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Machine learning modeling reveals divergent seasonal atmospheric carbon sensitivity in Mongolian dryland ecosystems, highlighting distinct carbon-water interactions.
Satellite-derived atmospheric CO2 observations provide important information for regional carbon monitoring, but their sparse spatial and temporal coverage limits their use in data-sparse drylands. We reconstructed seamless monthly column-averaged atmospheric CO2 (XCO2) over Mongolia at 0.1° resolution from 2015 to 2023 by integrating OCO-2 retrievals with vegetation, meteorological, and socioeconomic predictors using a LightGBM–Optuna method. The model achieved an R2 of 0.952, RMSE of 1.510 ppm, and MAE of 1.060 ppm. Detrended seasonal XCO2 was then related to SPEI-3-derived mean drought intensity (MDI) and drought frequency (DF) across typical steppe, forest, and Gobi Desert regions. The results showed that summer was the main season of XCO2 sensitivity to moisture limitation, but regional responses differed substantially. The typical steppe showed the strongest summer sensitivity; forest showed weak summer MDI sensitivity but greater relevance of repeated moisture-limited months; and spring XCO2 variability in the Gobi Desert was more closely associated with MDI. These findings indicate that seasonal moisture limitation is associated with contrasting atmospheric CO2 variability across Mongolia’s typical steppe, forest, and Gobi Desert regions, providing a regional atmospheric perspective on carbon–water interactions in arid and semi-arid drylands.
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Yi et al. (2026) studied this question.
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