The total carbon column observing network

Carbon emissions from the 2023 Canadian wildfires

The 2023 Canadian forest fires have been extreme in scale and intensity with more than seven times the average annual area burned compared to the previous four decades1. Here, we quantify the carbon emissions from these fires from May to September 2023 on the basis of inverse modelling of satellite carbon monoxide observations. We find that the magnitude of the carbon emissions is 647 TgC (570-727 TgC), comparable to the annual fossil fuel emissions of large nations, with only India, China and the USA releasing more carbon per year2. We find that widespread hot-dry weather was a principal driver of fire spread, with 2023 being the warmest and driest year since at least 19803. Although temperatures were extreme relative to the historical record, climate projections indicate that these temperatures are likely to be typical during the 2050s, even under a moderate climate mitigation scenario (shared socioeconomic pathway, SSP 2-4.5)4. Such conditions are likely to drive increased fire activity and suppress carbon uptake by Canadian forests, adding to concerns about the long-term durability of these forests as a carbon sink5-8.

The Greenhouse gas Observations of Biospheric and Local Emissions from the Upper sky (GOBLEU): a mission overview, instrument description, and results from the first flight

BACKGROUND

The Greenhouse gas Observations of Biospheric and Local Emissions from the Upper sky (GOBLEU) is a new joint project by Japan Aerospace Exploration Agency (JAXA) and ANA HOLDING INC. (ANAHD), which operates ANA flights. GOBLEU aims to visualizes our climate mitigation effort progress in support of subnational climate mitigation by collecting greenhouse gas (GHG) data as well as relevant data for emissions (nitrous dioxide, NO2) and removals (Solar-Induced Fluorescence, SIF) from regular passenger flights. We developed a luggage-sized instrument based on the space remote-sensing techniques that JAXA has developed for Japan's Greenhouse gas Observing SATellite (GOSAT). The instrument can be conveniently installed on a coach-class passenger seat without modifying the seat or the aircraft.

RESULTS

The first GOBLEU observation was made on the flight from the Tokyo Haneda Airport to the Fukuoka Airport, with only the NO2 module activated. The collected high-spatial-resolution NO2 data were compared to that from the TROPOspheric Monitoring Instrument (TROPOMI) satellite and surface NO2 data from ground-based air quality monitoring stations. While GOBLEU and TROPOMI data shared the major concentration patterns largely driven by cities and large point sources, regardless of different observation times, we found fine-scale concentration pattern differences, which might be an indication of potential room for GOBLEU to bring in new emission information and thus is worth further examination. We also characterized the levels of NO2 spatial correlation that change over time. The quickly degrading correlation level of GOBLEU and TROPOMI suggests a potentially significant impact of the time difference between CO2 and NO2 as an emission marker and, thus, the significance of co-located observations planned by future space missions.

CONCLUSIONS

GOBLEU proposes aircraft-based, cost-effective, frequent monitoring of greenhouse emissions by GOBLEU instruments carried on regular passenger aircraft. Theoretically, the GOBLEU instrument can be installed and operated in most commercially used passenger aircraft without modifications. JAXA and ANAHD wish to promote the observation technique by expanding the observation coverage and partnership to other countries by enhancing international cooperation under the Paris Agreement.

Slowdown in China's methane emission growth

The unprecedented surge in global methane levels has raised global concerns in recent years, casting a spotlight on China as a pivotal emitter. China has taken several actions to curb the methane emissions, but their effects remain unclear. Here, we developed the Global ObservatioN-based system for monitoring Greenhouse GAses for methane (GONGGA-CH4) and assimilate GOSAT XCH4 observations to assess changes in China's methane emissions. We find the average rate of increase in China's methane emissions (0.1 ± 0.3 Tg CH4 yr-2) during 2016-2021 slowed down compared to the preceding years (2011-2015) (0.9 ± 0.5 Tg CH4 yr-2), in contrast to the concurrent acceleration of global methane emissions. As a result, the contribution of China to global methane emissions dropped significantly. Notably, the slowdown of China's methane emission is mainly attributable to a reduction in biogenic emissions from wetlands and agriculture, associated with the drying trend in South China and the transition from double-season to single-season rice cropping, while fossil fuel emissions are still increasing. Our results suggest that GONGGA-CH4 provides the opportunity for independent assessment of China's methane emissions from an atmospheric perspective, providing insights into the implementation of methane-related policies that align with its ambitious climate objectives.

Rovibrational Energies of 13C16O2 Determined with Kilohertz Accuracy

Accurate spectroscopic data of carbon dioxide are widely used in many important applications, such as carbon monitoring missions. Here, we present comb-locked cavity ring-down saturation spectroscopy of the second most abundant isotopologue of CO2, 13C16O2. We determined the positions of 88 lines in three vibrational bands in the 1.6 μm region, 30011e/30012e/30013e-00001e, with an accuracy of a few kHz. Based on the analysis of combination differences, we obtained for the first time the ground-state rotational energies with kHz accuracy. We also provide a set of hybrid line positions for 150 13C16O2 transitions. The rotational energies (J < 50) in the 30013e vibrational state can be fitted by a set of rotational and centrifugal constants with deviations within a few kHz, indicating that the 30013e state is free of perturbations. These precise isotopic line positions will be utilized to improve the Hamiltonian model and quantitative remote sensing of carbon dioxide. Moreover, they will help to track changes in the carbon source and sink through isotopic analysis.

Weaker regional carbon uptake albeit with stronger seasonal amplitude in northern mid-latitudes estimated by higher resolution GEOS-Chem model

Terrestrial ecosystem in the Northern Hemisphere is characterized by a substantial carbon sink in recent decades. However, the carbon sink inferred from atmospheric CO2 data is usually larger than process- and inventory-based estimates, resulting in carbon release or near-neutral carbon exchange in the tropics. The atmospheric approach is known to be uncertain due to systematic biases of coarse atmospheric transport model simulation. Compared to a coarse-resolution inverse estimate at 4° × 5° using GEOS-Chem in the integrated region of N. America, E. Asia, and Europe from 2015 to 2018, the annual carbon sink estimate at a native high-resolution of 0.5° × 0.625° is reduced from -3.0±0.08 gigatons of carbon per year (GtC yr-1) to -2.15±0.08 GtC yr-1 due to prominent more carbon release during the non-growing seasons. The major reductions concentrate in the mid-latitudes (20°N-45°N), where the mean land carbon sinks in China and the USA are reduced from 0.64±0.03 and 0.35±0.02 GtC yr-1 to 0.14±0.03 and 0.15±0.02 GtC yr-1, respectively. The coarse-resolution GEOS-Chem tends to trap both the release and uptake signal within the planetary boundary layer, resulting in weaker estimates of biosphere seasonal strength. Since the strong fossil fuel emissions are persistently released from the surface, the trapped signal leads to the stronger estimates of annual carbon uptakes. These results suggest that high-resolution inversion with accurate vertical and meridional transport is urgently needed in targeting national carbon neutrality.

Synergistic monitoring of PM2.5 and CO2 based on active and passive remote sensing fusion during the 2022 Beijing Winter Olympics

Green and low-carbon are the keywords of the 2022 Beijing Winter Olympic Games (WOG) and the core of sustainable development. Beijing's P M 2.5 and C O 2 emissions attracted worldwide attention during WOG. However, the complex emission sources and frequently changing weather patterns make it impossible for a single monitoring approach to meet the high-resolution, full-coverage monitoring requirements. Therefore, we proposed an active-passive remote sensing fusion method to address this issue. The haze layer height (HLH) was first retrieved from vertical aerosol profiles measured by our high-spectral-resolution lidar located near Olympic venues, which provides new insights into the nonuniform boundary layer and the residual aerosol aloft above it. Second, we developed a bootstrap aggregating (bagging) method that assimilates the lidar-based HLH, satellite-based AOD, and meteorological data to estimate the hourly P M 2.5 with 1 km resolution. The P M 2.5 at Beijing region, Bird's Nest, and Yanqing venues during WOG was 23.00±18.33, 22.91±19.48, and 16.33±10.49µg/m 3, respectively. Third, we also derived the C O 2 enhancements, C O 2 spatial gradients resulting from human activities, and annual growth rate (AGR) to estimate the performance of carbon emission management in Beijing. Based on the top-down method, the results showed an average C O 2 enhancement of 1.62 ppm with an annual decline rate of 2.92 ppm. Finally, we compared the monitoring data with six other international cities. The results demonstrated that Beijing has the largest P M 2.5 annual decline rate of 7.43µg/m 3, while the C O 2 AGR is 1.46 ppm and keeps rising, indicating Beijing is still on its way to carbon peaking and needs to strive for carbon neutrality.

Spatiotemporal variability of near-surface CO2 and its affecting factors over Mongolia

We investigate the spatiotemporal variability of near-surface CO2 concentrations in Mongolia from 2010 to 2019 and the factors affecting it over four climate zones of Mongolia based on the Köppen-Geiger climate classification system, including arid desert climate (BWh), arid steppe climate (BSk), dry climate (Dw), and polar frost climate (ET). Initially, we validate the near-surface CO2 datasets obtained from the Greenhouse Gases Observing Satellite (GOSAT) using ground-based CO2 observations obtained from the World Data Center for Greenhouse Gases (WDCGG) and found good agreement. The results showed that CO2 concentrations over Mongolia steadily increased from 389.48 ppmv in 2010 to 409.72 ppmv in 2019, with an annual growth rate of 2.24 ppmv/year. Spatially, the southeastern Gobi desert region has the highest annual average CO2 concentration, while the northwestern Alpine and Meadow steppe region exhibits the most significant growth rate. Additionally, significant monthly and seasonal variations were observed in each climate zone, with CO2 levels decreasing to a minimum in summer and reaching a maximum in spring. Furthermore, our findings revealed a negative correlation between CO2 concentrations and vegetation parameters (NDVI, GPP, and LAI) during summer when photosynthesis is at its peak, while a positive correlation was observed during spring and autumn when the capacity for carbon sequestration is lower. Understanding CO2 concentrations in different climate zones and the uptake capacity of vegetation may help improve estimates of carbon sequestration in ecosystems such as deserts, steppes and forests.

Erbium-doped fiber amplifier (EDFA)-assisted laser heterodyne radiometer (LHR) working in the shot-noise-dominated regime

A near-infrared (NIR) laser heterodyne radiometer (LHR) using a 1603 nm distributed feedback (DFB) laser, associated with an erbium-doped fiber amplifier (EDFA), used as a local oscillator (LO) was developed. The EDFA was customized for automatic power control to amplify and stabilize the LO DFB laser power, which allowed to reduce baseline fluctuation and thus make the processed atmospheric transmission spectrum with higher precision. The operation of the EDFA-assisted LHR with a shot-noise-dominated performance was analyzed and experimentally achieved by optimizing the LO power. The performance of the developed LHR was evaluated and verified by measuring an atmospheric CO2 absorption spectrum, and the atmospheric CO2 column abundances were then retrieved based on the optimal estimation method (OEM). The results were in good agreement with the Greenhouse Gas Observation Satellite (GOSAT) data. The EDFA-assisted LHR firstly reported in this Letter has a potential to further improve the measurement precision of atmospheric greenhouse gases using ground-based LHR remote sensing.

Global evaluation of carbon neutrality and peak carbon dioxide emissions: current challenges and future outlook

With the acceleration of urbanization and industrialization, carbon neutrality and peak carbon dioxide emissions have become common sustainability goals worldwide. However, there are few literature statistics and econometric analyses targeting carbon neutrality and peak carbon dioxide emissions, especially the publication trends, geographic distribution, citation literature, and research hotspots. To conduct an in-depth analysis of existing research fields and future perspectives in this research area, 1615 publications from the Web of Science Core Collection, between 2010 and 2020, were evaluated by using three analysis tools, under the framework of the bibliometrics method. These publications are distributed between the start-up (2010-2015) and the stable development (2016-2020) phases. Cluster analysis suggests three areas of ongoing research: energy-related carbon emissions, methane emissions, and energy biomass. Overall, future trends in this field include cumulative carbon emissions, the residential building sector, methane emission measurement, nitrogen fertilization, land degradation neutrality, and sciamachy satellite methane measurement. Finally, this paper further examines the most comprehensive coverage of nitrogen fertilization and the most recent research of the residential building sector. In view of the statistical clusters from 1615 publications, this paper provides new insights and perspectives for climate-environment-related researchers and policymakers. Specifically, countries could apply nitrogen fertilizer to crops according to the conditions of different regions. Additionally, experiences from developed countries could be learned from, including optimizing the energy supply structure of buildings and increasing the use of clean energy to reduce CO2 emissions from buildings.

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.

Simultaneous detection of multi-component greenhouse gases based on an all-fibered near-infrared single-channel frequency-division multiplexing wavelength-modulated laser heterodyne radiometer

The performance of an all fibered near-infrared (NIR) single-channel frequency-division multiplexing wavelength-modulated laser heterodyne radiometer (FDM WM-LHR) is demonstrated in ground-based solar occultation mode. The system modulates the laser through the high-frequency signal output by the lock-in amplifier to replace the traditional chopper modulation, making it more stable and compact. Moreover, personal computers are used to simultaneously control the operating current of two distributed feedback (DFB) lasers through a general purpose interface bus-universal serial bus (GPIB-USB), thereby controlling the central wavelength of the laser at 1602.88 and 1653.727 nm, which serve as the absorption lines for the local oscillator detection of the two main greenhouse gases: CO₂ and CH₄. Firstly, the performance of traditional laser heterodyne radiometer (LHR) and the wavelength-modulated laser heterodyne radiometer (WM-LHR) are compared. The results reveal that both the radiometers have an optimized 2f signal when the modulation amplitude m = 2.2. In the actual measurement, 0.25 V and 0.21 V are selected as the modulation amplitude of the laser for the detection of CH₄ and CO₂. Under the same experimental parameters, at 1602.88 nm, the signal-to-noise ratio (SNR) for the 2f signal of CO₂ in the WM-LHR system is 500.24, while that for the direct absorption signal (DAS) of CO₂ in the traditional LHR system is 337.94. At 1653.727 nm, the SNR for the 2f signal in the WM-LHR system and the DAS of CH₄ in the traditional LHR system are 512.04 and 389.58, respectively. Obviously, the SNR for the WM-LHR system is greatly improved. Finally, the application of frequency-division multiplexing (FDM) technology in the WM-LHR system is discussed. The modulation frequency of the two lasers should be appropriately selected to avoid interference between the signals. Overall, the results show that the FDM WM-LHR system can not only detect multiple gases simultaneously but also reduce the implementation cost of the ground-based radiometer. In addition, this study provides useful insights on planetary atmosphere exploration.

Long-range transport of CO and aerosols from Siberian biomass burning over northern Japan during 18-20 May 2016

High CO concentration and dense aerosol layers at 1-6 km altitude in the free troposphere were observed over Rikubetsu, Japan, in ground-based Fourier transform spectrometer (FTS) and lidar measurements during 18-20 May 2016, days after intense wildfires east of Lake Baikal, Siberia. The column-averaged dry-air mole fraction of CO (XCO) was observed to be ∼150 ppb from 11:15 to 13:50 JST on 19 May, and peak aerosol optical depths (AODs) of 1.41 and 1.28 were observed at 15:40 JST 18 May and 11:20 JST 19 May, respectively. We used the HYSPLIT model to calculate five-day backward trajectories from Rikubetsu on May 18, 2016 at 2, 3 and 5 km altitude. The results show that the air parcels passed over the Siberian wildfires during 16-17 May. It was found that the high CO concentrations originated from forest fires were transported to the upper layers of Hokkaido. This will contribute to the understanding of the regional effects of air pollution in northern Japan due to air masses originating from forest fires. By combining these independent datasets such as AERONET aerosol optical thickness (AOT), MODIS fire data, and Infrared Atmospheric Sounding Interferometer (IASI) total CO columns, we confirmed that the lidar measurements of enhanced aerosol concentrations and FTS measurements of maximum XCO over Rikubetsu resulted from a persistent smoke plume transported from Siberian wildfires. Relatively large-scale forest fires have been frequently occurring in Siberia recently. However, the effects of CO and other gases released from them over northern Japan are not well known. We observed high concentrations of CO over the TCCON station in Rikubetsu, Japan, which we believe to be of forest fire origin. Therefore, we analyzed it as a case study to confirm its origin and impact on the upper atmosphere over northern Japan.

Characterizing Average Seasonal, Synoptic, and Finer Variability in Orbiting Carbon Observatory-2 XCO2 Across North America and Adjacent Ocean Basins

Variations in atmosphere total column-mean CO₂ (XCO₂) collected by the National Aeronautics and Space Administration's Orbiting Carbon Observatory-2 satellite can be used to constrain surface carbon fluxes if the influence of atmospheric transport and observation errors on the data is known and accounted for. Due to sparse validation data, the portions of fine-scale variability in XCO₂ driven by fluxes, transport, or retrieval errors remain uncertain, particularly over the ocean. To better understand these drivers, we characterize variability in OCO-2 Level 2 version 10 XCO₂ from the seasonal scale, synoptic-scale (order of days, thousands of kilometers), and mesoscale (within-day, hundreds of kilometers) for 10 biomes over North America and adjacent ocean basins. Seasonal and synoptic variations in XCO₂ reflect real geophysical drivers (transport and fluxes), following large-scale atmospheric circulation and the north-south distribution of biosphere carbon uptake. In contrast, geostatistical analysis of mesoscale and finer variability shows that real signals are obscured by systematic biases across the domain. Spatial correlations in along-track XCO₂ are much shorter and spatially coherent variability is much larger in magnitude than can be attributed to fluxes or transport. We characterize random and coherent along-track XCO₂ variability in addition to quantifying uncertainty in XCO₂ aggregates across typical lengths used in inverse modeling. Even over the ocean, correlated errors decrease the independence and increase uncertainty in XCO₂. We discuss the utility of computing geostatistical parameters and demonstrate their importance for XCO₂ science applications spanning from data reprocessing and algorithm development to error estimation and carbon flux inference.

Mapping contiguous XCO2 by machine learning and analyzing the spatio-temporal variation in China from 2003 to 2019

As China is the world's largest CO₂ emitter, it is important to understand the spatio-temporal variation of atmospheric CO₂ to reduce carbon emissions. Satellite remote sensing for carbon monitoring has been widely used and studied because of its long-term and large-scale characteristics. However, the satellite data results are very sparse with significant gaps due to narrow swath and other factors on CO₂ retrieval. The simple interpolation methods ignore the influential factors of CO₂ and loss the spatial resolution, which leads to the inability to quantify the spatio-temporal variation well. This study developed a machine learning method that considers carbon emissions, vegetation, and meteorology. Using the column-averaged dry-air mole fraction of CO₂ (XCO₂) data of SCIAMACHY, GOSAT, and OCO-2, we derived monthly-scale contiguous XCO₂ data across China from 2003 to 2019 with 0.25° resolution. The results showed a good agreement with the satellite measurements, with the bias and standard deviation of 0.11 and 1.38 ppmv for the validation dataset, respectively. Moreover, the results were consistent with the model simulation and in-situ sites, indicating the ability to reflect long-term spatio-temporal variation with a finer texture. We analyzed the spatial distribution, seasonal variation, and long-term trends of XCO₂ in China, revealing that the machine learning method has comparable performance to model simulations. The results showed that XCO₂ is dominated by anthropogenic emissions spatially and has a clear seasonal cycle, with a larger amplitude the further north. The long-term trend shows the XCO₂ increased by an average rate of 2.17 ppmv per year from 2003 to 2019 in China, which is consistent with the global. The method and data can further study the carbon cycle and climate change.

Validation of open-path dual-comb spectroscopy against an O2 background

Dual-comb spectroscopy measures greenhouse gas concentrations over kilometers of open air with high precision. However, the accuracy of these outdoor spectra is challenging to disentangle from the absorption model and the fluctuating, heterogenous concentrations over these paths. Relative to greenhouse gases, O₂ concentrations are well-known and evenly mixed throughout the atmosphere. Assuming a constant O₂ background, we can use O₂ concentration measurements to evaluate the consistency of open-path dual-comb spectroscopy with laboratory-derived absorption models. To this end, we construct a dual-comb spectrometer spanning 1240 nm to 1700nm, which measures O₂ absorption features in addition to CO₂ and CH₄. O₂ concentration measurements across a 560 m round-trip outdoor path reach 0.1% precision in 10 minutes. Over seven days of shifting meteorology and spectrometer conditions, the measured O₂ has -0.07% mean bias, and 90% of the measurements are within 0.4% of the expected hemisphere-average concentration. The excursions of up to 0.4% seem to track outdoor temperature and humidity, suggesting that accuracy may be limited by the O₂ absorption model or by water interference. This simultaneous O₂, CO₂, and CH₄ spectrometer will be useful for measuring accurate CO₂ mole fractions over vertical or many-kilometer open-air paths, where the air density varies.

Open-path measurement of stable water isotopologues using mid-infrared dual-comb spectroscopy

We present an open-path mid-infrared dual-comb spectroscopy (DCS) system capable of precise measurement of the stable water isotopologues H216O and HD16O. This system ran in a remote configuration at a rural test site for 3.75 months with 60% uptime and achieved a precision of < 2‰ on the normalized ratio of H216O and HD16O (δ D ) in 1000s. Here, we compare the δ D values from the DCS system to those from the National Ecological Observatory Network (NEON) isotopologue point sensor network. Over the multi-month campaign, the mean difference between the DCS δ D values and the NEON δ D values from a similar ecosystem is < 2‰ with a standard deviation of 18‰, which demonstrates the inherent accuracy of DCS measurements over a variety of atmospheric conditions. We observe time-varying diurnal profiles and seasonal trends that are mostly correlated between the sites on daily timescales. This observation motivates the development of denser ecological monitoring networks aimed at understanding regional- and synoptic-scale water transport. Precise and accurate open-path measurements using DCS provide new capabilities for such networks.

Range-resolved detection of boundary layer stable water vapor isotopologues using a ground-based 1.98 µm differential absorption LIDAR

This paper presents a first demonstration of range-resolved differential absorption LIDAR (DIAL) measurements of the water vapor main isotopologue H₂ ¹⁶O and the less abundant semi-heavy water isotopologue HD¹⁶O with the aim of determining the isotopic ratio. The presented Water Vapor and Isotope Lidar (WaVIL) instrument is based on a parametric laser source emitting nanosecond pulses at 1.98 µm and a direct-detection receiver utilizing a commercial InGaAs PIN photodiode. Vertical profiles of H₂ ¹⁶O and HD¹⁶O were acquired in the planetary boundary layer in the suburban Paris region up to a range of 1.5 km. For time averaging over 25 min, the achieved precision in the retrieved water vapor mixing ratio is 0.1 g kg^-1 (2.5% relative error) at 0.4 km above ground level (a.g.l.) and 0.6 g kg^-1 (20%) at 1 km a.g.l. for 150 m range bins along the LIDAR line of sight. For HD¹⁶O, weaker absorption has to be balanced with coarser vertical resolution (600 m range bins) in order to achieve similar relative precision. From the DIAL measurements of H₂ ¹⁶O and HD¹⁶O, the isotopic abundance δD was estimated as -51‰ at 0.4 km above the ground and -119‰ in the upper part of the boundary layer at 1.3 km a.g.l. Random and systematic errors are discussed in the form of an error budget, which shows that further instrumental improvements are required on the challenging path towards DIAL-profiling of the isotopic abundance with range resolution and precision suitable for water cycle studies.

Observations of atmospheric CO2 and CO based on in-situ and ground-based remote sensing measurements at Hefei site, China

The characteristics of long time series of CO₂ and CO surface concentrations, tropospheric and total column dry-air mole fractions (DMF) from May 2015 to December 2019 were investigated. Both CO₂ and CO show different seasonality for the three datasets. The annual increasing trend of CO₂ is similar for all three datasets. However, the annual decreasing trend of CO for surface concentration is high compared to the other two measurements, mainly due to the improved combustion efficiency from power generation in recent years. The correlation between the tropospheric and total atmospheric CO₂ and CO is higher than that between the surface concentration and tropospheric CO₂ and CO. This is because the tropospheric and total atmospheric results both have common vertical profiles for CO₂ and CO respective mole fractions that were observed in troposphere. Furthermore, the enhancement ratios of CO₂ to CO derived from the three datasets during the period from 2016 to 2019 were compared. The ratio of ∆CO₂ to ∆CO has an obvious increase with altitude each year, which means that the combustion efficiencies obtained from the three datasets are different. All ratios for the three datasets showed a slight increasing trend in recent years, which is attributed to increased combustion efficiency due to governmental measures for energy savings and emission reductions.

A review of datasets and methods for deriving spatiotemporal distributions of atmospheric CO2

As the most abundant greenhouse gas, atmospheric carbon dioxide (CO₂) is considered one of the main attributors to climate change. Atmospheric CO₂ concentrations can be measured by ground-based monitoring networks, mobile monitoring campaigns, and carbon-observing satellites. However, the worldwide ground-based monitoring networks are composed of sparsely distributed sites and are inadequate to represent the spatiotemporal distributions of CO₂. Satellite-based remote sensing features repeated, long-term, and large-scale measurements, so it plays a crucial role in monitoring the global distributions of atmospheric CO₂. However, due to the presence of heavy clouds (or aerosols) and the limitation of satellite orbiting tracks, there exist large amounts of missing data in satellite retrievals. Various methods, including chemical transport models (CTMs), geostatistical methods, and regression-based models, have been employed to derive full-coverage spatiotemporal distributions of CO₂ based on the limited CO₂ measurements. This review summarizes the strengths and limitations of these methods. However, CTMs simulation results can have high uncertainty due to imperfect knowledge of the real world, and the interpolation accuracy of all geostatistical methods is limited by the large amount of data gaps in current satellite retrieved CO₂ products. To overcome these limitations, regression-based methods (especially machine learning models) have the ability to predict CO₂ with superior predictive performance, so this review also summarizes the framework of the machine learning approach. Leveraging the ongoing advancements of satellite instrumentation, the satellite-based CO₂ products have been improving dramatically in recent decades, and this review will describe and critically assess the advantages and disadvantages of the currently used systems in detail. For future improvements, we recommend the fusion of data from multiple satellite retrievals and CTMs by using machine learning algorithms in order to obtain even longer-term, larger-scale, finer-resolution, and higher-accuracy CO₂ datasets.

Application and Analysis of XCO2 Data from OCO Satellite Using a Synthetic DINEOF–BME Spatiotemporal Interpolation Framework

Carbon dioxide (CO2) is one of the main greenhouse gases leading to global warming, and the ocean is the largest carbon reservoir on the earth that plays an important role in regulating CO2 concentration on a global scale. The column-averaged dry-air mole fraction of atmospheric CO2 (XCO2) is a key parameter in describing ocean carbon content. In this paper, the Data Interpolation Empirical Orthogonal Function (DINEOF) and the Bayesian Maximum Entropy (BME) methods are combined to interpolate XCO2 data of Orbiting Carbon Observatory 2 (OCO-2) and Orbiting Carbon Observatory 3 (OCO-3) from January to December 2020 occurring within the geographical range of 15–45°N and 120–150°E. At the first stage of our proposed analysis, spatiotemporal information was used by the DINEOF method to perform XCO2 interpolation that improved data coverage; at the second stage, the DINEOF-generated interpolation results were regarded as soft data and were subsequently assimilated using the BME method to obtain improved XCO2 interpolation values. The performance of the synthetic DINEOF–BME interpolation method was evaluated by means of a five-fold cross-validation method. The results showed that the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), and the Bias of the DINEOF-based OCO-2 and OCO-3 interpolations were 2.106 ppm, 3.046 ppm, and 1.035 ppm, respectively. On the other hand, the MAE, RMSE, and Bias of the cross-validation results obtained by the DINEOF–BME were 1.285 ppm, 2.422 ppm, and −0.085 ppm, respectively, i.e., smaller than the results obtained by DINEOF. In addition, based on the in situ measured XCO2 data provided by the Total Carbon Column Observing Network (TCCON), the original OCO-2 and OCO-3 data were combined and compared with the interpolated products of the synthetic DINEOF–BME framework. The accuracy of the original OCO-2 and OCO-3 products is lower than the DINEOF–BME-generated XCO2 products in terms of MAE (1.751 ppm vs. 2.616 ppm), RMSE (2.877 ppm vs. 3.566 ppm) and Bias (1.379 ppm vs 1.622 ppm), the spatiotemporal coverage of XCO2 product also improved dramatically from 16% to 100%. Lastly, this study demonstrated the feasibility of the synthetic DINEOF–BME approach for XCO2 interpolation purposes and the ability of the BME method to be successfully combined with other techniques.

A MEMS modulator-based dual-channel mid-infrared laser heterodyne radiometer for simultaneous remote sensing of atmospheric CH4, H2O and N2O

The performance of a micro-electro-mechanical system (MEMS) modulator-based dual-channel mid-infrared laser heterodyne radiometer (MIR-LHR) was demonstrated in ground-based solar occultation mode for the first time. A MEMS mirror was employed as an alternative modulator to the traditional mechanical chopper, which makes the system more stable and compact. Two inter-band cascade lasers (ICL) centered at 3.53 µm and 3.93 µm, were employed as local oscillators (LO) to probe absorption lines of methane (CH4), water vapor (H₂O) and nitrous oxide (N₂O). The system stability greater than 1000 s was evaluated by Allan variance. The experimental MIR-LHR spectra (acquired at Hefei, China, on February 24th 2022) of two channels were compared and were in good agreement with simulation spectra from atmospheric transmission modeling. The mixing ratio of CH₄, H₂O and N₂O were determined to be ∼1.906 ppm, 3069 ppm and ∼338 ppb, respectively. The reported MEMS modulator-based dual-channel MIR-LHR in this manuscript has great potential to be a portable and high spectral resolution instrument for remote sensing of multi-component gases in the atmospheric column.

Airborne Lidar Measurements of XCO2 in Synoptically Active Environment and Associated Comparisons With Numerical Simulations

Frontal boundaries have been shown to cause large changes in CO2 mole-fractions, but clouds and the complex vertical structure of fronts make these gradients difficult to observe. It remains unclear how the column average CO2 dry air mole-fraction (XCO2) changes spatially across fronts, and how well airborne lidar observations, data assimilation systems, and numerical models without assimilation capture XCO2 frontal contrasts (ΔXCO2, i.e., warm minus cold sector average of XCO2). We demonstrated the potential of airborne Multifunctional Fiber Laser Lidar (MFLL) measurements in heterogeneous weather conditions (i.e., frontal environment) to investigate the ΔXCO2 during four seasonal field campaigns of the Atmospheric Carbon and Transport-America (ACT-America) mission. Most frontal cases in summer (winter) reveal higher (lower) XCO2 in the warm (cold) sector than in the cold (warm) sector. During the transitional seasons (spring and fall), no clear signal in ΔXCO2 was observed. Intercomparison among the MFLL, assimilated fields from NASA's Global Modeling and Assimilation Office (GMAO), and simulations from the Weather Research and Forecasting--Chemistry (WRF-Chem) showed that (a) all products had a similar sign of ΔXCO2 though with different levels of agreement in ΔXCO2 magnitudes among seasons; (b) ΔXCO2 in summer decreases with altitude; and (c) significant challenges remain in observing and simulating XCO2 frontal contrasts. A linear regression analyses between ΔXCO2 for MFLL versus GMAO, and MFLL versus WRF-Chem for summer-2016 cases yielded a correlation coefficient of 0.95 and 0.88, respectively. The reported ΔXCO2 variability among four seasons provide guidance to the spatial structures of XCO2 transport errors in models and satellite measurements of XCO2 in synoptically-active weather systems.

Study of mid-latitude retrieval XCO2 greenhouse gas: Validation of satellite-based shortwave infrared spectroscopy with ground-based TCCON observations

Carbon dioxide (CO₂) is a major greenhouse gas. This study investigated the performance of three common algorithms, namely NIES, ACOS, and Remo Tec (SRFP). These algorithms were compared using GOSAT observation satellite data with reference data obtained from TCCON during the period 2009-2021. According to statistical evaluation, the SRFP and NIES algorithms achieved the lowest and highest correlation values of the 13 year (2009_2021) average of all sites, respectively. The average bias error values of NIES and ACOS was estimated to be less than that of SRFP approximately 0.5 ppm, while the bias within SRFP was of about 2 ppm. Comparing the RMSE and CRMS error values showed that the highest and lowest error values were related to the SRFP and NIES algorithms respectively, which were 0.37-1.67 and ppm 1.46-7.9. The researchers also compared them with monthly time changes based on ground measurements, and observed a time series of CO₂ concentration changes that well matched the trend of gas concentration values at ground stations obtained by NIES algorithm. The results showed that in most cases NIES was an effective algorithm to retrieve carbon dioxide gas concentrations, allowing the researchers to identify the main sources of greenhouse gas emissions in different areas. The clustering result in the study area showed that the continental CO₂ columnar concentration has a specific seasonal cycle, with the maximum and minimum values appearing in winter-early spring and spring-late summer, respectively. In conclusion, cluster analysis can classify the surface CO₂ column concentration values and determine the spatial distribution pattern of CO₂.

Using satellites to uncover large methane emissions from landfills

As atmospheric methane concentrations increase at record pace, it is critical to identify individual emission sources with high potential for mitigation. Here, we leverage the synergy between satellite instruments with different spatiotemporal coverage and resolution to detect and quantify emissions from individual landfills. We use the global surveying Tropospheric Monitoring Instrument (TROPOMI) to identify large emission hot spots and then zoom in with high-resolution target-mode observations from the GHGSat instrument suite to identify the responsible facilities and characterize their emissions. Using this approach, we detect and analyze strongly emitting landfills (3 to 29 t hour^-1) in Buenos Aires, Delhi, Lahore, and Mumbai. Using TROPOMI data in an inversion, we find that city-level emissions are 1.4 to 2.6 times larger than reported in commonly used emission inventories and that the landfills contribute 6 to 50% of those emissions. Our work demonstrates how complementary satellites enable global detection, identification, and monitoring of methane superemitters at the facility level.

Monitoring methane emissions from oil and gas operations‡

The atmospheric concentration of methane has more than doubled since the start of the Industrial Revolution. Methane is the second-most-abundant greenhouse gas created by human activities and a major driver of climate change. This APS-Optica report provides a technical assessment of the current state of monitoring U.S. methane emissions from oil and gas operations, which accounts for roughly 30% of U.S. anthropogenic methane emissions. The report identifies current technological and policy gaps and makes recommendations for the federal government in three key areas: methane emissions detection, reliable and systematized data and models to support mitigation measures, and effective regulation.

XCO2 and XCH4 Reconstruction Using GOSAT Satellite Data Based on EOF-Algorithm

The Greenhouse Gases Observing Satellite (GOSAT) can help to ascertain the global distribution of carbon dioxide (CO2) and methane (CH4), and how the sources and sinks of these gases vary by season, year, and location. However, the data provided by the GOSAT level 2 and 3 products have certain limitations due to their lack of spatial and temporal information; even with the application of the kriging geostatistical method on the level 2 products, the processing algorithms still need further upgrades. In this study, we apply an empirical orthogonal function (EOF)-based method on the GOSAT L3 products (137 images, from January 2010 to May 2021) to estimate the column average of carbon dioxide and methane (XCO2–XCH4) within the entire Earth. The reconstructed results are validated against the Total Carbon Column Observing Network (i.e., TCCON), with 31 in situ stations, and GOSAT L4B column-averaged data, using 107 layers. The results show an excellent agreement with the TCCON data and exhibit an R-squared coefficient of 0.95 regarding the CO2 measurements and 0.86 regarding the CH4 measurements. Therefore, this methodology can be incorporated into the processing steps used to map global greenhouse gases.

Modification of Fraser’s Method for the Atmospheric CO2 Mass Estimation by Using Satellite Data

One of the most critical greenhouse gases in the atmosphere is carbon dioxide (CO2) due to its long-lasting and negative impact on climate change. The global atmospheric monthly mean CO2 concentration is currently greater than 410 ppm which has changed dramatically since the industrial era. To choose suitable climate change mitigation and adaptation strategies it is necessary to define carbon dioxide mass distribution and global atmospheric carbon dioxide mass. The available method to estimate the global atmospheric CO2 mass was proposed in 1980. In this study, to increase the accuracy of the available method, various observation platforms such as ground-based stations, ground-based tall towers, aircrafts, balloons, ships, and satellites are compared to define the best available observations, considering the temporal and spatial resolution. In the method proposed in this study, satellite observations (OCO2 data), from January 2019 to December 2021, are used to estimate atmospheric CO2 mass. The global atmospheric CO2 mass is estimated around 3.24 × 1015 kg in 2021. For the sake of comparison, global atmospheric CO2 mass was estimated by Fraser’s method using NOAA data for the mentioned study period. The proposed methodology in this study estimated slightly greater amounts of CO2 in comparison to Fraser’s method. This comparison resulted in 1.23% and 0.15% maximum and average difference, respectively, between the proposed method and Fraser’s method. The proposed method can be used to estimate the required capacity of systems for carbon capturing and can be applied to smaller districts to find the most critical locations in the world to plan for climate change mitigation and adaptation.

Occurrence and Discrepancy of Surface and Column Mole Fractions of CO2 and CH4 at a Desert Site in Dunhuang, Western China

Carbon dioxide (CO2) and methane (CH4) are the two major radiative forcing factors of greenhouse gases. In this study, surface and column mole fractions of CO2 and CH4 were first measured at a desert site in Dunhuang, west China. The average column mole fractions of CO2 (XCO2) and CH4 (XCH4) were 413.00 ± 1.09 ppm and 1876 ± 6 ppb, respectively, which were 0.90 ppm and 72 ppb lower than their surface values. Diurnal XCO2 showed a sinusoidal mode, while XCH4 appeared as a unimodal distribution. Ground observed XCO2 and XCH4 were compared with international satellites, such as GOSAT, GOSAT-2, OCO-2, OCO-3, and Sentinel-5P. The differences between satellites and EM27/SUN observations were 0.26% for XCO2 and −0.38% for XCH4, suggesting a good consistency between different satellites and ground observations in desert regions in China. Hourly XCO2 was close to surface CO2 mole fractions, but XCH4 appeared to have a large gap with CH4, probably because of the additional chemical removals of CH4 in the upper atmosphere. It is necessary to carry out a long-term observation of column mole fractions of greenhouse gases in the future to obtain their temporal distributions as well as the differences between satellites and ground observations.

Tropical methane emissions explain large fraction of recent changes in global atmospheric methane growth rate

Large variations in the growth of atmospheric methane, a prominent greenhouse gas, are driven by a diverse range of anthropogenic and natural emissions and by loss from oxidation by the hydroxyl radical. We used a decade-long dataset (2010–2019) of satellite observations of methane to show that tropical terrestrial emissions explain more than 80% of the observed changes in the global atmospheric methane growth rate over this period. Using correlative meteorological analyses, we show strong seasonal correlations (r = 0.6–0.8) between large-scale changes in sea surface temperature over the tropical oceans and regional variations in methane emissions (via changes in rainfall and temperature) over tropical South America and tropical Africa. Existing predictive skill for sea surface temperature variations could therefore be used to help forecast variations in global atmospheric methane.

Methane is a powerful greenhouse gas with emissions that are challenging to constrain. Here the authors use 10 years of satellite observations and show tropical terrestrial emissions account for 80% of observed global methane increases.

Large CO2 Emitters as Seen From Satellite: Comparison to a Gridded Global Emission Inventory

Using the multiyear archive of the two Orbiting Carbon Observatories (OCO) of NASA, we have retrieved large fossil fuel CO₂ emissions (larger than 1.0 ktCO₂ h^-1 per 10^-2 square degree grid cell) over the globe with a simple plume cross-sectional inversion approach. We have compared our results with a global gridded and hourly inventory. The corresponding OCO emission retrievals explain more than one third of the inventory variance at the corresponding cells and hours. We have binned the data at diverse time scales from the year (with OCO-2) to the average morning and afternoon (with OCO-3). We see consistent variations of the median emissions, indicating that the retrieval-inventory differences (with standard deviations of a few tens of percent) are mostly random and that trends can be calculated robustly in areas of favorable observing conditions, when the future satellite CO₂ imagers provide an order of magnitude more data.

Variation patterns and driving factors of regional atmospheric CO2 anomalies in China

Atmospheric CO₂ anomaly (△XCO₂) is essential in evaluating regional carbon balance. However, it is difficult to understand △XCO₂ variation characteristics due to regional differences. This paper explored the inter-annual and inter-monthly variation patterns of △XCO₂ in different regions of China based on satellite observations. The relation model between regional △XCO₂ and anthropogenic emissions, gross primary productivity (GPP), wind speed, upwind region's emission, and upwind region's CO₂ concentration was established. Results show that the annual average △XCO₂ in the northwest and southeast regions is stable at around 0 and 1-2 ppm, respectively. Some municipalities directly under the central government and the southern coastal areas showed relatively intense inter-annual fluctuations. Four inter-monthly △XCO₂ variation patterns were observed: the northern region has a stable change, the northeast region has the lowest in summer, the southwest region has the highest in summer, and the central region has no obvious change rule. Furthermore, △XCO₂ in most areas can be explained by the emission-absorption-transportation model. Significant positive △XCO₂ in the southern coastal region in summer may be related to the stable GPP seasonal variation and increased power generation. In the southwestern plateau region, it may be related to the low wind speed and increased soil emission with rising temperature. The stability of the plateau carbon sink and inter-regional cooperation cannot be ignored for improving regional atmospheric environments.

Study of Atmospheric Carbon Dioxide Retrieval Method Based on Normalized Sensitivity

The satellite-based remote-sensing detection of CO2 can provide long-term and continuous observations at the global scale, and is the primary observational approach to monitoring CO2 changes. Surface albedo and aerosol are two significant challenges for retrieving CO2 concentrations from near-infrared remote-sensing data. This research addressed the dependence of radiance sensitivities on CO2 concentration, aerosol, surface albedo, and water vapor. The band ratio method was used to retrieve CO2 with band selection of the high- and low-sensitivity channels to restrain the influence of surface albedo and aerosol. Results showed that the band ratio method had better efficiency at reducing the impact of aerosol and surface albedo than that of the optical estimation method. The retrieval error of aerosol was reduced by 2.5% overall. We validated the band ratio method retrieval results with two TCCON sites and the GOSAT L3 product. Pearson’s correlation coefficient, mean bias, mean absolute bias, and root mean squared error of the monthly retrieval data of the band ratio method showed high agreement between the TCCON in Park Falls and Wollongong. These results indicate that the band ratio method based on normalized sensitivity can effectively reduce the influence of surface albedo and aerosol.

Spatiotemporal Geostatistical Analysis and Global Mapping of CH4 Columns from GOSAT Observations

Methane (CH4) is one of the most important greenhouse gases causing the global warming effect. The mapping data of atmospheric CH4 concentrations in space and time can help us better to understand the characteristics and driving factors of CH4 variation as to support the actions of CH4 emission reduction for preventing the continuous increase of atmospheric CH4 concentrations. In this study, we applied a spatiotemporal geostatistical analysis and prediction to develop an approach to generate the mapping CH4 dataset (Mapping-XCH4) in 1° grid and three days globally using column averaged dry air mole fraction of CH4 (XCH4) data derived from observations of the Greenhouse Gases Observing Satellite (GOSAT) from April 2009 to April 2020. Cross-validation for the spatiotemporal geostatistical predictions showed better correlation coefficient of 0.97 and a mean absolute prediction error of 7.66 ppb. The standard deviation is 11.42 ppb when comparing the Mapping-XCH4 data with the ground measurements from the total carbon column observing network (TCCON). Moreover, we assessed the performance of this Mapping-XCH4 dataset by comparing with the XCH4 simulations from the CarbonTracker model and primarily investigating the variations of XCH4 from April 2009 to April 2020. The results showed that the mean annual increase in XCH4 was 7.5 ppb/yr derived from Mapping-XCH4, which was slightly greater than 7.3 ppb/yr from the ground observational network during the past 10 years from 2010. XCH4 is larger in South Asia and eastern China than in the other regions, which agrees with the XCH4 simulations. The Mapping-XCH4 shows a significant linear relationship and a correlation coefficient of determination (R2) of 0.66, with EDGAR emission inventories over Monsoon Asia. Moreover, we found that Mapping-XCH4 could detect the reduction of XCH4 in the period of lockdown from January to April 2020 in China, likely due to the COVID-19 pandemic. In conclusion, we can apply GOSAT observations over a long period from 2009 to 2020 to generate a spatiotemporally continuous dataset globally using geostatistical analysis. This long-term Mpping-XCH4 dataset has great potential for understanding the spatiotemporal variations of CH4 concentrations induced by natural processes and anthropogenic emissions at a global and regional scale.

A Coupled BRDF CO2 Retrieval Method for the GF-5 GMI and Improvements in the Correction of Atmospheric Scattering

The Greenhouse Gases Monitoring Instrument (GMI), on board the Chinese Gaofen-5 (GF-5) satellite, provides rich observation data for the global remote sensing of atmospheric CO2. To meet the high-precision satellite retrieval needs of atmospheric CO2, this paper designs a coupled bidirectional reflectance distribution function (BRDF) CO2 retrieval (CBCR) method, which describes the surface reflectance characteristics by the BRDF, corrects for atmospheric scattering based on full physics retrieval theory, and ensures the stable retrieval of multiple parameters and atmospheric CO2 by enriching prior constraints. Theoretical analysis shows that the influence of atmospheric scattering induced by the surface bidirectional reflectance characteristics is significantly related to the aerosol optical depth (AOD), solar zenith angle (SZA), and viewing zenith angle (VZA). The validation of GMI CO2 retrievals shows that the CBCR method significantly reduced the influence of the surface bidirectional reflectance characteristics under high AOD and high SZA conditions, decreased the atmospheric CO2 retrieval error from 0.58 ± 5.64 ppm to −1.33 ± 3.13 ppm, and increased the correlation with the temporal variation of actual atmospheric CO2 from 34.7 to 76.8%. Our CBCR method can correct the influence of atmospheric scattering induced by the surface bidirectional reflectance characteristics on atmospheric CO2 retrieval, and this work demonstrates that describing the surface reflectance characteristics by using BRDF is a promising idea in the field of satellite CO2 retrievals.

Autonomous Differential Absorption Laser Device for Remote Sensing of Atmospheric Greenhouse Gases

A ground-based, integrated path, differential absorption (IPDA) light detection device capable of measuring multiple greenhouse gas (GHG) species in the atmosphere is presented. The device was developed to monitor greenhouse gas concentrations in small-scale areas with high emission activities. It is equipped with two low optical power tunable diode lasers in the near-infrared spectral range for the atmospheric detection of carbon dioxide, methane, and water vapors (CO2, CH4 and H2O). The device was tested with measurements of background concentrations of CO2 and CH4 in the atmosphere (Crete, Greece). Accuracies in the measurement retrievals of CO2 and CH4 were estimated at 5 ppm (1.2%) and 50 ppb (2.6%), respectively. A method that exploits the intensity of the recorded H2O absorption line in combination with weather measurements (water vapor pressure, temperature, and atmospheric pressure) to calculate the GHG concentrations is proposed. The method eliminates the requirement for measuring the range of the laser beam propagation. Accuracy in the measurement of CH4 using the H2O absorption line is estimated at 90 ppb (4.8%). The values calculated by the proposed method are in agreement with those obtained from the differential absorption LiDAR equation (DIAL).

Assimilation of GOSAT Methane in the Hemispheric CMAQ; Part I: Design of the Assimilation System

We present a parametric Kalman filter data assimilation system using GOSAT methane observations within the hemispheric CMAQ model. The assimilation system produces forecasts and analyses of concentrations and explicitly computes its evolving error variance while remaining computationally competitive with other data assimilation schemes such as 4-dimensional variational (4D-Var) and ensemble Kalman filter (EnKF). The error variance in this system is advected using the native advection scheme of the CMAQ model and updated at each analysis while the error correlations are kept fixed. We discuss extensions to the CMAQ model to include methane transport and emissions (both anthropogenic and natural) and perform a bias correction for the GOSAT observations. The results using synthetic observations show that the analysis error and analysis increments follow the advective flow while conserving the information content (i.e., total variance). We also demonstrate that the vertical error correlation contributes to the inference of variables down to the surface. In a companion paper, we use this assimilation system to obtain optimal assimilation of GOSAT observations.

Methane Emissions from Superemitting Coal Mines in Australia Quantified Using TROPOMI Satellite Observations

Two years of satellite observations were used to quantify methane emissions from coal mines in Queensland, the largest coal-producing state in Australia. The six analyzed surface and underground coal mines are estimated to emit 570 ± 98 Gg a-1 in 2018-2019. Together, they account for 7% of the national coal production while emitting 55 ± 10% of the reported methane emission from coal mining in Australia. Our results indicate that for two of the three locations, our satellite-based estimates are significantly higher than reported to the Australian government. Most remarkably, 40% of the quantified emission came from a single surface mine (Hail Creek) located in a methane-rich coal basin. Our findings call for increased monitoring and investment in methane recovery technologies for both surface and underground mines.

Societal shifts due to COVID-19 reveal large-scale complexities and feedbacks between atmospheric chemistry and climate change

The COVID-19 global pandemic and associated government lockdowns dramatically altered human activity, providing a window into how changes in individual behavior, enacted en masse, impact atmospheric composition. The resulting reductions in anthropogenic activity represent an unprecedented event that yields a glimpse into a future where emissions to the atmosphere are reduced. Furthermore, the abrupt reduction in emissions during the lockdown periods led to clearly observable changes in atmospheric composition, which provide direct insight into feedbacks between the Earth system and human activity. While air pollutants and greenhouse gases share many common anthropogenic sources, there is a sharp difference in the response of their atmospheric concentrations to COVID-19 emissions changes, due in large part to their different lifetimes. Here, we discuss several key takeaways from modeling and observational studies. First, despite dramatic declines in mobility and associated vehicular emissions, the atmospheric growth rates of greenhouse gases were not slowed, in part due to decreased ocean uptake of CO2 and a likely increase in CH4 lifetime from reduced NO x emissions. Second, the response of O3 to decreased NO x emissions showed significant spatial and temporal variability, due to differing chemical regimes around the world. Finally, the overall response of atmospheric composition to emissions changes is heavily modulated by factors including carbon-cycle feedbacks to CH4 and CO2, background pollutant levels, the timing and location of emissions changes, and climate feedbacks on air quality, such as wildfires and the ozone climate penalty.

The added value of satellite observations of methane forunderstanding the contemporary methane budget

Surface observations have recorded large and incompletely understood changes to atmospheric methane (CH₄) this century. However, their ability to reveal the responsible surface sources and sinks is limited by their geographical distribution, which is biased towards the northern midlatitudes. Data from Earth-orbiting satellites designed specifically to measure atmospheric CH₄ have been available since 2009 with the launch of the Japanese Greenhouse gases Observing SATellite (GOSAT). We assess the added value of GOSAT to data collected by the US National Oceanic and Atmospheric Administration (NOAA), which have been the lynchpin for knowledge about atmospheric CH₄ since the 1980s. To achieve that we use the GEOS-Chem atmospheric chemistry transport model and an inverse method to infer a posteriori flux estimates from the NOAA and GOSAT data using common a priori emission inventories. We find the main benefit of GOSAT data is from its additional coverage over the tropics where we report large increases since the 2014/2016 El Niño, driven by biomass burning, biogenic emissions and energy production. We use data from the European TROPOspheric Monitoring Instrument to show how better spatial coverage and resolution measurements allow us to quantify previously unattainable diffuse sources of CH₄, thereby opening up a new research frontier. This article is part of a discussion meeting issue 'Rising methane: is warming feeding warming? (part 1)'.

Measuring Atmospheric CO2 Enhancements From the 2017 British Columbia Wildfires Using a Lidar

During the summer 2017 ASCENDS/ABoVE airborne science campaign, the NASA Goddard CO₂ Sounder lidar overflew smoke plumes from wildfires in the British Columbia, Canada. In the flight path over Vancouver Island on 8 August 2017, the column XCO₂ retrievals from the lidar measurements at flight altitudes around 9 km showed an average enhancement of 4 ppm from the wildfires. A comparison of these enhancements with those from the Goddard Global Chemistry Transport model suggested that the modeled CO₂ emissions from wildfires were underestimated by more than a factor of 2. A spiral-down validation performed at Moses Lake airport, Washington showed a bias of 0.1 ppm relative to in situ measurements and a standard deviation of 1 ppm in lidar XCO₂ retrievals. The results show that future airborne campaigns and spaceborne missions with this type of lidar can improve estimates of CO₂ emissions from wildfires and estimates of carbon fluxes globally.

FTIR Measurements of Greenhouse Gases over Thessaloniki, Greece in the Framework of COCCON and Comparison with S5P/TROPOMI Observations

In this work, column-averaged dry-air mole fractions of carbon dioxide (XCO2), methane (XCH4) and carbon monoxide (XCO) are presented for the first time at a mid-latitude urban station, Thessaloniki, Greece, using the Bruker EM27/SUN ground-based low-resolution Fourier Transform spectrometer operated according to the requirements of the Collaborative Carbon Column Observing Network (COCCON). Two years of measurements are presented and examined for seasonal variability. The observed XCO2 levels show the expected seasonal cycle (spring maximum, late summer minimum) with a peak-to-peak amplitude of 12 ppm, with maximum values reported for winter 2021 exceeding 416 ppm. The XCH4 values are shown to increase in the second half of the year, with autumn showing the highest mean value of 1.878 ± 0.01 ppm. The XCO levels, following anthropogenic sources, show high winter and low summer values, exhibiting a rise again in August and September with a maximum value of 114 ± 3 ppb and a minimum in summer 2020 of 76 ± 3 ppb. Additionally, methane and carbon monoxide products obtained from the TROPOspheric Monitoring Instrument (TROPOMI), Sentinel-5P space borne sensor, are compared with the ground-based measurements. We report a good agreement between products. The relative mean bias for methane and carbon monoxide are −0.073 ± 0.647% and 3.064 ± 5.566%, respectively. Furthermore, a 15-day running average is subtracted from the original daily mean values to provide ΔXCO2, ΔXCO and ΔXCH4 residuals, so as to identify local sources at a synoptic scale. ΔXCO and ΔXCO2 show the best correlation in the winter (R2 = 0.898, slope = 0.007) season due to anthropogenic emissions in this period of the year (combustion of fossil fuels or industrial activities), while in summer no correlation is found. ΔXCO and ΔXCH4 variations are similar through both years of measurements and have a very good correlation in all seasons including winter (R2 = 0.804, slope = 1.209). The investigation of the X-gases comparison is of primary importance in order to identify local sources and quantify the impact of these trace gases to the deregulation of earth-climate system balance.

CO2 Flux over the Contiguous United States in 2016 Inverted by WRF-Chem/DART from OCO-2 XCO2 Retrievals

High spatial resolution carbon dioxide (CO2) flux inversion systems are needed to support the global stocktake required by the Paris Agreement and to complement the bottom-up emission inventories. Based on the work of Zhang, a regional CO2 flux inversion system capable of assimilating the column-averaged dry air mole fractions of CO2 (XCO2) retrieved from Orbiting Carbon Observatory-2 (OCO-2) observations had been developed. To evaluate the system, under the constraints of the initial state and boundary conditions extracted from the CarbonTracker 2017 product (CT2017), the annual CO2 flux over the contiguous United States in 2016 was inverted (1.08 Pg C yr−1) and compared with the corresponding posterior CO2 fluxes extracted from OCO-2 model intercomparison project (OCO-2 MIP) (mean: 0.76 Pg C yr−1, standard deviation: 0.29 Pg C yr−1, 9 models in total) and CT2017 (1.19 Pg C yr−1). The uncertainty of the inverted CO2 flux was reduced by 14.71% compared to the prior flux. The annual mean XCO2 estimated by the inversion system was 403.67 ppm, which was 0.11 ppm smaller than the result (403.78 ppm) simulated by a parallel experiment without assimilating the OCO-2 retrievals and closer to the result of CT2017 (403.29 ppm). Independent CO2 flux and concentration measurements from towers, aircraft, and Total Carbon Column Observing Network (TCCON) were used to evaluate the results. Mean bias error (MBE) between the inverted CO2 flux and flux measurements was 0.73 g C m−2 d−1, was reduced by 22.34% and 28.43% compared to those of the prior flux and CT2017, respectively. MBEs between the CO2 concentrations estimated by the inversion system and concentration measurements from TCCON, towers, and aircraft were reduced by 52.78%, 96.45%, and 75%, respectively, compared to those of the parallel experiment. The experiment proved that CO2 emission hotspots indicated by the inverted annual CO2 flux with a relatively high spatial resolution of 50 km consisted well with the locations of most major metropolitan/urban areas in the contiguous United States, which demonstrated the potential of combing satellite observations with high spatial resolution CO2 flux inversion system in supporting the global stocktake.

Monitoring Greenhouse Gases from Space

The increase in atmospheric greenhouse gas concentrations of CO2 and CH4, due to human activities, is the main driver of the observed increase in surface temperature by more than 1 °C since the pre-industrial era. At the 2015 United Nations Climate Change Conference held in Paris, most nations agreed to reduce greenhouse gas emissions to limit the increase in global surface temperature to 1.5 °C. Satellite remote sensing of CO2 and CH4 is now well established thanks to missions such as NASA’s OCO-2 and the Japanese GOSAT missions, which have allowed us to build a long-term record of atmospheric GHG concentrations from space. They also give us a first glimpse into CO2 and CH4 enhancements related to anthropogenic emission, which helps to pave the way towards the future missions aimed at a Monitoring & Verification Support (MVS) capacity for the global stock take of the Paris agreement. China plays an important role for the global carbon budget as the largest source of anthropogenic carbon emissions but also as a region of increased carbon sequestration as a result of several reforestation projects. Over the last 10 years, a series of projects on mitigation of carbon emission has been started in China, including the development of the first Chinese greenhouse gas monitoring satellite mission, TanSat, which was successfully launched on 22 December 2016. Here, we summarise the results of a collaborative project between European and Chinese teams under the framework of the Dragon-4 programme of ESA and the Ministry of Science and Technology (MOST) to characterize and evaluate the datasets from the TanSat mission by retrieval intercomparisons and ground-based validation and to apply model comparisons and surface flux inversion methods to TanSat and other CO2 missions, with a focus on China.

2019–20 Australian Bushfires and Anomalies in Carbon Monoxide Surface and Column Measurements

In Australia, bushfires are a natural part of the country’s landscape and essential for the regeneration of plant species; however, the 2019–20 bushfires were unprecedented in their extent and intensity. This paper is focused on the 2019–20 Australian bushfires and the resulting surface and column atmospheric carbon monoxide (CO) anomalies around Wollongong. Column CO data from the ground-based Total Carbon Column Observing Network (TCCON) and Network for the Detection of Atmospheric Composition Change (NDACC) site in Wollongong are used together with surface in situ measurements. A systematic comparison was performed between the surface in situ and column measurements of CO to better understand whether column measurements can be used as an estimate of the surface concentrations. If so, satellite column measurements of CO could be used to estimate the exposure of humans to CO and other fire-related pollutants. We find that the enhancements in the column measurements are not always significantly evident in the corresponding surface measurements. Topographical features play a key role in determining the surface exposures from column abundance especially in a coastal city like Wollongong. The topography at Wollongong, combined with meteorological effects, potentially exacerbates differences in the column and surface. Hence, satellite column amounts are unlikely to provide an accurate reflection of exposure at the ground during major events like the 2019–2020 bushfires.

Airborne Testing of 2-μm Pulsed IPDA Lidar for Active Remote Sensing of Atmospheric Carbon Dioxide

The capability of an airborne 2-μm integrated path differential absorption (IPDA) lidar for high-accuracy and high-precision active remote sensing of weighted-average column dry-air volume mixing ratio of atmospheric carbon dioxide (XCO2) is demonstrated. A test flight was conducted over the costal oceanic region of the USA to assess instrument performance during severe weather. The IPDA targets CO2 R30 absorption line using high-energy 2-μm laser transmitter. HgCdTe avalanche photodiode detection system is used in the receiver. Updated instrument model included range correction factor to account for platform attitude. Error budget for XCO2 retrieval predicts lower random error for longer sensing column length. Systematic error is dominated by water vapor (H2O) through dry-air number density derivation, followed by H2O interference and ranging related uncertainties. IPDA XCO2 retrieval results in 404.43 ± 1.23 ppm, as compared to 405.49 ± 0.01 ppm from prediction models, using consistent reflectivity and steady elevation oceanic surface target. This translates to 0.26% and 0.30% relative accuracy and precision, respectively. During gradual spiral descend, IPDA results in 404.89 ± 1.19 ppm as compared model of 404.75 ± 0.73 ppm indicating 0.04% and 0.23% relative accuracy, respectively. Challenging cloud targets limited retrieval accuracy and precision to 2.56% and 4.78%, respectively, due to H2O and ranging errors.

Design of a high-coverage ground-based CO2 monitoring layout using a novel information theory-based optimization model

Over the past decade, monitoring of the carbon cycle has become a major concern accented by the severe impacts of global warming. Here, we develop an information theory-based optimization model using the NSGA-II algorithm that determines an optimum ground-based CO₂ monitoring layout with the highest spatial coverage using a finite number of stations. The value of information (VOI) concept is used to assess the efficacy of the monitoring stations given their construction cost. In conjunction with VOI, the entropy theory-in terms of transinformation-is adopted to determine the redundant (overlapping) information rendered by the selected monitoring stations. The developed model is used to determine a ground-based CO₂ monitoring layout for Iran, the eighth-ranked country emitting CO₂ worldwide. An NSGA-II optimization model provides a tradeoff curve given the objectives of (1) minimizing the size of monitoring network; (2) maximizing VOI, i.e., spatial coverage; and (3) minimizing transinformation, i.e., overlapping information. Borda count method is then employed to select the most appropriate compromise monitoring layout from the Pareto-front solutions given regional priorities and concerns.

Remote Sensing of Atmospheric Hydrogen Fluoride (HF) over Hefei, China with Ground-Based High-Resolution Fourier Transform Infrared (FTIR) Spectrometry

Remote sensing of atmospheric hydrogen fluoride (HF) is challenging because it has weak absorption signatures in the atmosphere and is surrounded by strong absorption lines from interfering gases. In this study, we first present a multi-year time series of HF total columns over Hefei, China by using high-resolution ground-based Fourier transform infrared (FTIR) spectrometry. Both near-infrared (NIR) and mid-infrared (MIR) solar spectra suites, which are recorded following the requirements of Total Carbon Column Observing Network (TCCON) and Network for the Detection of Atmospheric Composition Change (NDACC), respectively, are used to retrieve total column of HF (THF) and column-averaged dry-air mole fractions of HF (XHF). The NIR and MIR observations are generally in good agreement with a correlation coefficient (R) of 0.87, but the NIR observations are found to be (6.90 ± 1.07 (1σ)) pptv, which is lower than the MIR observations. By correcting this bias, the combination of NIR and MIR observations discloses that the XHF over Hefei showed a maximum monthly mean value of (64.05 ± 3.93) pptv in March and a minimum monthly mean value of (45.15 ± 2.93) pptv in September. The observed XHF time series from 2015 to 2020 showed a negative trend of (−0.38 ± 0.22) % per year. The variability of XHF is inversely correlated with the tropopause height, indicating that the variability of tropopause height is a key factor that drives the seasonal cycle of HF in the stratosphere. This study can enhance the understanding of ground-based high-resolution remote sensing techniques for atmospheric HF and its evolution in the stratosphere and contribute to forming new reliable remote sensing data for research on climate change.