CO2 Mixing Ratios Research Articles

Spatial mapping of greenhouse gases using a UAV monitoring platform over a megacity in China

Urban environments are recognized as main anthropogenic contributors to greenhouse gas (GHG) emissions, characterized by unevenly distributed emission sources over the urban environments. However, spatial GHG distributions in urban regions are typically obtained through monitoring at only a limited number of locations, or through model studies, which can lead to incomplete insights into the heterogeneity in the spatial distribution of GHGs. To address such information gap and to evaluate the spatial representation of a planned GHG monitoring network, a custom-developed atmospheric sampler was deployed on a UAV platform in this study to map the CO2 and CH4 mixing ratios in the atmosphere over Zhengzhou in central China, a megacity of nearly 13 million people. The aerial survey was conducted along the main roads at an altitude of 150 m above ground, covering a total distance of 170 km from the city center to the suburbs. The spatial distributions of CO2 and CH4 mixing ratios in Zhengzhou exhibited distinct heterogeneities, with average mixing ratios of CO2 and CH4 at 439.2 ± 10.8 ppm and 2.12 ± 0.04 ppm, respectively. A spatial autocorrelation analysis was performed on the measured GHG mixing ratios across the city, revealing a spatial correlation range of approximately 2 km for both CO2 and CH4 in the urban area. Such a spatial autocorrelation distance suggests that the urban GHG monitoring network designed for emission inversion purposes need to have a spatial resolution of 4 km to characterize the spatial heterogeneities in the GHGs. This UAV-based measurement approach demonstrates its capability to monitor GHG mixing ratios across urban landscapes, providing valuable insights for GHG monitoring network design.

Carbon Monoxide in the Marine Atmosphere and Seawater: Spatiotemporal Distribution and Photobiogeochemical Cycling

AbstractCarbon monoxide (CO) plays a vital role in the biochemical processes of marine microorganisms, climate regulation, and global carbon cycle. In this study, seasonal and spatial variations of CO and oceanographic factors influencing these variations were investigated in the East China Sea (ECS) during two cruises from 16 to 30 May 2020 and from 8 to 30 October 2021. ECS was as a net source of atmospheric CO in both spring and autumn. The average values of seawater CO concentrations ([CO]surf), atmospheric CO mixing ratio ([CO]atm) and sea‐to‐air flux of CO were 2.22 ± 0.47 nmol L−1 and 0.97 ± 0.86 nmol L−1, 182.8 ± 71.8 ppbv and 102.7 ± 38.8 ppbv, 96.14 ± 25.82 nmol m−2 hr−1 and 31.96 ± 35.61 nmol m−2 hr−1 in spring and autumn, respectively. A steady‐state model was used to estimate the biogeochemical cycling rates of [CO]atm over the ECS. The effect of CO sea‐to‐air fluxes on atmospheric ·OH concentration was likely decreased from spring to autumn. Photoirradiation experiments further showed that the actual contributions of ultraviolet radiation and photosynthetically active radiation to CO photoproduction in the surface waters were 72.2% ± 13.8% and 27.8% ± 13.8%, respectively. The sea‐to‐air fluxes of CO in autumn were 5.15 μmol m−2 day−1 lower than in spring, which were due to larger bacterial consumption and lower [CO]surf in autumn in the ECS. Further, this study is expected to improve our understanding of the biogeochemical processes of CO in the eastern marginal seas of China.

Exploring how the heterogeneous urban landscape influences CO2 concentrations: The case study of the Metropolitan Area of Barcelona

Monitoring CO2 concentrations in urban areas is crucial for determining the efficacy of climate change mitigation policies. However, highly heterogeneous land use, local geography, and local convection patterns, which vary throughout the urban landscape, complicate this task. To establish continuous monitoring programs, it is important to first determine the heterogeneity of urban landscapes on the ground. To understand the role these factors play in the distribution of CO2 over an urban area, we conducted a CO2 measurement campaign over the Metropolitan Area of Barcelona (AMB) over four urban land uses: impervious, green, forest, and agricultural. There is a clear tendency for CO2 mixing ratios to decrease as the degree of urban vegetation increases, even in the midst of a developed boundary layer. For example, CO2 concentrations were 429 and 427 ppm at forest and agricultural sites, respectively, while 485 ppm was reported at urban sites. A decrease in atmospheric CO2 was observed from 458 to 428 ppm in the gradient from urban to suburban areas, in which the biosphere component increased. The biosphere component of the CO2 signal was significant and was observed in the gradient from urban to suburban areas, which averaged a reduction from 458 to 428 ppm. Our findings show that the large spatial variability in CO2 concentrations (ranging from 410 to 495 ppm) is best explained by anthropogenic activity. We propose increasing the spatiotemporal resolution of CO2 monitoring in the AMB to determine these trends more precisely over longer periods of time.

How Critical Is the Accuracy of the Atmospheric Transport Modeling to Improve the Urban CO2 Emission in India?—A Lagrangian‐Based Approach

AbstractAnthropogenic CO2 emission reduction strategies depend on how well we track emission enhancements at the urban scale. The estimation system, based on inverse modeling, relies on our knowledge of atmospheric transport and prior flux distributions. Hence, the analysis framework must account for uncertainties associated with each component in order to interpret the variations in observed CO2. Using an ensemble of simulations, we quantify the uncertainties in simulating anthropogenic CO2 mixing ratio enhancements at 15 locations in India. Differences in the representation of transport mechanisms and prior emission in the forward model induce a consistently large model spread of 62.2% and 41.9%, respectively, in the simulated mixing ratio over cities. The analysis reports an average uncertainty of 2.2 ppm with a maximum of 8 ppm for representing diurnally averaged anthropogenic CO2 enhancement. Diurnal variations in emissions and transport induce a rectification effect in those enhancements. The outcome of this study can thus inform future atmospheric CO2 inversion modeling at an urban scale on the expected forward model uncertainties, which are the essential components in the Bayesian inversion framework, typically lacking in the Indian region. The first‐order inversion experiments show that the change in the transport model induces significant uncertainty (up to 84.9%) in anthropogenic CO2 flux estimation at the national scale. Hence, the confidence level of inverse‐based emission estimation in India depends considerably on the accuracy of atmospheric transport modeling.

Characteristics of methane and carbon dioxide in ice caves at a high-mountain glacier of China

The exploration of the spatiotemporal distribution of greenhouse gas (GHG) exchange in the cryosphere (including ice sheet, glaciers, and permafrost) is important for understanding its future feedback to the atmosphere. Mountain glaciers and ice sheets may be potential sources of GHG emissions, but the magnitude and distribution of GHG emissions from glaciers and ice sheets remain unclear because observation data are lacking. In this study, in situ CH4 and CO2 and the mixing ratios of their carbon isotope signatures in the air inside an ice cave were measured, and CH4 and CO2 exchange in the meltwater of Laohugou glacier No. 12, a high-mountain glacier in an arid region of western China, was also analyzed and compared with the exchange in downstream rivers and a reservoir. The results indicated elevated CH4 mixing ratios (up to 5.7 ppm) and depleted CO2 (down to 168 ppm) in the ice cave, compared to ambient levels during field observations. The CH4 and CO2 fluxes in surface meltwater of the glacier were extremely low compared with their fluxes in rivers from the Tibetan Plateau (TP). CH4 and CO2 mixing ratios in the air inside the ice cave were mainly controlled by local meteorological conditions (air temperature, wind speed and direction) and meltwater runoff. The carbon isotopic compositions of CH4 and CO2 in the ice cave and terminus meltwater indicated δ13C-CH4 depletion compared to ambient air, suggesting an acetate fermentation pathway. The abundances of key genes for methanogenic archaea/genes encoding methyl coenzyme M reductase further indicated the production of CH4 by methanogenic archaea from the subglacial meltwater of high-mountain glaciers. The discovery of CH4 emissions from even small high-mountain glaciers indicates a more prevalent characteristic of glaciers to produce and release CH4 from the subglacial environment than previously believed. Nevertheless, further research is required to understand the relationship between this phenomenon and glacial dynamics in the third pole.

Aerosol interference with open-path eddy covariance measurement in a lake environment

Open-path eddy covariance (OPEC) is widely used for measuring trace gas fluxes between the surface and the atmosphere. At a lake in Eastern China (Lake Taihu), the CO2 flux measured with OPEC was often negative at night (with values as low as −22.1 μmol m−2 s−1) and was coherent across the whole lake, as if the lake were a large sink of atmospheric CO2. The purpose of this study is to investigate the cause of this negative flux phenomenon. In addition to OPEC, we also used closed-path eddy covariance (CPEC) and the transfer coefficient (TC) method to measure the flux. The results show that the persistent negative CO2 flux phenomenon was observed with OPEC but not with CPEC or TC. Because air drawn into the CPEC analyzer was filtered but the OPEC analyzer was influenced by aerosol contamination, the most logical explanation was that particles deposited on the optical lens of the OPEC analyzer changed its cross-sensitivity to water vapor. The direct evidence of this interference was a strong positive correlation between the OPEC analyzer's signal strength and the CO2 mixing ratio observed at 10 Hz. We suggest that it is possible to perform post-field correction to this negative flux bias using the 10 Hz signal strength data. In comparison, an OPEC system at a nearby land site did not experience aerosol interference due to low water vapor flux at night and lack of hygroscopic growth of particles on the optical lens in low humidity conditions. The type of aerosol interference reported here may also occur in high humidity and high pollution conditions elsewhere.

Distribution of ozone, carbon monoxide and oxides of nitrogen over an urban location in the foothills of the North-Western Himalayas

Increasing anthropogenic activities and biomass burning events over the Northern Indian region severely affect the pristine environment of North-Western Himalayas. To investigate their influence in this data sparse region, continuous monitoring of O3, CO and NOx are made during January 2018 to December 2019 over Dehradun, an urban location in the foothills of North-western Himalayas. Ozone shows daytime broad peak and CO and NOx show two peaks during morning and evening hours. The maximum daytime ozone is observed during spring with 58.2 ± 2.0 ppbv (56.2 ± 2.7 ppbv) during 2018 (2019). CO and NOx show maximum value during winter and minimum during summer-monsoon. Their concentrations are found to be influenced from stubble burning and forest fire emissions during late spring. WRF-Chem simulations are made with and without biomass burning emissions to quantify the contribution of these emissions on the variability of these gases over Dehradun. Sensitivity simulations reveal that biomass burning over northern Indian region contributed 43 ppbv, 79 ppbv and 1.2 ppbv increase in O3, CO and NOx mixing ratios over Dehradun. In addition, dust storm event in June 2018 contributed for lower ozone levels in 2018 (16.1 ± 9.3 ppbv) and stratosphere troposphere exchange process enhanced surface ozone in June 2019 (53.6 ± 22.3 ppbv).

The Lagrangian Atmospheric Radionuclide Transport Model (ARTM) – sensitivity studies and evaluation using airborne measurements of power plant emissions

Abstract. The Atmospheric Radionuclide Transport Model (ARTM) operates at the meso-γ scale and simulates the dispersion of radionuclides originating from nuclear facilities under routine operation within the planetary boundary layer. This study presents the extension and validation of this Lagrangian particle dispersion model and consists of three parts: (i) a sensitivity study that aims to assess the impact of key input parameters on the simulation results, (ii) the evaluation of the mixing properties of five different turbulence models using the well-mixed criterion, and (iii) a comparison of model results to airborne observations of carbon dioxide (CO2) emissions from a power plant and the evaluation of related uncertainties. In the sensitivity study, we analyse the effects of the stability class, roughness length, zero-plane displacement factor, and source height on the three-dimensional plume extent as well as the distance between the source and maximum concentration at the ground. The results show that the stability class is the most sensitive input parameter as expected. The five turbulence models are the default turbulence models of ARTM 2.8.0 and ARTM 3.0.0, one alternative built-in turbulence model of ARTM, and two further turbulence models implemented for this study. The well-mixed condition tests showed that all five turbulence models are able to preserve an initially well-mixed atmospheric boundary layer reasonably well. The models deviate only 6 % from the expected uniform concentration below 80 % of the mixing layer height, except for the default turbulence model of ARTM 3.0.0 with deviations of up to 18 %. CO2 observations along a flight path in the vicinity of the lignite power plant Bełchatów, Poland, measured by the Deutsches Zentrum für Luft- und Raumfahrt (DLR) Cessna aircraft during the Carbon Dioxide and Methane Mission (CoMet) campaign in 2018 allowed for evaluation of model performance for the different turbulence models under unstable boundary layer conditions. All simulated mixing ratios are of the same order of magnitude as the airborne in situ data. An extensive uncertainty analysis using probability distribution functions, statistical tests, and direct spatio-temporal comparisons of measurements and model results help to quantify the model uncertainties. With the default turbulence setups of ARTM versions 2.8.0 and 3.0.0, the plume widths are underestimated by up to 50 %, resulting in a strong overestimation of the maximum plume CO2 mixing ratios. The comparison of the three alternative turbulence models shows good agreement of the peak plume CO2 concentrations, the CO2 distribution within the plumes, and the plume width, with a 30 % deviation in the peak CO2 concentration and a less than 25 % deviation in the measured CO2 plume width. Uncertainties in the simulations may arise from the different spatial and temporal resolutions of simulations and measurements in addition to the turbulence parametrisation and boundary conditions. The results of this work may help to improve the accurate representation of real plumes in very unstable atmospheric conditions through the selection of distinct turbulence models. Further comparisons at different stability regimes are required for a final assessment of model uncertainties.

Development of the tangent linear and adjoint models of the global online chemical transport model MPAS-CO2 v7.3

Abstract. We describe the development of the tangent linear (TL) and adjoint models of the Model for Prediction Across Scales (MPAS)-CO2 transport model, which is a global online chemical transport model developed upon the non-hydrostatic Model for Prediction Across Scales – Atmosphere (MPAS-A). The primary goal is to make the model system a valuable research tool for investigating atmospheric carbon transport and inverse modeling. First, we develop the TL code, encompassing all CO2 transport processes within the MPAS-CO2 forward model. Then, we construct the adjoint model using a combined strategy involving re-calculation and storage of the essential meteorological variables needed for CO2 transport. This strategy allows the adjoint model to undertake a long-period integration with moderate memory demands. To ensure accuracy, the TL and adjoint models undergo vigorous verifications through a series of standard tests. The adjoint model, through backward-in-time integration, calculates the sensitivity of atmospheric CO2 observations to surface CO2 fluxes and the initial atmospheric CO2 mixing ratio. To demonstrate the utility of the newly developed adjoint model, we conduct simulations for two types of atmospheric CO2 observations, namely the tower-based in situ CO2 mixing ratio and satellite-derived column-averaged CO2 mixing ratio (XCO2). A comparison between the sensitivity to surface flux calculated by the MPAS-CO2 adjoint model with its counterpart from CarbonTracker–Lagrange (CT-L) reveals a spatial agreement but notable magnitude differences. These differences, particularly evident for XCO2, might be attributed to the two model systems' differences in the simulation configuration, spatial resolution, and treatment of vertical mixing processes. Moreover, this comparison highlights the substantial loss of information in the atmospheric CO2 observations due to CT-L's spatial domain limitation. Furthermore, the adjoint sensitivity analysis demonstrates that the sensitivities to both surface flux and initial CO2 conditions spread out throughout the entire Northern Hemisphere within a month. MPAS-CO2 forward, TL, and adjoint models stand out for their calculation efficiency and variable-resolution capability, making them competitive in computational cost. In conclusion, the successful development of the MPAS-CO2 TL and adjoint models, and their integration into the MPAS-CO2 system, establish the possibility of using MPAS's unique features in atmospheric CO2 transport sensitivity studies and in inverse modeling with advanced methods such as variational data assimilation.

Cenozoic stable isotope constraints on the Eurasian continental interior hydroclimate response to high CO2

The response of the terrestrial hydrologic cycle to higher atmospheric CO2 remains poorly constrained, largely due to difficulty in predicting how land surface processes may modify individual hydroclimate parameters such as precipitation (P), evapotranspiration (ET), and runoff (q). To interrogate how the terrestrial hydrologic cycle may change with warming and higher CO2, we utilize the Cenozoic geologic record of terrestrial stable oxygen isotopes (δ18O) as recorded in authigenic minerals, which reflect precipitation δ18O (δ18Op). Values of δ18Op are sensitive to changes in terrestrial hydroclimate, including to the ratio of P/ET and to precipitable water. In short, decreasing P/ET or higher precipitable water produces shallower δ18Op gradients inland, whereas increasing P/ET or lower precipitable water results in steeper δ18Op continental gradients. We compile nearly 15,000 samples of authigenic carbonate and tooth enamel across Eurasia—the largest continental landmass—that span the Cenozoic Era to reconstruct the past spatial distribution and zonal (i.e. east-to-west) gradients of δ18Op. We find that, in epochs with higher CO2, zonal δ18Op gradients increase, suggesting an increase in P/ET across Eurasia despite the expected increase in precipitable water in greenhouse climates. We compare these results to δ18Op gradients simulated by an isotope-enabled climate model (iCESM) forced with both pre-industrial (PI) and 4xPI CO2 mixing ratios. Simulated δ18Op gradients shallow as CO2 rises, in contrast to the reconstructed δ18Op gradients. This data-model mismatch suggests either that Earth system feedbacks not represented in our iCESM simulations, such as changes in Northern Hemisphere ice extent and vegetation, may have fundamentally changed Northern Hemisphere hydroclimate or that land-surface processes under high CO2 climates may be mis-represented in iCESM.

Airborne lidar measurements of atmospheric CO2 column concentrations to cloud tops made during the 2017 ASCENDS/ABoVE campaign

Abstract. We measured the column-averaged atmospheric CO2 mixing ratio (XCO2) to a variety of cloud tops with an airborne pulsed multi-wavelength integrated path differential absorption (IPDA) lidar during NASA's 2017 ASCENDS/ABoVE airborne campaign. Measurements of height-resolved atmospheric backscatter profiles allow this lidar to retrieve XCO2 to cloud tops, as well as to the ground, with accurate knowledge of the photon path length. We validated these measurements with those from an onboard in situ CO2 sensor during spiral-down maneuvers. These lidar measurements were 2–3 times better than those from previous airborne campaigns due to our using a wavelength step-locked laser transmitter and a high-efficiency detector for this campaign. Precisions of 0.6 parts per million (ppm) were achieved for 10 s average measurements to mid-level clouds and 0.9 ppm to low-level clouds at the top of the planetary boundary layer. This study demonstrates the lidar's capability to fill in XCO2 measurement gaps in cloudy regions and to help resolve the vertical and horizontal distributions of atmospheric CO2. Future airborne campaigns and spaceborne missions with this capability can be used to improve atmospheric transport modeling, flux estimation and carbon data assimilation.

Development of continuous monitoring of ambient carbon dioxide using near-infrared quartz-enhanced photoacoustic spectroscopy

We demonstrate continuous measurement of carbon dioxide (CO2) in the atmosphere using quartz-enhanced photoacoustic spectroscopy (QEPAS) at 2.008 μm with a fiber-coupled distributed-feedback laser. The QEPAS sensor is composed of a quartz tuning fork with a resonance frequency f 0 of 12.4 kHz. The photoacoustic second-harmonic (2f) signal resulting from CO2 absorption was detected using a digital lock-in amplifier. The photoacoustic signal is proportional to the laser power. The limit of detection at an absorption line of 4980.13161 cm−1 with a line strength of 3.87 × 10−22 cm2 molecule−1 cm−1 was 12 parts per million with an integration time of 10 s. The results of the continuous monitoring of ambient CO2 using the QEPAS sensor corresponded to those obtained using a nondispersive IR sensor. The QEPAS sensor exhibited a suitable performance for field measurements of the CO2 mixing ratio.

Complex Validation of Weather Research and Forecasting—Chemistry Modelling of Atmospheric CO2 in the Coastal Cities of the Gulf of Finland

The increase of the CO2 content in the atmosphere caused by anthropogenic emissions from the territories of large cities (~70%) is the critical factor in determining the accuracy of emission estimations. Advanced experiment-based methods of anthropogenic CO2 emission estimation are based on the solution of an inverse problem, using accurate measurements of CO2 content and numerical models of atmospheric transport and chemistry. The accuracy of such models decreases the errors of the emission estimations. The aim of the current study is to adapt numerical weather prediction and atmospheric chemistry model WRF-Chem and validate its capability to simulate atmospheric CO2 for the territories of the two large coastal cities of the Gulf of Finland—St. Petersburg (Russia) and Helsinki (Finland). The research has demonstrated that the WRF-Chem model is able to simulate annual variation, as well as the mean seasonal and diurnal variations of the near-surface CO2 mixing ratio, in Helsinki, at a high spatial resolution (2 km). Correlation between the modelled and measured CO2 mixing ratio is relatively high, at ~0.73, with a mean difference and its standard deviation of 0.15 ± 0.04 and 1.7%, respectively. The differences between the WRF-Chem data and the measurements might be caused by errors in the modelling of atmospheric transport and in a priori CO2 emissions and biogenic fluxes. The WRF-Chem model simulates well the column-averaged CO2 mixing ratio (XCO2) in St. Petersburg (January 2019–March 2020), with a correlation of ~0.95 relative to ground-based spectroscopic measurements by the IR–Fourier spectrometer Bruker EM27/SUN. The error of the XCO2 modelling constitutes ~0.3%, and most likely is related to inaccuracies in chemical boundary conditions and a priori anthropogenic CO2 emissions. The XCO2 time series in St. Petersburg by the WRF-Chem model fits well with global CAMS reanalysis and CarbonTracker-modelled data (the differences are less than ~1%). However, due to much higher spatial resolution (2 vs. over 100 km), the WRF-Chem data are in the best agreement with the ground-based remote measurements of XCO2. According to the study, the modelling errors of XCO2 in St. Petersburg during the whole simulated period are sufficiently minimal to fit the requirement of “Error ≤ 0.2%” in 60% of cases. This requirement should be satisfied to evaluate properly the anthropogenic CO2 emissions of St. Petersburg on a city-scale.

Multi-Frequency Differential Absorption LIDAR (DIAL) System for Aerosol and Cloud Retrievals of CO2/H2O and CH4/H2O

We discuss a remote sensing system that is used to simultaneously detect range-resolved differential absorption LIDAR (light detection and ranging; DIAL) signals and integrated path differential absorption LIDAR signals (IPDA LIDAR) from aerosol targets for ranges up to 22 km. The DIAL/IPDA LIDAR frequency converter consists of an OPO pumped at 1064 nm to produce light at 1.6 μm and operates at 100 Hz pulse repetition frequency. The probe light is free space coupled to a movable platform that contains one transmitter and two receiver telescopes. Hybrid photon counting/current systems increase the dynamic range for detection by two orders of magnitude. Range resolved and column integrated dry-air CO2 and CH4 mixing ratios are obtained from line shape fits of CO2 and CH4 centered at 1602.2 nm and 1645.5 nm, respectively, and measured at 10 different frequencies over ≈1.3 cm−1 bandwidth. The signal-to-noise ratios (SNRs) of the IPDA LIDAR returns from cloud aerosols approach 1000:1 and the uncertainties in the mixing ratios weighted according to the integrated counts over the cloud segments range from 0.1% to 1%. The range-averaged DIAL mixing ratios are in good agreement with the IPDA LIDAR mixing ratios at the 1% to 2% level for both CO2 and CH4. These results can serve as a validation method for future active and passive satellite observational systems.

Effect of land–sea air mass transport on spatiotemporal distributions of atmospheric CO2 and CH4 mixing ratios over the southern Yellow Sea

Abstract. To reveal the spatiotemporal distributions of atmospheric CO2 and CH4 mixing ratios and regulation mechanisms over the China shelf sea, two field surveys were conducted in the southern Yellow Sea in China in November 2012 and June 2013, respectively. The results observed showed that mean background atmospheric CO2 and CH4 mixing ratios were 403.94 (±13.77) ppm and 1924.8 (±27.8) ppb in November 2012 and 395.90 (±3.53) ppm and 1918.0 (±25.7) ppb in June 2013, respectively. An improved data-filtering method was optimised and established to flag atmospheric CO2 and CH4 emission from different sources in the survey area. We found that the spatiotemporal distributions of atmospheric CO2 and CH4 mixing ratios over the southern Yellow Sea were dominated by land–sea air mass transport, which was mainly driven by seasonal monsoon, while the influence of air–sea exchange was negligible. In addition, atmospheric CO2 and CH4 mixing ratios over the southern Yellow Sea could be elevated remarkably at a distance of approximately 20 km offshore by land-to-sea air mass transportation from the Asian continent during the early-winter monsoon.

Evolution of Earth's oxygen, nitrogen, and carbon polar outflow in the Archean eon

The habitability of Earth significantly depends on the evolution of its atmospheric properties, which are strongly influenced by escape processes. Polar outflow, which encompasses the atmospheric escape of ions through the open field lines of the Earth's magnetosphere, is the dominant escape process for modern Earth and likely played a significant role in Earth's early history as well. In this article, we investigate the evolution of Earth's polar outflow during the Archean eon 3-4 gigayears (Ga) ago for an N2-CO2 atmosphere. For this, we use a kinetic direct simulation Monte Carlo (DSMC) particle code to simulate the interactions between the solar wind and the Earth's upper atmosphere. We explore the effects of different CO2 mixing ratios (10%, 25%, 50%, 75%, 99%) on the polar outflow escape rates of oxygen, nitrogen and carbon. Using the ion production rates as an estimate for polar outflow, our results indicate that during the time 3.6-4.0 Ga ago, a CO2 mixing ratio larger than 25% may have been necessary to avoid excessive escape via polar outflow. Close to 4 Ga ago, even 50% CO2 may have been necessary due to the stronger solar winds. Our results align with other studies also suggesting a similarly high CO2 mixing ratio in the early Archean.

Anthropogenic carbon dioxide origin tracing study in Anmyeon-do, South Korea: Based on STILT-footprint and emissions data

Tracking the origin of greenhouse gases (GHGs) is critical for understanding regional GHG concentration variation and developing effective policies to reduce GHG emissions. This study provides quantitative information about the surface contribution to enhancement of carbon dioxide (CO2) concentration at Anmyeon-do (AMY), South Korea, using Stochastic Time-Inverted Lagrangian Transport (STILT) model and anthropogenic CO2 emission data. CO2 enhancement simulated by the STILT and emission data was positively correlated with measured CO2 anomalies at AMY with a correlation coefficient above 0.5. High and low CO2 days were selected using ground measurements of the CO2 mixing ratio at AMY during the winter season of 2018–2019. The surface contributions for the high and low CO2 days at AMY were compared quantitatively. When the high concentration was observed in AMY, the CO2 enhancements were dominated by domestic regions, especially from the metropolitan area in South Korea, due to the high footprint and large CO2 emissions. From the perspective of foreign regions, the surface contribution of eastern China regions (Shandong, Jiangsu-Shanghai) increased during high CO2 days compared to low CO2 days at AMY. During the high CO2 days, the ratio between CO2 and carbon monoxide, a co-emitted species, is large when the surface contribution of eastern China regions is relatively strong due to different regional combustion efficiency (i.e., high combustion efficiency in South Korea compared to that in China). The surface contribution based on STILT and emission data is useful for understanding the cause of high GHG concentration at the receptor (AMY in this study).

Three-dimensional view of CO2 variability in the atmosphere over the Indian region

The study reports diurnal and seasonal variations of atmospheric CO2 across multiple locations in India using the dedicated ground-based observations, satellite observations and model simulations. Data from seven sites collected during different time periods are analysed to understand the role of biospheric, fossil fuel fluxes on diurnal and seasonal variations of atmospheric CO2. The study also examines the impact of land use/land cover on the variability of atmospheric CO2 concentration. Results show that CO2 mixing ratios are highest during night and lowest in the afternoon. Ponmudi station shows homogeneous daily CO2 mixing ratios because of an active ecosystem and free of boundary layer influence. A high altitude site Ooty exhibits higher diurnal and seasonal variations, possibly due to entrapped emissions from tourism and settlements, while Nagpur's (an urban site), CO2 mixing ratios remain moderate throughout the year due to active plantations. The observed CO2 is further compared with the simulations from atmospheric chemistry-transport model. The model is able to capture the observed seasonal cycle all over the study locations.

Implications of Emission Sources and Biosphere Exchange on Temporal Variations of CO2 and δ13C Using Continuous Atmospheric Measurements at Shadnagar (India)

AbstractDiurnal variation of atmospheric CO2 and its driving factors are studied using high‐precision in situ mixing ratio measurements and stable carbon isotopic CO2 (δ13C‐CO2) for the first time over Shadnagar, a suburban site in India, from November 2018 to October 2019. The annual mean mixing ratios of atmospheric CO2 and δ13C‐CO2 are observed to be 414.76 ± 4.26 ppm and −11.19 ‰ ± 1.63 ‰. Maximum diurnal variability of atmospheric CO2 was observed in summer monsoon (17.30 ± 9.29 ppm), and the minimum was noticed in winter (7.19 ± 0.11 ppm), indicating strong seasonality in diurnal variability of amplitude. To characterize the atmospheric CO2 sources, the Miller‐Tans model was implemented separately using the CO2 and its δ13C‐CO2 isotopic data during day and night. However, a strong source signature of δ13C‐CO2 was observed during the night‐time of summer monsoon with a slope of −37.42‰ ± 0.73‰, obtained using a reduced major axis regression. The identified source indicates possible emissions from fossil fuel combustion. Further, a Lagrangian back trajectory analysis was performed to study the influence of transport on the observed CO2 mixing ratios. The model trajectory confirms the influence of the Indian summer monsoon on the variations of atmospheric CO2 mixing ratios. Our study shows that fossil fuel emissions are one of the major contributors of CO2 in the study location (about ∼2 to 4 ppm) with biogenic fluxes adding a clear seasonality ranging from −8 to 4 ppm.

CRIRES+ detection of CO emissions lines and temperature inversions on the dayside of WASP-18b and WASP-76b

The dayside atmospheres of ultra-hot Jupiters (UHJs) are predicted to possess temperature inversion layers with extremely high temperatures at high altitudes. We observed the dayside thermal emission spectra of WASP-18b and WASP-76b with the new CRIRES+ high-resolution spectrograph at near-infrared wavelengths. Using the cross-correlation technique, we detected strong CO emission lines in both planets, which confirms the existence of temperature inversions on their dayside hemispheres. The two planets are the first UHJs orbiting F-type stars with CO emission lines detected; previous detections were mostly for UHJs orbiting A-type stars. Evidence of weak H2O emission signals is also found for both planets. We further applied forward-model retrievals on the detected CO lines and retrieved the temperature-pressure profiles along with the CO volume mixing ratios. The retrieved logarithmic CO mixing ratio of WASP-18b (−2.2−1.5+1.4) is slightly higher than the value predicted by the self-consistent model assuming solar abundance. For WASP-76b, the retrieved CO mixing ratio (−3.6−1.6+1.8) is broadly consistent with the value of solar abundance. In addition, we included the equatorial rotation velocity (υeq) in the retrieval when analyzing the line profile broadening. The obtained υeq is 7.0 ± 2.9 km s−1 for WASP-18b and 5.2−3.0+2.5 km s−1 for WASP-76b, which are consistent with the tidally locked rotational velocities.