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.

/pdf/errors-and-uncertainties-in-a-gridded-carbon-dioxide-f5w5bxpuaw.pdf

Errors and uncertainties in a gridded carbon dioxide emissions inventory

Abstract. The version 10 (v10) Atmospheric Carbon Observations from Space (ACOS) Level 2 full-physics (L2FP) retrieval algorithm has been applied to multiyear records of observations from NASA's Orbiting Carbon Observatory 2 and 3 sensors (OCO-2 and OCO-3, respectively) to provide estimates of the carbon dioxide (CO2) column-averaged dry-air mole fraction (XCO2). In this study, a number of improvements to the ACOS v10 L2FP algorithm are described. The post-processing quality filtering and bias correction of the XCO2 estimates against multiple truth proxies are also discussed. The OCO v10 data volumes and XCO2 estimates from the two sensors for the time period of August 2019 through February 2022 are compared, highlighting differences in spatiotemporal sampling but demonstrating broad agreement between the two sensors where they overlap in time and space. A number of evaluation sources applied to both sensors suggest they are broadly similar in data and error characteristics. Mean OCO-3 differences relative to collocated OCO-2 data are approximately 0.2 and −0.3 ppm for land and ocean observations, respectively. Comparison of XCO2 estimates to collocated Total Carbon Column Observing Network (TCCON) measurements shows root mean squared errors (RMSEs) of approximately 0.8 and 0.9 ppm for OCO-2 and OCO-3, respectively. An evaluation against XCO2 fields derived from atmospheric inversion systems that assimilated only near-surface CO2 observations, i.e., did not assimilate satellite CO2 measurements, yielded RMSEs of 1.0 and 1.1 ppm for OCO-2 and OCO-3, respectively. Evaluation of uncertainties in XCO2 over small areas, as well as XCO2 biases across land–ocean crossings, also indicates similar behavior in the error characteristics of both sensors. Taken together, these results demonstrate a broad consistency of OCO-2 and OCO-3 XCO2 measurements, suggesting they may be used together for scientific analyses.


Evaluating the consistency between OCO-2 and OCO-3 XCO2 estimates derived from the NASA ACOS version 10 retrieval algorithm

The development of high-resolution greenhouse gas (GHG) inventories is an important step towards emission reduction in different sectors. However, most of the spatially explicit approaches that have been developed to date produce outputs at a coarse resolution or do not disaggregate the data by sector. In this study, we present a methodology for assessing GHG emissions from the residential sector by settlements at a fine spatial resolution. In many countries, statistical data about fossil fuel consumption is only available at the regional or country levels. For this reason, we assess energy demand for cooking and water and space heating for each settlement, which we use as a proxy to disaggregate regional fossil fuel consumption data. As energy demand for space heating depends heavily on climatic conditions, we use the heating degree day method to account for this phenomenon. We also take the availability of energy sources and differences in consumption patterns between urban and rural areas into account. Based on the disaggregated data, we assess GHG emissions at the settlement level using country and regional specific coefficients for Poland and Ukraine, two neighboring countries with different energy usage patterns. In addition, we estimate uncertainties in the results using a Monte Carlo method, which takes uncertainties in the statistical data, calorific values, and emission factors into account. We use detailed data on natural gas consumption in Poland and biomass consumption for several regions in Ukraine to validate our approach. We also compare our results to data from the EDGAR (Emissions Database for Global Atmospheric Research), which shows high agreement in places but also demonstrates the advantage of a higher resolution GHG inventory. Overall, the results show that the approach developed here is universal and can be applied to other countries using their statistical information.

/pdf/high-resolution-spatial-distribution-of-greenhouse-gas-yfpn1fdrah.pdf

High-resolution spatial distribution of greenhouse gas emissions in the residential sector

A growing constellation of satellites is providing near-global coverage of column-averaged CO2 observations. Launched in 2019, NASA's OCO-3 instrument is set to provide XCO2 observations at a high spatial and temporal resolution for regional domains (100 × 100 km). The atmospheric column version of the Stochastic Time-Inverted Lagrangian Transport (X-STILT) model is an established method of determining the influence of upwind sources on column measurements of the atmosphere, providing a means of analysis for current OCO-3 observations and future space-based column-observing missions. However, OCO-3 is expected to provide hundreds of soundings per targeted observation, straining this already computationally intensive technique. This work proposes a novel scheme to be used with the X-STILT model to generate upwind influence footprints with less computational expense. The method uses X-STILT generated influence footprints from a key subset of OCO-3 soundings. A nonlinear weighted averaging is applied to these footprints to construct additional footprints for the remaining soundings. The effects of subset selection, meteorological data, and topography are investigated for two test sites: Los Angeles, California, and Salt Lake City, Utah. The computational time required to model the source sensitivities for OCO-3 interpretation was reduced by 62% and 78% with errors smaller than other previously acknowledged uncertainties in the modeling system (OCO-3 retrieval error, atmospheric transport error, prior emissions error, etc.). Limitations and future applications for future CO2 missions are also discussed.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2020EA001343

An Interpolation Method to Reduce the Computational Time in the Stochastic Lagrangian Particle Dispersion Modeling of Spatially Dense XCO 2 Retrievals.

US Cities in the Dark

A crucial aspect of constructing a gridded model of anthropogenic fossil fuel CO2 (FFCO2) emissions involves careful consideration of uncertainty. Both the spatial resolution of the emissions estimates (grid scale) and the selection of proxy data to represent the spatial distribution of emissions, plus the quality of data on point sources of emissions, have important impacts on uncertainty. In earlier papers, we explored the uncertainties associated with grid selection and the available data on large point sources. In this work CO2 emissions data are spatially distributed using population density as the selected proxy, using three different treatments of large point sources, and with five levels of grid resolution (1o, 2o, 3o, 4o, and 5o). The methods of calculating uncertainty associated with grid size, proxy selection, and reported point-source emissions data are presented, with particular attention being drawn to grid size selection. We find that as the resolution becomes coarser, relative uncertainty (total uncertainty as a percentage of total emissions) at the grid cell level decreases. Relative uncertainty in most grid cells decreases as the portion of emissions attributed to specific point sources increases. Good data on large point sources is very important for spatially explicit emissions inventories.

Gridded estimates of CO 2 emissions: uncertainty as a function of grid size

The concentration of carbon dioxide (CO2) in Earth's atmosphere is increasing due to human activities and the resulting effects on the global climate system have initiated several policy‐driven approaches to reduce emissions of this greenhouse gas. Quantifying the effectiveness of such policies requires both bottom‐up and top‐down approaches to estimate CO2 emissions. This work investigates, for the first time, the potential of using Snapshot Area Map observations from NASA's OCO‐3 instrument to disaggregate sector‐specific emissions from instrument observations. Optimized sector‐specific timeseries were produced using Bayesian inversion techniques and compared to proxy activity data from transportation, commercial maritime, and industrial sectors in the Los Angeles Basin. Results demonstrate that dense space‐based observations of atmospheric CO2 are capable of disentangling sector‐specific CO2 fluxes, paving the way for accurate monitoring of the effects of carbon‐reduction policies and operational carbon monitoring systems.

Constraining Sector‐Specific CO2 Fluxes Using Space‐Based XCO2 Observations Over the Los Angeles Basin

. Bottom-up accounting methods of carbon dioxide ( CO 2 ) emissions can provide high-resolution emissions estimates at a global scale; however, the necessary in situ observations to verify these emissions are limited in coverage. Space-based observations of CO 2 in the Earth’s atmosphere expand this coverage to a near-global scale to inform carbon cycle science and record emission trends. This work applied an observing system simulation experiment (OSSE) to characterize the flux information contained in “Snapshot Area Map” (SAM) CO 2 measurements from the Orbiting Carbon Observatory-3 (OCO- 5 3). Unlike previous space-based carbon-observing systems, OCO-3 SAMs provide spatially dense observations of CO 2 over targeted urban areas at unprecedented coverage. A Bayesian inversion using synthetic data was applied to these SAMs to explore their effectiveness in optimizing estimates of fossil fuel CO 2 ( FFCO 2 ) emissions from the Los Angeles Basin. Results demonstrated that errors in the locations of large point sources diminished the inversion’s ability to reduce errors at the sub-city-level. Furthermore, reductions in atmospheric transport error exacerbated these issues. Only after geolocation errors in 10 large point source locations were removed and atmospheric transport error was reduced did individual SAM observations provide modest corrections to prior flux estimates. The aggregation of multiple SAMs proved to be effective in reducing systematic errors in manufacturing- and transportation-related estimates, demonstrating the need for similar measurements in future space-based X t was a square matrix containing the temporal differences between each backwards timestep associated with each SAM’s analysis. An autocorrelation function (ACF) was generated for each SAM by comparing the differences in total CO 2 300 between the prior and “ true ” inventories across the relevant timesteps. The number of significant timesteps ( p < 0 . 05 ) from

The Information Content of Dense Carbon Dioxide Measurements from Space: A High-Resolution Inversion Approach with Synthetic Data from the OCO-3 Instrument

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Gridded estimates of CO 2 emissions: uncertainty as a function of grid size

Constraining Sector‐Specific CO2 Fluxes Using Space‐Based XCO2 Observations Over the Los Angeles Basin

The Information Content of Dense Carbon Dioxide Measurements from Space: A High-Resolution Inversion Approach with Synthetic Data from the OCO-3 Instrument