As one of the largest countries in the world, China covers a vast territory (9.6 × 106 km2) with heterogeneities in climatic conditions, complex topographies, and ecological environments, which have caused strong variations in economic development and population growth. A combination of climatic diversity and human activity has resulted in a unique pattern of local carbon budgets with obvious spatial differences in CO₂ concentrations (Fang et al., 2014; Du et al., 2017). To address this issue, six sub-regions characterized by different climatic conditions and socioeconomic backgrounds were delineated in this study, and the detailed zonal information is illustrated in Figure 1.

Four types of datasets are used in this study, namely, in situ observations, satellite remote sensing, model simulations, and reanalysis. As part of the validation process for gridded CO₂ products, the in situ observation data from WDCGG were utilized, and the satellite and modeled CO₂ products were used to study the spatiotemporal variation of CO₂ at different altitude. A driving mechanism analysis was conducted using gridded data from the LAI and CDIAC, as well as ERA5 reanalysis data. Supplementary Table S1 provides a brief overview of all datasets used in this study.

Due to the possibility that different datasets may differ in terms of their spatial and temporal references, all datasets were projected to the GCS_WGS_1984 geographic coordinate system and converted to local time of Beijing to maintain consistency. Additionally, to make the datasets comparable and facilitate the following analysis, the products of CT, LAI, and CDIAC were aggregated or resampled to a 2.5 × 2.5 regular grid by using the spatial information of GOSAT as a standard.

Prior to applying the estimated CO₂ in the following analysis, the accuracy of GOSAT and CT in China was evaluated through comparison with WDCGG observations. Stations used for validation were screened based on the following criteria: (1) falling within the study area, (2) not being assimilated in generating the products of GOSAT and CT, and (3) having less than 20% of missing observations. For a gauge station, we first identified the grid cell in which that station was located in the spatial dataset of a satellite product. Then, the values of grid data were directly extracted and the Pearson linear correlation coefficient (R) and the root mean square error (RMSE) were used to measure the strength of the linear association and the magnitude of the deviation between observations and estimates. The formula is as follows: R = C o v P e − P o δ e δ o , (1) R M S E = ∑ i = 1 n P e − P o 2 n , (2) where Pe and Po are the estimated and observed CO₂, respectively; n is the sample size; δ is the standard deviation; and cov() is the covariance between the two variables.

In addition, the Pearson correlation method was also used to study the impacts of anthropogenic and biogenic activities on surface CO₂ concentrations by calculating the correlation coefficients between LAI, fossil fuel carbon emissions, and local CO₂ concentrations.

The interannual variation of CO₂ was evaluated using linear trend fit as expressed in Eq. 3. The slope and statistical significance of the trends were estimated using the ordinary least squares method and the two-tailed Student’s t-test, respectively. In this study, a trend was considered statistically significant when it is at the 95% confidence level. In addition, the coefficient of variation (CV) was used to quantify the seasonal variation of CO₂, and it was defined as the ratio of the standard deviation to the mean in Eq. 4. y = a t + b + ε , (3) C V = S X ¯ × 100 % , (4) where y is the time series of CO₂ concentrations; a and b are the corresponding trend and the intercept, respectively; t represents the year; and ε is the regression error. s and‾x are the standard deviation and the mean of CO₂, respectively.

As the concentration of CO₂ exhibits a strong seasonal variation, it is thus essential to calculate the seasonal indexes and remove the seasonal factor from the time series when studying the multi-year monthly average CO₂ concentrations and conducting the correlation analysis with fossil fuel carbon emissions and LAI (Dettinger and Ghil, 1998). In this study, the ts and decompose functions in R were used to deseasonalize the interannual variation of CO₂, and the original time series were divided into three components: the trend component, the seasonal component, and the random component. After that, the seasonal component was subtracted from the time series and was treated as an input to the subsequent analysis. t s a c t u a l = t s t r e n d + t s s e a s o n + t s r a n d o m , (5) where ts _actual is the actual value of the dataset and ts _trend , ts _season , and ts _random are the trend component, seasonal component, and random component, respectively, of the time series.

After conducting the screening process, only the stations of LLN and HKO were considered to meet the criteria in China. The scatterplots between the observations and estimates are presented in Figures 2A, B. Generally, the CO₂ estimates from GOSAT and CT agreed well with observations, with averaged correlation coefficients of 0.96 and 0.85 for LLN and HKO stations, respectively. The HKO station exhibited a slightly higher RMSE (6.88 ppm) than that in the LLN station (4.44 ppm). Both of the products had a CO₂ estimation accuracy lower than 2%, meeting the requirements for precision described in Rayner and O'Brien (2001). In addition, the general pattern of intra-annual variations in CO₂ can be well-captured by GOSAT and CT, which peaks in winter and reaches its lowest level in summer (Figures 2C, D). In light of the high accuracy and good stability of GOSAT and CT, the estimated CO₂ products can be used to study the spatiotemporal patterns of CO₂ in China.

In order to study the spatiotemporal variations of CO₂ concentrations at different heights of the atmosphere, three typical layers, namely, the 975 hPa, 500 hPa, and 100 hPa, were selected to represent the mean state of CO₂ at the near surface, the middle, and the upper troposphere. The annual, seasonal, and diurnal variations of CO₂ concentrations were analyzed at these three levels if there is no further specification.

The spatial pattern and magnitude of CO₂ concentrations varied among different heights of the atmosphere. A declining trend was observed with increasing atmospheric height, with a mean CO₂ concentration of 400.25 ppm at near surface decreasing to 398.41 ppm and 393.76 ppm at the middle and upper troposphere, respectively (Supplementary Table S2). Among them, the near surface witnessed a strong spatial heterogeneity with the highest concentrations of CO₂ occurring in East China and the lowest in Northwest China (Figure 3A). This pattern is consistent with the spatial distribution of China’s population and economy, indicating a considerable impact of local carbon emissions on near-surface CO₂. In contrast, CO₂ concentrations at the middle troposphere showed much less variation, with a standard deviation of only 0.23 (Figure 3C). The insignificant variations may be associated with horizontal and vertical winds, which transport near-surface CO₂ to the atmosphere and then sufficiently mixed, resulting in a uniform spatial pattern of CO₂ concentrations (Cao et al., 2019; Al-Bayati et al., 2020). In the upper levels of the atmosphere, a distinct gradient of CO₂ concentrations was observed from south to north (Figure 3E). High values of CO₂ are concentrated in the area of low latitudes, while low CO₂ values are observed at high latitudes. Such a phenomenon may be the result of large-scale circulation in the upper troposphere (Sohn et al., 2019).

A general increasing trend with a magnitude higher than 2.1 ppm/year was detected for all the three typical layers (Figure 4), and almost all the data points passed the significance test at the 0.05 level (Figures 3B, D, F). Similar to the variation of CO₂ concentrations, the annual change rate was significant at the near surface (2.38 ppm/year), where the high values of annual CO₂ growth were found in part of East and North China. The middle troposphere witnessed a stable increasing trend at about 2.34 ppm/year. When it comes to the upper troposphere, the annual increase of CO₂ shows large discrepancies across China, with high values in the Mid–South and low values in Northeast China. Based on the decreasing change rates from the lower to upper troposphere, it appears that the intensified anthropogenic activities tend to cause significant increase in CO₂ at near surface.

In order to study the intra-annual variation of CO₂, the linear regression method was used to remove the annual growth rate, and then the monthly CO₂ concentrations were derived by calculating the multi-year averages. At near surface, all the six regions exhibited a unimodal fluctuation pattern, with the peak value of CO₂ concentrations occurring in April, followed by a decline, and reaching its trough in August (Figure 5A). This may be the result of the combined effect of anthropogenic and biogenic activities (WMO, 2017; Buchwitz et al., 2018). The increased photosynthesis of vegetation in summer is responsible for a higher uptake of CO₂ from the atmosphere (Yang et al., 2021), while the intensified anthropogenic heating emissions and plant respiration lead to a higher level of CO₂ in early spring (Liu et al., 2012). The amplitude of the seasonal variation is found to be largest in Northeast and lowest in East, and the differences between the peak and the trough are 24.14 and 12.80 ppm, respectively. In Northeast China, there is a strong seasonal difference in anthropogenic heating emissions and vegetation activity, while the anthropogenic CO₂ is almost constant throughout the year in East; combined with the reduced seasonal variation in vegetation, the amplitude of seasonal variation of CO₂ at East is smaller than that in Northeast China (Fang et al., 2014). A similar trend of CO₂ seasonal variation was observed in the middle troposphere, with a high value in April and a low value in August (Figure 5B). The discrepancies among the six regions, however, are generally reduced, indicating the strong dilution effect of CO₂ by vertical wind. In contrast, the CO₂ variations at the upper troposphere exhibited an opposite trend, with the maximum CO₂ concentrations observed in summer and the minimum CO₂ concentrations in winter (Figure 5C). Such anomalies indicate that the upper troposphere CO₂ was less affected by anthropogenic and biogenic activities. In addition, it was also found that CO₂ concentrations in North China were generally higher than those in South China throughout the entire year. This may relate to the large-scale atmospheric circulation in the upper troposphere (Dargaville et al., 2000; Wang et al., 2011; Cao et al., 2019).

The seasonal variations of CO₂ were found largest at the near surface, with a high value in spring and winter and a low value in summer and autumn (Figure 6). In particular, an apparent east–west difference in CO₂ was observed across China, and the CO₂ concentrations in the coastal provinces of Southeast China were generally higher than those in Northwest China throughout the year. At the middle troposphere, however, such spatial differences almost disappeared, while the temporal variations remained consistent with the lower troposphere. When it comes to the upper troposphere at 100 hPa, both the spatial and temporal variations of CO₂ were the smallest among the three levels. The seasonal variation of the different levels was also reflected in the coefficient of variation, as shown in Supplementary Figure S1, with the largest CV of 1.45 found at 975 hPa and decreased to 0.79 and 0.36 at 500 hPa and 100 hPa, respectively. It is likely that the decreasing trend of CV is associated with the distance to the carbon sources and sinks at near surface. Therefore, we conclude that anthropogenic and vegetation activities are the main factors affecting the vertical distribution of CO₂.

The mean diurnal amplitudes of CO₂ variations at different levels and seasons are shown in Supplementary Table S3. In terms of vertical differences, the amplitude of the diurnal variation decreases from the near surface to the upper troposphere. The largest peak-to-trough diurnal amplitudes (8.90 ± 6.98 ppm) were found at the near surface, while the middle and the upper levels of the troposphere exhibited a smaller diurnal variability, with an amplitude of 0.88 ± 0.72 ppm and 0.44 ± 0.32 ppm, respectively. Such differences can be attributed to the transportation and dilution effects of the upper atmosphere, which make the diurnal cycles of CO₂ at the middle and upper levels less affected by local sources and sinks.

As for the smallest amplitudes of diurnal variation of CO₂ at the middle and upper troposphere, we are primarily interested in regional differences of daily CO₂ variation at the near surface. Overall, a uniform diurnal cycle of CO₂ was observed across the four seasons, with the trough occurring in the afternoon around 15:00, then turns to accumulate during nighttime and reaching its maximum at about 06:00 the next morning (Figure 7). It is possible that the obvious diurnal variations in CO₂ throughout the whole year may be caused by active photosynthetic/respiratory fluxes in local vegetation (Li et al., 2004). As compared with spring and winter, summer and autumn witnessed relatively large amplitudes of CO₂ variation. As for the regional differences in diurnal variations of CO₂, Northwest and Southwest China exhibit an overall smaller amplitude than East China and other subregions, and there is almost no clear diurnal cycle during winter, which could be related to weak human and vegetation activities at high altitudes in western China (Shi et al., 2020; Lin et al., 2021).

Figure 8 illustrates the spatial distribution of the multi-year average atmospheric CO₂ concentrations at different heights in China. CO₂ concentrations at the near surface are generally higher than those at the top levels. CO₂ concentrations exhibit a strong spatial heterogeneity at the height of 850 hPa and below, with high values observed in eastern China and lower values in western regions, probably caused by the local emissions of human activity. This east–west spatial difference gradually decreases with height and disappears at 150 hPa, a phenomenon that may be explained by the dilution and mixing effects of vertical wind. At the height of 100 hPa and above, there is a distinct south–north difference in the spatial distribution of CO₂, with high CO₂ concentrated at low latitudes and vice versa. This discrepancy may be attributed to the large-scale atmospheric circulation at the top troposphere.

In order to explore the regional differences of CO₂ at different heights, the vertical profiles of CO₂ for the six regions are shown in Figure 9A. The near surface (levels 1–5) witnessed the greatest discrepancy among the six regions, and then it decreased to 0 at the middle troposphere (levels 6–11). Smaller differences were observed at the upper troposphere (levels 12–17). The dilution effect of vertical wind allows local CO₂ emissions to move upward with an increase in convective PBL, explaining the linear decrease in lower tropospheric CO₂ in East and Mid–South China (Newman et al., 2013). When it comes to the middle troposphere, the mixing effect of zonal and vertical winds is the dominant factor, which makes the CO₂ fully mixed and results in a small variation, whereas the continuous and regular atmospheric circulation contributes most to the smaller regional differences of CO₂ at the upper troposphere (Dargaville et al., 2000; Sohn et al., 2019).

To better understand the seasonal variation of CO₂ at different heights, the linear regression method was used to remove the annual change rate from the calculation of the monthly mean CO₂ concentration. As shown in Figure 9B, the seasonal fluctuations are gradually diminishing with increasing height. The peak-to-trough seasonal amplitude can be reached to 14.36 ppm at the height of 975 hPa, but it is reduced to less than 1 ppm above 50 hPa. In addition, a unimodal pattern of CO₂ variation was observed below 500 hPa, with a peak value occurring in April and a trough in August. Nevertheless, at 400 hPa or higher, the peak and trough of CO₂ occur 1 or 2 months later than at near surface. This further indicates that CO₂ in the middle troposphere is transported by ground emissions.

According to the model results of CT, we evaluated the influence of anthropogenic, biogenic, oceanic, and fire sources in each of the six regions. It is to be noted that the biogenic and oceanic modules act as a carbon sink, a capability that has gradually strengthened over the past decade (Figures 10A, D). While the anthropogenic and fire activities exert a positive effect on local CO₂ and shows an increasing trend as well (Figures 10B, C), in particular, the biogenic CO₂ shows a similar pattern across the six regions, with maximum carbon sequestration occurring in September and the minimum occurring in May. The only difference is that there is another trough of carbon sequestration in January in the Northeast and the North, which is likely due to the inactive photosynthesis of vegetation in the cold season at high latitudes (Li et al., 2004). However, the regional differences in biogenic CO₂ are much smaller than those of anthropogenic CO₂. Carbon emissions from fossil fuels in the East and Mid-South are higher than those in the Northwest and Southwest throughout the year. The maximum difference of anthropogenic CO₂ can reach 15 ppm in winter. Furthermore, a gentle flat variation of seasonal fossil fuel CO₂ was also observed in western China. Contrary to the variation pattern of anthropogenic and biogenic CO₂, the fire and oceanic CO₂ exhibit the least regional differences with almost no seasonal variation, indicating that these two tracers of CO₂ in China are transported by wildfire emissions and oceanic absorption in other places.

Because of the large differences of simulated anthropogenic and biogenic CO₂ among the six regions, a completely independent dataset was used to verify the correlation between CO₂ concentrations and the fossil fuel emissions and the vegetation activity and to explore the spatial differentiation of this relationship. According to the results of correlation analysis, the near-surface CO₂ is significantly correlated with fossil fuel emissions on a national scale, with a correlation coefficient of 0.66 (Figure 11B). As for the grid scale, except for some sparsely populated areas such as desert, Gobi, and high mountains, where the correlation coefficient is negative, other areas show significant positive correlations (Figure 11A). Especially in parts of the Northwest and Southwest, the correlation coefficient is as high as 0.9, which indicates that fossil fuel emissions are the dominant factor affecting near-surface CO₂ in less developed regions. However, this strong correlation appears to be declining in the East and Mid-South (R ≈ 0.7), which can be partly explained by the intensive land use change and the massive cement production (Gregg et al., 2008; Herzog, 2009).

Although a general positive correlation was observed between near-surface CO₂ and fossil fuel emissions, however, there is no evidence to support that this is the cause of seasonal fluctuation in CO₂. According to the study of Fung et al. (1997) and Van Der Velde et al., 2013, the flux of δ¹³C in the process of carbon exchange between the atmosphere and biosphere is evidently greater than that between the atmosphere and ocean. In order to study the influence of terrestrial ecosystems on seasonal variation of near surface CO₂, the LAI was used to conduct a correlation analysis. As shown in Figures 12A, B, a completely opposite trend was observed between the near-surface CO₂ and LAI, with a negative correlation coefficient being as high as −0.85. Photosynthesis of vegetation removes a relatively small amount of CO₂ before March. From April onward, the LAI increases gradually and leads to a decrease in CO₂, with the largest photosynthesis CO₂ sequestration observed in August. Then, the monthly mean CO₂ increases with a decrease in the LAI from autumn to early spring of the following year. In terms of spatial distribution, approximately 96% of the grid cells show a negative correlation, with strong correlation mainly distributed in eastern China and weak correlation in part of western provinces (Figure 12A). This east–west spatial difference may relate to the patterns of land use and land cover in China (Liu et al., 2008; Lin et al., 2021). The seasonal variation of CO₂ is highly dependent on the LAI in areas where forestland, grassland, and cropland are concentrated, while showing a weak or even positive correlation in sparse vegetation and bare land.

A similar approach was used to investigate whether fossil fuel carbon emissions and vegetation activity in China may have affected the CO₂ concentrations at the middle and upper troposphere. As shown in Figure 13A, a completely opposite trend was observed between the middle tropospheric CO₂ and LAI, with a negative correlation of −0.57. The variations of CO₂ are lagged by 4 months on average relative to the LAI. Our results are consistent with those reported in the Northern Hemisphere, where the shortest lag phase was observed in the low latitudes and the longest in the region between 30°N and 40°N (Cao et al., 2019). When it comes to the upper levels of troposphere, the variation of CO₂ exhibits a uniform pattern with the LAI (R = 0.92). It appears that vegetation carbon sequestration does not have an evident impact on upper tropospheric CO₂. However, further research is required to determine why there is a strong correlation between CO₂ at high altitudes and surface vegetation. The strong influence of fossil-fuel CO₂ emissions on near-surface CO₂ tends to weaken at higher levels of the troposphere, with a correlation coefficient decreasing to 0.47 and 0.23, respectively, for the middle and upper troposphere (Figures 13B, C). The reduced correlation indicates a dissipating effect of local carbon emissions on atmospheric CO₂ with increasing altitude. As a result, we can conclude that the upper levels of atmospheric CO₂ are less affected by local carbon sources and sinks; it is therefore important to take into account the influence of regional atmospheric circulation when conducting the driving force analysis (Sohn et al., 2019; Al-Bayati et al., 2020).

Based on the wind data generated by ERA5, this study further explored the effects of atmospheric circulation in modulating the distribution of CO₂ at the upper atmosphere. The wind field was decomposed into zonal and vertical winds, and then their influence on CO₂ concentration at different heights was evaluated. At the height of 500 hPa, satellite observations indicate lower concentrations of CO₂ in the northwest and southwest of China. These areas have low CO₂ emission values and are dominated by the wind from the west and the southwest; the relatively low CO₂ concentrations from upstream countries, such as India, Pakistan, and Central Asia, have less impact on western China (Figure 14B). In addition, the frequent air motion in the upward and downward directions facilitates the mixing of the upper and lower air (Figure 14A), which assists in CO₂ dispersal. While the upward airflow was most prevalent in the Mid-south and eastern China, high CO₂ concentrations from the ground were carried to the upper levels, in combination with the westerly wind, resulting in high CO₂ concentrations in eastern China. Accordingly, the spatial distribution of CO₂ at the middle of the troposphere is the result of a combination of near-surface carbon emissions and zonal and vertical air motions (Cao et al., 2019). At the height of 100 hPa, an obviously zonal circulation stratification is observed with a uniform westerly wind, whereas the vertical airflow is weak (Figures 14C, D). The distinct differentiation of CO₂ from north to south reflects the zonal average distribution of atmospheric circulation at the global scale. Therefore, the spatial patterns of CO₂ at the upper levels of the troposphere may largely be explained by zonal winds (Dargaville et al., 2000).

Our results indicate an obvious increase in CO₂ at the near surface and the middle troposphere at 2.38 ppm/year and 2.34 ppm/year, respectively, over the period from 2009 to 2019. The growth rate of CO₂ at both levels is higher than the rate averaged for global areas (2.20 ppm/year and 2.34 ppm/year, respectively, for the near surface and middle troposphere) and Central Asia (Supplementary Table S4). As a result of the rapid development of China’s economy, such a trend is in accordance with the study of Peters et al. (2011), who reported a strong increment of atmospheric CO₂ well above the global mean in the 21st century. It should be noted that the rate derived from satellite products is much lower than the rate obtained from in situ measurements at the regional scale (Fang et al., 2014; Cao et al., 2017). This difference may be the results of different sampling points used in the studies. As traditional in situ observation systems are not able to obtain the data in complex terrain regions, where the concentrations of CO₂ are relatively low, using the values derived from satellite remote sensing may lower the regional averages. In addition, the different periods used may also have contributed to the differences. The growth rate of the study period including 2015, for example, is generally higher than that of the study period excluding 2015. Because the year 2015 is a typical El Niño–Southern Oscillation (ENSO) year, the growth rate of atmospheric CO₂ is expected to increase due to the anomalous sea surface warming, and covering the analysis period in that year may have yielded a higher rate of annual growth of CO₂ (Schwalm et al., 2011; Kim et al., 2016).