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

China's Fossil Fuel CO2 Emissions Estimated Using Surface Observations of Coemitted NO2

Accurate estimates of fossil fuel CO2 (FFCO2) emissions are of great importance for climate prediction and mitigation regulations but remain a significant challenge for accounting methods relying on economic statistics and emission factors. In this study, we employed a regional data assimilation framework to assimilate in situ NO2 observations, allowing us to combine observation-constrained NOx emissions coemitted with FFCO2 and grid-specific CO2-to-NOx emission ratios to infer the daily FFCO2 emissions over China. The estimated national total for 2016 was 11.4 PgCO2·yr-1, with an uncertainty (1σ) of 1.5 PgCO2·yr-1 that accounted for errors associated with atmospheric transport, inversion framework parameters, and CO2-to-NOx emission ratios. Our findings indicated that widely used "bottom-up" emission inventories generally ignore numerous activity level statistics of FFCO2 related to energy industries and power plants in western China, whereas the inventories are significantly overestimated in developed regions and key urban areas owing to exaggerated emission factors and inexact spatial disaggregation. The optimized FFCO2 estimate exhibited more distinct seasonality with a significant increase in emissions in winter. These findings advance our understanding of the spatiotemporal regime of FFCO2 emissions in China.

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.

Comprehensive Understandings of Hydrogen Bond Chemistry in Aqueous Batteries

Aqueous batteries are emerging as highly promising contenders for large-scale grid energy storage because of uncomplicated assembly, exceptional safety, and cost-effectiveness. The unique aqueous electrolyte with a rich hydrogen bond (HB) environment inevitably has a significant impact on the electrode materials and electrochemical processes. While numerous reviews have focused on the materials design and assembly of aqueous batteries, the utilization of HB chemistry is overlooked. Herein, instead of merely compiling recent advancements, this review presents a comprehensive summary and analysis of the profound implication exerted by HB on all components of the aqueous batteries. Intricate links between the novel HB chemistry and various aqueous batteries are ingeniously constructed within the critical aspects, such as self-discharge, structural stability of electrode materials, pulverization, solvation structures, charge carrier diffusion, corrosion reactions, pH sensitivity, water splitting, polysulfides shuttle, and H2 S evolution. By adopting a vantage point that encompasses material design, binder and separator functionalization, electrolyte regulation, and HB optimization, a critical examination of the key factors that impede electrochemical performance in diverse aqueous batteries is conducted. Finally, insights are rendered properly based on HB chemistry, with the aim of propelling the advancement of state-of-the-art aqueous batteries.

The role of satellite remote sensing in mitigating and adapting to global climate change

Climate change has critical adverse impacts on human society and poses severe challenges to global sustainable development. Information on essential climate variables (ECVs) that reflects the substantial changes that have occurred on Earth is critical for assessing the influence of climate change. Satellite remote sensing (SRS) technology has led to a new era of observations and provides multiscale information on ECVs that is independent of in situ measurements and model simulations. This enhances our understanding of climate change from space and supports policy-making in combating climate change. However, it remains challenging to remotely retrieve ECVs due to the complexity of the climate system. We provide an update on the studies on the role of SRS in climate change research, specifically in monitoring and quantifying ECVs in the atmosphere (greenhouse gases, clouds and aerosols), ocean (sea surface temperature, sea ice melt and sea level rise, ocean currents and mesoscale eddies, phytoplankton and ocean productivity), and terrestrial ecosystems (land use and land cover change and carbon flux, water resource and hydrological hazards, solar-induced chlorophyll fluorescence and terrestrial gross primary production). The benefits and challenges of applying SRS in climate change studies are also examined and discussed. This work will help us apply SRS and recommend future SRS studies to mitigate and adapt to global climate change.

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.

Robust algorithm for precise XCO2 retrieval using single observation of IPDA LIDAR

CO₂ column-weighted dry-air mixing ratio (X_CO2) products with high precision and spatial resolution are essential for inverting CO₂ fluxes and promoting our understanding of global climate change. Compared with passive remote sensing methods, IPDA LIDAR, as an active remote sensing technique, offers many advantages in measuring X_CO2. However, a significant random error in IPDA LIDAR measurements causes X_CO2 values calculated directly from LIDAR signals to be unqualified as the final X_CO2 products. Hence, we propose an efficient particle filter-based inversion of CO₂ for single observation (EPICSO) algorithm to precisely retrieve the X_CO2 of every LIDAR observation while preserving the high spatial resolution of LIDAR measurements. The EPICSO algorithm adopts the sliding average results as the first estimate of the local X_CO2; subsequently, it estimates the difference between two adjacent X_CO2 points and calculates the posterior probability of X_CO2 based on particle filter theory. To evaluate the performance of the EPICSO algorithm numerically, we perform an EPICSO to process pseudo-observation data. The simulation results show that the results retrieved by the EPICSO algorithm satisfy the required high precision and that the algorithm is robust to a significant amount of random errors. In addition, we utilize LIDAR observation data from actual experiments in Hebei, China, to validate the performance of the EPICSO algorithm. The results retrieved by the EPICSO algorithm are more consistent with the actual local X_CO2 than those of the conventional method, indicating that the EPICSO algorithm is efficient and practical for retrieving X_CO2 with high precision and spatial resolution.

Constraining China's land carbon sink from emerging satellite CO2 observations: Progress and challenges

Land carbon sink is a vital component for the achievement of China's ambitious carbon neutrality goal, but its magnitude is poorly known. Atmospheric observations and inverse models are valuable tools to constrain the China's land carbon sink. Space-based CO₂ measurements from satellites form an emerging data stream for application of such atmospheric inversions. Here, we reviewed the satellite missions that is dedicated to the monitoring of CO₂ , and the recent progresses on the inversion of China's land carbon sink using satellite CO₂ measurements. We summarized the limitations and challenges in current space platforms, retrieval algorithms, and the inverse modeling. It is shown that there are large uncertainties of contemporary satellite-based estimates of China's land carbon sink. We discussed future opportunities of continuous improvements in three aspects to better constrain China's land carbon sink with space-based CO₂ measurements.

Ames-2021 CO2 Dipole Moment Surface and IR Line Lists: Toward 0.1% Uncertainty for CO2 IR Intensities

A highly accurate CO₂ ab initio dipole moment surface (DMS), Ames-2021, is reported along with ¹²C¹⁶O₂ infrared (IR) intensity comparisons approaching a 1-4‰ level of agreement and uncertainty. The Ames-2021 DMS was accurately fitted from CCSD(T) finite-field dipoles computed with the aug-cc-pVXZ (X = T, Q, 5) basis for C atom and the d-aug-cc-pVXZ (X = T, Q, 5) basis for O atoms, and extrapolated to the one particle basis set limit. Fitting σ_rms is 3.8 × 10^-7 au for 4443 geometries below 15 000 cm^-1. The corresponding IR intensity, S_Ames-2021, are computed using the Ames-2 potential energy surface (PES), which is the best PES available for CO₂. Compared to high accuracy IR studies for 2001i-00001 and 3001i-00001 bands, S_Ames-2021 matches NIST experiment-based intensities [S_NIST-HIT16 or S_HIT20] to -1.0 ± 1.3‰, or matches DLR experiment-based intensities [S_{DLR-HIT16/UCL/Ames}] to 1.9 ± 3.7‰. This indicates the systematic deviations and uncertainties have been significantly reduced in S_Ames-2021. The S_UCL2015 (or S_HITRAN2016) have larger deviations (vs S_DLR) and uncertainties (vs S_DLR, S_NIST) which are attributed to the less accurate Ames-1 PES adopted in UCL-296 line list calculation. The S_Ames-2021 intensity of ¹²C¹⁶O₂ and ¹³C¹⁶O₂ is utilized to derive new absolute ¹³C/¹²C ratios for Vienna PeeDee Belemnite (VPDB) with uncertainty reduced by ¹/₃ or ²/₃. Further evaluation of S_Ames-2021 intensities are carried out on those CO₂ bands discussed in the HITRAN2020 update paper. Consistent improvements and better accuracies are found in band-by-band analysis, except for those bands strongly affected by Coriolis couplings, or very weak bands measured with relatively larger experimental uncertainties. The Ames-2021 296 K IR line lists are generated for 13 CO₂ isotopologues, with 18 000 cm^-1 and S_296 K > 1 × 10^-31 cm/molecule cutoff and then combined with CDSD line positions (except ¹⁴C¹⁶O₂). The Ames-2021 DMS and 296 K IR line lists represent a major improvement over previous CO₂ theoretical IR intensity studies, including Ames-2016, UCL-296, and recent UCL DMS 2021 update. A real 1 permille level of agreement and uncertainty will definitely require both more accurate PES and more accurate DMS.

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

Intrinsic Dimensionality as a Metric for the Impact of Mission Design Parameters

High-resolution space-based spectral imaging of the Earth's surface delivers critical information for monitoring changes in the Earth system as well as resource management and utilization. Orbiting spectrometers are built according to multiple design parameters, including ground sampling distance (GSD), spectral resolution, temporal resolution, and signal-to-noise ratio. Different applications drive divergent instrument designs, so optimization for wide-reaching missions is complex. The Surface Biology and Geology component of NASA's Earth System Observatory addresses science questions and meets applications needs across diverse fields, including terrestrial and aquatic ecosystems, natural disasters, and the cryosphere. The algorithms required to generate the geophysical variables from the observed spectral imagery each have their own inherent dependencies and sensitivities, and weighting these objectively is challenging. Here, we introduce intrinsic dimensionality (ID), a measure of information content, as an applications-agnostic, data-driven metric to quantify performance sensitivity to various design parameters. ID is computed through the analysis of the eigenvalues of the image covariance matrix, and can be thought of as the number of significant principal components. This metric is extremely powerful for quantifying the information content in high-dimensional data, such as spectrally resolved radiances and their changes over space and time. We find that the ID decreases for coarser GSD, decreased spectral resolution and range, less frequent acquisitions, and lower signal-to-noise levels. This decrease in information content has implications for all derived products. ID is simple to compute, providing a single quantitative standard to evaluate combinations of design parameters, irrespective of higher-level algorithms, products, applications, or disciplines.

Informing Nature-based Climate Solutions for the United States with the best-available science

Nature-based Climate Solutions (NbCS) are managed alterations to ecosystems designed to increase carbon sequestration or reduce greenhouse gas emissions. While they have growing public and private support, the realizable benefits and unintended consequences of NbCS are not well understood. At regional scales where policy decisions are often made, NbCS benefits are estimated from soil and tree survey data that can miss important carbon sources and sinks within an ecosystem, and do not reveal the biophysical impacts of NbCS for local water and energy cycles. The only direct observations of ecosystem-scale carbon fluxes, for example, by eddy covariance flux towers, have not yet been systematically assessed for what they can tell us about NbCS potentials, and state-of-the-art remote sensing products and land-surface models are not yet being widely used to inform NbCS policymaking or implementation. As a result, there is a critical mismatch between the point- and tree-scale data most often used to assess NbCS benefits and impacts, the ecosystem and landscape scales where NbCS projects are implemented, and the regional to continental scales most relevant to policymaking. Here, we propose a research agenda to confront these gaps using data and tools that have long been used to understand the mechanisms driving ecosystem carbon and energy cycling, but have not yet been widely applied to NbCS. We outline steps for creating robust NbCS assessments at both local to regional scales that are informed by ecosystem-scale observations, and which consider concurrent biophysical impacts, future climate feedbacks, and the need for equitable and inclusive NbCS implementation strategies. We contend that these research goals can largely be accomplished by shifting the scales at which pre-existing tools are applied and blended together, although we also highlight some opportunities for more radical shifts in approach.

A Comprehensive Understanding of Interlayer Engineering in Layered Manganese and Vanadium Cathodes for Aqueous Zn-ion Batteries

Rechargeable aqueous zinc-ion batteries (AZIBs) hold a budding technology for large-scale stationary energy storage devices due to their inherent safety, cost-effectiveness, eco-friendly, and acceptable electrochemical performance. However, developing a cathode material with fast kinetics and durable structural stability for Zn 2+ intercalation is still an arduous challenge. Compared with other cathode materials, layered manganese/vanadium (Mn/V) oxides that feature merits of adjustable interlayer spacing and considerable specific capacity have attracted much interest in AZIBs. However, the intrinsic sluggish reaction kinetics, inferior electrical conductivity, and notorious dissolution of active materials still obstruct the realization of their full potentials. Interlayer engineering of pre-intercalation is regarded as an effective solution to overcome these problems. In this review, we start from the crystal structure and reaction mechanism of layered Mn/V oxide cathodes to critical issues and recent progress in interlayer engineering. Finally, some future perspectives are outlined for the development of high-performance AZIBs.

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

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

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.

Detecting the Responses of CO2 Column Abundances to Anthropogenic Emissions from Satellite Observations of GOSAT and OCO-2

The continuing increase in atmospheric CO2 concentration caused by anthropogenic CO2 emissions significantly contributes to climate change driven by global warming. Satellite measurements of long-term CO2 data with global coverage improve our understanding of global carbon cycles. However, the sensitivity of the space-borne measurements to anthropogenic emissions on a regional scale is less explored because of data sparsity in space and time caused by impacts from geophysical factors such as aerosols and clouds. Here, we used global land mapping column averaged dry-air mole fractions of CO2 (XCO2) data (Mapping-XCO2), generated from a spatio-temporal geostatistical method using GOSAT and OCO-2 observations from April 2009 to December 2020, to investigate the responses of XCO2 to anthropogenic emissions at both global and regional scales. Our results show that the long-term trend of global XCO2 growth rate from Mapping-XCO2, which is consistent with that from ground observations, shows interannual variations caused by the El Niño Southern Oscillation (ENSO). The spatial distributions of XCO2 anomalies, derived from removing background from the Mapping-XCO2 data, reveal XCO2 enhancements of about 1.5–3.5 ppm due to anthropogenic emissions and seasonal biomass burning in the wintertime. Furthermore, a clustering analysis applied to seasonal XCO2 clearly reveals the spatial patterns of atmospheric transport and terrestrial biosphere CO2 fluxes, which help better understand and analyze regional XCO2 changes that are associated with atmospheric transport. To quantify regional anomalies of CO2 emissions, we selected three representative urban agglomerations as our study areas, including the Beijing-Tian-Hebei region (BTH), the Yangtze River Delta urban agglomerations (YRD), and the high-density urban areas in the eastern USA (EUSA). The results show that the XCO2 anomalies in winter well capture the several-ppm enhancement due to anthropogenic CO2 emissions. For BTH, YRD, and EUSA, regional positive anomalies of 2.47 ± 0.37 ppm, 2.20 ± 0.36 ppm, and 1.38 ± 0.33 ppm, respectively, can be detected during winter months from 2009 to 2020. These anomalies are slightly higher than model simulations from CarbonTracker-CO2. In addition, we compared the variations in regional XCO2 anomalies and NO2 columns during the lockdown of the COVID-19 pandemic from January to March 2020. Interestingly, the results demonstrate that the variations of XCO2 anomalies have a positive correlation with the decline of NO2 columns during this period. These correlations, moreover, are associated with the features of emitting sources. These results suggest that we can use simultaneously observed NO2, because of its high detectivity and co-emission with CO2, to assist the analysis and verification of CO2 emissions in future studies.

Biotechnology for carbon capture and fixation: Critical review and future directions

To mitigate the growing threat of climate change and develop novel technologies that can eliminate carbon dioxide, the most abundant greenhouse gas derived from the flue gas stream of the fossil fuel-fired power stations, is momentous. The development of carbon capture and sequestration-based technologies may play a significant role in this regard. Carbon fixation mostly occurs by photosynthesizing plants as well as photo and chemoautotrophic microbes that turn the atmospheric carbon dioxide into organic materials via their enzymes. Biofuel can offer a sustainable solution for carbon mitigation. The pragmatic implementation of biofuel production processes is neither cost-effective nor has been proven safe over the long term. Searching for ways to enhance biofuel generation by the employment of genetic engineering is vital. Carbon biosequestration can help to curb the greenhouse effect. In addition, new genomic approaches, which are able to use gene-splicing biotechnology techniques and recombinant DNA technology to produce genetically modified organisms, can contribute to improvement in sustainable and renewable biofuel and biomaterial production from microorganisms. Biopolymers, Biosurfactants, and Biochars are suggested as sustainable future trends. This study aims to pave the way for implementing biotechnology methods to capture carbon and decrease the demand and consumption of fossil fuels as well as the emissions of greenhouse gases. Having a better image of microorganisms' potential role in carbon capture and storage can be prolific in developing powerful techniques to reduce CO₂ emissions.

Unusual characteristics of the carbon cycle during the 2015-2016 El Niño

The 2015-2016 El Niño was one of the strongest on record, but its influence on the carbon balance is less clear. Using Northern Hemisphere atmospheric CO₂ observations, we found both detrended atmospheric CO₂ growth rate (CGR) and CO₂ seasonal-cycle amplitude (SCA) of 2015-2016 were much higher than that of other El Niño events. The simultaneous high CGR and SCA were unusual, because our analysis of long-term CO₂ observations at Mauna Loa revealed a significantly negative correlation between CGR and SCA. Atmospheric inversions and terrestrial ecosystem models indicate strong northern land carbon uptake during spring but substantially reduced carbon uptake (or high emissions) during early autumn, which amplified SCA but also resulted in a small anomaly in annual carbon uptake of northern ecosystems in 2015-2016. This negative ecosystem carbon uptake anomaly in early autumn was primarily due to soil water deficits and more litter decomposition caused by enhanced spring productivity. Our study demonstrates a decoupling between seasonality and annual carbon cycle balance in northern ecosystems over 2015-2016, which is unprecedented in the past five decades of El Niño events.

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.

Ab initio investigation of the CO-N2 quantum scattering: The collisional perturbation of the pure rotational R(0) line in CO

We report fully quantum calculations of the collisional perturbation of a molecular line for a system that is relevant for Earth's atmosphere. We consider the N₂-perturbed pure rotational R(0) line in CO. The results agree well with the available experimental data. This work constitutes a significant step toward populating the spectroscopic databases with ab initio collisional line-shape parameters for atmosphere-relevant systems. The calculations were performed using three different recently reported potential energy surfaces (PESs). We conclude that all three PESs lead to practically the same values of the pressure broadening coefficients.

Plant-based CO2 drawdown and storage as SiC

This study demonstrates how integration of plants into an artificial carbon cycle is capable of harmoniously operating with Earth's natural cycles as one means to recycle atmospheric CO₂ for economically lucrative green materials.

Catalytic transfer hydrogenation of ethyl levulinate to γ-valerolactone over supported MoS2 catalysts

A MoS₂ catalytic system was developed for the efficient catalytic transfer hydrogenation of levulinate esters to γ-valerolactone, and the support effect and reaction mechanism were discussed for this novel system.

Satellite-based estimates of decline and rebound in China's CO2 emissions during COVID-19 pandemic

Changes in CO₂ emissions during the COVID-19 pandemic have been estimated from indicators on activities like transportation and electricity generation. Here, we instead use satellite observations together with bottom-up information to track the daily dynamics of CO₂ emissions during the pandemic. Unlike activity data, our observation-based analysis deploys independent measurement of pollutant concentrations in the atmosphere to correct misrepresentation in the bottom-up data and can provide more detailed insights into spatially explicit changes. Specifically, we use TROPOMI observations of NO₂ to deduce 10-day moving averages of NO _x and CO₂ emissions over China, differentiating emissions by sector and province. Between January and April 2020, China's CO₂ emissions fell by 11.5% compared to the same period in 2019, but emissions have since rebounded to pre-pandemic levels before the coronavirus outbreak at the beginning of January 2020 owing to the fast economic recovery in provinces where industrial activity is concentrated.

Local Anomalies in the Column-Averaged Dry Air Mole Fractions of Carbon Dioxide Across the Globe During the First Months of the Coronavirus Recession

We use a global transport model and satellite retrievals of the carbon dioxide (CO₂) column average to explore the impact of CO₂ emissions reductions that occurred during the economic downturn at the start of the Covid-19 pandemic. The changes in the column averages are substantial in a few places of the model global grid, but the induced gradients are most often less than the random errors of the retrievals. The current necessity to restrict the quality-assured column retrievals to almost cloud-free areas appears to be a major obstacle in identifying changes in CO₂ emissions. Indeed, large changes have occurred in the presence of clouds, and in places that were cloud free in 2020, the comparison with previous years is hampered by different cloud conditions during these years. We therefore recommend to favor all-weather CO₂ monitoring systems, at least in situ, to support international efforts to reduce emissions.

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.

Spatio-Temporal Validation of AIRS CO2 Observations Using GAW, HIPPO and TCCON

Carbon dioxide (CO2) is a significant atmospheric greenhouse gas and its concentrations can be observed by in situ surface stations, aircraft flights and satellite sensors. This paper investigated the ability of the CO2 satellite observations to monitor, analyze and predict the horizontal and vertical distribution of atmospheric CO2 concentration at global scales. CO2 observations retrieved by an Atmospheric Infrared Sounder (AIRS) were inter-compared with the Global Atmosphere Watch Program (GAW) and HIAPER Pole-to-Pole Observations (HIPPOs), with reference to the measurements obtained using high-resolution ground-based Fourier Transform Spectrometers (FTS) in the Total Carbon Column Observing Network (TCCON) from near-surface level to the mid-to-high troposphere. After vertically integrating the AIRS-retrieved values with the column averaging kernels of TCCON measurements, the AIRS observations are spatio-temporally compared with HIPPO-integrated profiles in the mid-to-high troposphere. Five selected GAW stations are used for comparisons with TCCON sites near the surface of the Earth. The results of AIRS, TCCON (5–6 km), GAW and TCCON (1 km) CO2 measurements from 2007 to 2013 are compared, analyzed and discussed at their respective altitudes. The outcomes indicate that the difference of about 3.0 ppmv between AIRS and GAW or other highly accurate in situ surface measurements is mainly due to the different vertical altitudes, rather than the errors in the AIRS. The study reported here also explores the potential of AIRS satellite observations for analyzing the spatial distribution and seasonal variation of CO2 concentration at global scales.

Measuring and Monitoring Urban Impacts on Climate Change from Space

As urban areas continue to expand and play a critical role as both contributors to climate change and hotspots of vulnerability to its effects, cities have become battlegrounds for climate change adaptation and mitigation. Large amounts of earth observations from space have been collected over the last five decades and while most of the measurements have not been designed specifically for monitoring urban areas, an increasing number of these observations is being used for understanding the growth rates of cities and their environmental impacts. Here we reviewed the existing tools available from satellite remote sensing to study urban contribution to climate change, which could be used for monitoring the progress of climate change mitigation strategies at the city level. We described earth observations that are suitable for measuring and monitoring urban population, extent, and structure; urban emissions of greenhouse gases and other air pollutants; urban energy consumption; and extent, intensity, and effects on surrounding regions, including nearby water bodies, of urban heat islands. We compared the observations available and obtainable from space with the measurements desirable for monitoring. Despite considerable progress in monitoring urban extent, structure, heat island intensity, and air pollution from space, many limitations and uncertainties still need to be resolved. We emphasize that some important variables, such as population density and urban energy consumption, cannot be suitably measured from space with available observations.

The potential of a constellation of low earth orbit satellite imagers to monitor worldwide fossil fuel CO2 emissions from large cities and point sources

BACKGROUND

Satellite imagery will offer unparalleled global spatial coverage at high-resolution for long term cost-effective monitoring of CO₂ concentration plumes generated by emission hotspots. CO₂ emissions can then be estimated from the magnitude of these plumes. In this paper, we assimilate pseudo-observations in a global atmospheric inversion system to assess the performance of a constellation of one to four sun-synchronous low-Earth orbit (LEO) imagers to monitor anthropogenic CO₂ emissions. The constellation of imagers follows the specifications from the European Spatial Agency (ESA) for the Copernicus Anthropogenic Carbon Dioxide Monitoring (CO2M) concept for a future operational mission dedicated to the monitoring of anthropogenic CO₂ emissions. This study assesses the uncertainties in the inversion estimates of emissions ("posterior uncertainties").

RESULTS

The posterior uncertainties of emissions for individual cities and power plants are estimated for the 3 h before satellite overpasses, and extrapolated at annual scale assuming temporal auto-correlations in the uncertainties in the emission products that are used as a prior knowledge on the emissions by the Bayesian framework of the inversion. The results indicate that (i) the number of satellites has a proportional impact on the number of 3 h time windows for which emissions are constrained to better than 20%, but it has a small impact on the posterior uncertainties in annual emissions; (ii) having one satellite with wide swath would provide full images of the XCO₂ plumes, and is more beneficial than having two satellites with half the width of reference swath; and (iii) an increase in the precision of XCO₂ retrievals from 0.7 ppm to 0.35 ppm has a marginal impact on the emission monitoring performance.

CONCLUSIONS

For all constellation configurations, only the cities and power plants with an annual emission higher than 0.5 MtC per year can have at least one 8:30-11:30 time window during one year when the emissions can be constrained to better than 20%. The potential of satellite imagers to constrain annual emissions not only depend on the design of the imagers, but also strongly depend on the temporal error structure in the prior uncertainties, which is needed to be objectively assessed in the bottom-up emission maps.

OCO-2 Solar-Induced Chlorophyll Fluorescence Variability across Ecoregions of the Amazon Basin and the Extreme Drought Effects of El Niño (2015–2016)

Amazonian ecosystems are major biodiversity hotspots and carbon sinks that may lose species to extinction and become carbon sources due to extreme dry or warm conditions. We investigated the seasonal patterns of high-resolution solar-induced chlorophyll fluorescence (SIF) measured by the satellite Orbiting Carbon Observatory-2 (OCO-2) across the Amazonian ecoregions to assess the area´s phenology and extreme drought vulnerability. SIF is an indicator of the photosynthetic activity of chlorophyll molecules and is assumed to be directly related to gross primary production (GPP). We analyzed SIF variability in the Amazon basin during the period between September 2014 and December 2018. In particular, we focused on the SIF drought response under the extreme drought period during the strong El Niño in 2015–2016, as well as the 6-month drought peak period. During the drought´s peak months, the SIF decreased and increased with different intensities across the ecoregions of the Amazonian moist broadleaf forest (MBF) biome. Under a high temperature, a high vapor pressure deficit, and extreme drought conditions, the SIF presented differences from −31.1% to +17.6%. Such chlorophyll activity variations have been observed in plant-level measurements of active fluorescence in plants undergoing physiological responses to water or heat stress. Thus, it is plausible that the SIF variations in the ecoregions’ ecosystems occurred as a result of water and heat stress, and arguably because of drought-driven vegetation mortality and collateral effects in their species composition and community structures. The SIF responses to drought at the ecoregional scale indicate that there are different levels of resilience to drought across MBF ecosystems that the currently used climate- and biome-region scales do not capture. Finally, we identified monthly SIF values of 32 ecoregions, including non-MBF biomes, which may give the first insights into the photosynthetic activity dynamics of Amazonian ecoregions.

Spatio-Temporal Mapping of Multi-Satellite Observed Column Atmospheric CO2 Using Precision-Weighted Kriging Method

Column-averaged dry air mole fraction of atmospheric CO2 (XCO2), obtained by multiple satellite observations since 2003 such as ENVISAT/SCIAMACHY, GOSAT, and OCO-2 satellite, is valuable for understanding the spatio-temporal variations of atmospheric CO2 concentrations which are related to carbon uptake and emissions. In order to construct long-term spatio-temporal continuous XCO2 from multiple satellites with different temporal and spatial periods of observations, we developed a precision-weighted spatio-temporal kriging method for integrating and mapping multi-satellite observed XCO2. The approach integrated XCO2 from different sensors considering differences in vertical sensitivity, overpass time, the field of view, repeat cycle and measurement precision. We produced globally mapped XCO2 (GM-XCO2) with spatial/temporal resolution of 1 × 1 degree every eight days from 2003 to 2016 with corresponding data precision and interpolation uncertainty in each grid. The predicted GM-XCO2 precision improved in most grids compared with conventional spatio-temporal kriging results, especially during the satellites overlapping period (0.3–0.5 ppm). The method showed good reliability with R2 of 0.97 from cross-validation. GM-XCO2 showed good accuracy with a standard deviation of bias from total carbon column observing network (TCCON) measurements of 1.05 ppm. This method has potential applications for integrating and mapping XCO2 or other similar datasets observed from multiple satellite sensors. The resulting GM-XCO2 product may be also used in different carbon cycle research applications with different precision requirements.

Interannual variation of terrestrial carbon cycle: Issues and perspectives

With accumulation of carbon cycle observations and model developments over the past decades, exploring interannual variation (IAV) of terrestrial carbon cycle offers the opportunity to better understand climate-carbon cycle relationships. However, despite growing research interest, uncertainties remain on some fundamental issues, such as the contributions of different regions, constituent fluxes and climatic factors to carbon cycle IAV. Here we overviewed the literature on carbon cycle IAV about current understanding of these issues. Observations and models of the carbon cycle unanimously show the dominance of tropical land ecosystems to the signal of global carbon cycle IAV, where tropical semiarid ecosystems contribute as much as the combination of all other tropical ecosystems. Vegetation photosynthesis contributes more than ecosystem respiration to IAV of the global net land carbon flux, but large uncertainties remain on the contribution of fires and other disturbance fluxes. Climatic variations are the major drivers to the IAV of net land carbon flux. Although debate remains on whether the dominant driver is temperature or moisture variability, their interaction,that is, the dependence of carbon cycle sensitivity to temperature on moisture conditions, is emerging as key regulators of the carbon cycle IAV. On timescales from the interannual to the centennial, global carbon cycle variability will be increasingly contributed by northern land ecosystems and oceans. Therefore, both improving Earth system models (ESMs) with the progressive understanding on the fast processes manifested at interannual timescale and expanding carbon cycle observations at broader spatial and longer temporal scales are critical to better prediction on evolution of the carbon-climate system.

Exploiting OMI NO2 satellite observations to infer fossil-fuel CO2 emissions from U.S. megacities

Fossil-fuel CO₂ emissions and their trends in eight U.S. megacities during 2006-2017 are inferred by combining satellite-derived NO_X emissions with bottom-up city-specific NO_X-to-CO₂ emission ratios. A statistical model is fit to a collection NO₂ plumes observed from the Ozone Monitoring Instrument (OMI), and is used to calculate top-down NO_X emissions. Decreases in OMI-derived NO_X emissions are observed across the eight cities from 2006 to 2017 (-17% in Miami to -58% in Los Angeles), and are generally consistent with long-term trends of bottom-up inventories (-25% in Miami to -49% in Los Angeles), but there are some interannual discrepancies. City-specific NO_X-to-CO₂ emission ratios, used to calculate inferred CO₂, are estimated through annual bottom-up inventories of NO_X and CO₂ emissions disaggregated to 1 × 1 km² resolution. Over the study period, NO_X-to-CO₂ emission ratios have decreased by ~40% nationwide (-24% to -51% for our studied cities), which is attributed to a faster reduction in NO_X when compared to CO₂ due to policy regulations and fuel type shifts. Combining top-down NO_X emissions and bottom-up NO_X-to-CO₂ emission ratios, annual fossil-fuel CO₂ emissions are derived. Inferred OMI-based top-down CO₂ emissions trends vary between +7% in Dallas to -31% in Phoenix. For 2017, we report annual fossil-fuel CO₂ emissions to be: Los Angeles 113 ± 49 Tg/yr; New York City 144 ± 62 Tg/yr; and Chicago 55 ± 24 Tg/yr. A study in the Los Angeles area, using independent methods, reported a 2013-2016 average CO₂ emissions rate of 104 Tg/yr and 120 Tg/yr, which suggests that the CO₂ emissions from our method are in good agreement with other studies' top-down estimates. We anticipate future remote sensing instruments - with better spatial and temporal resolution - will better constrain the NO_X-to-CO₂ ratio and reduce the uncertainty in our method.

Flux towers in the sky: global ecology from space

Global ecology - the study of the interactions among the Earth's ecosystems, land, atmosphere and oceans - depends crucially on global observations: this paper focuses on space-based observations of global terrestrial ecosystems. Early global ecology relied on an extrapolation of detailed site-level observations, using models of increasing complexity. Modern global ecology has been enabled largely by vegetation indices (greenness) from operational space-based imagery but current capabilities greatly expand scientific possibilities. New observations from spacecraft in orbit allowed an estimation of gross carbon fluxes, photosynthesis, biomass burning, evapotranspiration and biomass, to create virtual eddy covariance sites in the sky. Planned missions will reveal the dimensions of the diversity of life itself. These observations will improve our understanding of the global productivity and carbon storage, land use, carbon cycle-climate feedback, diversity-productivity relationships and enable improved climate forecasts. Advances in remote sensing challenge ecologists to relate information organised by biome and species to new data arrayed by pixels and develop theory to address previously unobserved scales.

Mechanistic evidence for tracking the seasonality of photosynthesis with solar-induced fluorescence

Northern hemisphere evergreen forests assimilate a significant fraction of global atmospheric CO2 but monitoring large-scale changes in gross primary production (GPP) in these systems is challenging. Recent advances in remote sensing allow the detection of solar-induced chlorophyll fluorescence (SIF) emission from vegetation, which has been empirically linked to GPP at large spatial scales. This is particularly important in evergreen forests, where traditional remote-sensing techniques and terrestrial biosphere models fail to reproduce the seasonality of GPP. Here, we examined the mechanistic relationship between SIF retrieved from a canopy spectrometer system and GPP at a winter-dormant conifer forest, which has little seasonal variation in canopy structure, needle chlorophyll content, and absorbed light. Both SIF and GPP track each other in a consistent, dynamic fashion in response to environmental conditions. SIF and GPP are well correlated (R2 = 0.62-0.92) with an invariant slope over hourly to weekly timescales. Large seasonal variations in SIF yield capture changes in photoprotective pigments and photosystem II operating efficiency associated with winter acclimation, highlighting its unique ability to precisely track the seasonality of photosynthesis. Our results underscore the potential of new satellite-based SIF products (TROPOMI, OCO-2) as proxies for the timing and magnitude of GPP in evergreen forests at an unprecedented spatiotemporal resolution.

A Theoretical Analysis for Improving Aerosol-Induced CO2 Retrieval Uncertainties Over Land Based on TanSat Nadir Observations Under Clear Sky Conditions

Aerosols significantly affect carbon dioxide (CO2) retrieval accuracy and precision by modifying the light path. Hyperspectral measurements in the near infrared and shortwave infrared (NIR/SWIR) bands from the generation of new greenhouse gas satellites (e.g., the Chinese Global Carbon Dioxide Monitoring Scientific Experimental Satellite, TanSat) contain aerosol information for correction of scattering effects in the retrieval. Herein, a new approach is proposed for optimizing the aerosol model used in the TanSat CO2 retrieval algorithm to reduce CO2 uncertainties associated with aerosols. The weighting functions of hyperspectral observations with respect to elements in the state vector are simulated by a forward radiative transfer model. Using the optimal estimation method (OEM), the information content and each component of the CO2 column-averaged dry-air mole fraction (XCO2) retrieval errors from the TanSat simulations are calculated for typical aerosols which are described by Aerosol Robotic Network (AERONET) inversion products at selected sites based on the a priori and measurement assumptions. The results indicate that the size distribution parameters (reff, veff), real refractive index coefficient of fine mode (arf) and fine mode fraction (fmf) dominate the interference errors, with each causing 0.2–0.8 ppm of XCO2 errors. Given that only 4–7 degrees of freedom for signal (DFS) of aerosols can be obtained simultaneously and CO2 information decreases as more aerosol parameters are retrieved, four to seven aerosol parameters are suggested as the most appropriate for inclusion in CO2 retrieval. Focusing on only aerosol-induced XCO2 errors, forward model parameter errors, rather than interference errors, are dominant. A comparison of these errors across different aerosol parameter combination groups reveals that fewer aerosol-induced XCO2 errors are found when retrieving seven aerosol parameters. Therefore, the model selected as the optimal aerosol model includes aerosol optical depth (AOD), peak height of aerosol profile (Hp), width of aerosol profile (Hw), effective variance of fine mode aerosol (vefff), effective radius of coarse mode aerosol (reffc), coefficient a of the real part of the refractive index for the fine mode and coarse mode (arf and arc), with the lowest error of less than 1.7 ppm for all aerosol and surface types. For marine aerosols, only five parameters (AOD, Hp, Hw, reffc and arc) are recommended for the low aerosol information. This optimal aerosol model therefore offers a theoretical foundation for improving CO2 retrieval precision from real TanSat observations in the future.

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.

100 Years of Progress in Gas-Phase Atmospheric Chemistry Research

Remarkable progress has occurred over the last 100 years in our understanding of atmospheric chemical composition, stratospheric and tropospheric chemistry, urban air pollution, acid rain, and the formation of airborne particles from gas-phase chemistry. Much of this progress was associated with the developing understanding of the formation and role of ozone and of the oxides of nitrogen, NO and NO2, in the stratosphere and troposphere. The chemistry of the stratosphere, emerging from the pioneering work of Chapman in 1931, was followed by the discovery of catalytic ozone cycles, ozone destruction by chlorofluorocarbons, and the polar ozone holes, work honored by the 1995 Nobel Prize in Chemistry awarded to Crutzen, Rowland, and Molina. Foundations for the modern understanding of tropospheric chemistry were laid in the 1950s and 1960s, stimulated by the eye-stinging smog in Los Angeles. The importance of the hydroxyl (OH) radical and its relationship to the oxides of nitrogen (NO and NO2) emerged. The chemical processes leading to acid rain were elucidated. The atmosphere contains an immense number of gas-phase organic compounds, a result of emissions from plants and animals, natural and anthropogenic combustion processes, emissions from oceans, and from the atmospheric oxidation of organics emitted into the atmosphere. Organic atmospheric particulate matter arises largely as gas-phase organic compounds undergo oxidation to yield low-volatility products that condense into the particle phase. A hundred years ago, quantitative theories of chemical reaction rates were nonexistent. Today, comprehensive computer codes are available for performing detailed calculations of chemical reaction rates and mechanisms for atmospheric reactions. Understanding the future role of atmospheric chemistry in climate change and, in turn, the impact of climate change on atmospheric chemistry, will be critical to developing effective policies to protect the planet.

Modeling leaf CO2 assimilation and Photosystem II photochemistry from chlorophyll fluorescence and the photochemical reflectance index

We present a simple model to assess the quantum yield of photochemistry (Φ_P ) and CO₂ assimilation rate from two parameters that are detectable by remote sensing: chlorophyll (chl) fluorescence and the photochemical reflectance index (PRI). Φ_P is expressed as a simple function of the chl fluorescence yield (Φ_F ) and nonphotochemical quenching (NPQ): Φ_P  = 1-bΦ_F (1 + NPQ). Because NPQ is known to be related with PRI, Φ_P can be remotely assessed from solar-induced fluorescence and the PRI. The CO₂ assimilation rate can be assessed from the estimated Φ_P value with either the maximum carboxylation rate (V_cmax ), the intercellular CO₂ concentration (C_i ), or parameters of the stomatal conductance model. The model was applied to experimental data obtained for Chenopodium album leaves under various environmental conditions and was able to successfully predict Φ_F values and the CO₂ assimilation rate. The present model will improve the accuracy of assessments of gas exchange rates and primary productivity by remote sensing.

Global atmospheric CO2 inverse models converging on neutral tropical land exchange, but disagreeing on fossil fuel and atmospheric growth rate

We have compared a suite of recent global CO2 atmospheric inversion results to independent airborne observations and to each other, to assess their dependence on differences in northern extratropical (NET) vertical transport and to identify some of the drivers of model spread. We evaluate posterior CO2 concentration profiles against observations from the High-Performance Instrumented Airborne Platform for Environmental Research (HIAPER) Pole-to-Pole Observations (HIPPO) aircraft campaigns over the mid-Pacific in 2009-2011. Although the models differ in inverse approaches, assimilated observations, prior fluxes, and transport models, their broad latitudinal separation of land fluxes has converged significantly since the Atmospheric Carbon Cycle Inversion Intercomparison (TransCom 3) and the REgional Carbon Cycle Assessment and Processes (RECCAP) projects, with model spread reduced by 80% since TransCom 3 and 70% since RECCAP. Most modeled CO2 fields agree reasonably well with the HIPPO observations, specifically for the annual mean vertical gradients in the Northern Hemisphere. Northern Hemisphere vertical mixing no longer appears to be a dominant driver of northern versus tropical (T) annual flux differences. Our newer suite of models still gives northern extratropical land uptake that is modest relative to previous estimates (Gurney et al., 2002; Peylin et al., 2013) and near-neutral tropical land uptake for 2009-2011. Given estimates of emissions from deforestation, this implies a continued uptake in intact tropical forests that is strong relative to historical estimates (Gurney et al., 2002; Peylin et al., 2013). The results from these models for other time periods (2004-2014, 2001-2004, 1992-1996) and reevaluation of the TransCom 3 Level 2 and RECCAP results confirm that tropical land carbon fluxes including deforestation have been near neutral for several decades. However, models still have large disagreements on ocean-land partitioning. The fossil fuel (FF) and the atmospheric growth rate terms have been thought to be the best-known terms in the global carbon budget, but we show that they currently limit our ability to assess regional-scale terrestrial fluxes and ocean-land partitioning from the model ensemble.

Comment on “Contrasting carbon cycle responses of the tropical continents to the 2015–2016 El Niño”

Liu et al. (Research Articles, 13 October 2017) inferred carbon flux anomalies in tropical continents with enough confidence to constrain the driving carbon-exchange processes. I show that they underestimated their error budget and that more effort must be invested in the satellite concentration retrievals and in the atmospheric transport models before such precision can be achieved.

A Global Synthesis Inversion Analysis of Recent Variability in CO2 Fluxes Using GOSAT and In Situ Observations

The precise contribution of the two major sinks for anthropogenic CO₂ emissions, terrestrial vegetation and the ocean, and their location and year-to-year variability are not well understood. Top-down estimates of the spatiotemporal variations in emissions and uptake of CO₂ are expected to benefit from the increasing measurement density brought by recent in situ and remote CO₂ observations. We uniquely apply a batch Bayesian synthesis inversion at relatively high resolution to in situ surface observations and bias-corrected GOSAT satellite column CO₂ retrievals to deduce the global distributions of natural CO₂ fluxes during 2009-2010. Our objectives include evaluating bottom-up prior flux estimates, assessing the value added by the satellite data, and examining the impacts of inversion technique and assumptions on posterior fluxes and uncertainties. The GOSAT inversion is generally better constrained than the in situ inversion, with smaller posterior regional flux uncertainties and correlations, because of greater spatial coverage, except over North America and high-latitude ocean. Complementarity of the in situ and GOSAT data enhances uncertainty reductions in a joint inversion; however, spatial and temporal gaps in sampling still limit the ability to accurately resolve fluxes down to the sub-continental scale. The GOSAT inversion produces a shift in the global CO₂ sink from the tropics to the north and south relative to the prior, and an increased source in the tropics of ~2 Pg C y^-1 relative to the in situ inversion, similar to what is seen in studies using other inversion approaches. This result may be driven by sampling and residual retrieval biases in the GOSAT data, as suggested by significant discrepancies between posterior CO₂ distributions and surface in situ and HIPPO mission aircraft data. While the shift in the global sink appears to be a robust feature of the inversions, the partitioning of the sink between land and ocean in the inversions using either in situ or GOSAT data is found to be sensitive to prior uncertainties because of negative correlations in the flux errors. The GOSAT inversion indicates significantly less CO₂ uptake in summer of 2010 than in 2009 across northern regions, consistent with the impact of observed severe heat waves and drought. However, observations from an in situ network in Siberia imply that the GOSAT inversion exaggerates the 2010-2009 difference in uptake in that region, while the prior CASA-GFED model of net ecosystem production and fire emissions reasonably estimates that quantity. The prior, in situ posterior, and GOSAT posterior all indicate greater uptake over North America in spring to early summer of 2010 than in 2009, consistent with wetter conditions. The GOSAT inversion does not show the expected impact on fluxes of a 2010 drought in the Amazon; evaluation of posterior mole fractions against local aircraft profiles suggests that time-varying GOSAT coverage can bias estimation of flux interannual variability in this region.

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.