Atmospheric aerosol pollution has seriously affected the ecological environment and human health in recent years. There are great differences in aerosol optical properties and types due to the influence of environmental conditions, meteorology, industrial and agricultural activities, and other factors of each ecological functional zones. Using MODIS aerosol products (including MCD19A2 and MOD04_3K), this study discussed the temporal and spatial distribution of aerosol optical depth (AOD), Ångström wavelength index (AE) and aerosol types in the Beijing–Tianjin–Hebei region (BTH region) based on the ecological functional zones from 2015 to 2020. The results showed as follows: (1) The AOD in BTH region showed an obviously spatial pattern of low in the north and high in the south, while the spatial pattern of AE was opposite to that of AOD. In addition, the dominant aerosol type of the north part was clean aerosol, the dominant aerosol type of the middle part was biomass burning or urban-industrial aerosol, while that of the other part was mixed aerosols. (2) The seasonal changes of AOD and AE indexes in each ecological functional area had obvious seasonal changes, and the AOD and AE values were highest in summer. At the same time, the proportion of biomass combustion or urban industrial aerosol was the highest in summer. (3) The ecological functional areas with fewer human activities were dominated by clean aerosols, with lower AOD and higher AE value. The ecological functional areas dominated by cities were dominated by mixed aerosols, with higher AOD. The ecological functional areas dominated by agriculture and heavy industry were dominated by biomass combustion or urban industrial aerosols, with the largest AOD. (4) Compared with 2015, the average AOD of each ecological functional area decreased significantly to 2020, and biomass combustion or urban industrial aerosols changed to mixed aerosols.

As a key component of the climate system, terrestrial carbon dynamics are commonly simulated using Dynamic Global Vegetation Models (DGVMs), which provide essential information for estimating the global carbon budget. However, substantial uncertainties persist in DGVM‐based estimates of land‐use change carbon emissions, and their process‐level origins remain poorly constrained. Here, we harmonize DGVM‐based net land‐use emissions (E _NLUC ) from 23 TRENDY2025 DGVMs using a transient, time‐varying PFT‐level land‐cover correction that explicitly removes the loss of additional sink capacity (LASC), and apply a hierarchical Bayesian mixed‐effects variance decomposition framework to characterize between‐DGVM heterogeneity in fitted coefficients associated with land‐use predictors. LASC correction reveals that uncorrected DGVMs artificially converge toward bookkeeping estimates, masking persistent structural deficiencies—most notably the limited representation of wood harvest, forest management, and other peat‐related activities incorporated in bookkeeping models. Between‐DGVM heterogeneity in fitted coefficients is greater for persistent regrowth area than for current deforestation, particularly at decadal‐to‐centennial timescales and in the tropics, where the fitted association with persistent regrowth area varies substantially among models. This uncertainty can propagate into residual land sink estimates, complicating the diagnosis of land–atmosphere carbon exchange. Our results highlight that improving the representation of secondary forest recovery, including sub‐grid land‐use processes and forest management, is critical for reducing uncertainties in land‐use and land‐use change emissions in next‐generation DGVMs.