Impacts of different biomass burning emission inventories: Simulations of atmospheric CO2 concentrations based on GEOS-Chem

Biomass burning has substantial spatiotemporal variabilities. It contributes significantly to the dynamics of global CO₂ distributions and variances. Quantifying the impacts of biomass burning emissions on atmospheric CO₂ concentrations is essential for global and regional carbon cycles and budgets. In this study, we performed several numerical experiments by switching and replacing inventories to estimate the impacts of four biomass burning emission inventories on atmospheric CO₂ concentration simulations in 2006-2010 based on the global chemical transport model, GEOS-Chem. The results highlighted similarities and differences in the annual and seasonal variability of biomass burning emissions and simulated CO₂ concentrations at global and regional scales. Based on four different biomass burning emission inventories, we found that biomass burning emissions could lead to a global CO₂ concentration increase of 2.4 ppm annually. Africa contributed the largest global CO₂ emissions among all continental regions, where the maximum CO₂ concentration increase could reach 7.9-13.0 ppm in summer. Model evaluation results showed that simulation using the Quick Fire Emissions Database (QFED) as the model priori biomass burning emission inventory had the best performance compared with the satellite and surface observations. The sensitivity of simulated CO₂ concentrations to the uncertainties in different biomass burning emission inventories was high in southern South America and most areas of the Eurasian continent, and low in central Africa and Southeast Asia. This study furthers our understanding of the critical role of biomass burning in atmospheric CO₂ and indicates an urgent need to improve the accuracy of biomass burning emission estimates in CO₂ simulations.

Spatiotemporal Variations and Uncertainty in Crop Residue Burning Emissions over North China Plain: Implication for Atmospheric CO2 Simulation

Large uncertainty exists in the estimations of greenhouse gases and aerosol emissions from crop residue burning, which could be a key source of uncertainty in quantifying the impact of agricultural fire on regional air quality. In this study, we investigated the crop residue burning emissions and their uncertainty in North China Plain (NCP) using three widely used methods, including statistical-based, burned area-based, and fire radiative power-based methods. The impacts of biomass burning emissions on atmospheric carbon dioxide (CO2) were also examined by using a global chemical transport model (GEOS-Chem) simulation. The crop residue burning emissions were found to be high in June and followed by October, which is the harvest times for the main crops in NCP. The estimates of CO2 emission from crop residue burning exhibits large interannual variation from 2003 to 2019, with rapid growth from 2003 to 2012 and a remarkable decrease from 2013 to 2019, indicating the effects of air quality control plans in recent years. Through Monte Carlo simulation, the uncertainty of each estimation was quantified, ranging from 20% to 70% for CO2 emissions at the regional level. Concerning spatial uncertainty, it was found that the crop residue burning emissions were highly uncertain in small agricultural fire areas with the maximum changes of up to 140%. While in the areas with large agricultural fire, i.e., southern parts of NCP, the coefficient of variation mostly ranged from 30% to 100% at the gridded level. The changes in biomass burning emissions may lead to a change of surface CO2 concentration during the harvest times in NCP by more than 1.0 ppmv. The results of this study highlighted the significance of quantifying the uncertainty of biomass burning emissions in a modeling study, as the variations of crop residue burning emissions could affect the emission-driven increases in CO2 and air pollutants during summertime pollution events by a substantial fraction in this region.

Contributions of economic growth, terrestrial sinks, and atmospheric transport to the increasing atmospheric CO2 concentrations over the Korean Peninsula

BACKGROUND

Understanding a carbon budget from a national perspective is essential for establishing effective plans to reduce atmospheric CO₂ growth. The national characteristics of carbon budgets are reflected in atmospheric CO₂ variations; however, separating regional influences on atmospheric signals is challenging owing to atmospheric CO₂ transport. Therefore, in this study, we examined the characteristics of atmospheric CO₂ variations over South and North Korea during 2000-2016 and unveiled the causes of their regional differences in the increasing rate of atmospheric CO₂ concentrations by utilizing atmospheric transport modeling.

RESULTS

The atmospheric CO₂ concentration in South Korea is rising by 2.32 ppm year^- 1, which is more than the globally-averaged increase rate of 2.05 ppm year^- 1. Atmospheric transport modeling indicates that the increase in domestic fossil energy supply to support manufacturing export-led economic growth leads to an increase of 0.12 ppm year^- 1 in atmospheric CO₂ in South Korea. Although enhancements of terrestrial carbon uptake estimated from both inverse modeling and process-based models have decreased atmospheric CO₂ by up to 0.02 ppm year^- 1, this decrease is insufficient to offset anthropogenic CO₂ increases. Meanwhile, atmospheric CO₂ in North Korea is also increasing by 2.23 ppm year^- 1, despite a decrease in national CO₂ emissions close to carbon neutrality. The great increases estimated in both South Korea and North Korea are associated with changes in atmospheric transport, including increasing emitted and transported CO₂ from China, which have increased the national atmospheric CO₂ concentrations by 2.23 ppm year^- 1 and 2.27 ppm year^- 1, respectively.

CONCLUSIONS

This study discovered that economic activity is the determinant of regional differences in increasing atmospheric CO₂ in the Korea Peninsula. However, from a global perspective, changes in transported CO₂ are a major driver of rising atmospheric CO₂ over this region, yielding an increase rate higher than the global mean value. Our findings suggest that accurately separating the contributions of atmospheric transport and regional sources to the increasing atmospheric CO₂ concentrations is important for developing effective strategies to achieve carbon neutrality at the national level.

Validation of GOSAT and OCO-2 against In Situ Aircraft Measurements and Comparison with CarbonTracker and GEOS-Chem over Qinhuangdao, China

Carbon dioxide (CO2) is the most important greenhouse gas and several satellites have been launched to monitor the atmospheric CO2 at regional and global scales. Evaluation of the measurements obtained from these satellites against accurate and precise instruments is crucial. In this work, aircraft measurements of CO2 were carried out over Qinhuangdao, China (39.9354°N, 119.6005°E), on 14, 16, and 19 March 2019 to validate the Greenhous gases Observing SATellite (GOSAT) and the Orbiting Carbon Observatory 2 (OCO-2) CO2 retrievals. The airborne in situ instruments were mounted on a research aircraft and the measurements were carried out between the altitudes of ~0.5 and 8.0 km to obtain the vertical profiles of CO2. The profiles captured a decrease in CO2 concentration from the surface to maximum altitude. Moreover, the vertical profiles from GEOS-Chem and the National Oceanic and Atmospheric Administration (NOAA) CarbonTracker were also compared with in situ and satellite datasets. The satellite and the model datasets captured the vertical structure of CO2 when compared with in situ measurements, which showed good agreement among the datasets. The dry-air column-averaged CO2 mole fractions (XCO2) retrieved from OCO-2 and GOSAT showed biases of 1.33 ppm (0.32%) and −1.70 ppm (−0.41%), respectively, relative to the XCO2 derived from in situ measurements.

Anthropogenic CH4 Emissions in the Yangtze River Delta Based on A “Top-Down” Method

There remains significant uncertainty in the estimation of anthropogenic CH4 emissions at local and regional scales. We used atmospheric CH4 and CO2 concentration data to constrain the anthropogenic CH4 emission in the Yangtze River Delta one of the most populated and economically important regions in China. The observation of atmospheric CH4 and CO2 concentration was carried out from May 2012 to April 2017 at a rural site. A tracer correlation method was used to estimate the anthropogenic CH4 emission in this region, and compared this “top-down” estimate with that obtained with the IPCC inventory method. The annual growth rates of the atmospheric CO2 and CH4 mole fractions are 2.5 ± 0.7 ppm year−1 and 9.5 ± 4.7 ppb year−1, respectively, which are 9% and 53% higher than the values obtained at Waliguan (WLG) station. The average annual anthropogenic CH4 emission is 4.37 (± 0.61) × 109 kg in the YRD (excluding rice cultivation). This “top-down” estimate is 20–70% greater than the estimate based on the IPCC method. We suggest that possible sources for the discrepancy include low biases in the IPCC calculation of emission from landfills, ruminants and the transport sector.

An Assessment of Anthropogenic CO₂ Emissions by Satellite-Based Observations in China

Carbon dioxide (CO₂) is the most important anthropogenic greenhouse gas and its concentration in atmosphere has been increasing rapidly due to the increase of anthropogenic CO₂ emissions. Quantifying anthropogenic CO₂ emissions is essential to evaluate the measures for mitigating climate change. Satellite-based measurements of greenhouse gases greatly advance the way of monitoring atmospheric CO₂ concentration. In this study, we propose an approach for estimating anthropogenic CO₂ emissions by an artificial neural network using column-average dry air mole fraction of CO₂ (XCO₂) derived from observations of Greenhouse gases Observing SATellite (GOSAT) in China. First, we use annual XCO₂ anomalies (dXCO₂) derived from XCO₂ and anthropogenic emission data during 2010⁻2014 as the training dataset to build a General Regression Neural Network (GRNN) model. Second, applying the built model to annual dXCO₂ in 2015, we estimate the corresponding emission and verify them using ODIAC emission. As a results, the estimated emissions significantly demonstrate positive correlation with that of ODIAC CO₂ emissions especially in the areas with high anthropogenic CO₂ emissions. Our results indicate that XCO₂ data from satellite observations can be applied in estimating anthropogenic CO₂ emissions at regional scale by the machine learning. This developed method can estimate carbon emission inventory in a data-driven way. In particular, it is expected that the estimation accuracy can be further improved when combined with other data sources, related CO₂ uptake and emissions, from satellite observations.

Ratios of greenhouse gas emissions observed over the Yellow Sea and the East China Sea

During a cruise of the survey vessel Dongfanghong II on the Yellow Sea and the East China Sea in the spring of 2017 we performed accurate measurements of the mole fractions of carbon dioxide (CO₂), methane (CH₄), carbon monoxide (CO) and nitrous oxide (N₂O) using two types of Cavity Ring-Down Spectrometers (CRDS). The spatial variations of the mole fraction of the four trace gases were very similar. The emission sources of these gases were divided into several regions by using the NOAA HYSPLIT model. Then we analyzed the variations of the ratios of the mole fraction enhancements between every pair of trace gases downwind of these source areas. The ratios showed that the distributions of these trace gases over the Yellow Sea and the East China Sea in the spring were mainly caused by the emissions from Eastern China. The much higher enhancement ratio of ΔCO/ΔCO₂ and the lower ratio of ΔCH₄/ΔCO observed in the air parcels from big cities like Beijing and Shanghai indicated high CO emission from the cities during our time of observation. Compared with the values of NOAA's Marine Boundary Layer (MBL), the ratios of the averages in the air coming from the Northern sector (Russia) were on average closer to the MBL, and the air that stayed over the Yellow Sea and the East China Sea was a mixture of emissions from wide regional areas. The highly variable N₂O data of the air from Qingdao and Shanghai showed much more fluctuation.

A comparison of satellite observations with the XCO2 surface obtained by fusing TCCON measurements and GEOS-Chem model outputs

Ground observations can capture CO₂ concentrations accurately but the number of available TCCON (Total Carbon Column Observing Network) sites is too small to support a comprehensive analysis (i.e. validation) of satellite observations. Atmospheric transport models can provide continuous atmospheric CO₂ concentrations in space and time, but some information is difficult to generate with model simulations. The HASM platform can model continuous column-averaged CO₂ dry air mole fraction (XCO₂) surface taking TCCON observations as its optimum control constraints and an atmospheric transport model as its driving field. This article presents a comparison of the satellite observations with a HASM XCO₂ surface obtained by fusing TCCON measurements with GEOS-Chem model results. We first verified the accuracy of the HASM XCO₂ surface using six years (2010-2015) of TCCON observations and the GEOS-Chem model XCO₂ results. The validation results show that the largest MAE of bias between the HASM results and observations was 0.85ppm and the smallest MAE was only 0.39ppm. Next, we modeled the HASM XCO₂ surface by fusing the TCCON measurements and GEOS-Chem XCO₂ model results for the period 9/1/14 to 8/31/15. Finally, we compared the GOSAT and OCO-2 observations with the HASM XCO₂ surface and found that the global OCO-2 XCO₂ estimates more closely resembled the HASM XCO₂ surface than the GOSAT XCO₂ estimates.

Separating the influence of temperature, drought, and fire on interannual variability in atmospheric CO2

The response of the carbon cycle in prognostic Earth system models (ESMs) contributes significant uncertainty to projections of global climate change. Quantifying contributions of known drivers of interannual variability in the growth rate of atmospheric carbon dioxide (CO2) is important for improving the representation of terrestrial ecosystem processes in these ESMs. Several recent studies have identified the temperature dependence of tropical net ecosystem exchange (NEE) as a primary driver of this variability by analyzing a single, globally averaged time series of CO2 anomalies. Here we examined how the temporal evolution of CO2 in different latitude bands may be used to separate contributions from temperature stress, drought stress, and fire emissions to CO2 variability. We developed atmospheric CO2 patterns from each of these mechanisms during 1997-2011 using an atmospheric transport model. NEE responses to temperature, NEE responses to drought, and fire emissions all contributed significantly to CO2 variability in each latitude band, suggesting that no single mechanism was the dominant driver. We found that the sum of drought and fire contributions to CO2 variability exceeded direct NEE responses to temperature in both the Northern and Southern Hemispheres. Additional sensitivity tests revealed that these contributions are masked by temporal and spatial smoothing of CO2 observations. Accounting for fires, the sensitivity of tropical NEE to temperature stress decreased by 25% to 2.9 ± 0.4 Pg C yr-1 K-1. These results underscore the need for accurate attribution of the drivers of CO2 variability prior to using contemporary observations to constrain long-term ESM responses.

Toward verifying fossil fuel CO2 emissions with the CMAQ model: motivation, model description and initial simulation

Motivated by the question of whether and how a state-of-the-art regional chemical transport model (CTM) can facilitate characterization of CO2 spatiotemporal variability and verify CO2 fossil-fuel emissions, we for the first time applied the Community Multiscale Air Quality (CMAQ) model to simulate CO2. This paper presents methods, input data, and initial results for CO2 simulation using CMAQ over the contiguous United States in October 2007. Modeling experiments have been performed to understand the roles of fossil-fuel emissions, biosphere-atmosphere exchange, and meteorology in regulating the spatial distribution of CO2 near the surface over the contiguous United States. Three sets of net ecosystem exchange (NEE) fluxes were used as input to assess the impact of uncertainty of NEE on CO2 concentrations simulated by CMAQ. Observational data from six tall tower sites across the country were used to evaluate model performance. In particular, at the Boulder Atmospheric Observatory (BAO), a tall tower site that receives urban emissions from Denver CO, the CMAQ model using hourly varying, high-resolution CO2 fossil-fuel emissions from the Vulcan inventory and Carbon Tracker optimized NEE reproduced the observed diurnal profile of CO2 reasonably well but with a low bias in the early morning. The spatial distribution of CO2 was found to correlate with NO(x), SO2, and CO, because of their similar fossil-fuel emission sources and common transport processes. These initial results from CMAQ demonstrate the potential of using a regional CTM to help interpret CO2 observations and understand CO2 variability in space and time. The ability to simulate a full suite of air pollutants in CMAQ will also facilitate investigations of their use as tracers for CO2 source attribution. This work serves as a proof of concept and the foundation for more comprehensive examinations of CO2 spatiotemporal variability and various uncertainties in the future.

IMPLICATIONS

Atmospheric CO2 has long been modeled and studied on continental to global scales to understand the global carbon cycle. This work demonstrates the potential of modeling and studying CO2 variability at fine spatiotemporal scales with CMAQ, which has been applied extensively, to study traditionally regulated air pollutants. The abundant observational records of these air pollutants and successful experience in studying and reducing their emissions may be useful for verifying CO2 emissions. Although there remains much more to further investigate, this work opens up a discussion on whether and how to study CO2 as an air pollutant.

Quantifying global terrestrial methanol emissions using observations from the TES satellite sensor

We employ new global space-based measurements of atmospheric methanol from the Tropospheric Emission Spectrometer (TES) with the adjoint of the GEOS-Chem chemical transport model to quantify terrestrial emissions of methanol to the atmosphere. Biogenic methanol emissions in the model are based on version 2.1 of the Model of Emissions of Gases and Aerosols from Nature (MEGANv2.1), using leaf area data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) and GEOS-5 assimilated meteorological fields. We first carry out a pseudo observation test to validate the overall approach, and find that the TES sampling density is sufficient to accurately quantify regional- to continental-scale methanol emissions using this method. A global inversion of two years of TES data yields an optimized annual global surface flux of 122 Tg yr^-1 (including biogenic, pyrogenic, and anthropogenic sources), an increase of 60 % from the a priori global flux of 76 Tg yr^-1. Global terrestrial methanol emissions are thus nearly 25 % those of isoprene (~540 Tg yr^-1), and are comparable to the combined emissions of all anthropogenic volatile organic compounds (~100-200 Tg yr^-1). Our a posteriori terrestrial methanol source leads to a strong improvement of the simulation relative to an ensemble of airborne observations, and corroborates two other recent top-down estimates (114-120 Tg yr^-1) derived using in situ and space-based measurements. Inversions testing the sensitivity of optimized fluxes to model errors in OH, dry deposition, and oceanic uptake of methanol, as well as to the assumed a priori constraint, lead to global fluxes ranging from 118 to 126 Tg yr^-1. The TES data imply a relatively modest revision of model emissions over most of the tropics, but a significant upward revision in midlatitudes, particularly over Europe and North America. We interpret the inversion results in terms of specific source types using the methanol : CO correlations measured by TES, and find that biogenic emissions are overestimated relative to biomass burning and anthropogenic emissions in central Africa and southeastern China, while they are underestimated in regions such as Brazil and the US. Based on our optimized emissions, methanol accounts for > 25 % of the photochemical source of CO and HCHO over many parts of the northern extratropics during springtime, and contributes ~6 % of the global secondary source of those compounds annually.

Carbon monoxide and carbon dioxide concentrations in Santiago de Chile associated with traffic emissions

CO/CO(2) ratios have been measured in different locations of Santiago de Chile city. Measurements were carried out in a tunnel (prevailing emissions from cars with catalytic converter) and close to heavy traffic streets. Concentrations measured along the city traffic tunnel or temporal profiles of concentrations measured near heavy traffic streets allow an estimation of CO/CO(2) ratios emitted from mobile sources. Values obtained range from 0.0045 +/- 0.0006 to 0.0100 +/- 0.0004 and depend on the prevailing type of mobile sources. In particular, lowest values were found close to a street with heavy traffic dominated by diesel-powered public transportation, while the highest values were found at the city tunnel. Places located near streets of mixed mobile sources (public buses and cars) showed intermediate values. Average CO/CO(2) ratios are compatible with emission factors proposed for Santiago's main mobile sources.

Global partitioning of NOx sources using satellite observations: Relative roles of fossil fuel combustion, biomass burning and soil emissions

We use space-based observations of NO2 columns from the Global Ozone Monitoring Experiment (GOME) to derive monthly top-down NOx emissions for 2000 via inverse modeling with the GEOS-CHEM chemical transport model. Top-down NOx sources are partitioned among fuel combustion (fossil fuel and biofuel), biomass burning and soils by exploiting the spatio-temporal distribution of remotely sensed fires and a priori information on the location of regions dominated by fuel combustion. The top-down inventory is combined with an a priori inventory to obtain an optimized a posteriori estimate of the relative roles of NOx sources. The resulting a posteriori fuel combustion inventory (25.6 TgN year(-1)) agrees closely with the a priori (25.4 TgN year(-1)), and errors are reduced by a factor of 2, from +/- 80% to +/- 40%. Regionally, the largest differences are found over Japan and South Africa, where a posteriori estimates are 25% larger than a priori. A posteriori fuel combustion emissions are aseasonal, with the exception of East Asia and Europe where winter emissions are 30-40% larger relative to summer emissions, consistent with increased energy use during winter for heating. Global a posteriori biomass burning emissions in 2000 resulted in 5.8 TgN (compared to 5.9 TgN year(-1) in the a priori), with Africa accounting for half of this total. A posteriori biomass burning emissions over Southeast Asia/India are decreased by 46% relative to a priori; but over North equatorial Africa they are increased by 50%. A posteriori estimates of soil emissions (8.9 TgN year(-1)) are 68% larger than a priori (5.3 TgN year(-1)). The a posteriori inventory displays the largest soil emissions over tropical savanna/woodland ecosystems (Africa), as well as over agricultural regions in the western U.S. (Great Plains), southern Europe (Spain, Greece, Turkey), and Asia (North China Plain and North India), consistent with field measurements. Emissions over these regions are highest during summer at mid-latitudes and during the rainy season in the Tropics. We estimate that 2.5-4.5 TgN year(-1) are emitted from N-fertilized soils, at the upper end of previous estimates. Soil and biomass burning emissions account for 22% and 14% of global surface NOx emissions, respectively. We infer a significant role for soil NOx emissions at northern mid-latitudes during summer, where they account for nearly half that of the fuel combustion source, a doubling relative to the a priori. The contribution of soil emissions to background ozone is thus likely to be underestimated by the current generation of chemical transport models.