFCO2 emissions in LA, of which 41% is from arterials (intermediate road type). Arterials also have the largest C emissions intensity—FFCO2 per VKT—possibly from high traffic congestion and fleet composition. Non-interstate emissions hotspots (> 419 tC lane-km-1) are equally dominated by arterials and collectors (the lowest road type) in terms of FFCO2 emissions though collectors have a higher VKT. These hotspots occur in densely populated areas and developed landuse classes, largely in LA (67%) and Orange (18%) counties, and provide specific targets for emissions reduction efforts. The estimated uncertainties for interstate, arterial and collector emissions per road length are ± 2.1, ± 0.5 and ± 18.0%, respectively. Our overall estimates compare reasonably well with other products, DARTE and FIVE but with substantial differences in spatial distribution. The method for developing this dataset is easily replicable in other urban landscapes, and represents a powerful tool for carbon cycle science and regional policy makers.

Reconciling the differences between a bottom-up and inverse-estimated FFCO2 emissions estimate in a large US urban area

The INFLUX experiment has taken multiple approaches to estimate the carbon dioxide (CO2) flux in a domain centered on the city of Indianapolis, Indiana. One approach, Hestia, uses a bottom-up technique relying on a mixture of activity data, fuel statistics, direct flux measurement and modeling algorithms. A second uses a Bayesian atmospheric inverse approach constrained by atmospheric CO2 measurements and the Hestia emissions estimate as a prior CO2 flux. The difference in the central estimate of the two approaches comes to 0.94 MtC (an 18.7% difference) over the eight-month period between September 1, 2012 and April 30, 2013, a statistically significant difference at the 2-sigma level. Here we explore possible explanations for this apparent discrepancy in an attempt to reconcile the flux estimates. We focus on two broad categories: 1) biases in the largest of bottom-up flux contributions and 2) missing CO2 sources. Though there is some evidence for small biases in the Hestia fossil fuel carbon dioxide (FFCO2) flux estimate as an explanation for the calculated difference, we find more support for missing CO2 fluxes, with biological respiration the largest of these. Incorporation of these differences bring the Hestia bottom-up and the INFLUX inversion flux estimates into statistical agreement and are additionally consistent with wintertime measurements of atmospheric 14CO2. We conclude that comparison of bottom-up and top-down approaches must consider all flux contributions and highlight the important contribution to urban carbon budgets of animal and biotic respiration. Incorporation of missing CO2 fluxes reconciles the bottom-up and inverse-based approach in the INFLUX domain.

Gridded uncertainty in fossil fuel carbon dioxide emission maps, a CDIAC example

Abstract. Due to a current lack of physical measurements at appropriate spatial and temporal scales, all current global maps and distributions of fossil fuel carbon dioxide (FFCO2) emissions use one or more proxies to distribute those emissions. These proxies and distribution schemes introduce additional uncertainty into these maps. This paper examines the uncertainty associated with the magnitude of gridded FFCO2 emissions. This uncertainty is gridded at the same spatial and temporal scales as the mass magnitude maps. This gridded uncertainty includes uncertainty contributions from the spatial, temporal, proxy, and magnitude components used to create the magnitude map of FFCO2 emissions. Throughout this process, when assumptions had to be made or expert judgment employed, the general tendency in most cases was toward overestimating or increasing the magnitude of uncertainty. The results of the uncertainty analysis reveal a range of 4–190 %, with an average of 120 % (2σ) for populated and FFCO2-emitting grid spaces over annual timescales. This paper also describes a methodological change specific to the creation of the Carbon Dioxide Information Analysis Center (CDIAC) FFCO2 emission maps: the change from a temporally fixed population proxy to a temporally varying population proxy.

Urban high-resolution fossil fuel CO2 emissions quantification and exploration of emission drivers for potential policy applications

Fossil fuel carbon dioxide (FFCO2) emissions are the largest driver of anthropogenic climate change. Approximately three-quarters of the world’s fossil fuels carbon dioxide emissions are generated in urban areas. We used the Hestia high resolution approach to quantify FFCO2 for Salt Lake County, Utah, USA and demonstrate the importance of high resolution quantification to urban emissions mitigation policymaking. We focus on the residential and onroad sectors across both urbanized and urbanizing parts of the valley. Stochastic Impact by Regression on Population, Affluence, and Technology (STIRPAT) regression models using sociodemographic data at the census block group level shows that population, per capita income, and building age exhibit positive relationships while household size shows a negative relationship with FFCO2 emissions. Compact development shows little effect on FFCO2 emissions in this domain. FFCO2 emissions in high income block groups is twice as sensitive to income than low income block groups. Emissions are four times as sensitive to household size in low-income versus high-income block groups. These results suggest that policy options targeting personal responsibility or knowledge feedback loops may be the most effective strategies. Examples include utility bill performance comparison or publicly available energy maps identifying high-emitting areas. Within the onroad sector, high emissions density (FFCO2/km) is associated with primary roads, while high emissions intensity (FFCO2/VMT) is associated with secondary roads. Opportunities exist for alignment of public transportation extension with remaining high emission road segments, offering a prioritization of new onroad transportation policy in Salt Lake County.

A comparison of five high-resolution spatially-explicit, fossil-fuel, carbon dioxide emission inventories for the United States

The quantification of fossil-fuel-related emissions of carbon dioxide to the atmosphere is necessary in order to accurately represent carbon cycle fluxes and to understand and project the details of the global carbon cycle. In addition, the monitoring, reporting, and verification (MRV) of carbon dioxide emissions is necessary for the success of international agreements to reduce emissions. However, existing fossil-fuel carbon dioxide (FFCO2) emissions inventories vary in terms of the data and methods used to estimate and distribute FFCO2. This paper compares how the approaches used to create spatially explicit FFCO2 emissions inventories affect the spatial distribution of emissions estimates and the magnitude of emissions estimates in specific locales. Five spatially explicit FFCO2 emission inventories were compared: Carbon Dioxide Information and Analysis Center (CDIAC), Emission Database for Global Atmospheric Research (EDGAR), Fossil Fuel Data Assimilation System (FFDAS), Open-source Data Inventory for Anthropogenic CO2 (ODIAC), and Vulcan. The effects of using specific data and approaches in the creation of spatially explicit FFCO2 emissions inventories, and the effect of resolution on data representation are analyzed using graphical, numerical, and cartographic approaches. We examined the effect of using top-down versus bottom-up approaches, nightlights versus population proxies, and the inclusion of large point sources. The results indicate that the approach used to distribute emissions in space creates distinct patterns in the distribution of emissions estimates and hence in the estimates of emissions in specific locations. The different datasets serve different purposes but collectively show the key role of large point sources and urban centers and the strong relationship between scale and uncertainty.

A sparse reconstruction method for the estimation of multi-resolution emission fields via atmospheric inversion

Abstract. Atmospheric inversions are frequently used to estimate fluxes of atmospheric greenhouse gases (e.g., biospheric CO2 flux fields) at Earth's surface. These inversions typically assume that flux departures from a prior model are spatially smoothly varying, which are then modeled using a multi-variate Gaussian. When the field being estimated is spatially rough, multi-variate Gaussian models are difficult to construct and a wavelet-based field model may be more suitable. Unfortunately, such models are very high dimensional and are most conveniently used when the estimation method can simultaneously perform data-driven model simplification (removal of model parameters that cannot be reliably estimated) and fitting. Such sparse reconstruction methods are typically not used in atmospheric inversions. In this work, we devise a sparse reconstruction method, and illustrate it in an idealized atmospheric inversion problem for the estimation of fossil fuel CO2 (ffCO2) emissions in the lower 48 states of the USA. Our new method is based on stagewise orthogonal matching pursuit (StOMP), a method used to reconstruct compressively sensed images. Our adaptations bestow three properties to the sparse reconstruction procedure which are useful in atmospheric inversions. We have modified StOMP to incorporate prior information on the emission field being estimated and to enforce non-negativity on the estimated field. Finally, though based on wavelets, our method allows for the estimation of fields in non-rectangular geometries, e.g., emission fields inside geographical and political boundaries. Our idealized inversions use a recently developed multi-resolution (i.e., wavelet-based) random field model developed for ffCO2 emissions and synthetic observations of ffCO2 concentrations from a limited set of measurement sites. We find that our method for limiting the estimated field within an irregularly shaped region is about a factor of 10 faster than conventional approaches. It also reduces the overall computational cost by a factor of 2. Further, the sparse reconstruction scheme imposes non-negativity without introducing strong nonlinearities, such as those introduced by employing log-transformed fields, and thus reaps the benefits of simplicity and computational speed that are characteristic of linear inverse problems.

A multiresolution spatial parameterization for the estimation of fossil-fuel carbon dioxide emissions via atmospheric inversions

Abstract. The characterization of fossil-fuel CO2 (ffCO2) emissions is paramount to carbon cycle studies, but the use of atmospheric inverse modeling approaches for this purpose has been limited by the highly heterogeneous and non-Gaussian spatiotemporal variability of emissions. Here we explore the feasibility of capturing this variability using a low-dimensional parameterization that can be implemented within the context of atmospheric CO2 inverse problems aimed at constraining regional-scale emissions. We construct a multiresolution (i.e., wavelet-based) spatial parameterization for ffCO2 emissions using the Vulcan inventory, and examine whether such a~parameterization can capture a realistic representation of the expected spatial variability of actual emissions. We then explore whether sub-selecting wavelets using two easily available proxies of human activity (images of lights at night and maps of built-up areas) yields a low-dimensional alternative. We finally implement this low-dimensional parameterization within an idealized inversion, where a sparse reconstruction algorithm, an extension of stagewise orthogonal matching pursuit (StOMP), is used to identify the wavelet coefficients. We find that (i) the spatial variability of fossil-fuel emission can indeed be represented using a low-dimensional wavelet-based parameterization, (ii) that images of lights at night can be used as a proxy for sub-selecting wavelets for such analysis, and (iii) that implementing this parameterization within the described inversion framework makes it possible to quantify fossil-fuel emissions at regional scales if fossil-fuel-only CO2 observations are available.

Detecting fossil fuel emissions patterns from subcontinental regions using North American in situ CO2 measurements.

The ability to monitor fossil fuel carbon dioxide (FFCO2) emissions from subcontinental regions using atmospheric CO2 observations remains an important but unrealized goal. Here we explore a necessary but not sufficient component of this goal, namely, the basic question of the detectability of FFCO2 emissions from subcontinental regions. Detectability is evaluated by examining the degree to which FFCO2 emissions patterns from specific regions are needed to explain the variability observed in high-frequency atmospheric CO2 observations. Analyses using a CO2 monitoring network of 35 continuous measurement towers over North America show that FFCO2 emissions are difficult to detect during nonwinter months. We find that the compounding effects of the seasonality of atmospheric transport patterns and the biospheric CO2 flux signal dramatically hamper the detectability of FFCO2 emissions. Results from several synthetic data case studies highlight the need for advancements in data coverage and transport model accuracy if the goal of atmospheric measurement-based FFCO2 emissions detection and estimation is to be achieved beyond urban scales.Key PointsPoor detectability of fossil fuel CO2 emissions from subcontinental regionsDetectability assessed via attribution of emissions patterns in atmospheric dataLoss in detectability due to transport modeling errors and biospheric signal

Can we evaluate a fine-grained emission model using high-resolution atmospheric transport modelling and regional fossil fuel CO2 observations?

Quantifying carbon dioxide emissions from fossil fuel burning (FFCO2) is a crucial task to assess continental carbon fluxes and to track anthropogenic emissions changes in the future. In the present study, we investigate potentials and challenges when combining observational data with simulations using high-resolution atmospheric transport and emission modelling. These challenges concern, for example, erroneous vertical mixing or uncertainties in the disaggregation of national total emissions to higher spatial and temporal resolution. In our study, the hourly regional fossil fuel CO2 offset (ΔFFCO2) is simulated by transporting emissions from a 5 min×5 min emission model (IER2005) that provides FFCO2 emissions from different emission categories. Our Lagrangian particle dispersion model (STILT) is driven by 25 km×25 km meteorological data from the European Center for Medium-Range Weather Forecast (ECMWF). We evaluate this modelling framework (STILT/ECMWF+IER2005) for the year 2005 using hourly ΔFFCO2 estimates derived from 14C, CO and 222Radon (222Rn) observations at an urban site in south-western Germany (Heidelberg). Analysing the mean diurnal cycles of ΔFFCO2 for different seasons, we find that the large seasonal and diurnal variation of emission factors used in the bottom-up emission model (spanning one order of magnitude) are adequate. Furthermore, we show that the use of 222Rn as an independent tracer helps to overcome problems in timing as well as strength of the vertical mixing in the transport model. By applying this variability correction, the model-observation agreement is significantly improved for simulated ΔFFCO2. We found a significant overestimation of ΔFFCO2 concentrations during situations where the air masses predominantly originate from densely populated areas. This is most likely caused by the spatial disaggregation methodology for the residential emissions, which to some extent relies on a constant per capita-based distribution. In the case of domestic heating emissions, this does not appear to be sufficient.