Thermal and near infrared sensor for carbon observation Fourier-transform spectrometer on the Greenhouse Gases Observing Satellite for greenhouse gases monitoring

Estimation of daily XCO2 at 1 km resolution in China using a spatiotemporal ResNet model

Carbon dioxide (CO2) serves as a crucial greenhouse gas that traps heat and regulates the Earth's temperature. High spatiotemporal resolution CO2 estimation can provide valuable information to understand the characteristics of fine-scale climate change trends and to formulate more effective emission reduction strategies. This study presents a spatiotemporal ResNet model (ST-ResNet) specifically developed to estimate the highest resolution (1 km × 1 km) daily column-averaged dry-air mole fraction of CO2 (XCO2) in China from 2015 to 2020. The ST-ResNet model excels in estimating XCO2 by comprehensively considering the complex relationships between XCO2 and its various influencing factors, while efficiently capturing both temporal and spatial correlations, thereby demonstrating remarkable generalization capability. The results show that the ST-ResNet generates a highly accurate XCO2 dataset, outperforming the traditional ResNet. Ground-based validation results further confirm the high accuracy and spatiotemporal resolution of our estimated data product. Using this dataset, the spatial and temporal characteristics of XCO2 across the entire China and several urban agglomerations have been analyzed. The high spatiotemporal resolution estimated XCO2 dataset for China is made publicly available at [https://doi.org/10.6084/m9.figshare.25272868], offering substantial potential for fine-scale carbon research.

Inverse modeling of 2010-2022 satellite observations shows that inundation of the wet tropics drove the 2020-2022 methane surge

Atmospheric methane concentrations rose rapidly over the past decade and surged in 2020-2022 but the causes have been unclear. We find from inverse analysis of GOSAT satellite observations that emissions from the wet tropics drove the 2010-2019 increase and the subsequent 2020-2022 surge, while emissions from northern mid-latitudes decreased. The 2020-2022 surge is principally contributed by emissions in Equatorial Asia (43%) and Africa (30%). Wetlands are the major drivers of the 2020-2022 emission increases in Africa and Equatorial Asia because of tropical inundation associated with La Niña conditions, consistent with trends in the GRACE terrestrial water storage data. In contrast, emissions from major anthropogenic emitters such as the United States, Russia, and China are relatively flat over 2010-2022. Concentrations of tropospheric OH (the main methane sink) show no long-term trend over 2010-2022 but a decrease over 2020-2022 that contributed to the methane surge.

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.

Near-infrared detection based on the excitation of hot electrons in Au/Si microcone array

Although the photoresponse cut-off wavelength of Si is about 1100 nm due to the Si bandgap energy, the internal photoemission effect (IPE) of the Au/Si junction in Schottky detector can extend the absorption wavelength, which makes it a promising candidate for the Si-based infrared detector. However, due to low light absorption, low photon-electron interaction, and poor electron injection efficiency, the near-infrared light detection efficiency of the Schottky detector is still insufficient. The synergistic effect of Si nano/microstructures with a strong light trapping effect and nanoscale Au films with surface plasmon enhanced absorption may provide an effective solution for improving the detection efficiency. In this paper, a large-area periodic Si microcone array covered by an Au film has successfully been fabricated by one-time dry etching based on the mature polystyrene microspheres lithography technique and vacuum thermal deposition, and its properties for hot electron-based near infrared photodetection are investigated. Optical measurements show that the 20 nm-thick Au covered Si microcone array exhibits a low reflectance and a strong absorption (about 85%) in wide wavelength range (900-2500 nm), and the detection responsivity can reach a value as high as 17.1 and 7.0 mA W-1at 1200 and 1310 nm under the front illumination, and 35.9 mA W-1at 1310 nm under the back illumination respectively. Three-dimensional finite difference time domain (3D-FDTD) simulation results show that the enhanced local electric field in the Au layer distributes near the air/Au interface under the front illumination and close to the Au/Si interface under the back illumination. The back illumination favors the injection of photo-generated hot electrons in Au layer into Si, which can explain the higher responsivity under the back illumination. Our research is expected to promote the practical application of Schottky photodetectors to Si-compatible near infrared photodetectors.

Invert global and China's terrestrial carbon fluxes over 2019-2021 based on assimilating richer atmospheric CO2 observations

With China's commitment to reach carbon peak by 2030 and achieve carbon neutrality by 2060, it is particularly important to obtain terrestrial ecosystem carbon fluxes with low uncertainty both globally and in China. The use of more observation data may help reduce the uncertainty of inverting carbon fluxes. This study uses the observation data from global stations, background stations and provincial stations in China, as well as the OCO-2 satellite, and uses the China Carbon Monitoring, Verification and Supporting System for Global (CCMVS-G) to estimate the carbon fluxes of global and Chinese terrestrial ecosystems from 2019 to 2021. The results revealed that the global terrestrial ecosystem carbon sink was approximately -3.40 Pg C/yr from 2019 to 2021. The carbon sinks in the Northern Hemisphere are large, especially in Asia, North America, and Europe. From 2019 to 2021, the carbon sink of China's terrestrial ecosystem was approximately -0.44 Pg C/yr. Carbon sinks exhibit significant seasonal and interannual variations in China. After assimilating the observation data, the uncertainty of the posterior flux is smaller than that of the prior flux, a more reasonable distribution of carbon sources and sinks can be obtained, and more accurate boundary conditions can be provided for the China Carbon Monitoring, Verification and Supporting System for Regional (CCMVS-R). In the future, it is important to establish a well-designed CO2 ground-based observation network.

Preliminary analysis of global column-averaged CO2 concentration data from the spaceborne aerosol and carbon dioxide detection lidar onboard AEMS

In contrast to the passive remote sensing of global CO2 column concentrations (XCO2), active remote sensing with a lidar enables continuous XCO2 measurements throughout the entire atmosphere in daytime and nighttime. The lidar could penetrate most cirrus and is almost unaffected by aerosols. Atmospheric environment monitoring satellite (AEMS, also named DQ-1) aerosol and carbon dioxide detection Lidar (ACDL) is a novel spaceborne lidar that implements a 1572 nm integrated path differential absorption (IPDA) method to measure the global XCO2 for the first time. In this study, special methods have been developed for ACDL data processing and XCO2 retrieval. The CO2 measurement data products of ACDL, including the differential absorption optical depth between the online and offline wavelengths, the integral weighting function, and XCO2, are presented. The results of XCO2 measurements over the period from 1st June 2022 to 30th June 2022 (first month data of ACDL) are analyzed to demonstrate the measurement capabilities of the spaceborne ACDL system.

Underestimated Dry Season Methane Emissions from Wetlands in the Pantanal

Tropical wetlands contribute ∼30% of the global methane (CH4) budget. Limited observational constraints on tropical wetland CH4 emissions lead to large uncertainties and disparities in representing emissions. In this work, we combine remote sensing observations with atmospheric and wetland models to investigate dry season wetland CH4 emissions from the Pantanal region of South America. We incorporate inundation maps generated from the Cyclone Global Navigation Satellite System (CYGNSS) satellite constellation together with traditional inundation maps to generate an ensemble of wetland CH4 emission realizations. We challenge these realizations with daily satellite observations for May-July when wetland CH4 emission predictions diverge. We find that the CYGNSS inundation products predict larger emissions in May, in better agreement with observations. We use the model ensemble to generate an empirical observational constraint on CH4 emissions independent of choice of inundation map, finding large dry season wetland CH4 emissions (31.7 ± 13.6 and 32.0 ± 20.2 mg CH4/m2/day in May and June/July during 2018/2019, respectively). These May/June/July emissions are 2-3 times higher than current models, suggesting that annual wetland emissions may be higher than traditionally simulated. Observed trends in the early dry season indicate that dynamics during this period are of importance in representing tropical wetland CH4 behaviors.

Characterizing the regional XCO2 variability and its association with ENSO over India inferred from GOSAT and OCO-2 satellite observations

India is primarily concerned with comprehending regional carbon source-sink response in the context of changes in atmospheric CO2 concentrations or anthropogenic emissions. Recent advancements in high-resolution satellite's fine-scale XCO2 measurements provide an opportunity to understand unprecedented details of source-sink activity on a regional scale. In this study, we investigated the long-term variations of XCO2 concentration and growth rates as well as its covarying relationship with ENSO and regional climate parameters (temperature, precipitation, soil moisture, and NDVI) over India from 2010 to 2021 using GOSAT and OCO-2 retrievals. The results show since the launch of OCO-2 in 2014, the number of monthly high-quality XCO2 soundings over India has grown nearly 100-fold compared to GOSAT, launched in 2009. Also, the discrepancy in XCO2 increase of 2.54(2.43) ppm/yr was observed in GOSAT (OCO-2) retrieval during an overlapping measurement period (2015-2021). Additionally, wavelet analysis indicated that the OCO-2 retrieval is able to capture a better frequency of local-scale XCO2 variability compared to GOSAT, owing to its high-resolution cloud-free XCO2 soundings, providing more well-defined regional-scale source-sink features. Furthermore, dominant spatial pattern of XCO2 variability observed over south and southeast of India in both satellites, with XCO2 semi-annual and annual variability more distinctly present in OCO-2 compared to GOSAT. A cross-correlation analysis suggested GOSAT XCO2 growth rate positively correlates with ENSO in different homogeneous monsoon regions of India, with ENSO leading the GOSAT XCO2 growth rate in all homogeneous regions by 3-9 months. The South Peninsular region sensitive to ENSO changes, especially during 2015-2016 ENSO event, where a decrease in CO2 uptake was observed is closely linked with precipitation, soil moisture, and temperature anomalies. However, regional climate parameters show a low correlation with XCO2 growth since CO2 is a long-lived well-mixed gas primarily having an imprint of large-scale transport in column CO2.

Greenhouse gas monitoring instrument on the GF-5 satellite-II: on-orbit spectral calibration

The greenhouse gas monitoring instruments were carried on the Gaofen-5-II satellite, which was launched into orbit from the Taiyuan Satellite Launch Center on September 7, 2021. In order to improve the on-orbit data quantification level, a calibration device based on diffuse reflector system was designed, which can realize on-orbit spectral and radiation calibration. In this paper, the principle of standard spectral line selection is given, and the characteristic spectral lines that can be used for on-orbit spectral calibration are extracted. The wavelength deviation evaluation function is established by using the method of matching the high-resolution reference spectrum after the linear function of the convolution instrument with the on-orbit calibration measurement spectrum, and finally using the Levenberg-Marquardt algorithm to evaluate the function. The optimization solution is the on-orbit wavelength calibration result. According to the above method, the on-orbit calibration data are processed. After calibration, the maximum deviation of the on-orbit spectral offset is changed from 0.133 to 0.009c m -1, and variations in magnitude less than 10% of the spectral resolution for C O 2 (1.57 µm) band have been detected.

Satellite observation of atmospheric CO2 and water storage change over Iran

Like many other Middle East countries, Iran has been suffering from severe water shortages over the last two decades, as evidenced by significant decline in surface water and groundwater levels. The observed changes in water storage can be attributed to the mutually reinforcing effects of human activities, climatic variability, and of course the climate change. The objective of this study is to analyze the dependency of atmospheric CO₂ increase on the water shortage of Iran, for which we investigate the spatial relationship between water storage change and CO₂ concentration using large scale satellite data. We conduct our analysis using water storage change data from GRACE satellite and atmospheric CO₂ concentration from GOSAT and SCIAMACHY satellites during 2002-2015. To analyze the long-term behavior of time series we benefit from Mann-Kendal test and for the investigation of the relationship between atmospheric CO₂ concentration and total water storage we use Canonical Correlation Analysis (CCA) and Regression model. Our Results show that the water storage change anomaly and CO₂ concentration are negatively correlated especially in northern, western, southwest (Khuzestan province), and also southeast (Kerman, Hormozgan, Sistan, and Baluchestan provinces) of Iran. CCA results reveal that in the most of northern regions, the decrease in water storage is significantly influenced by the increase of CO₂ concentration. The results further show that precipitation in the highland and peaks does not seem to be influenced by the long and short-term variation in CO₂ concentration. Besides, our results show that the CO₂ concentration is slightly correlated with a weak positive trend in evapotranspiration over agricultural areas. Thus, the indirect effect of CO₂ on increasing evapotranspiration is observed spatially in the whole of Iran. The results of the regression model between total water storage change and carbon dioxide (R² = 0.91)/water discharge/water consumption show that carbon dioxide has the highest effect on total water storage change at large scale. The results of this study will contribute to both water resource management and mitigation plans to achieve the goal of CO₂ emission reduction.

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.

Role of space station instruments for improving tropical carbon flux estimates using atmospheric data

The tropics is the nexus for many of the remaining gaps in our knowledge of environmental science, including the carbon cycle and atmospheric chemistry, with dire consequences for our ability to describe the Earth system response to a warming world. Difficulties associated with accessibility, coordinated funding models and economic instabilities preclude the establishment of a dense pan-tropical ground-based atmospheric measurement network that would otherwise help to describe the evolving state of tropical ecosystems and the associated biosphere-atmosphere fluxes on decadal timescales. The growing number of relevant sensors aboard sun-synchronous polar orbiters provide invaluable information over the remote tropics, but a large fraction of the data collected along their orbits is from higher latitudes. The International Space Station (ISS), which is in a low-inclination, precessing orbit, has already demonstrated value as a proving ground for Earth observing atmospheric sensors and as a testbed for new technology. Because low-inclination orbits spend more time collecting data over the tropics, we argue that the ISS and its successors, offer key opportunities to host new Earth-observing atmospheric sensors that can lead to a step change in our understanding of tropical carbon fluxes.

Detection of Anthropogenic CO2 Emission Signatures with TanSat CO2 and with Copernicus Sentinel-5 Precursor (S5P) NO2 Measurements: First Results

China's first carbon dioxide (CO₂) measurement satellite mission, TanSat, was launched in December 2016. This paper introduces the first attempt to detect anthropogenic CO₂ emission signatures using CO₂ observations from TanSat and NO₂ measurements from the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Copernicus Sentinel-5 Precursor (S5P) satellite. We focus our analysis on two selected cases in Tangshan, China and Tokyo, Japan. We found that the TanSat XCO₂ measurements have the capability to capture the anthropogenic variations in the plume and have spatial patterns similar to that of the TROPOMI NO₂ observations. The linear fit between TanSat XCO₂ and TROPOMI NO₂ indicates the CO₂-to-NO₂ ratio of 0.8 × 10^-16 ppm (molec cm^-2)^-1 in Tangshan and 2.3 × 10^-16 ppm (molec cm^-2)^-1 in Tokyo. Our results align with the CO₂-to-NO _x emission ratios obtained from the EDGAR v6 emission inventory.

Observed changes in China's methane emissions linked to policy drivers

China intends to significantly reduce its methane emissions in the 2020s. A better understanding of methane emissions at regional and national levels provides valuable inputs to the formulation of the action plan. Our observation-based analysis reveals complex and even unexpected linkages between recent changes in China’s methane emissions and related policy drivers: China’s energy policy that prioritizes the phase out of small coal mines leads to region-varying responses in coal mining methane emissions, while agricultural and environmental policies aimed at improving crop production and air quality may have contributed to increased methane emissions from rice cultivation. These findings highlight the importance of integrated considerations in designing methane policy to achieve energy, food, health, and climate targets.

China is set to actively reduce its methane emissions in the coming decade. A comprehensive evaluation of the current situation can provide a reference point for tracking the country’s future progress. Here, using satellite and surface observations, we quantify China’s methane emissions during 2010–2017. Including newly available data from a surface network across China greatly improves our ability to constrain emissions at subnational and sectoral levels. Our results show that recent changes in China’s methane emissions are linked to energy, agricultural, and environmental policies. We find contrasting methane emission trends in different regions attributed to coal mining, reflecting region-dependent responses to China’s energy policy of closing small coal mines (decreases in Southwest) and consolidating large coal mines (increases in North). Coordinated production of coalbed methane and coal in southern Shanxi effectively decreases methane emissions, despite increased coal production there. We also detect unexpected increases from rice cultivation over East and Central China, which is contributed by enhanced rates of crop-residue application, a factor not accounted for in current inventories. Our work identifies policy drivers of recent changes in China’s methane emissions, providing input to formulating methane policy toward its climate goal.

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.

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.

Optical Design of a Novel Wide-Field-of-View Space-Based Spectrometer for Climate Monitoring

We report on a near-infrared imaging spectrometer for sensing the three most prominent greenhouse gases in the atmosphere (water vapor, carbon dioxide and methane). The optical design of the spectrometer involves freeform optics, which enables achieving exceptional performance and allows progressing well beyond the state-of-the-art in terms of compactness, field-of-view, and spatial resolution. The spectrometer is intended to be launched on a small satellite orbiting at 700 km and observing the Earth with a wide field-of-view of 120° and a spatial resolution of 2.6 km at nadir. The satellite will ultimately allow for improved climate change monitoring.

The increasing atmospheric CO2 over India: Comparison to global trends

Atmospheric CO₂ is the key Greenhouse Gas in terms of its global warming potential and anthropogenic sources. Therefore, it is important to analyze the changes in the concentration of atmospheric CO₂ to monitor regional and global climate change. Here, we use ground-based and satellite measurements for the 2002-2020 period to assess CO₂ over India. The average CO₂ trend over India is about 2.1 ppm/yr, and the highest trends are in agreement with the increase in total energy consumption during the period, and the highest trends are found in the areas of mines and refineries in the west and east India. The estimated CO₂ trends for India are comparable to that of global tropical and mid-latitude regions. The increasing CO₂ implies serious anthropogenic global warming and thus, calls for mitigation measures and continuous monitoring for timely policy interventions.

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All satellite CO₂ measurements show a bias between −0.5 and 3.0 ppm

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Coastal India shows high concentrations and the highest trend is 2.4 ppm/yr

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The global average CO₂ trends are similar to that of India, about 1.8-2.1 ppm/yr

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The Increasing CO₂ is a concern for regional warming and global climate change

Anthropogenic climate change drives melting of glaciers in the Himalaya

The Himalayan glaciers provide water to a large population in south Asia for a variety of purposes and ecosystem services. As a result, regional monitoring of glacier melting and identification of the drivers are important for understanding and predicting future cryospheric melting trends. Using multi-date satellite images from 2000 to 2020, we investigated the shrinkage, snout retreat, thickness changes, mass loss and velocity changes of 77 glaciers in the Drass basin, western Himalaya, India. During this period, the total glacier cover has shrunk by 5.31 ± 0.33 km2. The snout retreat ranged from 30 to 430 m (mean 155 ± 9.58 m). Debris cover had a significant impact on glacier melting, with clean glaciers losing ~ 5% more than debris-covered glaciers (~ 2%). The average thickness change and mass loss of glacier have been - 1.27 ± 0.37 and - 1.08 ± 0.31 m w.e.a-1, respectively. Because of the continuous melting and the consequent mass loss, average glacier velocity has reduced from 21.35 ± 3.3 m a-1 in 2000 to 16.68 ± 1.9 m a-1 by 2020. During the observation period, the concentration of greenhouse gases (GHGs), black carbon (BC) and other pollutants from vehicular traffic near the glaciers increased significantly. Increasing temperatures, caused by a significant increase in GHGs, black carbon and other pollutants in the atmosphere, are driving glacier melting in the study area. If the current trend continues in the future, the Himalayan glaciers may disappear entirely, having a significant impact on regional water supplies, hydrological processes, ecosystem services and transboundary water sharing.

The status of carbon neutrality of the world's top 5 CO2 emitters as seen by carbon satellites

China, the Unite States (US), the European Union (EU), India, and Russia are the world's top 5 fossil fuel and cement CO2 (FFC) emitting countries or regions (CRs). It is very important to understand their status of carbon neutrality, and to monitor their future changes of net carbon fluxes (NCFs). In this study, we implemented a well-established global carbon assimilation system (GCAS, Version 2) to infer global surface carbon fluxes from May 2009 to December 2019 using both GOSAT and OCO-2 XCO2 retrievals. The reductions of flux uncertainty and XCO2 bias, and the evaluation of posterior flux show that GCAS has comparable and good performance in the 5 CRs. The results suggest that Russia has achieved carbon neutrality, but the other 4 are still far from being carbon neutral, especially China. The mean annual NCFs in China, the US, the EU, India, and Russia are 2.33 ± 0.29, 0.82 ± 0.20, 0.42 ± 0.16, 0.50 ± 0.12, and -0.33 ± 0.23 PgC yr-1, respectively. From 2010 to 2019, the NCFs showed an increasing trend in the US and India, a slight downward trend after 2013 in China, and were stable in the EU. The changes of land sinks in China and the US might be the main reason for their trends. India's trend was mainly due to the increase of FFC emission. The relative contributions of NCFs to the global land net carbon emission of China and the EU have decreased, while those of the US and India have increased, implying the US and India must take more active measures to control carbon emissions or increase their sinks. This study indicates that satellite XCO2 could be successfully used to monitor the changes of regional NCFs, which is of great significance for major countries to achieve greenhouse gas control goals.

Pixel-scale miniaturization of guided mode resonance transmission filters in short wave infrared

The effects of miniaturization on theoretically predicted performance of dual-period guided mode resonance (GMR) transmission filters, which demonstrate immense potential for multispectral imaging in short wave infrared (SWIR), have been compared with experimental findings. With reducing filter size from 112 periods (90 µm) to 12 periods (10 µm), peak transmittance (Tpeak) of simulated and measured filters reduced gradually from 84% to 55% and from 76% to 65%, respectively, with a moderate change of 1 - 3 nm in full width at half maximum (FWHM). For 6 period filters (5 µm), simulations predict drastically reduced Tpeak = 14% accompanied by increase in FWHM by 12 nm. The Tpeak value is theoretically shown to increase to 46% with FWHM reduced by 7 nm upon placing metal reflectors at the optimum positions to increase the optical path length. Our findings indicate that four 5 µm × 5 µm size filters with metal reflectors designed for different resonance wavelengths can be used to form a single, 20 µm × 20 µm mosaic pixel for SWIR multispectral imaging.

Benefits of satellite XCO2 and newly proposed atmospheric CO2 observation network over India in constraining regional CO2 fluxes

Top-down modeling estimates are among the most reliable information available on the CO₂ fluxes of the earth system. The inadequate coverage of CO₂ observing stations over the tropical regions adds a limitation to this estimate, especially when the satellite XCO₂ is strictly screened for cloud contamination, aerosol, dust, etc. In this study, we investigated the potential benefit of a global ground-based observing station network, 17 newly proposed stations over India, and global satellite XCO₂ in reducing the uncertainty of terrestrial biospheric fluxes of Tropical Asia-Eurasia in TransCom cyclo-stationary inversion. The data from selected 80 global ground-based CO₂ observation stations, together with two additional stations from India (i.e., Cape Rama and Sinhagad) and satellite XCO₂, helps to reduce the temperate Eurasian terrestrial flux uncertainty by 23.8%, 26.4%, and 36.2%, respectively. This further improved to 54.7% by adding the newly proposed stations over India into the inversion. By separating the Indian sub-continent from temperate Eurasia (as inspired by the heterogeneity in the terrestrial ecosystems, prevailing meteorological conditions, and the orography of this vast region), the inversion evinces the capacity of existing CO₂ observations to reduce the Indian terrestrial flux uncertainty by 20.5%. The largest benefit (70% reduction of annual mean uncertainty) for estimating Indian terrestrial fluxes could be achieved by combining these global observations with data from the newly proposed stations over India. The existing two stations from India suggest Temperate Eurasia as a mild source of CO₂ (0.33 ± 0.57 Pg C yr^-1), albeit with prominent anthropogenic influences visible in these two stations during the dry seasons. This implies that the proposed new stations should be cautiously placed to avoid such effects. The study also finds that the newly proposed stations over India also have an impact in constraining nearby oceanic CO₂ fluxes.

Global monthly gridded atmospheric carbon dioxide concentrations under the historical and future scenarios

Increases in atmospheric carbon dioxide (CO₂) concentrations is the main driver of global warming due to fossil fuel combustion. Satellite observations provide continuous global CO₂ retrieval products, that reveal the nonuniform distributions of atmospheric CO₂ concentrations. However, climate simulation studies are almost based on a globally uniform mean or latitudinally resolved CO₂ concentrations assumption. In this study, we reconstructed the historical global monthly distributions of atmospheric CO₂ concentrations with 1° resolution from 1850 to 2013 which are based on the historical monthly and latitudinally resolved CO₂ concentrations accounting longitudinal features retrieved from fossil-fuel CO₂ emissions from Carbon Dioxide Information Analysis Center. And the spatial distributions of nonuniform CO₂ under Shared Socio-economic Pathways and Representative Concentration Pathways scenarios were generated based on the spatial, seasonal and interannual scales of the current CO₂ concentrations from 2015 to 2150. Including the heterogenous CO₂ distributions could enhance the realism of global climate modeling, to better anticipate the potential socio-economic implications, adaptation practices, and mitigation of climate change.

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.

Effect of assimilating CO2 observations in the Korean Peninsula on the inverse modeling to estimate surface CO2 flux over Asia

To investigate the impact of two CO2 observation datasets obtained from the Korean Peninsula on the surface CO2 flux estimation over Asia, the two datasets are assimilated into the CarbonTracker (CT) inverse modeling system and the estimated surface CO2 fluxes are analyzed. Anmyeon-do (AMY) and Gosan (GSN) sites in the Korean Peninsula have observed surface CO2 mole fraction since the late 1990s. To investigate the effect of assimilating the additional Korean observations on the surface CO2 flux estimation over Asia, two experiments are conducted. The reference experiment (CNTL) only assimilates observations provided by National Oceanic and Atmospheric Administration (NOAA), while the other experiment (EXP1) assimilates both NOAA observations and two Korean observation datasets. The results are analyzed for 9 years from 2003 to 2011 in Asia region because both AMY and GSN datasets exist almost completely for this period. The annual average of estimated biosphere CO2 flux of EXP1 shows more flux absorption in summer and less flux emission from fall to spring compared to CNTL, mainly on Eurasia Temperate and Eurasia Boreal regions. When comparing model results to independent CO2 concentration data from surface stations and aircraft, the root mean square error is smaller for EXP1 than CNTL. The EXP1 yields more reduction on uncertainty of estimated biosphere CO2 flux over Asia, and the observation impact of AMY, GSN sites on flux estimation is approximately 11%, which is greater than other observation sites around the world. Therefore, the two CO2 observation sets in the Korean Peninsula are useful in reducing uncertainties for regional as well as global scale CO2 flux estimation.

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.

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.

Seasonal Variations of Solar-Induced Fluorescence, Precipitation, and Carbon Dioxide Over the Amazon

Previous studies suggested that the Amazon, the largest rainforest on Earth, changes from a CO₂ sink to a CO₂ source during the dry/fire season. However, the biospheric contributions to atmospheric CO₂ are not well understood during the two main seasons, the dry/fire season and the wet season. In this article, we utilize Orbiting Carbon Observatory 2 (OCO-2) Solar-Induced Fluorescence (SIF) to explore photosynthetic activity during the different seasons. The spatiotemporal variability of OCO-2 SIF, OCO-2 CO₂, precipitation, and burned area are investigated over the Amazon from September 2014 to December 2019. Averaging over the entire Amazon region, we found a positive temporal correlation (0.94) between OCO-2 SIF and Global Precipitation Climatology Project precipitation and a negative temporal correlation (-0.64) between OCO-2 SIF and OCO-2 CO₂, consistent with the fact that precipitation enhances photosynthesis, which results in higher values for SIF and rate of removal of CO₂ from the atmosphere above the Amazon region. We also observed seasonality in the spatial variability of these variables within the Amazon region. During the dry/fire (August-October) season, low SIF values, low precipitation, high vapor pressure deficit (VPD), large burned areas, and high atmospheric CO₂ are mainly found over the southern Amazon region. In contrast, during the wet season (January-March), high SIF values, high precipitation, low VPD, smaller burned areas, and low CO₂ are found over both the central and southern Amazon regions. The seasonal difference in SIF suggests that photosynthetic activity is reduced during the dry/fire season relative to the wet season as a result of low precipitation and high VPD, especially over the southern Amazon region, which will contribute to more CO₂ in the atmosphere during the dry/fire season.

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.

Detecting mid-infrared light by molecular frequency upconversion in dual-wavelength nanoantennas

[Figure: see text].

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)'.

Regional impacts of COVID-19 on carbon dioxide detected worldwide from space

Activity reductions in early 2020 due to the coronavirus disease 2019 pandemic led to unprecedented decreases in carbon dioxide (CO₂) emissions. Despite their record size, the resulting atmospheric signals are smaller than and obscured by climate variability in atmospheric transport and biospheric fluxes, notably that related to the 2019–2020 Indian Ocean Dipole. Monitoring CO₂ anomalies and distinguishing human and climatic causes thus remain a new frontier in Earth system science. We show that the impact of short-term regional changes in fossil fuel emissions on CO₂ concentrations was observable from space. Starting in February and continuing through May, column CO₂ over many of the world’s largest emitting regions was 0.14 to 0.62 parts per million less than expected in a pandemic-free scenario, consistent with reductions of 3 to 13% in annual global emissions. Current spaceborne technologies are therefore approaching levels of accuracy and precision needed to support climate mitigation strategies with future missions expected to meet those needs.

A balloon-borne imaging Fourier transform spectrometer for atmospheric trace gas profiling

The upper troposphere and lower stratosphere (UTLS) region is a highly variable region of the atmosphere and critical for understanding climate. Yet, it remains undersampled in the observational satellite record. Due to recent advances in interferometer and infrared detection technologies, imaging Fourier transform spectrometer (FTS) technology has been identified as a feasible remote sensing approach to obtain the required precision and spatial resolution of atmospheric trace gas composition in the UTLS. Building on the success of instruments such as the Michelson Interferometer for Passive Atmospheric Sounding and gimbaled limb observer for radiance imaging of the atmosphere, the limb imaging Fourier transform spectrometer experiment (LIFE) instrument, of which this paper details the design and performance, is a balloon-borne infrared imaging FTS developed as an early prototype of a low earth orbit satellite instrument. LIFE is constructed primarily with commercially available off-the-shelf components, with a design emphasis on greatly reducing the complexity of the instrument, particularly the cooling requirements, with a minimal reduction in information gain on the target atmospheric greenhouse gases of water vapor, methane, ozone, and nitrous oxide. The developed instrument was characterized through a series of thermal and vacuum tests and validated through a successful demonstration balloon flight during the 2019 Strato-Science campaign in Canada. In the calibration of the data from the balloon flight, an issue was identified regarding a lack of knowledge in the emissivity of the on-board blackbody calibration sources. These systematic effects were minimized through the application of an emissivity ratio determined from the characterization tests where a wider range of known blackbody temperatures were available. Despite this identified calibration issue, the results demonstrate that the instrument is capable of meeting primary performance requirements for trace gas retrievals of the target atmospheric species.

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.

The TSIS-1 Hybrid Solar Reference Spectrum

We present a new solar irradiance reference spectrum representative of solar minimum conditions between solar cycles 24 and 25. The Total and Spectral Solar Irradiance Sensor-1 (TSIS-1) Hybrid Solar Reference Spectrum (HSRS) is developed by applying a modified spectral ratio method to normalize very high spectral resolution solar line data to the absolute irradiance scale of the TSIS-1 Spectral Irradiance Monitor (SIM) and the CubeSat Compact SIM (CSIM). The high spectral resolution solar line data are the Air Force Geophysical Laboratory ultraviolet solar irradiance balloon observations, the ground-based Quality Assurance of Spectral Ultraviolet Measurements In Europe Fourier transform spectrometer solar irradiance observations, the Kitt Peak National Observatory solar transmittance atlas, and the semi-empirical Solar Pseudo-Transmittance Spectrum atlas. The TSIS-1 HSRS spans 202-2730 nm at 0.01 to ∼0.001 nm spectral resolution with uncertainties of 0.3% between 460 and 2365 nm and 1.3% at wavelengths outside that range.

Comparative Evaluation of Top-Down GOSAT XCO2 vs. Bottom-Up National Reports in the European Countries

Submitting national inventory reports (NIRs) on emissions of greenhouse gases (GHGs) is obligatory for parties of the United Nations Framework Convention on Climate Change (UNFCCC). The NIR forms the basis for monitoring individual countries’ progress on mitigating climate change. Countries prepare NIRs using the default bottom–up methodology of the Intergovernmental Panel on Climate Change (IPCC), as approved by the Kyoto protocol. We provide tangible evidence of the discrepancy between official bottom–up NIR reporting (unit: tons) versus top–down XCO2 reporting (unit: ppm) within the European continent, as measured by the Greenhouse Gases Observing Satellite (GOSAT). Bottom–up NIR (annual growth rate of CO2 emission from 2010 to 2016: −1.55%) does not show meaningful correlation (geographically weighted regression coefficient = −0.001, R2 = 0.024) to top–down GOSAT XCO2 (annual growth rate: 0.59%) in the European countries. The top five countries within the European continent on carbon emissions in NIR do not match the top five countries on GOSAT XCO2 concentrations. NIR exhibits anthropogenic carbon-generating activity within country boundaries, whereas satellite signals reveal the trans-boundary movement of natural and anthropogenic carbon. Although bottom–up NIR reporting has already gained worldwide recognition as a method to track national follow-up for treaty obligations, the single approach based on bottom–up did not present background atmospheric CO2 density derived from the air mass movement between the countries. In conclusion, we suggest an integrated measuring, reporting, and verification (MRV) approach using top–down observation in combination with bottom–up NIR that can provide sufficient countrywide objective evidence for national follow-up activities.

Innovative Mechanical Design Strategy for Actualizing 80 kg-Class X-Band Active SAR Small Satellite of S-STEP

The Small SAR Technology Experimental Project (S-STEP) mission aims to develop a new (space-based 80 kg-class active X-band synthetic aperture radar (SAR)) satellite with a main imaging mode of 1 m resolution stripmap. In the S-STEP mission, to achieve the design goal of developing faster, cheaper, better, and lighter small SAR satellite systems, innovative thermo-mechanical design approaches have been proposed and investigated. The major design approaches are the bus-payload integrated flat plate-type structure, multifunctional transmit/receive (TR) module, and dedicated vibration-free orbit deployer (VFOD) with the function of whole spacecraft vibration isolation. To validate the feasibility of the innovative mechanical design of S-STEP, a structural analysis considering launch and on-orbit environments is performed. In addition, development test results are presented to confirm the effectiveness of the proposed design approach for VFOD.

GOSAT CH4 Vertical Profiles over the Indian Subcontinent: Effect of a Priori and Averaging Kernels for Climate Applications

We examined methane (CH4) variability over different regions of India and the surrounding oceans derived from thermal infrared (TIR) band observations (TIR CH4) by the Thermal and Near-infrared Sensor for carbon Observation—Fourier Transform Spectrometer (TANSO-FTS) onboard the Greenhouse gases Observation SATellite (GOSAT) for the period 2009–2014. This study attempts to understand the sensitivity of the vertical profile retrievals at different layers of the troposphere and lower stratosphere, on the basis of the averaging kernel (AK) functions and a priori assumptions, as applied to the simulated concentrations by the MIROC4.0-based Atmospheric Chemistry-Transport Model (MIROC4-ACTM). We stress that this is of particular importance when the satellite-derived products are analyzed using different ACTMs other than those used as retrieved a priori. A comparison of modeled and retrieved CH4 vertical profiles shows that the GOSAT/TANSO-FTS TIR instrument has sufficient sensitivity to provide critical information about the transport of CH4 from the top of the boundary layer to the upper troposphere. The mean mismatch between TIR CH4 and model is within 50 ppb, except for the altitude range above 150 hPa, where the sensitivity of TIR CH4 observations becomes very low. Convolved model profiles with TIR CH4 AK reduces the mismatch to less than the retrieval uncertainty. Distinct seasonal variations of CH4 have been observed near the atmospheric boundary layer (800 hPa), free troposphere (500 hPa), and upper troposphere (300 hPa) over the northern and southern regions of India, corresponding to the southwest monsoon (July–September) and post-monsoon (October–December) seasons. Analysis of the transport and emission contributions to CH4 suggests that the CH4 seasonal cycle over the Indian subcontinent is governed by both the heterogeneous distributions of surface emissions and the influence of the global monsoon divergent wind circulations. The major contrast between monsoon, and pre- and post-monsoon profiles of CH4 over Indian regions are noticed near the boundary layer heights, which is mainly caused by seasonal change in local emission strength with a peak during summer due to increased emissions from the paddy fields and wetlands. A strong difference between seasons in the middle and upper troposphere is caused by convective transport of the emission signals from the surface and redistribution in the monsoon anticyclone of upper troposphere. TIR CH4 observations provide additional information on CH4 in the region compared to what is known from in situ data and total-column (XCH4) measurements. Based on two emission sensitivity simulations compared to TIR CH4 observations, we suggest that the emissions of CH4 from the India region were 51.2 ± 4.6 Tg year−1 during the period 2009–2014. Our results suggest that improvements in the a priori profile shape in the upper troposphere and lower stratosphere (UT/LS) region would help better interpretation of CH4 cycling in the earth’s environment.

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

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

A simple and quick sensitivity analysis method for methane isotopologues detection with GOSAT-TANSO-FTS

Measurements of methane isotopologues can differentiate between different source types, be they biogenic (e.g. marsh lands) or abiogenic (e.g. industry). Global measurements of these isotopologues would greatly benefit the current disconnect between 'top-down' (knowledge from chemistry transport models and satellite measurements) and 'bottom-up' (in situ measurement inventories) methane measurements. However, current measurements of these isotopologues are limited to a small number of in situ studies and airborne studies. In this paper we investigate the potential for detecting the second most common isotopologue of methane (¹³CH₄) from space using the Japanese Greenhouse Gases Observing Satellite applying a quick and simple residual radiance analysis technique. The method allows for a rapid analysis of spectral regions, and can be used to teach university students or advanced school students about radiative transfer analysis. Using this method we find limited sensitivity to ¹³CH₄, with detections limited to total column methane enhancements of >6%, assuming a desert surface albedo of >0.3.

Spatial Retrievals of Atmospheric Carbon Dioxide from Satellite Observations

Modern remote-sensing retrievals often invoke a Bayesian approach to infer atmospheric properties from observed radiances. In this approach, plausible mean states and variability for the quantities of interest are encoded in a prior distribution. Recent developments have devised prior assumptions for the correlation among atmospheric constituents and across observing locations. This work formulates a spatial statistical framework for simultaneous multi-footprint retrievals of carbon dioxide (CO2) with application to the Orbiting Carbon Observatory-2/3 (OCO-2/3). Formally, the retrieval state vector is extended to include atmospheric and surface conditions at many footprints in a small region, and a prior distribution that assumes spatial correlation across these locations is assumed. This spatial prior allows the length-scale, or range, of spatial correlation to vary between different elements of the state vector. Various single- and multi-footprint retrievals are compared in a simulation study. A spatial prior that also includes relatively large prior variances for CO2 results in posterior inferences that most accurately represent the true state and that reduce the correlation in retrieval error across locations.

The potential of CO2 satellite monitoring for climate governance: A review

Good-quality CO₂ emission data are fundamental for effective climate policy and governance. Data manipulation should be deterred, while developing countries are generally weaker than developed countries in compiling bottom-up CO₂ emission inventories due to less adequate data collection capacity. This paper assesses the capabilities of CO₂ satellites as objective, independent, potentially low-cost and external data sources for monitoring energy-related anthropogenic CO₂ emissions at regional/national, megacity and point-source geographical scales. After overviewing all major CO₂ satellites, SCIAMACHY, GOSAT and OCO-2 are focused on due to their wider research applications and higher CO₂ sensitivity in total column measurements that include near surface emissions. Nighttime light satellite data for proxy CO₂ monitoring are also brought into comparison to distinguish the importance of direct CO₂ satellite monitoring. Studies are reviewed from the perspectives of spatial and temporal capability and accuracy to comprehend the current statuses of applications, assess the strengths and weaknesses of research methods, investigate major challenges and propose suggestions for future progress. We conclude that CO₂ satellite monitoring can strengthen the data foundation for implementing international climate treaties and domestic climate policies.

Correcting model biases of CO in East Asia: impact on oxidant distributions during KORUS-AQ

Global coupled chemistry-climate models underestimate carbon monoxide (CO) in the Northern Hemisphere, exhibiting a pervasive negative bias against measurements peaking in late winter and early spring. While this bias has been commonly attributed to underestimation of direct anthropogenic and biomass burning emissions, chemical production and loss via OH reaction from emissions of anthropogenic and biogenic volatile organic compounds (VOCs) play an important role. Here we investigate the reasons for this underestimation using aircraft measurements taken in May and June 2016 from the Korea-United States Air Quality (KORUS-AQ) experiment in South Korea and the Air Chemistry Research in Asia (ARIAs) in the North China Plain (NCP). For reference, multispectral CO retrievals (V8J) from the Measurements of Pollution in the Troposphere (MOPITT) are jointly assimilated with meteorological observations using an ensemble adjustment Kalman filter (EAKF) within the global Community Atmosphere Model with Chemistry (CAM-Chem) and the Data Assimilation Research Testbed (DART). With regard to KORUS-AQ data, CO is underestimated by 42% in the control run and by 12% with the MOPITT assimilation run. The inversion suggests an underestimation of anthropogenic CO sources in many regions, by up to 80% for northern China, with large increments over the Liaoning Province and the North China Plain (NCP). Yet, an often-overlooked aspect of these inversions is that correcting the underestimation in anthropogenic CO emissions also improves the comparison with observational O₃ datasets and observationally constrained box model simulations of OH and HO₂. Running a CAM-Chem simulation with the updated emissions of anthropogenic CO reduces the bias by 29% for CO, 18% for ozone, 11% for HO₂, and 27% for OH. Longer-lived anthropogenic VOCs whose model errors are correlated with CO are also improved, while short-lived VOCs, including formaldehyde, are difficult to constrain solely by assimilating satellite retrievals of CO. During an anticyclonic episode, better simulation of O₃, with an average underestimation of 5.5 ppbv, and a reduction in the bias of surface formaldehyde and oxygenated VOCs can be achieved by separately increasing by a factor of 2 the modeled biogenic emissions for the plant functional types found in Korea. Results also suggest that controlling VOC and CO emissions, in addition to widespread NO _x controls, can improve ozone pollution over East Asia.

Toward High Precision XCO2 Retrievals From TanSat Observations: Retrieval Improvement and Validation Against TCCON Measurements

TanSat is the 1st Chinese carbon dioxide (CO2) measurement satellite, launched in 2016. In this study, the University of Leicester Full Physics (UoL-FP) algorithm is implemented for TanSat nadir mode XCO2 retrievals. We develop a spectrum correction method to reduce the retrieval errors by the online fitting of an 8th order Fourier series. The spectrum-correction model and its a priori parameters are developed by analyzing the solar calibration measurement. This correction provides a significant improvement to the O2 A band retrieval. Accordingly, we extend the previous TanSat single CO2 weak band retrieval to a combined O2 A and CO2 weak band retrieval. A Genetic Algorithm (GA) has been applied to determine the threshold values of post-screening filters. In total, 18.3% of the retrieved data is identified as high quality compared to the original measurements. The same quality control parameters have been used in a footprint independent multiple linear regression bias correction due to the strong correlation with the XCO2 retrieval error. Twenty sites of the Total Column Carbon Observing Network (TCCON) have been selected to validate our new approach for the TanSat XCO2 retrieval. We show that our new approach produces a significant improvement on the XCO2 retrieval accuracy and precision when compared to TCCON with an average bias and RMSE of -0.08 ppm and 1.47 ppm, respectively. The methods used in this study can help to improve the XCO2 retrieval from TanSat and subsequently the Level-2 data production, and hence will be applied in the TanSat operational XCO2 processing.