Satellite measurements of the spatiotemporal distributions of atmospheric CO 2 concentrations are a key component for better understanding global carbon cycle characteristics. Currently, several satellite instruments such as the Greenhouse gases Observing SATellite (GOSAT), SCanning Imaging Absorption Spectrometer for Atmospheric CHartographY (SCIAMACHY), and Orbiting Carbon Observatory-2 can be used to measure CO 2 column-averaged dry air mole fractions. However, because of cloud effects, a single satellite can only provide limited CO 2 data, resulting in significant uncertainty in the characterization of the spatiotemporal distribution of atmospheric CO 2 concentrations. In this study, a new physical data fusion technique is proposed to combine the GOSAT and SCIAMACHY measurements. On the basis of the fused dataset, a gap-filling method developed by modeling the spatial correlation structures of CO 2 concentrations is presented with the goal of generating global land CO 2 distribution maps with high spatiotemporal resolution. The results show that, compared with the single satellite dataset (i.e., GOSAT or SCIAMACHY), the global spatial coverage of the fused dataset is significantly increased (reaching up to approximately 20%), and the temporal OPEN ACCESSAtmosphere 2014, 5 871 resolution is improved by two or three times. The spatial coverage and monthly variations of the generated global CO 2 distributions are also investigated. Comparisons with ground-based Total Carbon Column Observing Network (TCCON) measurements reveal that CO 2 distributions based on the gap-filling method show good agreement with TCCON records despite some biases. These results demonstrate that the fused dataset as well as the gap-filling method are rather effective to generate global CO 2 distribution with high accuracies and high spatiotemporal resolution.

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In recent years, global warming caused by emission of CO 2 has attracted considerable attention from the public. Although the measurements from AIRS, GOSAT, SCIAMACHY and IASI have been frequently used to derive atmospheric CO 2 concentration, comprehensive quantification of the differences among these CO 2 products is still not fully investigated yet. In this paper, a series of strategies have been proposed to allow the CO 2 products from different instruments to be physically inter-comparable. Based on this, these CO 2 products are inter-compared in terms of magnitude and their spatiotemporal distributions. The results reveal that the correlations among these CO 2 products are relatively weak, and some discrepancies are detected in terms of the CO 2 spatiotemporal characteristics, demonstrating more efforts should be made in the future to improve the retrievals of CO 2 . Their spatial coverage differences reflected in this study imply the great necessity to generate consistent products with improved spatial and temporal continuities by combining these CO 2 measurements.

The air quality in China has experienced dramatic changes during the last few decades. To improve understanding of distribution, variations, and main influence factors of air pollution in central China, long-term multiple satellite observations from moderate resolution imaging spectroradiometer (MODIS) and ozone monitoring instrument (OMI) are used to characterize particle pollution and their primary gaseous precursors, sulfur dioxide (SO2), and nitrogen dioxide (NO2) in Hubei province during 2005–2017. Unlike other regions in eastern China, particle and gaseous pollutants exhibit distinct spatial and temporal patterns in central China due to differences in emission sources and control measures. OMI SO2 of the whole Hubei region reached the highest value of ~0.2 Dobson unit (DU) in 2007 and then declined by more than 90% to near background levels. By contrast, OMI NO2 grew from ~3.2 to 5.9 × 1015 molecules cm−2 during 2005–2011 and deceased to ~3.9 × 1015 molecules cm−2 in 2017. Unlike the steadily declining SO2, variations of OMI NO2 flattened out in 2016 and increased ~0.5 × 1015 molecules cm−2 during 2017. As result, MODIS AOD at 550 nm increased from 0.55 to the peak value of 0.7 during 2005–2011 and then decreased continuously to 0.38 by 2017. MODIS AOD and OMI SO2 has a high correlation (R > 0.8), indicating that annual variations of SO2 can explain most changes of AOD. The air pollution in central China has notable seasonal variations, which is heaviest in winter and light in summer. While air quality in eastern Hubei is dominated by gaseous pollution such as O3 and NOx, particle pollutants are mainly concentrated in central Hubei. The high consistency with ground measurements demonstrates that satellite observation can well capture variations of air pollution in regional scales. The increasing ozone (O3) and NO2 since 2016 suggests that more control measures should be made to reduce O3-related emissions. To improve the air quality in regional scale, it is necessary to monitor the dynamic emission sources with satellite observations at a finer resolution.

Satellite aerosol optical depth (AOD) products have been widely used in estimating fine particulate matter (PM2.5) concentrations near the surface at a regional scale, and perform well compared with ground measurements. However, the influence of limitations such as retrieval frequency and the spatial resolution of satellite AODs on the applicability of predicted PM2.5 values has been rarely considered. With three widely used MODIS AOD products, including Multi-Angle Implementation of Atmospheric Correction (MAIAC), Deep Blue (DB) and Dark Target (DT), here we evaluate the influence of their spatial resolution and sampling frequency by estimating daily PM2.5 concentrations in the Beijing-Tianjin-Hebei (BTH) region of northern China during 2017 utilizing a mixed effects model. The daily concentrations of PM2.5 derived from MAIAC, DB and DT AOD all have high correlations (R2: 0.78, 0.8, and 0.78) with the observed values, but the predicted annual PM2.5 exhibits a distinct spatial distribution. DT estimation obviously underestimates annual PM2.5 in polluted areas due to lower sampling of heavy pollution events. By contrast, the retrieval frequency (~40-60%) of MAIAC and DB AOD can represent well annual PM2.5 in nearly all 83 sites tested. However, MAIAC and DB-derived PM2.5 have a larger bias compared with observed values than DT, indicating that the large spatial variation of aerosol properties can exert an influence on the reliability of the statistical AOD-PM2.5 relationship. Also, there is notable difference between MAIAC and DB PM2.5 due to their different cloud screening methods. The MAIAC PM2.5 with high spatial resolution at 1 km can capture much finer hotpots than DB and DT at 10 km. Our results suggest that it is crucial to consider the applicability of satellite-predicted PM2.5 values derived from different aerosol products according to the specific requirements besides modeling the AOD-PM2.5 relationship.

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

Accurate quantification of the distribution and variability of atmospheric CO 2 is crucial for a better understanding of global carbon cycle characteristics and climate change. Model simulation and observations are only two ways to globally estimate CO 2 concentrations and fluxes. However, large uncertainties still exist. Therefore, quantifying the differences between model and observations is rather helpful for reducing their uncertainties and further improving model estimations of global CO 2 sources and sinks. In this paper, the GEOS-Chem model was selected to simulate CO 2 concentration and then compared with the Greenhouse Gases Observing Satellite (GOSAT) observations, CarbonTracker (CT) and the Total Carbon Column Observing Network (TCCON) measurements during 2009-2011 for quantitatively evaluating the uncertainties of CO 2 simulation. The results revealed that the CO 2 simulated from GEOS-Chem is in good agreement with other CO 2 data sources, but some discrepancies exist including: (1) compared with GOSAT retrievals, modeled XCO 2 from GEOS-Chem is somewhat overestimated, with 0.78 ppm on average; (2) compared with CT, the simulated XCO 2 from GEOS-Chem is slightly underestimated at most regions, although their time series and correlation show pretty good consistency; (3) compared with the TCCON sites, modeled XCO 2 is also underestimated within 1 ppm at most sites, except at Garmisch, Karlsruhe, Sodankylä and Ny-Ålesund. Overall, the results demonstrate that the modeled XCO 2 is underestimated on average, however, obviously overestimated XCO 2 from GEOS-Chem were found at high latitudes of the Northern Hemisphere in summer. These results are helpful for understanding the model uncertainties as well as to further improve the CO 2 estimation.

Asian dust storms markedly affect the ecosystem, environment, ocean biogeochemical cycle, and regional climate.