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.

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.

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.

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.

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

On the Detection of COVID-Driven Changes in Atmospheric Carbon Dioxide

We assess the detectability of COVID-like emissions reductions in global atmospheric CO2 concentrations using a suite of large ensembles conducted with an Earth system model. We find a unique fingerprint of COVID in the simulated growth rate of CO2 sampled at the locations of surface measurement sites. Negative anomalies in growth rates persist from January 2020 through December 2021, reaching a maximum in February 2021. However, this fingerprint is not formally detectable unless we force the model with unrealistically large emissions reductions (2 or 4 times the observed reductions). Internal variability and carbon-concentration feedbacks obscure the detectability of short-term emission reductions in atmospheric CO2. COVID-driven changes in the simulated, column-averaged dry air mole fractions of CO2 are eclipsed by large internal variability. Carbon-concentration feedbacks begin to operate almost immediately after the emissions reduction; these feedbacks reduce the emissions-driven signal in the atmosphere carbon reservoir and further confound signal detection.

Fully quantum calculations of O2-N2 scattering using a new potential energy surface: Collisional perturbations of the oxygen 118 GHz fine structure line

A proper description of the collisional perturbation of the shapes of molecular resonances is important for remote spectroscopic studies of the terrestrial atmosphere. Of particular relevance are the collisions between the O₂ and N₂ molecules-the two most abundant atmospheric species. In this work, we report a new highly accurate O₂(X³Σ_g ^-)-N₂(X¹Σ_g ⁺) potential energy surface and use it for performing the first quantum scattering calculations addressing line shapes for this system. We use it to model the shape of the 118 GHz fine structure line in O₂ perturbed by collisions with N₂ molecules, a benchmark system for testing our methodology in the case of an active molecule in a spin triplet state. The calculated collisional broadening of the line agrees well with the available experimental data over a wide temperature range relevant for the terrestrial atmosphere. This work constitutes a step toward populating the spectroscopic databases with ab initio line shape parameters for atmospherically relevant systems.

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.

Constraining tectonic uplift and advection from the main drainage divide of a mountain belt

One of the most conspicuous features of a mountain belt is the main drainage divide. Divide location is influenced by a number of parameters, including tectonic uplift and horizontal advection. Thus, the topography of mountain belts can be used as an archive to extract tectonic information. Here we combine numerical landscape evolution modelling and analytical solutions to demonstrate that mountain asymmetry, determined by the location of the main drainage divide, increases with increasing uplift gradient and advection velocity. Then, we provide a conceptual framework to constrain the present or previous tectonic uplift and advection of a mountain belt from the location and migration direction of its main drainage divide. Furthermore, we apply our model to Wula Shan horst, Northeastern Sicily, and Southern Taiwan.

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.

Retrieval and Validation of XCO2 from TanSat Target Mode Observations in Beijing

Satellite observation is one of the main methods used to monitor the global distribution and variation of atmospheric carbon dioxide (CO2). Several CO2 monitoring satellites have been successfully launched, including Japan’s Greenhouse Gases Observing SATellite (GOSAT), the USA’s Orbiting Carbon Observatory-2 (OCO-2), and China’s Carbon Dioxide Observation Satellite Mission (TanSat). Satellite observation targeting the ground-based Fourier transform spectrometer (FTS) station is the most effective technique for validating satellite CO2 measurement precision. In this study, the coincident observations from TanSat and ground-based FTS were performed numerous times in Beijing under a clear sky. The column-averaged dry-air mole fraction of carbon dioxide (XCO2) obtained from TanSat was retrieved by the Department for Eco-Environmental Informatics (DEEI) of China’s State Key Laboratory of Resources and Environmental Information System based on a full physical model. The comparison and validation of the TanSat target mode observations revealed that the average of the XCO2 bias between TanSat retrievals and ground-based FTS measurements was 2.62 ppm, with a standard deviation (SD) of the mean difference of 1.41 ppm, which met the accuracy standard of 1% required by the mission tasks. With bias correction, the mean absolute error (MAE) improved to 1.11 ppm and the SD of the mean difference fell to 1.35 ppm. We compared simultaneous observations from GOSAT and OCO-2 Level 2 (L2) bias-corrected products within a ±1° latitude and longitude box centered at the ground-based FTS station in Beijing. The results indicated that measurements from GOSAT and OCO-2 were 1.8 ppm and 1.76 ppm higher than the FTS measurements on 20 June 2018, on which the daily observation bias of the TanSat XOC2 results was 1.87 ppm. These validation efforts have proven that TanSat can measure XCO2 effectively. In addition, the DEEI-retrieved XCO2 results agreed well with measurements from GOSAT, OCO-2, and the Beijing ground-based FTS.

Optical-Optical Double-Resonance Absorption Spectroscopy of Molecules with Kilohertz Accuracy

Selective pumping and probing of highly excited states of molecules are essential in various studies but are also challenging because of high density of states, weak transition moments, and lack of precise spectroscopy data. We develop a comb-locked cavity-assisted double-resonance spectroscopy (COCA-DR) method for precision measurements using low-power continuous-wave lasers. A high-finesse cavity locked with an optical frequency comb is used to enhance both the pumping power and the probing sensitivity. As a demonstration, Doppler-free stepwise two-photon absorption spectra of CO₂ were recorded by using two milliwatt diode lasers (1.60 and 1.67 μm), and the rotation energies in a highly excited state (CO-stretching quanta = 8) were determined with an unprecedented accuracy of a few kilohertz.

A well-isolated vibrational state of CO2verified by near-infrared saturated spectroscopy with kHz accuracy

Quantitative determination of atmospheric CO₂concentration by remote sensing relies on accurate line parameters.

Cavity ring-down spectroscopy of CO2 near λ = 2.06 μm: Accurate transition intensities for the Orbiting Carbon Observatory-2 (OCO-2) "strong band"

The λ = 2.06 μm absorption band of CO₂ is widely used for the remote sensing of atmospheric carbon dioxide, making it relevant to many important top-down measurements of carbon flux. The forward models used in the retrieval algorithms employed in these measurements require increasingly accurate line intensity and line shape data from which absorption cross-sections can be computed. To overcome accuracy limitations of existing line lists, we used frequency-stabilized cavity ring-down spectroscopy to measure 39 transitions in the ¹²C¹⁶O₂ absorption band. The line intensities were measured with an estimated relative combined standard uncertainty of u _r = 0.08 %. We predicted the J-dependence of the measured intensities using two theoretical models: a one-dimensional spectroscopic model with Herman-Wallis rotation-vibration corrections, and a line-by-line ab initio dipole moment surface model [Zak et al. JQSRT 2016;177:31-42]. For the second approach, we fit only a single factor to rescale the theoretical integrated band intensity to be consistent with the measured intensities. We find that the latter approach yields an equally adequate representation of the fitted J-dependent intensity data and provides the most physically general representation of the results. Our recommended value for the integrated band intensity equal to 7.183 × 10^-21 cm molecule^-1 ± 6 × 10^-24 cm molecule^-1 is based on the rescaled ab initio model and corresponds to a fitted scale factor of 1.0069 ± 0.0002. Comparisons of literature intensity values to our results reveal systematic deviations ranging from -1.16 % to +0.33 %.

Pixel Size and Revisit Rate Requirements for Monitoring Power Plant CO2 Emissions from Space

The observational requirements for space-based quantification of anthropogenic CO 2 emissions are of interest to space agencies and related organizations that may contribute to a possible satellite constellation to support emission monitoring in the future. We assess two key observing characteristics for space-based monitoring of CO2 emissions: pixel size and revisit rate, and we introduce a new method utilizing multiple images simultaneously to significantly improve emission estimates. The impact of pixel size ranging from 2–10 km for space-based imaging spectrometers is investigated using plume model simulations, accounting for biases in the observations. Performance of rectangular pixels is compared to square pixels of equal area. The findings confirm the advantage of the smallest pixels in this range and the advantage of square pixels over rectangular pixels. A method of averaging multiple images is introduced and demonstrated to be able to estimate emissions from small sources when the individual images are unable to distinguish the plume. Due to variability in power plant emissions, results from a single overpass cannot be directly extrapolated to annual emissions, the most desired timescale for regulatory purposes. We investigate the number of overpasses required to quantify annual emissions with a given accuracy, based on the mean variability from the 50 highest emitting US power plants. Although the results of this work alone are not sufficient to define the full architecture of a future CO 2 monitoring constellation, when considered along with other studies, they may assist in informing the design of a space-based system to support anthropogenic CO 2 emission monitoring.

Spatio-Temporal Consistency Evaluation of XCO2 Retrievals from GOSAT and OCO-2 Based on TCCON and Model Data for Joint Utilization in Carbon Cycle Research

The global carbon cycle research requires precise and sufficient observations of the column-averaged dry-air mole fraction of CO 2 (XCO 2 ) in addition to conventional surface mole fraction observations. In addition, assessing the consistency of multi-satellite data are crucial for joint utilization to better infer information about CO 2 sources and sinks. In this work, we evaluate the consistency of long-term XCO 2 retrievals from the Greenhouse Gases Observing Satellite (GOSAT), Orbiting Carbon Observatory 2 (OCO-2) in comparison with Total Carbon Column Observing Network (TCCON) and the 3D model of CO 2 mole fractions data from CarbonTracker 2017 (CT2017). We create a consistent joint dataset and compare it with the long-term model data to assess their abilities to characterize the carbon cycle climate. The results show that, although slight increasing differences are found between the GOSAT and TCCON XCO 2 in the northern temperate latitudes, the GOSAT and OCO-2 XCO 2 retrievals agree well in general, with a mean bias ± standard deviation of differences of 0.21 ± 1.3 ppm. The differences are almost within ±2 ppm and are independent of time, indicating that they are well calibrated. The differences between OCO-2 and CT2017 XCO 2 are much larger than those between GOSAT and CT XCO 2 , which can be attributed to the significantly different spatial representatives of OCO-2 and the CT-transport model 5 (TM5). The time series of the combined OCO-2/GOSAT dataset and the modeled XCO 2 agree well, and both can characterize significantly increasing atmospheric CO 2 under the impact of a large El Niño during 2015 and 2016. The trend calculated from the dataset using the seasonal Kendall (S-K) method indicates that atmospheric CO 2 is increasing by 2–2.6 ppm per year.

Increase of Atmospheric Methane Observed from Space-Borne and Ground-Based Measurements

It has been found that the concentration of atmospheric methane (CH4) has rapidly increased since 2007 after a decade of nearly constant concentration in the atmosphere. As an important greenhouse gas, such an increase could enhance the threat of global warming. To better quantify this increasing trend, a novel statistic method, i.e. the Ensemble Empirical Mode Decomposition (EEMD) method, was used to analyze the CH4 trends from three different measurements: the mid–upper tropospheric CH4 (MUT) from the space-borne measurements by the Atmospheric Infrared Sounder (AIRS), the CH4 in the marine boundary layer (MBL) from NOAA ground-based in-situ measurements, and the column-averaged CH4 in the atmosphere (XCH4) from the ground-based up-looking Fourier Transform Spectrometers at Total Carbon Column Observing Network (TCCON) and the Network for the Detection of Atmospheric Composition Change (NDACC). Comparison of the CH4 trends in the mid–upper troposphere, lower troposphere, and the column average from these three data sets shows that, overall, these trends agree well in capturing the abrupt CH4 increase in 2007 (the first peak) and an even faster increase after 2013 (the second peak) over the globe. The increased rates of CH4 in the MUT, as observed by AIRS, are overall smaller than CH4 in MBL and the column-average CH4. During 2009–2011, there was a dip in the increase rate for CH4 in MBL, and the MUT-CH4 increase rate was almost negligible in the mid-high latitude regions. The increase of the column-average CH4 also reached the minimum during 2009–2011 accordingly, suggesting that the trends of CH4 are not only impacted by the surface emission, however that they also may be impacted by other processes like transport and chemical reaction loss associated with [OH]. One advantage of the EEMD analysis is to derive the monthly rate and the results show that the frequency of the variability of CH4 increase rates in the mid–high northern latitude regions is larger than those in the tropics and southern hemisphere.

Analysis of Four Years of Global XCO2 Anomalies as Seen by Orbiting Carbon Observatory-2

NASA’s carbon dioxide mission, Orbiting Carbon Observatory-2, began operating in September 2014. In this paper, we analyze four years (2015–2018) of global (60°S–60°N) XCO2 anomalies and their annual variations and seasonal patterns. We show that the anomaly patterns in the column-averaged CO2 dry air mole fraction, XCO2, are robust and consistent from year-to-year. We evaluate the method by comparing the anomalies to fluxes from anthropogenic, biospheric, and biomass burning and to model-simulated local concentration enhancements. We find that, despite the simplicity of the method, the anomalies describe the spatio-temporal variability of XCO2 (including anthropogenic emissions and seasonal variability related to vegetation and biomass burning) consistently with more complex model-based approaches. We see, for example, that positive anomalies correspond to fossil fuel combustion over the major industrial areas (e.g., China, eastern USA, central Europe, India, and the Highveld region in South Africa), shown as large positive XCO2 enhancements in the model simulations. We also find corresponding positive anomalies and fluxes over biomass burning areas during different fire seasons. On the other hand, the largest negative anomalies correspond to the growing season in the northern middle latitudes, characterized by negative XCO2 enhancements from simulations and high solar-induced chlorophyll fluorescence (SIF) values (indicating the occurrence of photosynthesis). The largest discrepancies between the anomaly patterns and the model-based results are observed in the tropical regions, where OCO-2 shows persistent positive anomalies over every season of every year included in this study. Finally, we demonstrate how XCO2 anomalies enable the detection of anthropogenic signatures for several local scale case studies, both in the Northern and Southern Hemisphere. In particular, we analyze the XCO2 anomalies collocated with the recent TROPOspheric Monitoring Instrument NO2 observations (used as indicator of anthropogenic fossil fuel combustion) over the Highveld region in South Africa. The results highlight the capability of satellite-based observations to monitor natural and man-made CO2 signatures on global scale.

Distribution of CO2 in Western Pacific, Studied Using Isotope Data Made in Taiwan, OCO-2 Satellite Retrievals, and CarbonTracker Products

To assess sources and processes that affect the variability of CO2 at local to regional scales, we have analyzed the mixing ratio [CO2] and stable isotopic compositions (δ13C and δ18O) of atmospheric CO2 for three years (2014-2016) in urban and sub-urban areas in Taipei, Taiwan. The data are compared with those from some background sites, viz., Lulin, Mauna Loa, and Minamitorishima, to evaluate how local emissions affect CO2 level regionally. [CO2] over the urban and sub-urban stations are significantly higher than that observed at the three aforementioned remote sites mainly due to local emissions, which partly mask the seasonal cycle caused by photosynthesis and respiration. Likewise, significantly low δ13C and δ18O values observed at two Taipei stations also point to anthropogenic emissions. The seasonal cycles in [CO2] and in the isotopic compositions are retrieved using the ensemble empirical mode decomposition method. Regional impact is assessed using CO2 products from the Orbiting Carbon Observatory-2 satellite, the NOAA/EARL CarbonTracker project, and meteorological data from European Centre for Medium range Weather Forecast-Interim. We found that besides local emissions, Taiwan is largely affected by external CO2 in winter and spring originated from north, west and southwest landmasses. In winter air masses with elevated CO2 concentrations, originated in eastern China influence Taipei. In spring season, about 2 ppmv enhancement in CO2 observed at the top of Lulin, a high mountain station (2.8 km), could be linked to CO2 produced by biomass burning in the southeast Asian countries and transported to the region by easterly winds.

Spaceborne detection of localized carbon dioxide sources

Spaceborne measurements by NASA's Orbiting Carbon Observatory-2 (OCO-2) at the kilometer scale reveal distinct structures of atmospheric carbon dioxide (CO₂) caused by known anthropogenic and natural point sources. OCO-2 transects across the Los Angeles megacity (USA) show that anthropogenic CO₂ enhancements peak over the urban core and decrease through suburban areas to rural background values more than ~100 kilometers away, varying seasonally from ~4.4 to 6.1 parts per million. A transect passing directly downwind of the persistent isolated natural CO₂ plume from Yasur volcano (Vanuatu) shows a narrow filament of enhanced CO₂ values (~3.4 parts per million), consistent with a CO₂ point source emitting 41.6 kilotons per day. These examples highlight the potential of the OCO-2 sensor, with its unprecedented resolution and sensitivity, to detect localized natural and anthropogenic CO₂ sources.

The Orbiting Carbon Observatory-2 early science investigations of regional carbon dioxide fluxes

NASA's Orbiting Carbon Observatory-2 (OCO-2) mission was motivated by the need to diagnose how the increasing concentration of atmospheric carbon dioxide (CO2) is altering the productivity of the biosphere and the uptake of CO2 by the oceans. Launched on 2 July 2014, OCO-2 provides retrievals of the column-averaged CO2 dry-air mole fraction ([Formula: see text]) as well as the fluorescence from chlorophyll in terrestrial plants. The seasonal pattern of uptake by the terrestrial biosphere is recorded in fluorescence and the drawdown of [Formula: see text] during summer. Launched just before one of the most intense El Niños of the past century, OCO-2 measurements of [Formula: see text] and fluorescence record the impact of the large change in ocean temperature and rainfall on uptake and release of CO2 by the oceans and biosphere.

High-Accuracy CO(2) Line Intensities Determined from Theory and Experiment

Atmospheric CO(2) concentrations are being closely monitored by remote sensing experiments which rely on knowing line intensities with an uncertainty of 0.5% or better. Most available laboratory measurements have uncertainties much larger than this. We report a joint experimental and theoretical study providing rotation-vibration line intensities with the required accuracy. The ab initio calculations are extendible to all atmospherically important bands of CO(2) and to its isotologues. As such, they will form the basis for detailed CO(2) spectroscopic line lists for future studies.

Observations of Dicke narrowing and speed dependence in air-broadened CO₂ lineshapes near 2.06 μm

Frequency-stabilized cavity ring-down spectroscopy was used to study CO2 lineshapes in the (20013) ← (00001) band centered near 2.06 μm. Two rovibrational transitions were chosen for this study to measure non-Voigt collisional effects for air-broadened lines over the pressure range of 7 kPa-28 kPa. Lineshape analysis for both lines revealed evidence of simultaneous Dicke (collisional) narrowing and speed-dependent effects that would introduce biases exceeding 2% in the retrieved air-broadening parameters if not incorporated in the modeling of CO2 lineshapes. Additionally, correlations between velocity- and phase/state changing collisions greatly reduced the observed Dicke narrowing effect. As a result, it was concluded that the most appropriate line profile for modeling CO2 lineshapes near 2.06 μm was the correlated speed-dependent Nelkin-Ghatak profile, which includes all of the physical effects mentioned above and leads to a consistent set of line shape parameters that are linear with gas pressure.

Combining XCO2 measurements derived from SCIAMACHY and GOSAT for potentially generating global CO2 maps with high spatiotemporal resolution

Global warming induced by atmospheric CO₂ has attracted increasing attention of researchers all over the world. Although space-based technology provides the ability to map atmospheric CO₂ globally, the number of valid CO₂ measurements is generally limited for certain instruments owing to the presence of clouds, which in turn constrain the studies of global CO₂ sources and sinks. Thus, it is a potentially promising work to combine the currently available CO₂ measurements. In this study, a strategy for fusing SCIAMACHY and GOSAT CO₂ measurements is proposed by fully considering the CO₂ global bias, averaging kernel, and spatiotemporal variations as well as the CO₂ retrieval errors. Based on this method, a global CO₂ map with certain UTC time can also be generated by employing the pattern of the CO₂ daily cycle reflected by Carbon Tracker (CT) data. The results reveal that relative to GOSAT, the global spatial coverage of the combined CO₂ map increased by 41.3% and 47.7% on a daily and monthly scale, respectively, and even higher when compared with that relative to SCIAMACHY. The findings in this paper prove the effectiveness of the combination method in supporting the generation of global full-coverage XCO₂ maps with higher temporal and spatial sampling by jointly using these two space-based XCO₂ datasets.

AIRS retrieved CO2 and its association with climatic parameters over India during 2004-2011

Atmospheric Infrared Sounder (AIRS) retrieved mid-tropospheric Carbon Dioxide (CO2) have been used to study the variability and its association with the climatic parameters over India during 2004 to 2011. The study also aims in understanding transport of CO2 from surface to mid-troposphere over India. The annual cycle of mid-tropospheric CO2 shows gradual increase in concentration from January till the month of May at the rate ~0.6 ppm/month. It decreases continuously in summer monsoon (JJAS) at the same rate during which strong westerlies persists over the region. A slight increase is seen during winter monsoon (DJF). Being a greenhouse gas, annual cycle of CO2 show good resemblance with annual cycle of surface air temperature with correlation coefficient (CC) of +0.8. Annual cycle of vertical velocity indicate inverse pattern compared to annual cycle of CO2. High values of mid-tropospheric CO2 correspond to upward wind, while low values of mid-tropospheric CO2 correspond to downward wind. In addition to vertical motion, zonal winds are also contributing towards the transport of CO2 from surface to mid-troposphere. Vegetation as it absorbs CO2 at surface level, show inverse annual cycle to that of annual cycle of CO2 (CC-0.64). Seasonal variation of rainfall-CO2 shows similarities with seasonal variation of NDVI-CO2. However, the use of long period data sets for CO2 at the surface and at the mid-troposphere will be an advantage to confirm these results.

Characterization and correction of spectral distortions induced by microvibrations onboard the GOSAT Fourier transform spectrometer

Microvibrations onboard greenhouse gases observing satellite (GOSAT) cause scan speed variations in the TANSO Fourier transform spectrometer. The associated periodic sampling errors generate ghost features in O2 A-band spectra, where surface pressure and aerosol properties are retrieved to determine the optical path through the atmosphere. A correction algorithm has been developed to re-compute the interferograms at equally spaced sampling intervals. The key is to determine iteratively the amplitude and phase of sinusoidal perturbations with predetermined frequencies to minimize the magnitude of the out-of-band ghosts artifacts after correction of the sampling grid. This correction algorithm drastically reduces errors in retrieved surface pressure and improves agreement with ground-based observations.

Atmospheric CO2 column measurements with an airborne intensity-modulated continuous wave 1.57 μm fiber laser lidar

The 2007 National Research Council (NRC) Decadal Survey on Earth Science and Applications from Space recommended Active Sensing of CO(2) Emissions over Nights, Days, and Seasons (ASCENDS) as a midterm, Tier II, NASA space mission. ITT Exelis, formerly ITT Corp., and NASA Langley Research Center have been working together since 2004 to develop and demonstrate a prototype laser absorption spectrometer for making high-precision, column CO(2) mixing ratio measurements needed for the ASCENDS mission. This instrument, called the multifunctional fiber laser lidar (MFLL), operates in an intensity-modulated, continuous wave mode in the 1.57 μm CO(2) absorption band. Flight experiments have been conducted with the MFLL on a Lear-25, UC-12, and DC-8 aircraft over a variety of different surfaces and under a wide range of atmospheric conditions. Very high-precision CO(2) column measurements resulting from high signal-to-noise ratio (>1300) column optical depth (OD) measurements for a 10 s (~1 km) averaging interval have been achieved. In situ measurements of atmospheric CO(2) profiles were used to derive the expected CO(2) column values, and when compared to the MFLL measurements over desert and vegetated surfaces, the MFLL measurements were found to agree with the in situ-derived CO(2) columns to within an average of 0.17% or ~0.65 ppmv with a standard deviation of 0.44% or ~1.7 ppmv. Initial results demonstrating ranging capability using a swept modulation technique are also presented.

Simultaneous retrieval of atmospheric CO2 and light path modification from space-based spectroscopic observations of greenhouse gases: methodology and application to GOSAT measurements over TCCON sites

This paper presents an improved photon path length probability density function method that permits simultaneous retrievals of column-average greenhouse gas mole fractions and light path modifications through the atmosphere when processing high-resolution radiance spectra acquired from space. We primarily describe the methodology and retrieval setup and then apply them to the processing of spectra measured by the Greenhouse gases Observing SATellite (GOSAT). We have demonstrated substantial improvements of the data processing with simultaneous carbon dioxide and light path retrievals and reasonable agreement of the satellite-based retrievals against ground-based Fourier transform spectrometer measurements provided by the Total Carbon Column Observing Network (TCCON).

The total carbon column observing network

A global network of ground-based Fourier transform spectrometers has been founded to remotely measure column abundances of CO(2), CO, CH(4), N(2)O and other molecules that absorb in the near-infrared. These measurements are directly comparable with the near-infrared total column measurements from space-based instruments. With stringent requirements on the instrumentation, acquisition procedures, data processing and calibration, the Total Carbon Column Observing Network (TCCON) achieves an accuracy and precision in total column measurements that is unprecedented for remote-sensing observations (better than 0.25% for CO(2)). This has enabled carbon-cycle science investigations using the TCCON dataset, and allows the TCCON to provide a link between satellite measurements and the extensive ground-based in situ network.

Economic value of improved quantification in global sources and sinks of carbon dioxide

On average, about 45 per cent of global annual anthropogenic carbon dioxide (CO(2)) emissions remain in the atmosphere, while the remainder are taken up by carbon reservoirs on land and in the oceans-the CO(2) 'sinks'. As sink size and dynamics are highly variable in space and time, cross-verification of reported anthropogenic CO(2) emissions with atmospheric CO(2) measurements is challenging. Highly variable CO(2) sinks also limit the capability to detect anomolous changes in natural carbon reservoirs. This paper argues that significant uncertainty reduction in annual estimates of the global carbon balance could be achieved rapidly through coordinated up-scaling of existing methods, and that this uncertainty reduction would provide incentive for accurate reporting of CO(2) emissions at the country level. We estimate that if 5 per cent of global CO(2) emissions go unreported and undetected, the associated marginal economic impacts could reach approximately US$20 billion each year by 2050. The net present day value of these impacts aggregated until 2200, and discounted back to the present would have a mean value exceeding US$10 trillion. The costs of potential impacts of unreported emissions far outweigh the costs of enhancement of measurement infrastructure to reduce uncertainty in the global carbon balance.

Atmospheric CO2 measurements with a 2 μm airborne laser absorption spectrometer employing coherent detection

We report airborne measurements of CO(2) column abundance conducted during two 2009 campaigns using a 2.05 μm laser absorption spectrometer. The two flight campaigns took place in the California Mojave desert and in Oklahoma. The integrated path differential absorption (IPDA) method is used for the CO(2) column mixing ratio retrievals. This instrument and the data analysis methodology provide insight into the capabilities of the IPDA method for both airborne measurements and future global-scale CO(2) measurements from low Earth orbit pertinent to the NASA Active Sensing of CO(2) Emissions over Nights, Days, and Seasons mission. The use of a favorable absorption line in the CO(2) 2 μm band allows the on-line frequency to be displaced two (surface pressure) half-widths from line center, providing high sensitivity to the lower tropospheric CO(2). The measurement repeatability and measurement precision are in good agreement with predicted estimates. We also report comparisons with airborne in situ measurements conducted during the Oklahoma campaign.

Frequency stabilization of distributed-feedback laser diodes at 1572 nm for lidar measurements of atmospheric carbon dioxide

We demonstrate a wavelength-locked laser source that rapidly steps through six wavelengths distributed across a 1572.335 nm carbon dioxide (CO(2)) absorption line to allow precise measurements of atmospheric CO(2) absorption. A distributed-feedback laser diode (DFB-LD) was frequency-locked to the CO(2) line center by using a frequency modulation technique, limiting its peak-to-peak frequency drift to 0.3 MHz at 0.8 s averaging time over 72 hours. Four online DFB-LDs were then offset locked to this laser using phase-locked loops, retaining virtually the same absolute frequency stability. These online and two offline DFB-LDs were subsequently amplitude switched and combined. This produced a precise wavelength-stepped laser pulse train, to be amplified for CO(2) measurements.