Spatial and Temporal Distributions of Atmospheric CO₂ in East China Based on Data from Three Satellites

Carbon dioxide (CO₂) is the single most important anthropogenic greenhouse gas (GHG) in the atmosphere, responsible for more than 80% of the increase in radiative forcing over the past five years. Since the industrial revolution, human emissions from fossil fuel combustion and cement production, deforestation and other land-use changes have resulted in the dramatic increase of CO₂ mole fractions. The global average CO₂ mole fraction reached 405.5 ± 0.1 ppm in 2017, 146% of the pre-industrial level (WMO, 2018). Temporal and spatial observations of GHGs constitute an important way to understand the mechanism of the carbon cycle and verify the effect of emissions reduction plans. Thus, so far, a network of 31 global stations and more than 400 regional stations has been built to measure GHG concentrations, which is the backbone of the Global Atmosphere Watch (GAW) Program. Since the 1990s, China has established its own national GHG observation network, consisting mainly of Waliguan (WLG) global background station in Qinghai Province, Shangdianzi regional background station in Beijing, Linan regional background station in Zhejiang Province, and Longfengshan regional background station in Heilongjiang Province (Zhang et al., 2013; Fang et al., 2014; Xia et al., 2015; Cheng et al., 2018; Liu et al., 2018). Moreover, provincial networks have also been established in several regions, such as the provinces of Jiangsu, Guangdong, Shanxi and Jiangxi. However, these monitoring stations are still too sparse to constrain estimates of local to regional carbon budgets. Satellite measurements overcome the deficiency of sparse ground-based observation stations and the uneven spatial distribution, and have the potential to obtain a broad spatial coverage of data on GHG concentrations globally. With the development of remote sensing technology, satellite observations have become an effective approach to study the temporal and spatial distributions of CO₂ mixing ratios (Aumann et al., 2003; Zeng et al., 2013; Yin et al., 2018).

In recent years, some countries have successfully launched satellite series for high spatiotemporal resolution monitoring of the global CO₂ distribution. For instance, the Scanning Imaging Absorption Spectrometer (SCIAMACHY) onboard the Environmental Satellite (ENVISAT) launched by the European Space Agency was the first satellite instrument whose measurements are sensitive to the variations of CO₂ and methane (CH₄) column dry air mole fractions (Schneising et al., 2008; Bergamaschi et al., 2013). However, contact with ENVISAT was lost in April 2012. The Atmospheric Infrared Sounder (AIRS) onboard the Aqua mission carried out by NASA in 2002 can be used to retrieve CO₂ mixing ratios of the middle troposphere (Aumann et al., 2003). The Greenhouse Gases Observing Satellite (GOSAT)—the first satellite mission dedicated to monitoring CO₂ and CH₄ from space—was successfully launched by Japan in 2009 (Yoshida et al., 2011; Monteil et al., 2013; Zeng et al., 2013; Wang et al., 2019). The Orbiting Carbon Observatory-2 (OCO-2), used to retrieve the column-averaged CO₂ dry air mole fraction, was launched by NASA on 2 July 2014 from Vandenberg Air Force Base on the California coast (Crisp et al., 2004; Frankenberg et al., 2015; Taylor et al., 2016). China has also launched (in 2016) its first satellite to observe GHGs, named TANSAT, into a 700-km sun-synchronous orbit; however, thus far, no CO₂ data from TANSAT are publicly available.

Previous research has sought to understand the distributions of CO₂ in China based on the retrieval products from the abovementioned satellites. For instance, Yang et al. (2016) obtained the spatial and temporal distributions of CO₂ over China in 2010 based on four retrieval products from GOSAT. Zhang et al. (2014) validated CO₂ products from SCIAMACHY and GOSAT based on the ground observation data published on the Total Carbon Column Observing Network (TCCON) website (https://tccon-wiki.caltech.edu/). Liao et al. (2012) discussed the retrieval of CO₂ using SCIAMACHY data. Therefore, most of the literature in this field is related to the distributions of CO₂ over the whole of China based on satellite observations or using satellite data in inversion methods; whereas, specific CO₂ distributions in the typical regions of China are still poorly constrained. For example, the East China, a typical area with rapid development of industry, includes one of China’s main manufacturing hubs Yangtze River Delta region. Analysis of CO₂ spatial and temporal variabilities is extremely important, but few studies have focused on such typical regions in China—particularly East China.

To address this issue, based on GOSAT monthly products, this study compared these monthly CO₂ data with those of WLG global background station, and then focused on the East China region, comparing the annual average CO₂ and average seasonal cycles in this area with six other regions in China. We also investigated the spatial and temporal distributions of near-surface and mid-tropospheric CO₂ in East China from 2014 to 2017 based on GOSAT, AIRS and OCO-2 retrieval products, and analyzed the reason for differences.

The variabilities of near-surface and mid-tropospheric CO₂ in East China were compared in this study. The East China region is one of the most economically developed regions in China, including Shanghai, Jiangsu, Zhejiang, Shandong, Anhui, Jiangxi, and Fujian. Figure 1 maps these provinces and the surrounding regions of China. Based on newly published national statistical data of China from http://data.stats.gov.cn/, we found that the area of East China is 808 000 km², and the Gross Domestic Product (GDP) in 2018 reached 34573.7 billion RMB. While East China only accounted for 8.7% of the landmass of China, it occupied a relatively significant share (37.8%) of the total GDP in China in 2018. The population of East China reached 0.41 billion in 2018, accounting for 29.5% of the total population of the country, and the region’s energy consumption was 1.36 billion tons of standard coal in 2017.

Figure 1.  East China (Shanghai, Jiangsu, Zhejiang, Shandong, Anhui, Jiangxi and Fujian provinces) and surrounding regions of China. WLG station is located in Northwest China. The Yangtze River Delta region in East China is highlighted in the right-hand figure.

The orbit height of Japan’s GOSAT is about 666 km, in a sun-synchronous orbit with an inclination of 98°. It returns to the same location in 3 days and utilizes 44 orbits to cover the entire globe. The Thermal and Near Infrared Sensor for Carbon Observation instrument (known as TANSO) onboard GOSAT acquires CO₂ spectra in the 1.6-µm and 2.0-µm bands, and thus it is sensitive to surface fluxes of CO₂ and CO₂ concentrations in the mid to lower troposphere (Basu et al., 2013). The GOSAT data collected in this study were drawn from the FTS-SWIR L3 global distribution products. The global CO₂ column-averaged mixing ratio quantifies the average concentration of CO₂ in a column of dry air extending from Earth’s surface to the top of the atmosphere (Saito et al., 2012). The monthly CO₂ data of GOSAT L3 products (available at http://www.gosat.nies.go.jp/index_e.html), from 2014 to 2017, were used to discuss the variations of near-surface CO₂ in East China (monthly data of ecember 2014 and January 2015 are not published on the website). The GOSAT L3 CO₂ data have a grid resolution of 2.5° × 2.5°.

AIRS is one of six instruments onboard the Aqua satellite that launched into Earth-orbit on 4 May 2002. AIRS is an infrared spectrometer/radiometer that covers the 3.7–15.4-μm spectral range with 2378 spectral channels (Aumann el al., 2003). AIRS measures the concentration of CO₂ with peak sensitivity at the 400-hPa pressure level, and the measurement accuracy of AIRS can reach 1.5–2 ppm, making it ideal for mapping the distribution and transport of CO₂ levels in the free troposphere. Generally, CO₂ retrieved from AIRS mainly reflects the variations of mid-tropospheric CO₂. The monthly CO₂ data from 2014 to 2016 were acquired from AIRS L3 products (available at https://airs.jpl.nasa.gov/), but the monthly CO₂ data after March 2017 were not published. Level 3 CO₂ data have a grid resolution of 2° latitude × 2.5° longitude.

OCO-2 retrieves CO₂ from its grating spectrometer measurements reflected sunlight in three near-infrared regions (0.765, 1.61 and 2.06 µm) (Wang et al., 2019). OCO-2 flies in the EOS Afternoon Constellation (A-Train) on a 705-km sun-synchronous orbit, which needs 16 days to return to the same location. OCO-2 has a high spatial resolution, with approximately 2 km along its ground track and a cross-track resolution of approximately 1 km (Taylor et al., 2016). The CO₂ data of OCO-2 from January 2015 to December 2017 drawn from OCO-2 level 2 bias-corrected CO₂ products of version 9r (available at https://disc.gsfc.nasa.gov/) were collected in this study. OCO-2 level 2 products contain information about full orbits or fractions of orbits of geographically located estimates of CO₂ (OCO, 2015).

WLG station (36.12°N, 100.06°E; 3816 MSL) is one of WMO’s GAW global background sites, and is located on Mt. Waliguan in Qinghai Province (Fig. 1). The weekly air samples have been collected in glass flasks since May 1991, and then analyzed by NOAA’s Earth System Research Laboratory (Fang et al., 2014). The monthly CO₂ data of WLG from 2014 to 2017 were obtained from monthly flask data contributed by NOAA (available at https://gaw.kishou.go.jp/).

WLG is a background station and representative of well-mixed atmospheric compositions for a large region. Monthly CO₂ data from GOSAT L3 products were interpolated by Ordinary Kriging in ArcGIS, to the WLG station location (36.12°N, 100.06°E), to identify the satellite representation of the WLG CO₂ value. Comparison results of CO₂ retrieved from GOSAT with observed CO₂ at WLG background station are shown in Fig. 2. Figure 2a shows that the variation of CO₂ retrieved from GOSAT is in good agreement with that observed at WLG global background station. The correlation coefficient (R) can reach 0.92 (as presented in Fig. 2d). As shown in Fig. 2b, the monthly CO₂ retrieved from GOSAT presents a similar seasonal cycle with that of WLG, with slightly lower seasonal amplitude compared to WLG background station, indicating more impacts from anthropogenic activity and the terrestrial biosphere on the ground observation station. The annual variations of CO₂ retrieved from GOSAT exhibited in Fig. 2c present a similar positive trend with those observed at WLG station, with a growth rate of 2.54 ppm yr⁻¹ for GOSAT, lower than that of WLG background station (2.96 ppm yr⁻¹). To summarize, the CO₂ retrieved from GOSAT presents good consistency with that of the background station observations, suggesting that the CO₂ products from GOSAT can be used to study the variations of CO₂ in the surface layer.

Figure 2.  Comparison of the CO₂ retrieved from GOSAT with the observed CO₂ at WLG background station. Panels (a–c) represent the monthly, seasonal and yearly variations during 2014–17, respectively. Panel (d) presents a comparison of monthly CO₂ (46 samples) retrieved from GOSAT and observations at WLG station.

The annual average CO₂ and seasonal cycle in East China were compared with six other regions in China in this study. As listed in Table 1, the annual means of the CO₂ retrieved from GOSAT in East China were higher than those of the other regions in China. The annual CO₂ in East China increased from 398.96 ± 0.24 ppm in 2014 to 407.39 ± 0.20 ppm in 2017, much higher than the globally averaged CO₂ mole fraction. The long-term trend of the CO₂ mixing ratio was calculated by linear fitting the annual CO₂ data, revealing a growth rate of 2.82 ± 0.05 ppm yr⁻¹ in East China, higher than that of the globally averaged CO₂ mole fraction (2.67 ± 0.15 ppm yr⁻¹) (WMO, 2015, 2016, 2017, 2018) and other regions in China. This is probably due to the rapid economic development in East China, with a GDP that accounted for 37.1% ± 0.7% of China’s as a whole during 2014–17 (National Bureau of Statistics of China, 2019).

The monthly data from AIRS could be acquired until February 2017, so we compared the monthly variation of CO₂ retrieved from AIRS and GOSAT in East China from 2014 to 2016. We also calculated the corresponding annual means of CO₂ retrieved from AIRS for the different regions in China (as listed in Table 2). In East China, the annual means of CO₂ were similar to Central China, and slightly lower than that in the northern region of China, but higher than that in the southern and western regions of China. The annual CO₂ retrieved from AIRS in East China ranged from 398.54 ± 0.53 ppm in 2014 to 403.13 ± 0.50 ppm in 2016, with a growth rate of 2.30 ± 0.17 ppm yr⁻¹, which was lower than the result from GOSAT. The annual growth rate of CO₂ in East China was similar to that of Northeast and Southwest China, slightly lower than that of South China, but higher than that of North, Central and Northwest China. The results were different with GOSAT, probably because of the horizontal movement of the middle troposphere. In East China, the mid-tropospheric CO₂ retrieved from AIRS was lower than the near-surface CO₂ retrieved from GOSAT (as listed in Tables 1 and 2), indicating East China had more CO₂ emissions near the surface.

OCO-2 data could be acquired from September 2014, so we selected data from 2015 to 2017, but the data from 31 July 2017 to 18 September 2017 were missing. As listed in Tables 1 and 3, the annual CO₂ retrieved from OCO-2 was close to that from GOSAT, and the annual CO₂ deviation between GOSAT and OCO-2 was within 0.38% in East China, while it was 0.88% for the whole of China, during 2015–17. The CO₂ retrieved from OCO-2 in East China was similar to that for Central China, slightly lower than that in the northern region of China, but higher than that in the southern and western regions of China; plus, the results were in good agreement with the results inferred from AIRS. The annual CO₂ in East China ranged from 400.02 ± 1.17 ppm in 2015 to 406.14 ± 1.73 ppm in 2017, with a growth rate of 3.06 ± 0.09 ppm yr⁻¹, which was slightly higher than the result from GOSAT, possibly due to the missing data. Both OCO-2 and GOSAT satellite products reflected the atmospheric CO₂ column mixing ratio well.

As shown in Fig. 3, the CO₂ retrieved from GOSAT, AIRS, and OCO-2 in East China all exhibited similar and distinct seasonal patterns, with an increasing trend year by year during 2014–17. The monthly CO₂ from OCO-2 in East China was closer to that from GOSAT than that from AIRS. The average seasonal amplitude of CO₂ retrieved from OCO-2 was 8.79 ppm, which was close to that from GOSAT (8.0 ppm), and the value from AIRS was 3.57 ppm, which was lower than that from GOSAT and OCO-2, due to the fact that GOSAT, OCO-2 and AIRS products reflect CO₂ over different heights in the troposphere, influenced by their respective averaging kernels. AIRS reflected the mid-tropospheric CO₂ mixing ratio, and the CO₂ was well-mixed in the atmosphere, which was less affected by human activities and ground sources–sinks than the near-surface CO₂ retrieved from GOSAT.

Figure 3.  Time series of CO₂ retrieved from three satellites (GOSAT, AIRS and OCO-2) in East China.

This paper uses the Northern Hemisphere definitions of spring (March, April and May), summer (June, July and August), autumn (September, October and November) and winter (December, January and February). As seen from Fig. 3, the seasonal cycles of the three satellites all presented a consistent peak in spring and valley in late summer (August) or early autumn (September). In addition, the monthly maximum and minimum CO₂ retrieved from AIRS lagged slightly behind that from GOSAT in 2014 and 2016, indicating that the seasonal variation of mid-tropospheric CO₂ lagged behind that of the near-surface CO₂, mainly due to the vertical diffusion of CO₂ from the near-surface to mid-troposphere, and horizontal mixing movement in the mid-troposphere (Xia et al., 2018).

The annual spatial distributions from 2014 and 2017 are shown in Fig. 4. High values of CO₂ always appeared in the Yangtze River Delta region, while lower values were observed in Jiangxi Province and the northern areas of Fujian Province, probably due to the high emissions from fossil fuel combustion in the Yangtze River Delta region. The annual energy consumptions were 2366.0 tons of standard coal per square kilometer for Shandong, 2858.7 tons for Jiangsu, 893.8 tons for Anhui, 18154.5 tons for Shanghai, 1889.6 tons for Zhejiang, 512.9 tons for Jiangxi, and 998.7 tons for Fujian, during 2014–17 (National Bureau of Statistics of China, 2019).

Figure 4.  Spatial variations of annual CO₂ retrieved from GOSAT in East China (units: ppm).

Based on Fig. 4, we obtained a GOSAT CO₂ spatial distribution with high values appearing in the Yangtze River Delta region and lower values in the southern region. However, the result from AIRS was different. As presented in Fig. 5, the annual CO₂ retrieved from AIRS had a zonal distribution with high values in the north and low values in the south, probably influenced by the high emissions of fossil fuel combustion in northern regions and the high uptake of vegetation in southern regions. In 2014, the high annual CO₂ in East China was mainly concentrated in Shandong Province, and the low values were concentrated in Fujian, southern Jiangxi and Zhejiang provinces. In 2015, the high values were concentrated in Shandong, northern Anhui and Jiangsu provinces, and the low values were concentrated in southern Jiangxi and Fujian provinces. In 2016, its high values were concentrated in eastern Shandong and Jiangsu provinces, while low values were concentrated in Fujian Province.

Figure 5.  Spatial variations of annual CO₂ retrieved from AIRS in East China (unit: ppm).

The annual CO₂ retrieved from OCO-2 showed an increasing trend year by year, and overall a distribution with high values in the north and low values in the south during 2015–17 (Fig. 6). The southern region had high forest cover, such as in Fujian and Jiangxi provinces, which had the highest and second-highest forest coverages in China, respectively, and thus CO₂ was lower because of its absorption by green vegetation. In 2015, the high annual CO₂ in East China was mainly concentrated in Shandong Province, western Jiangsu and northern Anhui Province, and the low values were concentrated in Fujian and Jiangxi provinces. Over time, affected by human activities and economic development, the high values of annual CO₂ retrieved from OCO-2 spread southwards, while the low-value region was concentrated in Fujian Province and surrounding regions in 2017.

Figure 6.  Spatial variations of annual CO₂ retrieved from OCO-2 in East China (units: ppm).

From Table 1 and Fig. 4, there was a long-term increase in CO₂ over East China. To determine which part of the region influenced this increase, we calculated the year-to-year differences in spatial annual averages. The annual enhancement of CO₂ (ΔCO₂) retrieved from GOSAT in East China from 2014 to 2017 is shown in Fig. 7. The smallest increase of CO₂ mainly occurred in Fujian Province and part of Jiangxi Province during 2014–15, with a growth rate lower than 1.0 ppm. In comparison, the growth rate of WLG in 2015 was higher than 2.0 ppm, which was considered to be a representative station of China. Thus, we inferred that there might have been absorption by vegetation in the southern part of East China in 2015. The forest cover of Fujian and Jiangxi provinces reached 66.8% and 63.1% in 2015, ranking them first and second in China, respectively (National Bureau of Statistics of China, 2019). However, as presented in Fig. 7, the significant annual enhancement of CO₂ also appeared in the southern part (Fujian Province and its surroundings) of East China during 2015–16. The highest growth rate of global CO₂ was also observed in 2016, as reported by WMO (2015, 2016, 2017, 2018) during 2014–17, mainly caused by drought and forest fires in the tropics strongly influenced by ENSO. The combined contribution of global impacts and local emissions might have caused larger increases of CO₂ in the southern part of East China in 2016. Although the economy of the Yangtze River Delta region has developed rapidly, the enhancements of CO₂ in 2015 from 2014, 2016 from 2015, and 2017 from 2016, were relatively consistent. Carbon emissions control and reduction policies carried out in the Yangtze River Delta region might have contributed to the low growth rate in these years. The CO₂ growth rate of the Yangtze River Delta region was 2.40 ± 0.02 ppm yr⁻¹ from 2014 to 2017, which was lower than that of China as a whole (2.83 ± 0.14 ppm yr⁻¹) and globally (2.67 ± 0.15 ppm yr⁻¹). To accomplish the carbon reduction targets of the 13th Five-year Plan for GHG emissions control (State Council of China, 2016), plans for carbon emissions reduction were drafted and executed for Jiangsu, Zhejiang and Shanghai provinces from 2015. Moreover, five cities of Jiangsu, five cities of Zhejiang, and Shanghai, were designated as national low-carbon city pilots (National Development and Reform Commission, 2012, 2017).

Figure 7.  Spatial variations of annual ΔCO₂ retrieved from GOSAT in East China (units: ppm). Panels (a–c) represent the enhancements of CO₂ for 2015 relative to 2014, 2016 relative to 2015, and 2017 relative to 2016, respectively.

To evaluate CO₂ seasonal cycles, we obtained detrended monthly data of CO₂ retrieved from GOSAT during 2014–17 using the method recommended by Zhou et al. (2006). As shown in Fig. 8, CO₂ presented significant seasonal variations over China, but with slight differences among different areas. The seasonal amplitude in East China was 8.0 ppm, similar to that in Central China, and higher than those in South (7.15 ppm), Southwest (7.30 ppm) and Northwest China (7.39 ppm), but lower than that in North (9.53 ppm) and Northeast China (10.92 ppm). CO₂ absorption by vegetation in summer and the emissions of fossil fuel combustion in winter in different regions might be key factors for the differences in seasonal amplitudes (Liu et al., 2009; Xia et al., 2018). Due to the strong absorption of CO₂ by photosynthesis in terrestrial ecosystems in summer, the minimum CO₂ in East China always occurred in August or September, similar to in other regions of China. The peak of CO₂ seasonal variation in East China mostly appears in April, which is influenced by the combination of the regional terrestrial biosphere, anthropogenic activities, and local meteorological conditions (Zhang et al., 2008).

Figure 8.  Seasonal variations of CO₂ retrieved from GOSAT in different regions of China. The seasonal variations are averaged between 2014 and 2017.

Seasonal distributions of averaged CO₂ from 2014 to 2017 in East China retrieved from GOSAT are shown in Fig. 9. High values were mainly observed in winter and spring, and low values appeared in summer and autumn in East China. In terms of spatial distribution, low values mostly occurred in the south of East China in spring, autumn and winter, probably due to the high absorption by vegetation from forests in Fujian and Jiangxi. However, in summer, the CO₂ mixing ratios observed in the south were higher than in other regions in East China; the airmass transport from the Pearl River Delta and Yangtze River Delta regions might have been the main reason (Xia et al., 2020). In spring, high values of CO₂ were concentrated in Jiangsu, Shanghai, eastern Anhui and northern Zhejiang provinces. In winter, higher CO₂ mixing ratios were concentrated in the northern areas of East China, probably owing to the CO₂ emissions of fossil fuel combustion from central heating over northern China.

Figure 9.  Seasonal distributions of CO₂ retrieved from GOSAT in East China (units: ppm). The seasonal variations are averaged between 2014 and 2017.

As illustrated in Fig. 10, the seasonal CO₂ retrieved from AIRS had obvious characteristics, with high values in spring and low values in autumn in East China during 2014–17. The high values of CO₂ in East China were mainly concentrated in Shandong Province, and mid-tropospheric CO₂ increased from south to north in all seasons. Combined with the GOSAT results, it is speculated that the air mass carrying high CO₂ mixing ratios in the near-surface spread to the mid-troposphere, and then moved from south to north along with the mid-tropospheric transport in East China.

Figure 10.  Seasonal distributions of CO₂ retrieved from AIRS in East China (units: ppm).

As shown in Fig. 11, the seasonal CO₂ retrieved from OCO-2 also had obvious characteristics, with high values in winter and spring and low values in summer and autumn in East China during 2015–17. Similar to GOSAT, the CO₂ retrieved from OCO-2 showed zonal distribution characteristics that were generally high in the north and low in the south during spring, autumn and winter in East China, while lower CO₂ values were presented in the north of East China and high values in the Yangtze River Delta region for summer. In addition to potential transport from the Yangtze River Delta region in summer, CO₂ absorption by vegetation may be weakened in southern locations owing to the high temperatures inhibiting photosynthesis.

Figure 11.  Seasonal distributions of CO₂ retrieved from OCO-2 in East China (units: ppm).

We have analyzed the spatial and temporal properties of atmospheric CO₂ over East China measured with three satellite instruments. Our main findings are as follows:

(1) The monthly, seasonal and yearly results all showed that GOSAT had good consistency with WLG background station observations, suggesting that the CO₂ product from GOSAT can represent the CO₂ variations in the background atmosphere near the surface layer.

(2) The annual means of the CO₂ mixing ratio retrieved from GOSAT, AIRS and OCO-2 in East China all showed an increasing trend during 2014–17, with growth rates of 2.82 ± 0.15 ppm yr⁻¹, 2.30 ± 0.17 ppm yr⁻¹ and 3.06 ± 0.09 ppm yr⁻¹, respectively. Also, they had apparent seasonal characteristics, with high values in spring and low values in summer or autumn.

(3) The annual CO₂ and growth rate retrieved from GOSAT in East China were higher than six other regions in China. The annual CO₂ in East China ranged from 398.96 ± 0.24 ppm in 2014 to 407.39 ± 0.20 ppm in 2017; the highest values were mainly concentrated in the coastal areas of Zhejiang Province, and the lowest values in Jiangxi Province and northern areas of Fujian Province. CO₂ in Fujian and part of Jiangxi Province showed little increase during 2014–15, due to the region’s high forest cover with high absorption by vegetation, but it enhanced significantly during 2015–16 influenced by local emissions and global impacts. However, because of the carbon emissions control and reduction policies, the annual enhancements of CO₂ in the Yangtze River Delta region were relatively low and stable during 2014–17.

(4) The annual and seasonal CO₂ retrieved from AIRS had a zonal distribution with high values in the north and low values in the south. The annual CO₂ retrieved from AIRS in East China ranged from 398.54 ± 0.53 ppm in 2014 to 403.13 ± 0.50 ppm in 2016. The annual values and seasonal amplitude of CO₂ retrieved from AIRS were lower than those from GOSAT, possibly because AIRS and GOSAT products reflect CO₂ over different heights in the troposphere, and the CO₂ retrieved from GOSAT better reflects near-surface conditions that might be more affected by human activities and local emission sources.

(5) The monthly, annual values and seasonal amplitudes of CO₂ retrieved from OCO-2 were close to those from GOSAT. The annual CO₂ in East China ranged from 400.02 ± 1.17 ppm in 2015 to 406.14 ± 1.73 ppm in 2017. The minimum CO₂ retrieved from OCO-2 in East China occurred in August and the maximum in March and April. The same pattern was also found based on the GOSAT results. From the spatial perspective, it showed distribution characteristics with high values in the north and low values in the south.

Acknowledgements. The authors thank the staff of WLG station in Qinghai Province for their data sampling and maintenance of the observation system. We are also grateful to NASA for providing the CO₂ products retrieved from AIRS, OCO-2 and JAXA for providing the CO₂ product retrieved from GOSAT. This research was supported by the China Meteorological Administration Climate Change Special Project (No. CCSF20 2035) and the Jiangxi Meteorological Science and Technology Project (201805, 201905).