2026-10-14T22:54:46Z https://www.scidb.cn/oai

10.57760/sciencedb.cjae.00115 2026-06-22T10:23:51Z

2026-06-22 Spatiotemporal variation of atmospheric CO2 concentration in China based on the Köppen-Geiger climate classification doi:10.57760/sciencedb.cjae.00115 en Spatiotemporal dynamics of atmospheric CO2 concentrations are crucial to understanding the global carbon cycle and predicting regional climate change. China spans multiple climatic zones and features complex and diverse ecosystems. Elucidating the patterns of CO2 differentiation across climatic zones are essential for formulating refined and differentiated reduction strategies. Here, we employed the satellite-retrieved XCO2 datasets from 2003 to 2023 and the Köppen-Geiger climate classification to systematically investigate the spatiotemporal variations of atmospheric CO2 concentrations in China. The results showed that the average of CO2 concentrations ranked as: temperate zone>tropical zone>cold zone≈arid zone>polar zone, while the growth rates ranked as: tropical zone>temperatezone>polar zone>cold zone≈arid zone. CO2 concentrations had increased synchronously across climatic zones, yet the interregional CO2 concentration differences had further widened, revealing an intensifying trend of spatial differentiation. Nearly half of the regions exhibited significant spatial clustering of atmospheric CO2 concentration. Specifically, high-high clusters were primarily concentrated in the temperate zone, which was densely populated and economically developed, whereas low-low clusters were mainly distributed in the cold and polar zones, where ecological conservation was relatively robust. The spatial clustering was weakest in 2006 (Moran's I=0.68, Z=70.00) and peaked in 2017 (Moran's I=0.88, Z=90.33). Correlation analysis revealed that, except for downward shortwave radiation (r=-0.542), population density (r=0.992), carbon emissions (r=0.970), leaf area index (r=0.845), temperature (r=0.565), and precipitation (r=0.481) all exhibited significant positive correlations with CO2 concentration, while soil water (r=0.072) showed a non-significant positive correlation. According to the optimal parameters-based geographical detector, population density exerted the strongest influence on the spatial pattern of CO2 concentration (q=0.55), followed by temperature (q=0.43). The interaction types between factors were predominantly characterized by bi-linear or nonlinear enhancement. remote sensing; atmospheric CO2 concentration; Köppen-Geiger climate classification; spatial autocorrelation; optimal parameters-based geographical detector Zhu Weixin Zhang Lei Wang Jieyu Li Yaodong Niu Junjie PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset Science Data Bank