Orbiting Carbon Observatory-2 Research Articles

Grassland productivity response to droughts in northern China monitored by satellite-based solar-induced chlorophyll fluorescence

Solar-induced chlorophyll fluorescence (SIF) has been applied to a wide range of ecological studies, such as monitoring and assessing drought, vegetation productivity, and crop yield. Previous studies have shown that SIF is highly related to gross primary production (GPP), but its correlation with aboveground biomass (AGB) still needs further exploration. In this study, we explored the potential of SIF for monitoring and assessing the effects of climate change and meteorological drought on grassland AGB changes in the northern grassland of China. By examining the relationship between the Orbiting Carbon Observatory 2 (OCO-2) SIF and drought indices, we assessed the response of northern grassland productivity to meteorological drought conditions. The results show that SIF is very sensitive to meteorological drought and can capture drought events and the dynamics of grassland growth in different grassland types. The correlation between SIF, drought indices, and AGB varied with grassland type. A gradient boosting decision tree (GBDT) was used to explore the relationships between SIF and the impact variables in the grassland ecosystem. We found that climatic factors (e.g., annual mean growing season precipitation, annual mean growing season temperature, and annual mean vapor pressure deficit) and human activity (e.g., grazing intensity) significantly impacted the interannual variability of grassland productivity. Our results indicate that SIF changes can reflect the seasonal dynamics of vegetation growth in the northern grassland of China. Therefore, SIF can be used as benchmark data for evaluating the performance of terrestrial ecosystem models in simulating ecosystem productivity in this region. The high sensitivity of SIF to drought suggests that it is a useful tool for monitoring and assessing drought events.

Beyond Matérn: On A Class of Interpretable Confluent Hypergeometric Covariance Functions

The Matérn covariance function is a popular choice for prediction in spatial statistics and uncertainty quantification literature. A key benefit of the Matérn class is that it is possible to get precise control over the degree of mean-square differentiability of the random process. However, the Matérn class possesses exponentially decaying tails, and thus, may not be suitable for modeling polynomially decaying dependence. This problem can be remedied using polynomial covariances; however, one loses control over the degree of mean-square differentiability of corresponding processes, in that random processes with existing polynomial covariances are either infinitely mean-square differentiable or nowhere mean-square differentiable at all. We construct a new family of covariance functions called the Confluent Hypergeometric (CH) class using a scale mixture representation of the Matérn class where one obtains the benefits of both Matérn and polynomial covariances. The resultant covariance contains two parameters: one controls the degree of mean-square differentiability near the origin and the other controls the tail heaviness, independently of each other. Using a spectral representation, we derive theoretical properties of this new covariance including equivalent measures and asymptotic behavior of the maximum likelihood estimators under infill asymptotics. The improved theoretical properties of the CH class are verified via extensive simulations. Application using NASA’s Orbiting Carbon Observatory-2 satellite data confirms the advantage of the CH class over the Matérn class, especially in extrapolative settings. Supplementary materials for this article are available online.

Solar-Induced Chlorophyll Fluorescence Trends and Mechanisms in Different Ecosystems in Northeastern China

Solar-induced chlorophyll fluorescence (SIF), when used as a proxy for plant photosynthesis, can provide an indication of the photosynthesis rate and has the potential to improve our understanding of carbon exchange mechanisms within an ecosystem. However, the relationships between SIF and vegetation indices (VIs) operating within different ecological contexts and the effect of other environmental factors on SIF remain unclear. This study focused on three ecosystems (cropland, forest, and grassland), with different ecological characteristics, located in Northeast China. These areas provide case studies where numerous relationships can be explored, including the correlations between the Orbiting Carbon Observatory-2 (OCO-2) SIF and MODIS products, meteorological factors, and the differences in the relationships between the three different ecosystems. Some interesting results and conclusions were obtained. First, in different ecosystems, the relationships between SIF and MODIS products show different correlations, whereby the enhanced vegetation index (EVI) has a close relationship with SIF in all the three ecosystems of forest, cropland, and grassland. Second, forest-type ecosystems appear to be sensitive to changes in daily temperature, whereas cropland and grassland areas respond more closely to changes in previous 16-day daily minimum temperature. Compared with forest and cropland areas, grasslands were more sensitive to precipitation (although the R2 value was small). Third, different ecosystems have different mechanisms of photosynthesis. Hence, we suggest that it is better to use SIF in areas exhibiting different ecological characteristics, and different models should be employed while simulating SIF.

Global land 1° mapping dataset of XCO2 from satellite observations of GOSAT and OCO-2 from 2009 to 2020

ABSTRACT A global mapping data of atmospheric carbon dioxide (CO2) concentrations can help us to better understand the spatiotemporal variations of CO2 and the driving factors of the variations to support the actions for emissions reduction and control. Greenhouse gases satellites that measure atmospheric CO2, such as the Greenhouse Gases Observing Satellite (GOSAT) and Orbiting Carbon Observatory (OCO-2), have been providing global observations of the column averaged dry-air mole fractions of CO2 (XCO2) since 2009. However, these XCO2 retrievals are irregular in space and time with many gaps. In this paper, we mapped a global spatiotemporally continuous XCO2 dataset (Mapping-XCO2) using the XCO2 retrievals from GOSAT and OCO-2 during the period from April 2009 to December 2020 based on a geostatistical approach that fills those data gaps. The dataset covers a geographic range from 56° S to 65° N and 169° W to 180° E for a 1° grid interval in space and 3-day time interval. The uncertainties of the mapped XCO2 values are generally less than 1.5 parts per million (ppm). The spatiotemporal characteristics of global XCO2 that are revealed by the Mapping-XCO2 are similar to the model data obtained from CarbonTracker. Compared to the ground observations, the overall standard bias is 1.13 ppm. The results indicate that this long-term Mapping-XCO2 dataset can be used to investigate the spatiotemporal variations of global atmospheric XCO2 and can support studies related to the carbon cycle and anthropogenic CO2 emissions. The dataset is available at http://www.doi.org/10.7910/DVN/4WDTD8 and https://www.scidb.cn/en/detail?dataSetId=c2c3111b421043fc8d9b163c39e6f56e.

Distinguishing ecological outcomes of pathways in the Grain for Green Program in the subtropical areas of China

Effective forestation policies are urgently required across the globe under the initiative of the UN Decade on Ecosystem Restoration. Rather than simply planting trees, such initiatives involve complex components of societal and biophysical systems. However, the underlying pathways by which forestation influences ecological outcomes are not well understood, especially given the lack of a unified quantification framework. In this study, such a framework was developed to reveal the pathways by which reforestation programs influenced ecological outcomes by identifying the linkages among reforestation efforts, societal changes, land system changes and ecological outcomes. The framework was applied to the reforestation program of the Grain for Green Program (GFGP), to explore how the GFGP influenced vegetation dynamics and ecosystem functioning in Guizhou Province, China, through direct and indirect pathways. Two remote sensing based indicators, namely the enhanced vegetation index, derived from the Moderate Resolution Imaging Spectroradiometer, and gross primary production (GPP), obtained from the Orbiting Carbon Observatory-2 solar-induced chlorophyll fluorescence (SIF) fine-resolution dataset GOSIF, were combined with inventory data and land-use maps to detect changes in social and ecological outcomes. Using the structural equation model to apply the framework, the results showed that the GFGP positively contributed to the increasing greenness and GPP of the study area through the direct conservation pathway. Although implementation of the GFGP encouraged the rural outmigration and led to a decrease in the area of farmland, the GFGP showed negative indirect effects on greenness and GPP because of the difficulty of reforestation during land-use conversion from farmland to forest land. This study revealed divergent impacts of the reforestation program through multiple pathways, which could provide valuable information for other parts of the globe for more precise design of ecological restoration policies.

An 11-year record of XCO<sub>2</sub> estimates derived from GOSAT measurements using the NASA ACOS version 9 retrieval algorithm

Abstract. The Thermal And Near infrared Sensor for carbon Observation – Fourier Transform Spectrometer (TANSO-FTS) on the Japanese Greenhouse gases Observing SATellite (GOSAT) has been returning data since April 2009. The version 9 (v9) Atmospheric Carbon Observations from Space (ACOS) Level 2 Full Physics (L2FP) retrieval algorithm (Kiel et al., 2019) was used to derive estimates of carbon dioxide (CO2) dry air mole fraction (XCO2) from the TANSO-FTS measurements collected over its first 11 years of operation. The bias correction and quality filtering of the L2FP XCO2 product were evaluated using estimates derived from the Total Carbon Column Observing Network (TCCON) as well as values simulated from a suite of global atmospheric inversion systems (models) which do not assimilate satellite-derived CO2. In addition, the v9 ACOS GOSAT XCO2 results were compared with collocated XCO2 estimates derived from NASA's Orbiting Carbon Observatory-2 (OCO-2), using the version 10 (v10) ACOS L2FP algorithm. These tests indicate that the v9 ACOS GOSAT XCO2 product has improved throughput, scatter, and bias, when compared to the earlier v7.3 ACOS GOSAT product, which extended through mid 2016. Of the 37 million soundings collected by GOSAT through June 2020, approximately 20 % were selected for processing by the v9 L2FP algorithm after screening for clouds and other artifacts. After post-processing, 5.4 % of the soundings (2×106 out of 37×106) were assigned a “good” XCO2 quality flag, as compared to 3.9 % in v7.3 (<1 ×106 out of 24×106). After quality filtering and bias correction, the differences in XCO2 between ACOS GOSAT v9 and both TCCON and models have a scatter (1σ) of approximately 1 ppm for ocean-glint observations and 1 to 1.5 ppm for land observations. Global mean biases against TCCON and models are less than approximately 0.2 ppm. Seasonal mean biases relative to the v10 OCO-2 XCO2 product are of the order of 0.1 ppm for observations over land. However, for ocean-glint observations, seasonal mean biases relative to OCO-2 range from 0.2 to 0.6 ppm, with substantial variation in time and latitude. The ACOS GOSAT v9 XCO2 data are available on the NASA Goddard Earth Science Data and Information Services Center (GES-DISC) in both the per-orbit full format (https://doi.org/10.5067/OSGTIL9OV0PN, OCO-2 Science Team et al., 2019b) and in the per-day lite format (https://doi.org/10.5067/VWSABTO7ZII4, OCO-2 Science Team et al., 2019a). In addition, a new set of monthly super-lite files, containing only the most essential variables for each satellite observation, has been generated to provide entry level users with a light-weight satellite product for initial exploration (CaltechDATA, https://doi.org/10.22002/D1.2178, Eldering, 2021). The v9 ACOS Data User's Guide (DUG) describes best-use practices for the GOSAT data (O'Dell et al., 2020). The GOSAT v9 data set should be especially useful for studies of carbon cycle phenomena that span a full decade or more and may serve as a useful complement to the shorter OCO-2 v10 data set, which begins in September 2014.

OCO‐2 Satellite‐Imposed Constraints on Terrestrial Biospheric CO2 Fluxes Over South Asia

AbstractThe spatiotemporal variability of terrestrial biospheric carbon dioxide (CO2) fluxes over South Asia has large uncertainty. The Orbiting Carbon Observatory 2 (OCO‐2) satellite provides much‐needed retrievals of column‐average CO2 on a global‐scale, with the highest sensitivity to surface CO2 fluxes and spatiotemporal resolution available to‐date. This study conducted global inverse model simulations, assimilating in situ (IS) data and OCO‐2 retrievals, to assess optimized CO2 net ecosystem exchange (NEE) fluxes for South Asia. Annual Net Biome Exchange (NBE = NEE + biomass burning) fluxes over South Asia were estimated to be near neutral (0.04 ± 0.14 PgC yr−1) using both IS and OCO‐2 observations. The most robust result found by assimilating OCO‐2 observations was the constraint imposed on the seasonal cycle of NBE fluxes. The amplitude of the seasonal cycle of NEE was found to be larger than previously assumed. The OCO‐2 inversion led to an NBE seasonal amplitude of 0.34 PgC month−1, which was larger compared to IS constrained NBE (0.19 PgC month−1) and MsTMIP ensemble mean NEE (0.16 PgC month−1). Moreover, OCO‐2 data imposed a phase shift in the NBE seasonal cycle predicted by the prior model. The larger magnitude of NEE seasonality, and phase shift, simulated when assimilating OCO‐2 observations are in general agreement with previous studies assimilating regional aircraft observations in addition to global IS observations. This result suggests that OCO‐2 provides valuable data that allows for the estimate of NBE on a regional scale in a similar manner as regional in situ aircraft networks.

Spatio-temporal variability of XCO2 over Indian region inferred from Orbiting Carbon Observatory (OCO-2) satellite and Chemistry Transport Model

Investigation of spatio-temporal variability of column-averaged dry-air mole fraction of CO2 (XCO2) over the Indian region using remote sensing satellite measurements is of interest due to the sparseness of ground-based observations. In this study, we utilized OCO-2 satellite retrievals in conjunction with an atmospheric chemistry-transport model (ACTM) simulations for a set of known (bottom-up) and optimized (top-down) flux for the period September 2014 to December 2018. Results showed the highest XCO2 during the pre-monsoon season, due to prevailing seasonal-high temperatures and drier soil conditions that resulted in increased respiration and suppressed photosynthesis. In contrast, a reduction in XCO2 during the monsoon season is found as precipitation increased soil moisture and moderated the air temperature, driving vegetation growth by which photosynthesis exceeded respiration by the land biosphere. Model - observation differences of XCO2 have shown the overestimation (underestimation) during monsoon (pre-monsoon) especially over the central India region, which might be due to underestimation of the modeled peak-to-trough biospheric fluxes. Analyses of the latitudinal distribution of XCO2 averaged over Indian landmass shows a clear increasing trend and seasonality. An enormous increase in XCO2 of about ~12 ppm during 2015–2018 is estimated from OCO-2, which is in good agreement with model-simulated XCO2 (optimized flux case) and consistent with the global growth rate from surface observations. The time series and seasonal cycle of XCO2 have also been examined using model simulations over different parts of the Indian region and agreed well with those from OCO-2. Over the northern regions, especially over the Indo Gangetic Plain, the peak-to-trough seasonal cycle amplitudes of 2–3 ppm are twice than those in the southern and oceanic regions,~1–1.5 ppm. An annual total CO2 flux of −397 ± 99 TgC/yr is estimated by the inversion and that is consistent with the XCO2 measurements.

A new exponentially decaying error correlation model for assimilating OCO-2 column-average CO<sub>2</sub> data using a length scale computed from airborne lidar measurements

Abstract. To check the accuracy of column-average dry air CO2 mole fractions (XCO2) retrieved from Orbiting Carbon Observatory (OCO-2) data, a similar quantity has been measured from the Multi-functional Fiber Laser Lidar (MFLL) aboard aircraft flying underneath OCO-2 as part of the Atmospheric Carbon and Transport (ACT) – America flight campaigns. Here we do a lagged correlation analysis of these MFLL–OCO-2 column CO2 differences and find that their correlation spectrum falls off rapidly at along-track separation distances under 10 km, with a correlation length scale of about 10 km, and less rapidly at longer separation distances, with a correlation length scale of about 20 km. The OCO-2 satellite takes many CO2 measurements with small (∼3 km2) fields of view (FOVs) in a thin (<10 km wide) swath running parallel to its orbit: up to 24 separate FOVs may be obtained per second (across a ∼6.75 km distance on the ground), though clouds, aerosols, and other factors cause considerable data dropout. Errors in the CO2 retrieval method have long been thought to be correlated at these fine scales, and methods to account for these when assimilating these data into top-down atmospheric CO2 flux inversions have been developed. A common approach has been to average the data at coarser scales (e.g., in 10 s long bins) along-track, then assign an uncertainty to the averaged value that accounts for the error correlations. Here we outline the methods used up to now for computing these 10 s averages and their uncertainties, including the constant-correlation-with-distance error model that was used to summarize the OCO-2 version 9 XCO2 retrievals as part of the OCO-2 flux inversion model intercomparison project. We then derive a new one-dimensional error model using correlations that decay exponentially with separation distance, apply this model to the OCO-2 data using the correlation length scales derived from the MFLL–OCO-2 differences, and compare the results (for both the average and its uncertainty) to those given by the current constant correlation error model. To implement this new model, the data are averaged first across 2 s spans to collapse the cross-track distribution of the real data onto the 1-D path assumed by the new model. Considering correlated errors can cause the average value to fall outside the range of the values averaged; two strategies for preventing this are presented. The correlation lengths over the ocean, which the land-based MFLL data do not clarify, are assumed to be twice those over the land. The new correlation model gives 10 s XCO2 averages that are only a few tenths of 1 ppm different from the constant correlation model. Over land, the uncertainties in the mean are also similar, suggesting that the +0.3 constant correlation coefficient currently used in the model there is accurate. Over the oceans, the twice-the-land correlation lengths that we assume here result in a significantly lower uncertainty on the mean than the +0.6 constant correlation currently gives – measurements similar to the MFLL ones are needed over the oceans to do better. Finally, we show how our 1-D exponential error correlation model may be used to account for correlations in inversion methods that choose to assimilate each XCO2 retrieval individually and also to account for correlations between separate 10 s averages when these are assimilated instead.

Four years of global carbon cycle observed from the Orbiting Carbon Observatory 2 (OCO-2) version 9 and in situ data and comparison to OCO-2 version 7

Abstract. The Orbiting Carbon Observatory 2 (OCO-2) satellite has been providing information to estimate carbon dioxide (CO2) fluxes at global and regional scales since 2014 through the combination of CO2 retrievals with top–down atmospheric inversion methods. Column average CO2 dry-air mole fraction retrievals have been constantly improved. A bias correction has been applied in the OCO-2 version 9 retrievals compared to the previous OCO-2 version 7r improving data accuracy and coverage. We study an ensemble of 10 atmospheric inversions all characterized by different transport models, data assimilation algorithms, and prior fluxes using first OCO-2 v7 in 2015–2016 and then OCO-2 version 9 land observations for the longer period 2015–2018. Inversions assimilating in situ (IS) measurements have also been used to provide a baseline against which the satellite-driven results are compared. The time series at different scales (going from global to regional scales) of the models emissions are analyzed and compared to each experiment using either OCO-2 or IS data. We then evaluate the inversion ensemble based on the dataset from the Total Carbon Column Observing Network (TCCON), aircraft, and in situ observations, all independent from assimilated data. While we find a similar constraint of global total carbon emissions between the ensemble spread using IS and both OCO-2 retrievals, differences between the two retrieval versions appear over regional scales and particularly in tropical Africa. A difference in the carbon budget between v7 and v9 is found over this region, which seems to show the impact of corrections applied in retrievals. However, the lack of data in the tropics limits our conclusions, and the estimation of carbon emissions over tropical Africa require further analysis.

WOMBAT v1.0: a fully Bayesian global flux-inversion framework

Abstract. WOMBAT (the WOllongong Methodology for Bayesian Assimilation of Trace-gases) is a fully Bayesian hierarchical statistical framework for flux inversion of trace gases from flask, in situ, and remotely sensed data. WOMBAT extends the conventional Bayesian synthesis framework through the consideration of a correlated error term, the capacity for online bias correction, and the provision of uncertainty quantification on all unknowns that appear in the Bayesian statistical model. We show, in an observing system simulation experiment (OSSE), that these extensions are crucial when the data are indeed biased and have errors that are spatio-temporally correlated. Using the GEOS-Chem atmospheric transport model, we show that WOMBAT is able to obtain posterior means and variances on non-fossil-fuel CO2 fluxes from Orbiting Carbon Observatory-2 (OCO-2) data that are comparable to those from the Model Intercomparison Project (MIP) reported in Crowell et al. (2019). We also find that WOMBAT's predictions of out-of-sample retrievals obtained from the Total Column Carbon Observing Network (TCCON) are, for the most part, more accurate than those made by the MIP participants.

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

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

Inflight Radiometric Calibration and Performance of the Orbiting Carbon Observatory 3 for Version 10 Products

The Orbiting Carbon Observatory-3 (OCO-3) measures carbon dioxide and solar-induced fluorescence from the International Space Station (ISS). It uses the flight spare spectrometers from its predecessor, OCO-2, and produces spectral images in three near-infrared channels. Its preflight radiometric calibration was performed at NASA’s Jet Propulsion Laboratory and is constantly updated inflight to account for both gradual and abrupt changes in instrument response that display a wavelength dependency within the bands. Some of these are caused by the accumulation of contaminants and their subsequent removal after scheduled decontamination events, but sudden changes of the overall gain state of the two longest wavelength bands, unrelated to contamination, are also observed. They were found to be triggered by instrument resets as well as by decontamination events. OCO-3 cannot perform solar calibration due to its position on the ISS and the inflight updates to its radiometric calibration depend solely on its on-board calibrator, which consists of three lamps and a reflective diffuser. The lamps are observed with different cadences and thus degrade at different rates. Information from all three lamps is combined to provide high temporal resolution and minimize the impact of lamp aging on the gain degradation coefficients that describe the changes in the radiometric response of the instrument inflight. We use thousands of soundings from ocean scenes to assess the relative calibration within the two shortest wavelength bands. Here we present the algorithm developed for the inflight relative radiometric calibration of OCO-3 Version 10 products and discuss the state of the calibration.

Machine learning-based aerosol characterization using OCO-2 O2 A-band observations

Aerosol scattering influences the retrieval of the column-averaged dry-air mole fraction of CO2 (XCO2) from the Orbiting Carbon Observatory-2 (OCO-2). This is especially true for surfaces with reflectance close to a critical value where there is very low sensitivity to aerosol loading. A spectral sorting approach was introduced to improve the characterization of aerosols over coastal regions. Here, we generalize this procedure to land surfaces and use a two-step neural network to retrieve aerosol parameters from OCO-2 measurements. We show that, by using a combination of radiance measurements in the continuum and inside the absorption band, both the aerosol optical depth and layer height, as well as their uncertainties, can be accurately predicted. Using the improved aerosol estimates as a priori, we demonstrate that the accuracy of the XCO2 retrieval can be significantly improved compared to the OCO-2 Level-2 Standard product. Furthermore, using simulated observations, we obtain estimates of the error in the retrieved XCO2. These simulations indicate that the bias-corrected OCO-2 Lite data, which is used for flux inversions, may have remaining biases due to interference of aerosol effects.

The result of the comparative use of OCO-2 information to find the relationship between changes in the atmospheric concentration of CO2 and air temperature in the Argentine Islands and Ukraine.

The results of comparative analysis of data of data of changes CO2 concentrations in the atmosphere over the polar regions adjacent to the "Akademik Vernadsky" station (the size of the study area 1.5° × 1.5°, or approximately 166 km by 75 km) were obtained satellite Orbiting Carbon Observatory (OCO-2) and air temperature according to ground meteorological observations at the "Akademik Vernadsky" station for 2014-2020. From obtained graph, we can see that the air temperature and CO2 concentration in the atmosphere over the study area tend to increase slowly. In addition, the analysis of the above results showed that in this way it is possible to determine the integrated trends in the impact of greenhouse gas content in the atmosphere on air temperature. A comparative analysis of data of data of changes CO2 concentrations according to OCO-2 between regions where there is a direct anthropogenic impact (Ukraine) and the region where anthropogenic impact is minimized (areas adjacent to the "Akademik Vernadsky" station) for 2014-2020. It was found that CO2 concentrations over areas with direct anthropogenic impact are growing much faster than over areas where there is no such impact at all. However, we see that CO2 concentrations tend to increase in both cases. Based on the obtained results, it was determined that conducting a comprehensive, simultaneous and consistent with satellite observations measurements of various series of variables, will provide an opportunity to optimize information of changes in climatic parameters (air temperature) in the polar regions.

Assessing the feasibility of using a neural network to filter Orbiting Carbon Observatory 2 (OCO-2) retrievals at northern high latitudes

Abstract. Satellite retrievals of XCO2 at northern high latitudes currently have sparser coverage and lower data quality than most other regions of the world. We use a neural network (NN) to filter Orbiting Carbon Observatory 2 (OCO-2) B10 bias-corrected XCO2 retrievals and compare the quality of the filtered data to the quality of the data filtered with the standard B10 quality control filter. To assess the performance of the NN filter, we use Total Carbon Column Observing Network (TCCON) data at selected northern high latitude sites as a truth proxy. We found that the NN filter decreases the overall bias by 0.25 ppm (∼ 50 %), improves the precision by 0.18 ppm (∼ 12 %), and increases the throughput by 16 % at these sites when compared to the standard B10 quality control filter. Most of the increased throughput was due to an increase in throughput during the spring, fall, and winter seasons. There was a decrease in throughput during the summer, but as a result the bias and precision were improved during the summer months. The main drawback of using the NN filter is that it lets through fewer retrievals at the highest-latitude Arctic TCCON sites compared to the B10 quality control filter, but the lower throughput improves the bias and precision.

Was Australia a sink or source of CO<sub>2</sub> in 2015? Data assimilation using OCO-2 satellite measurements

Abstract. In this study, we present the assimilation of data from the Orbiting Carbon Observatory-2 (OCO-2) (land nadir and glint data, version 9) to estimate the Australian carbon surface fluxes for the year 2015. To perform this estimation, we used both a regional-scale atmospheric transport–dispersion model and a four-dimensional variational assimilation scheme. Our results suggest that Australia was a carbon sink of −0.41 ± 0.08 PgC yr−1 compared to the prior estimate 0.09 ± 0.20 PgC yr−1 (excluding fossil fuel emissions). Most of the carbon uptake occurred in northern Australia over the savanna ecotype and in the western region over areas with sparse vegetation. Analysis of the enhanced vegetation index (EVI) suggests that the majority of the carbon uptake over the savanna ecosystem was due to an increase of vegetation productivity (positive EVI anomalies) amplified by an anomalous increase of rainfall in summer. Further from this, a slight increase of carbon uptake in Western Australia over areas with sparse vegetation (the largest ecosystem in Australia) was noted due to increased land productivity in the area caused by positive rainfall anomalies. The stronger carbon uptake estimate in this ecosystem was partially due to the land surface model (CABLE-BIOS3) underestimating the gross primary productivity of the ecosystem. To evaluate the accuracy of our carbon flux estimates from OCO-2 retrievals, we compare our posterior concentration fields against the column-averaged carbon retrievals from the Total Carbon Column Observing Network (TCCON) and ground-based in situ monitoring sites located around our domain. The validation analysis against TCCON shows that our system is able to reduce bias mainly in the summer season. Comparison with surface in situ observations was less successful, particularly over oceanic monitoring sites that are strongly affected by oceanic fluxes and subject to less freedom by the inversion. For stations located far from the coast, the comparison with in situ data was more variable, suggesting difficulties matching the column-integrated and surface data by the inversion, most likely linked to model vertical transport. Comparison of our fluxes against the OCO-2 model intercomparison (MIP) was encouraging. The annual carbon uptake estimated by our inversion falls within the ensemble of the OCO-2 MIP global inversions and presents a similar seasonal pattern.

The Carbon Cycle of Southeast Australia During 2019–2020: Drought, Fires, and Subsequent Recovery

Abstract2019 was the hottest and driest year on record for southeast Australia leading to bushfires of unprecedented extent. Ecosystem carbon losses due to drought and fire are believed to have been substantial, but have not been well quantified. Here, we utilize space‐based measurements of trace gases (TROPOspheric Monitoring Instrument XCO, Orbiting Carbon Observatory 2 ) and up‐scaled GPP (FluxSat GPP) to quantify the carbon cycle anomalies resulting from drought and fire in southeast Australia during the 2019–2020 growing season. We find that biomass burning released 113–236 TgC of CO2 while drought and fire‐induced anomalies in net ecosystem exchange reduced growing season carbon uptake by an additional 19–52 TgC of CO2. These carbon losses were concentrated during the spring and early summer, when hot‐dry conditions were most severe. A shift to cooler conditions with above average rainfall during February is found to result in a partial recovery and greening in unburned ecosystems, but not in fire‐impacted areas. The net 2019–2020 carbon loss substantially exceeded interannual variations in net uptake over 2010–2019 estimated from top‐down constraints (∼5σ anomaly), and exceeded Australia's annual fossil fuel emissions (∼104 TgC year−1). Top‐down constraints show that the regional carbon budget is strongly regulated by climate variability, and suggest that cool‐wet conditions are required for a rapid recovery of carbon stocks. This has implications for the regional carbon budget as more frequent climate‐change‐driven heat and drought events may increase the frequency of fire events and the recovery time of ecosystems, threatening the carbon stocks of the region.

Neural-network-based estimation of regional-scale anthropogenic CO<sub>2</sub> emissions using an Orbiting Carbon Observatory-2 (OCO-2) dataset over East and West Asia

Abstract. Atmospheric carbon dioxide (CO2) is the most significant greenhouse gas, and its concentration is continuously increasing, mainly as a consequence of anthropogenic activities. Accurate quantification of CO2 is critical for addressing the global challenge of climate change and for designing mitigation strategies aimed at stabilizing CO2 emissions. Satellites provide the most effective way to monitor the concentration of CO2 in the atmosphere. In this study, we utilized the concentration of the column-averaged dry-air mole fraction of CO2, i.e., XCO2 retrieved from a CO2 monitoring satellite, the Orbiting Carbon Observatory-2 (OCO-2), and the net primary productivity (NPP) provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate the anthropogenic CO2 emissions using the Generalized Regression Neural Network (GRNN) over East and West Asia. OCO-2 XCO2, MODIS NPP, and the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) CO2 emission datasets for a period of 5 years (2015–2019) were used in this study. The annual XCO2 anomalies were calculated from the OCO-2 retrievals for each year to remove the larger background CO2 concentrations and seasonal variability. The XCO2 anomaly, NPP, and ODIAC emission datasets from 2015 to 2018 were then used to train the GRNN model, and, finally, the anthropogenic CO2 emissions were estimated for 2019 based on the NPP and XCO2 anomalies derived for the same year. The estimated and the ODIAC CO2 emissions were compared, and the results showed good agreement in terms of spatial distribution. The CO2 emissions were estimated separately over East and West Asia. In addition, correlations between the ODIAC emissions and XCO2 anomalies were also determined separately for East and West Asia, and East Asia exhibited relatively better results. The results showed that satellite-based XCO2 retrievals can be used to estimate the regional-scale anthropogenic CO2 emissions, and the accuracy of the results can be enhanced by further improvement of the GRNN model with the addition of more CO2 emission and concentration datasets.

Spatial distributions of <i>X</i><sub>CO<sub>2</sub></sub> seasonal cycle amplitude and phase over northern high-latitude regions

Abstract. Satellite-based observations of atmospheric carbon dioxide (CO2) provide measurements in remote regions, such as the biologically sensitive but undersampled northern high latitudes, and are progressing toward true global data coverage. Recent improvements in satellite retrievals of total column-averaged dry air mole fractions of CO2 (XCO2) from the NASA Orbiting Carbon Observatory 2 (OCO-2) have allowed for unprecedented data coverage of northern high-latitude regions, while maintaining acceptable accuracy and consistency relative to ground-based observations, and finally providing sufficient data in spring and autumn for analysis of satellite-observed XCO2 seasonal cycles across a majority of terrestrial northern high-latitude regions. Here, we present an analysis of XCO2 seasonal cycles calculated from OCO-2 data for temperate, boreal, and tundra regions, subdivided into 5∘ latitude by 20∘ longitude zones. We quantify the seasonal cycle amplitudes (SCAs) and the annual half drawdown day (HDD). OCO-2 SCAs are in good agreement with ground-based observations at five high-latitude sites, and OCO-2 SCAs show very close agreement with SCAs calculated for model estimates of XCO2 from the Copernicus Atmosphere Monitoring Services (CAMS) global inversion-optimized greenhouse gas flux model v19r1 and the CarbonTracker2019 model (CT2019B). Model estimates of XCO2 from the GEOS-Chem CO2 simulation version 12.7.2 with underlying biospheric fluxes from CarbonTracker2019 (GC-CT2019) yield SCAs of larger magnitude and spread over a larger range than those from CAMS, CT2019B, or OCO-2; however, GC-CT2019 SCAs still exhibit a very similar spatial distribution across northern high-latitude regions to that from CAMS, CT2019B, and OCO-2. Zones in the Asian boreal forest were found to have exceptionally large SCA and early HDD, and both OCO-2 data and model estimates yield a distinct longitudinal gradient of increasing SCA from west to east across the Eurasian continent. In northern high-latitude regions, spanning latitudes from 47 to 72∘ N, longitudinal gradients in both SCA and HDD are at least as pronounced as latitudinal gradients, suggesting a role for global atmospheric transport patterns in defining spatial distributions of XCO2 seasonality across these regions. GEOS-Chem surface contact tracers show that the largest XCO2 SCAs occur in areas with the greatest contact with land surfaces, integrated over 15–30 d. The correlation of XCO2 SCA with these land surface contact tracers is stronger than the correlation of XCO2 SCA with the SCA of CO2 fluxes or the total annual CO2 flux within each 5∘ latitude by 20∘ longitude zone. This indicates that accumulation of terrestrial CO2 flux during atmospheric transport is a major driver of regional variations in XCO2 SCA.