Orbiting Carbon Observatory-2 Research Articles

Inter-comparison and evaluation of global satellite XCO2 products

ABSTRACT Carbon dioxide (CO2) is one of the main greenhouse gases and has become a major concern as its concentration has been growing in recent years. Satellite remote sensing is an efficient way to monitor CO2 in the atmosphere, and several satellites are already used for CO2 monitoring. It is imperative to investigate the spatial coverage and spatio-temporal trends of satellite products, as well as identify the satellites with higher levels of accuracy. Additionally, examining the disparities between the older and new generations of satellites would be meaningful. Therefore, this paper provides a comprehensive evaluation and inter-comparison for the commonly used satellite column-averaged dry-air mole fraction of CO2 (XCO2) products. Specifically, the temporal trends and monthly coverage of the Greenhouse Gases Observing SATellite (GOSAT), Greenhouse Gases Observing SATellite-2 (GOSAT-2), Orbiting Carbon Observatory-2 (OCO-2), Orbiting Carbon Observatory-3 (OCO-3), and SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) are investigated. The accuracy of these satellite products is evaluated and analyzed based on Total Carbon Column Observing Network (TCCON) data. The results indicate that the XCO2 of all the satellite products show a year-by-year increase, with seasonal periodicity. In terms of overall accuracy, the OCO series satellites exhibit a slightly higher level of accuracy compared to the GOSAT series. The products of the new generation of satellites are less stable than those of the older generation, probably due to the impacts of the inversion algorithm and platforms.

Spatiotemporal variation in bamboo Solar-induced chlorophyll fluorescence (SIF) in China based on the global Orbiting Carbon Observatory-2 (OCO-2) carbon satellite and study on the response to climate and terrain

ABSTRACT Bamboo forests have a high carbon sequestration potential. Solar-induced chlorophyll fluorescence (SIF) provides fresh insights into the temporal and geographical variations in bamboo development and its interactions with the environment. SIF can serve as a probe for photosynthesis. In this study, we used distribution information on Chinese bamboo forests as well as a global SIF product, the global Orbiting Carbon Observatory-2 (OCO-2) SIF (GOSIF), which is derived from the OCO-2 carbon satellite. Thus, we used SIF as an indicator of bamboo forest growth. We investigated the spatial and temporal variations as well as the trends of bamboo forest SIF in China from 2008 to 2019 and explained the response mechanisms of bamboo forest growth to climate change and terrain differences in the region. We also evaluated the combined effects of climate and terrain on bamboo forest growth using the partial least squares path model (PLS-PM). The results were as follows: (1) Low values were found in the west and north, while high values were found in the east and south, according to the average bamboo forest SIF values in China from 2008 to 2019. More than 88% of the pixels had significantly increasing values. In conclusion, bamboo forests in China are growing well. (2) Solar radiation (Rad) and minimum temperature (Tmin) promoted the growth of most bamboo stands, while precipitation (Pre) and maximum temperature (Tmax) did the opposite. (3) The terrain, with a digital elevation model (DEM) less than 1000 m and a slope less than 9°, had a positive effect on bamboo forest SIF. (4) Climate and terrain jointly explained 45% of the bamboo forest growth change, and the major factor was climate change.

Spatial Statistical Prediction of Solar-Induced Chlorophyll Fluorescence (SIF) from Multivariate OCO-2 Data

Solar-induced chlorophyll fluorescence, or SIF, is a part of the natural process of photosynthesis. SIF can be measured from space by instruments such as the Orbiting Carbon Observatory-2 (OCO-2), making it a useful proxy for monitoring gross primary production (GPP), which is a critical component of Earth’s carbon cycle. The complex physical relationship between SIF and GPP is frequently studied using OCO-2 observations of SIF since they offer the finest spatial resolution available. However, measurement error (noise) and large gaps in spatial coverage limit the use of OCO-2 SIF to highly aggregated scales. To study the relationship between SIF and GPP across varying spatial scales, de-noised and gap-filled (i.e., Level 3) SIF data products are needed. Using a geostatistical methodology called cokriging, which includes kriging as a special case, we develop coSIF: a Level 3 SIF data product at a 0.05-degree resolution. As a natural secondary variable for cokriging, OCO-2 observes column-averaged atmospheric carbon dioxide concentrations (XCO2) simultaneously with SIF. There is a suggested lagged spatio-temporal dependence between SIF and XCO2, which we characterize through spatial covariance and cross-covariance functions. Our approach is highly parallelizable and accounts for non-stationary measurement errors in the observations. Importantly, each datum in the resulting coSIF data product is accompanied by a measure of uncertainty. Extant approaches do not provide formal uncertainty quantification, nor do they leverage the cross-dependence with XCO2.

Seamless mapping of long-term (2010–2020) daily global XCO2 and XCH4 from the Greenhouse Gases Observing Satellite (GOSAT), Orbiting Carbon Observatory 2 (OCO-2), and CAMS global greenhouse gas reanalysis (CAMS-EGG4) with a spatiotemporally self-supervised fusion method

Abstract. Precise and continuous monitoring of long-term carbon dioxide (CO2) and methane (CH4) over the globe is of great importance, which can help study global warming and achieve the goal of carbon neutrality. Nevertheless, the available observations of CO2 and CH4 from satellites are generally sparse, and current fusion methods to reconstruct their long-term values on a global scale are few. To address this problem, we propose a novel spatiotemporally self-supervised fusion method to establish long-term daily seamless XCO2 and XCH4 products from 2010 to 2020 over the globe on grids of 0.25∘. A total of three datasets are applied in our study, including the Greenhouse Gases Observing Satellite (GOSAT), the Orbiting Carbon Observatory 2 (OCO-2), and CAMS global greenhouse gas reanalysis (CAMS-EGG4). Attributed to the significant sparsity of data from GOSAT and OCO-2, the spatiotemporal discrete cosine transform is considered for our fusion task. Validation results show that the proposed method achieves a satisfactory accuracy, with standard deviations of bias (σ) of ∼1.18 ppm for XCO2 and 11.3 ppb for XCH4 against Total Carbon Column Observing Network (TCCON) measurements from 2010 to 2020. Meanwhile, the determination coefficients (R2) of XCO2 and XCH4 reach 0.91 or 0.95 (2010–2014 or 2015–2020) and 0.9 (2010–2020), respectively, after fusion. Overall, the performance of fused results distinctly exceeds that of CAMS-EGG4, which is also superior or close to those of GOSAT and OCO-2. In particular, our fusion method can effectively correct the large biases in CAMS-EGG4 due to the issues from assimilation data, such as the unadjusted anthropogenic emission inventories for COVID-19 lockdowns in 2020. Moreover, the fused results present coincident spatial patterns with GOSAT and OCO-2, which accurately display the long-term and seasonal changes in globally distributed XCO2 and XCH4. The daily global seamless gridded (0.25∘) XCO2 and XCH4 from 2010 to 2020 can be freely accessed at https://doi.org/10.5281/zenodo.7388893 (Wang et al., 2022a).

Improved Constraints on the Recent Terrestrial Carbon Sink Over China by Assimilating OCO‐2 XCO2 Retrievals

AbstractThe magnitude and distribution of China's terrestrial carbon sink remain uncertain due to insufficient observational constraints; satellite column‐average dry‐air mole fraction carbon dioxide (XCO2) retrievals may fill some of this gap. Here, we estimate China's carbon sink using atmospheric inversions of the Orbiting Carbon Observatory 2 (OCO‐2) XCO2 retrievals within different platforms, including the Global Carbon Assimilation System (GCAS) v2, the Copernicus Atmosphere Monitoring Service, and the OCO‐2 Model Inter‐comparison Project (MIP). We find that they consistently place the largest net biome production (NBP) in the south on an annual basis compared to the northeast and other main agricultural areas during peak growing season, coinciding well with the distribution of forests and crops, respectively. Moreover, the mean seasonal cycle amplitude of NBP in OCO‐2 inversions is obviously larger than that of biosphere model simulations and slightly greater than surface CO2 inversions. More importantly, the mean seasonal cycle of the OCO‐2 inversions is well constrained in the temperate, tropical, and subtropical monsoon climate zones, with better inter‐model consistency at a sub‐regional scale compared to in situ inversions and biosphere model simulations. In addition, the OCO‐2 inversions estimate the mean annual NBP in China for 2015–2019 to be between 0.34 (GCASv2) and 0.47 ± 0.16 PgC/yr (median ± std; OCO‐2 v10 MIP), and indicate the impacts of climate extremes (e.g., the 2019 drought) on the interannual variations of NBP. Our results suggest that assimilating OCO‐2 XCO2 retrievals is crucial for improving our understanding of China's terrestrial carbon sink regime.

Elucidating climatic drivers of photosynthesis by tropical forests.

Tropical forests play a pivotal role in regulating the global carbon cycle. However, the response of these forests to changes in absorbed solar energy and water supply under the changing climate is highly uncertain. Three-year (2018-2021) spaceborne high-resolution measurements of solar-induced chlorophyll fluorescence (SIF) from the TROPOspheric Monitoring Instrument (TROPOMI) provide a new opportunity to study the response of gross primary production (GPP) and more broadly tropical forest carbon dynamics to differences in climate. SIF has been shown to be a good proxy for GPP on monthly and regional scales. Combining tropical climate reanalysis records and other contemporary satellite products, we find that on the seasonal timescale, the dependence of GPP on climate variables is highly heterogeneous. Following the principal component analyses and correlation comparisons, two regimes are identified: water limited and energy limited. GPP variations over tropical Africa are more correlated with water-related factors such as vapor pressure deficit (VPD) and soil moisture, while in tropical Southeast Asia, GPP is more correlated with energy-related factors such as photosynthetically active radiation (PAR) and surface temperature. Amazonia is itself heterogeneous: with an energy-limited regime in the north and water-limited regime in the south. The correlations of GPP with climate variables are supported by other observation-based products, such as Orbiting Carbon Observatory-2 (OCO2) SIF and FluxSat GPP. In each tropical continent, the coupling between SIF and VPD increases with the mean VPD. Even on the interannual timescale, the correlation of GPP with VPD is still discernable, but the sensitivity is smaller than the intra-annual correlation. By and large, the dynamic global vegetation models in the TRENDY v8 project do not capture the high GPP seasonal sensitivity to VPD in dry tropics. The complex interactions between carbon and water cycles in the tropics illustrated in this study and the poor representation of this coupling in the current suite of vegetation models suggest that projections of future changes in carbon dynamics based on these models may not be robust.

Generating high-resolution total canopy SIF emission from TROPOMI data: Algorithm and application

Solar-induced chlorophyll fluorescence (SIF) is a rapidly advancing front in modeling global terrestrial gross primary production (GPP). Canopy total SIF emissions (SIFtotal) are mechanistically linked to the plant photosynthesis, and can be estimated from satellite observed SIF (SIFobs) through radiative transfer modeling. However, the current satellite SIFobs and thus SIFtotal are available only at coarse spatial resolutions from several kilometers to tens of kilometers, inhibiting the application at fine spatial scales. Here, we proposed an algorithm to generate both global high-resolution SIFtotal (HSIFtotal) and high-resolution SIFobs (HSIFobs) at 1 km from low-resolution SIFobs (LSIFobs) from the TROPOspheric Monitoring Instrument (TROPOMI), which has a spatial resolution at nadir of 3.5 km by 5.6–7 km. Our statistical method is based on the law of energy conservation and uses satellite derived fraction of absorbed photosynthetically active radiation, fluorescence efficiency, and the escape probability of fluorescence. We evaluated the accuracy of our HSIFtotal using the Orbiting Carbon Observatory-2 SIF (R2 = 0.78). We found that the spatial resolution had clear effects on the relationship between HSIFtotal and GPP. We also compared HSIFtotal to 8-day averaged tower GPP from 135 flux sites and found that they were better correlated when HSIFtotal was averaged over a 1-km radius around the tower than when averaged over a larger radius. Our study provided a unique high-resolution HSIFtotal product, which will advance the estimation of GPP by extrapolating site-level relationships to the global scale.

Evaluating the consistency between OCO-2 and OCO-3 XCO2 estimates derived from the NASA ACOS version 10 retrieval algorithm

Abstract. The version 10 (v10) Atmospheric Carbon Observations from Space (ACOS) Level 2 full-physics (L2FP) retrieval algorithm has been applied to multiyear records of observations from NASA's Orbiting Carbon Observatory 2 and 3 sensors (OCO-2 and OCO-3, respectively) to provide estimates of the carbon dioxide (CO2) column-averaged dry-air mole fraction (XCO2). In this study, a number of improvements to the ACOS v10 L2FP algorithm are described. The post-processing quality filtering and bias correction of the XCO2 estimates against multiple truth proxies are also discussed. The OCO v10 data volumes and XCO2 estimates from the two sensors for the time period of August 2019 through February 2022 are compared, highlighting differences in spatiotemporal sampling but demonstrating broad agreement between the two sensors where they overlap in time and space. A number of evaluation sources applied to both sensors suggest they are broadly similar in data and error characteristics. Mean OCO-3 differences relative to collocated OCO-2 data are approximately 0.2 and −0.3 ppm for land and ocean observations, respectively. Comparison of XCO2 estimates to collocated Total Carbon Column Observing Network (TCCON) measurements shows root mean squared errors (RMSEs) of approximately 0.8 and 0.9 ppm for OCO-2 and OCO-3, respectively. An evaluation against XCO2 fields derived from atmospheric inversion systems that assimilated only near-surface CO2 observations, i.e., did not assimilate satellite CO2 measurements, yielded RMSEs of 1.0 and 1.1 ppm for OCO-2 and OCO-3, respectively. Evaluation of uncertainties in XCO2 over small areas, as well as XCO2 biases across land–ocean crossings, also indicates similar behavior in the error characteristics of both sensors. Taken together, these results demonstrate a broad consistency of OCO-2 and OCO-3 XCO2 measurements, suggesting they may be used together for scientific analyses.

Generating daily high-resolution and full-coverage XCO2 across China from 2015 to 2020 based on OCO-2 and CAMS data

China has set a goal to achieve carbon neutrality by 2060, and satellite remote sensing allows for acquiring large-range and high-resolution carbon dioxide (CO2) data, which can aid in achieving this goal. However, satellite-derived column-averaged dry-air mole fraction of CO2 (XCO2) products often suffer from substantial spatial gaps due to the impacts of narrow swath and clouds. Here, this paper generates daily full-coverage XCO2 data at a high spatial resolution of 0.1° over China during 2015–2020, by fusing satellite observations and reanalysis data in a deep neural network (DNN) framework. Specifically, DNN constructs the relationships between Orbiting Carbon Observatory-2 satellite XCO2 retrievals, Copernicus Atmosphere Monitoring Service (CAMS) XCO2 reanalysis data, and environmental factors. Then, daily full-coverage XCO2 data can be generated based on CAMS XCO2 and environmental factors. Results show that a satisfactory performance is reported in multiform validations, with RMSE and R2 of 0.99 ppm and 0.963 in terms of the sample-based cross-validation, respectively. The independent in-situ validation also indicates high consistency (R2 = 0.866 and RMSE = 1.71 ppm) between XCO2 estimates and ground measurements. Based on the generated dataset, spatial and seasonal distributions of XCO2 across China are investigated, and a growth rate of 2.71 ppm/yr is found from 2015 to 2020. This paper generates long time series of full-coverage XCO2 data, which helps promote our understanding of carbon cycling. The dataset is available from https://doi.org/10.5281/zenodo.7793917.

Monitoring and quantifying CO2emissions of isolated power plants from space

Abstract. Top-down CO2 emission estimates based on satellite observations are of great importance for independently verifying the accuracy of reported emissions and emission inventories. Difficulties in verifying these satellite-derived emissions arise from the fact that emission inventories often provide annual mean emissions, while estimates from satellites are available only for a limited number of overpasses. Previous studies have derived CO2 emissions for power plants from the Orbiting Carbon Observatory-2 and 3 (OCO-2 and OCO-3) satellite observations of their exhaust plumes, but the accuracy and the factors affecting these emissions are uncertain. Here we advance monitoring and quantifying point source carbon emissions by focusing on how to improve the accuracy of carbon emission using different wind data estimates. We have selected only isolated power plants for this study, to avoid complications linked to multiple sources in close proximity. We first compared the Gaussian plume model and cross-sectional flux methods for estimating CO2 emission of power plants. Then we examined the sensitivity of the emission estimates to possible choices for the wind field. For verification we have used power plant emissions that are reported on an hourly basis by the Environmental Protection Agency (EPA) in the US. By using the OCO-2 and OCO-3 observations over the past 4 years we identified emission signals of isolated power plants and arrived at a total of 50 collocated cases involving 22 power plants. We correct for the time difference between the moment of the emission and the satellite observation. We found the wind field halfway the height of the planetary boundary layer (PBL) yielded the best results. We also found that the instantaneous satellite estimated emissions of these 50 cases, and reported emissions display a weak correlation (R2=0.12). The correlation improves with averaging over multiple observations of the 22 power plants (R2=0.40). The method was subsequently applied to 106 power plant cases worldwide and yielded a total emission of 1522 ± 501 Mt CO2 yr−1, estimated to be about 17 % of the power sector emissions of our selected countries. The improved correlation highlights the potential for future planned satellite missions with a greatly improved coverage to monitor a significant fraction of global power plant emissions.

Do State‐Of‐The‐Art Atmospheric CO2 Inverse Models Capture Drought Impacts on the European Land Carbon Uptake?

AbstractThe European land carbon uptake has been heavily impacted by several recent severe droughts, yet quantitative estimates of carbon uptake anomalies are uncertain. Atmospheric CO2 inverse models (AIMs) provide observation‐based estimates of the large‐scale carbon flux dynamics, but how well they capture drought impacts on the terrestrial carbon uptake is poorly known. Here we assessed the capacity of state‐of‐the‐art AIMs in monitoring drought impacts on the European carbon uptake over 2001–2015 using observations of environmental variability and vegetation function and made comparisons with bottom‐up estimates of carbon uptake anomalies. We found that global inversions with only limited surface CO2 observations give divergent estimates of drought impacts. Regional inversions assimilating denser CO2 observations over Europe demonstrated some improved consistency, with all inversions capturing a reduction in carbon uptake during the 2012 drought. However, they failed to capture the reduction caused by the 2015 drought. Finally, we found that a set of inversions that assimilated satellite XCO2 or assimilated environmental variables plus surface CO2 observations better captured carbon uptake anomalies induced by both the 2012 and 2015 droughts. In addition, the recent Orbiting Carbon Observatory—2 XCO2 inversions showed good potential in capturing drought impacts, with better performances for larger‐scale droughts like the 2018 drought. These results suggest that surface CO2 observations may still be too sparse to fully capture the impact of drought on the carbon cycle at subcontinental scales over Europe, and satellite XCO2 and ancillary environmental data can be used to improve observational constraints in atmospheric inversion systems.

Anomalous Net Biome Exchange Over Amazonian Rainforests Induced by the 2015/16 El Niño: Soil Dryness‐Shaped Spatial Pattern but Temperature‐dominated Total Flux

AbstractThe magnitude and spatial pattern of anomalous net biome exchange (NBE) induced by the 2015/16 El Niño over Amazonian rainforests remain uncertain. We here investigated them using multi‐model posterior NBE products in the Orbiting Carbon Observatory‐2 (OCO‐2) version 10 modeling intercomparison project. Results suggest that relative to the annual NBE average in 2017/18, larger anomalous carbon release occurred over the eastern and northern Amazonian rainforests in 2015/16, with a total flux of approximately 0.4 PgC yr−1 after assimilating satellite‐observed column CO2 concentrations (XCO2) over land. We further find that this anomalous spatial pattern was predominantly determined by soil dryness, while the total positive NBE anomaly was dominated by higher temperature with its contribution of approximately 68～70%. We believe that atmospheric inversions assimilating more satellite‐observed XCO2 in future can provide us more comprehensive understanding how Amazonian rainforests cope with the abiotic stresses induced by El Niño events.

Potential improvement of XCO2 retrieval of the OCO-2 by having aerosol information from the A-train satellites

ABSTRACT Near-real time observations of aerosol properties could have a potential to improve the accuracy of XCO2 retrieval algorithm in operational satellite missions. In this study, we developed a retrieval algorithm of XCO2 (Yonsei Retrieval Algorithm; YCAR) based on the Optimal Estimation (OE) method that used aerosol information at the location of the Orbiting Carbon Observatory-2 (OCO-2) measurement from co-located measurement of the Afternoon constellation (A-train) such as the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Observation (CALIPSO) and the MODerate-resolution Imaging Spectrometer (MODIS) onboard the Aqua. Specifically, we used optical depth, vertical profile, and optical properties of aerosol from MODIS and CALIOP data. We validated retrieval results to the Total Carbon Column Observing Network (TCCON) ground-based measurements and found general consistency. The impact of observed aerosol information and its constraint was examined by retrieval tests using different settings. The effect of using additional aerosol information was analyzed in connection with the bias correction process of the operational retrieval algorithm. YCAR using a priori aerosol loading parameters from co-located satellite measurements and less constraint of aerosol optical properties made comparable results with operational data with the bias correction process in three of the four cases subject to this study. Our work provides evidence supporting the bias correction process of operational algorithms and quantitatively presents the effectiveness of synergic use of multiple satellites (e.g. A-train) and better treatment of aerosol information.

Characteristics of interannual variability in space-based XCO2 global observations

Abstract. Atmospheric carbon dioxide (CO2) accounts for the largest radiative forcing among anthropogenic greenhouse gases. There is, therefore, a pressing need to understand the rate at which CO2 accumulates in the atmosphere, including the interannual variations (IAVs) in this rate. IAV in the CO2 growth rate is a small signal relative to the long-term trend and the mean annual cycle of atmospheric CO2, and IAV is tied to climatic variations that may provide insights into long-term carbon–climate feedbacks. Observations from the Orbiting Carbon Observatory-2 (OCO-2) mission offer a new opportunity to refine our understanding of atmospheric CO2 IAV since the satellite can measure over remote terrestrial regions and the open ocean, where traditional in situ CO2 monitoring is difficult, providing better spatial coverage compared to ground-based monitoring techniques. In this study, we analyze the IAV of column-averaged dry-air CO2 mole fraction (XCO2) from OCO-2 between September 2014 and June 2021. The amplitude of the IAV, which is calculated as the standard deviation of the time series, is up to 1.2 ppm over the continents and around 0.4 ppm over the open ocean. Across all latitudes, the OCO-2-detected XCO2 IAV shows a clear relationship with El Niño–Southern Oscillation (ENSO)-driven variations that originate in the tropics and are transported poleward. Similar, but smoother, zonal patterns of OCO-2 XCO2 IAV time series compared to ground-based in situ observations and with column observations from the Total Carbon Column Observing Network (TCCON) and the Greenhouse Gases Observing Satellite (GOSAT) show that OCO-2 observations can be used reliably to estimate IAV. Furthermore, the extensive spatial coverage of the OCO-2 satellite data leads to smoother IAV time series than those from other datasets, suggesting that OCO-2 provides new capabilities for revealing small IAV signals despite sources of noise and error that are inherent to remote-sensing datasets.

Insights into 3D cloud radiative transfer effects for the Orbiting Carbon Observatory

Abstract. Clouds impose radiance perturbations upon Orbiting Carbon Observatory (OCO-2)-measured spectra. The Spherical Harmonic Discrete Ordinate radiative transfer Method (SHDOM) code is applied in both idealized bar cloud and scene-specific calculations of 1D and 3D radiances in order to understand 3D cloud effects for a wide range of gas vertical optical depths and solar- and sensor-viewing geometries for ocean and land scenes. SHDOM calculations for 36 scenes over the Amazon and the Pacific are co-analyzed with Moderate Resolution Imaging Spectroradiometer (MODIS) radiance-based cloud distance data and the OCO-2 Lite file rawXCO2 for both quality flag =0 (QF0; best quality) and quality flag =1 (QF1; poor quality) data. SHDOM calculations of the ocean and land scenes indicate that the 1D / 3D radiance intensity ratios and rawXCO2 decrease concurrently as the nearest-cloud distance decreases towards zero, especially for the ocean glint QF1 data, which provide the clearest evidence of 3D cloud effects in OCO-2 retrievals. Yearly analysis of OCO-2 O2 A-band continuum radiances indicate that 3D cloud-brightening events are predominant over cloud-shadowing events; therefore, 1D / 3D intensity ratios are predominantly less than unity. Bias corrected (bcXCO2) at cloud distances between 0 and 20 km are calculated for 20∘ latitude bands for 2015–2018. These zonal averages are used to calculate 3D-cloud-effect biases for bcXCO2 data (with a positive bias indicating that OCO-2 underestimates bcXCO2). Averages of 3D-cloud-effect biases, weighted by the number of Lite file data points in each of the nearest-cloud distance bins, in the Northern and Southern hemispheres, are 0.16 (1.31) and 0.26 (1.41) ppm (parts per million), respectively, over the ocean, and −0.13 (0.51) and −0.08 (0.47) ppm over land for QF0 (QF1) data.

Effect of meteorological data assimilation using 3DVAR on high-resolution simulations of atmospheric CO2 concentrations in East Asia

In this study, the effects of meteorological data assimilation (DA) on high-resolution CO2 concentration simulations in East Asia were investigated using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and three-dimensional variational data assimilation (3DVAR). Experiments with and without meteorological DA were performed for June and December 2018. To determine an appropriate DA cycling interval, two experiments were performed at cycling intervals of 6 and 12 h. Compared to the meteorological fields simulated from experiment without DA, those simulated from experiments with DA were more similar to atmospheric observations. The CO2 concentrations simulated from the experiment with a 6 h DA cycling interval were highly similar to the observed CO2 concentrations. Compared to the root mean square error (RMSE) of the experiment without DA, the RMSE of simulated CO2 concentrations in the experiment with a 6 h DA interval decreased by 8.19% in June and 1.43% in December with respect to surface CO2 concentration observations, and decreased by 2.52% in June and 0.47% in December against column-averaged CO2 concentration (XCO2) observations of the Orbiting Carbon Observatory 2 (OCO-2). The improved 2 m temperature forecasts by DA enhanced biogenic CO2 simulations using the Vegetation Photosynthesis and Respiration Model (VPRM) in WRF-Chem. The improved wind speed and direction forecasts by DA led to improved simulations of the horizontal transport of CO2 concentrations and planetary boundary layer height. Overall, enhanced meteorological forecasts by meteorological DA using 3DVAR improved the accuracy of high-resolution CO2 concentration simulations using WRF-Chem in East Asia.

The Education and Research 3D Radiative Transfer Toolbox (EaR3T) – towards the mitigation of 3D bias in airborne and spaceborne passive imagery cloud retrievals

Abstract. We introduce the Education and Research 3D Radiative Transfer Toolbox (EaR3T, pronounced []) for quantifying and mitigating artifacts in atmospheric radiation science algorithms due to spatially inhomogeneous clouds and surfaces and show the benefits of automated, realistic radiance and irradiance generation along extended satellite orbits, flight tracks from entire aircraft field missions, and synthetic data generation from model data. EaR3T is a modularized Python package that provides high-level interfaces to automate the process of 3D radiative transfer (3D-RT) calculations. After introducing the package, we present initial findings from four applications, which are intended as blueprints to future in-depth scientific studies. The first two applications use EaR3T as a satellite radiance simulator for the NASA Orbiting Carbon Observatory 2 (OCO-2) and Moderate Resolution Imaging Spectroradiometer (MODIS) missions, which generate synthetic satellite observations with 3D-RT on the basis of cloud field properties from imagery-based retrievals and other input data. In the case of inhomogeneous cloud fields, we show that the synthetic radiances are often inconsistent with the original radiance measurements. This lack of radiance consistency points to biases in heritage imagery cloud retrievals due to sub-pixel resolution clouds and 3D-RT effects. They come to light because the simulator's 3D-RT engine replicates processes in nature that conventional 1D-RT retrievals do not capture. We argue that 3D radiance consistency (closure) can serve as a metric for assessing the performance of a cloud retrieval in presence of spatial cloud inhomogeneity even with limited independent validation data. The other two applications show how airborne measured irradiance data can be used to independently validate imagery-derived cloud products via radiative closure in irradiance. This is accomplished by simulating downwelling irradiance from geostationary cloud retrievals of Advanced Himawari Imager (AHI) along all the below-cloud aircraft flight tracks of the Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP2Ex, NASA 2019) and comparing the irradiances with the colocated airborne measurements. In contrast to case studies in the past, EaR3T facilitates the use of observations from entire field campaigns for the statistical validation of satellite-derived irradiance. From the CAMP2Ex mission, we find a low bias of 10 % in the satellite-derived cloud transmittance, which we are able to attribute to a combination of the coarse resolution of the geostationary imager and 3D-RT biases. Finally, we apply a recently developed context-aware Convolutional Neural Network (CNN) cloud retrieval framework to high-resolution airborne imagery from CAMP2Ex and show that the retrieved cloud optical thickness fields lead to better 3D radiance consistency than the heritage independent pixel algorithm, opening the door to future mitigation of 3D-RT cloud retrieval biases.

Spatiotemporal analysis of atmospheric XCH4 as related to fires in the Amazon biome during 2015–2020

Studies that focus on the concentration of methane and its relationship with fires in the Amazon have become relevant in the current scenario, especially due to the increasing environmental degradation associated with climate change. Therefore, the main objective of the study was to investigate the spatial–temporal variability in the observations of XCH4 in the time series of 2015 a 2020 and understand the correlations between XCH4 and the fire foci number, CO2 anomalies and biophysical variables (temperature and soil moisture) under the Amazon biome. For number of active fires foci (Fire Foci) and Land Surface Temperature (LST) were obtained through the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, and the atmospheric concentrations of the averaged column of methane (XCH4) and carbon dioxide (XCO2) were obtained by the Greenhouse gases Observing SATellite (GOSAT) satellites and Orbiting Carbon Observatory-2 (OCO-2), respectively. The Soil Moisture (SMAP) was obtained through satellite Soil Moisture Active Passive (SMAP). The analysis was carried out in the dry and wet seasons, and the XCH4 presented an annual mean and standard error of 1794 ± 5.3 ppb for the rainy season, while for the dry period, the XCH4 was 1789 ± 5.6 ppb. For the spatial distribution of XCH4, a significant correlation (r = 0.53 and p < 0.05) was observed between XCH4 and Anomaly XCO2 in the dry season, possibly justified by the increase in fire foci. Additionally, in the dry period, XCH4 was significantly correlated with SMAP (r = 0.97, p < 0.01), validating the hypothesis of a strong relationship between the variables. The temporal variability of XCH4 was significant for SMAP (r = 0.65 and p < 0.01), similar to the significance of the LST variable (r = 0.66 and p < 0.01). Thus, the temporal distribution of XCH4 was positively related to both soil moisture and land surface temperature. Therefore, considering more frequent droughts and the predominance of fires in the region, as well as the increase in global average temperature, there will be an increase in greenhouse gas (GHG) emissions, especially methane, further impacting the Amazonian ecosystem, which is already vulnerable to climate change.

Spatiotemporal Analysis of XCO2 and Its Relationship to Urban and Green Areas of China’s Major Southern Cities from Remote Sensing and WRF-Chem Modeling Data from 2010 to 2019

Monitoring CO2 concentrations is believed to be an effective measure for assisting in the control of greenhouse gas emissions. Satellite measurements compensate for the sparse and uneven spatial distribution of ground observation stations, allowing for the collection of a wide range of CO2 concentration data. However, satellite monitoring’s spatial coverage remains limited. This study fills the knowledge gaps of column-averaged dry-air mole fraction of CO2 (XCO2) products retrieved from the Greenhouse Gases Observing Satellite (GOSAT) and Orbiting Carbon Observatory Satellite (OCO-2) based on the normalized output of atmospheric chemical models, WRF-Chem, in Southern China during 2010–2019. Hefei (HF)/Total Carbon Column Observing Network (TCCON), Lulin (LLN)/World Data Centre for Greenhouse Gases (WDCGG) station observations were used to validate the results of void filling with an acceptable accuracy for spatiotemporal analysis (R = 0.96, R2 = 0.92, RMSE = 2.44 ppm). Compared to the IDW (inverse distance weighting) and Kriging (ordinary Kriging) interpolation methods, this method has a higher validation accuracy. In addition, spatiotemporal distributions of CO2, as well as the sensitivity of CO2 concentration to the urban built-up areas and urban green space areas in China’s major southern cities during 2010–2019, are discussed. The approximate annual average concentrations have gradually increased from 388.56 to 414.72 ppm, with an annual growth rate of 6.73%, and the seasonal cycle presents a maximum in spring and a minimum in summer or autumn from 2010 to 2019. CO2 concentrations have a strong positive correlation with the impervious area to city area ratio, while anomaly values of the impervious area to urban green area ratio occurred in individual cities. The experimental findings demonstrate the viability of the study hypothesis that combines remote sensing data with the WRF-Chem model to produce a local area dataset with high spatial resolution and an extracted urban unit from statistical data.

Satellite solar-induced chlorophyll fluorescence tracks physiological drought stress development during 2020 southwest US drought.

Monitoring and estimating drought impact on plant physiological processes over large regions remains a major challenge for remote sensing and land surface modeling, with important implications for understanding plant mortality mechanisms and predicting the climate change impact on terrestrial carbon and water cycles. The Orbiting Carbon Observatory 3 (OCO-3), with its unique diurnal observing capability, offers a new opportunity to track drought stress on plant physiology. Using radiative transfer and machine learning modeling, we derive a metric of afternoon photosynthetic depression from OCO-3 solar-induced chlorophyll fluorescence (SIF) as an indicator of plant physiological drought stress. This unique diurnal signal enables a spatially explicit mapping of plants' physiological response to drought. Using OCO-3 observations, we detect a widespread increasing drought stress during the 2020 southwest US drought. Although the physiological drought stress is largely related to the vapor pressure deficit (VPD), our results suggest that plants' sensitivity to VPD increases as the drought intensifies and VPD sensitivity develops differently for shrublands and grasslands. Our findings highlight the potential of using diurnal satellite SIF observations to advance the mechanistic understanding of drought impact on terrestrial ecosystems and to improve land surface modeling.