Decadal sink-source shifts of forest aboveground carbon since 1988

Abstract

Forest ecosystems are vital to the global carbon cycle, yet their long-term aboveground carbon (AGC) dynamics remain uncertain. Here, we integrate multi-source satellite observations with probabilistic deep learning models to reconstruct a harmonized, uncertainty-aware global forest AGC record from 1988 to 2021 at 0.25-deg. We find that, although global forests sequestered 6.2 PgC, moist tropical and boreal forests have progressively transitioned toward carbon sources since the early 2000s. This shift coincides with a strengthening negative correlation between tropical AGC variability and atmospheric CO2 growth rates (r = -0.63 in 2011-2021), suggesting tropical forests increasingly modulate the global carbon cycle. Notably, in the Brazilian Amazon, the contribution of intact forests to the year-to-year variations in AGC losses increased from 33% in the 1990s to 76% in the 2010s, surpassing that of deforested areas (from 60% to 13%). Our findings highlight the vulnerability of carbon stocks in key biomes and provide a benchmark to track emerging sink-source shifts under anthropogenic climate change.

Figures (6)

• Figure 1: Comparison of our forest AGC estimates with national inventory-based data and other remote sensing products.a, Scatter plots comparing our regional AGC stocks against the living biomass carbon stocks reported by Pan et al. panEnduringWorldForest2024 across boreal, temperate, and tropical forests. Each data point represents a specific geographic region or country for a given year (1990, 2000, 2010, or 2020), following the definitions in Pan et al. panEnduringWorldForest2024. Solid red lines indicate the linear fits, and dashed grey lines represent the 1:1 relationships. The consistent slopes of $<1$ reflect the fundamental distinction between our estimator (AGC only) and Pan et al.'s estimator (total living biomass carbon, which includes below-ground components). b, Comparison of decadal net carbon stock changes across regions for the periods 1990s (1990--1999), 2000s (2000--2009), and 2010s (2010--2019). Our AGC change estimates (black circles) are compared alongside inventory-based living biomass changes (red squares) by Pan et al. panEnduringWorldForest2024 and other available remote sensing-based AGC products (Xu et al. xuChangesGlobalTerrestrial2021, Liu et al. liuRecentReversalLoss2015, and Boitard et al. boitardAbovegroundBiomassDataset2025). Regions are vertically grouped by their corresponding dominant biomes following the definitions in Pan et al. panEnduringWorldForest2024.
• Figure 2: Spatial patterns, predictive uncertainty, and biome-level distribution of global forest AGC.a, Multi-year averaged forest AGC density at 0.25° resolution, calculated from the long-term (1988--2021) time series reconstructed in this study. b, Multi-year averaged predictive uncertainty (standard deviation), which jointly reflects observation noise and model underrepresentation (see Methods). c, Latitudinal profiles of zonal AGC stock sums. Profiles represent multi-year means for each reference dataset, except for ESA CCI AGC, which is averaged over 2010 and 2021 to exclude our model training period. d, Total AGC stocks of different biomes, with moist tropical forests dominating (119 PgC, 52%), followed by temperate (52 PgC, 23%), dry tropical & subtropical (35 PgC, 15%), and boreal (24 PgC, 11%) forests. Black error bars indicate the uncertainty range. e, Boxplots of biome-specific AGC density at the grid-cell level, exhibiting a similar cross-biome gradient to the total stocks. The boxplot boundaries from top to bottom represent the maximum, third quartile, median, first quartile, and minimum, and black triangles mark the mean.
• Figure 3: Spatially explicit trends and biome-level dynamics of global forest AGC.a, Overall AGC density trends across the entire study period at 0.25° resolution. b--d, Decadal AGC density trends for the periods 1988--2000 (b), 2001--2010 (c), and 2011--2021 (d). Grid-cell-wise trends are computed via the Theil–Sen slope and a modified Mann–Kendall test to account for serial autocorrelation, with increases shown in blue and declines in red, retaining only grid cells with $p < 0.05$. e, Boxplots of AGC density trends for the globe (grey) as well as moist tropical (green), dry tropical & subtropical (brown), temperate (orange), and boreal (blue) forests. Each box denotes the median, quartiles, and range; black triangles indicate mean values. f, Time series of AGC stock changes with respect to 1988 at both global and biome levels, revealing overall increasing AGC stocks in global, temperate, boreal, and dry tropical & subtropical forests, and slightly decreasing AGC in moist tropical forests. Only grid cells with valid data across all years are considered. Shaded areas depict the 95% uncertainty interval (see Methods). The vertical grey band denotes the time period affected by the Mt. Pinatubo eruption (1991-1992), which is not included in the time series analysis.
• Figure 4: Spatially explicit net AGC changes across decades from 1988 to 2021.a, Annual mean values of net AGC change over the full 1988--2021 period at 0.25° resolution. b--d, Annual mean values of net AGC changes for 1988--2000, 2001--2010, and 2011--2021, respectively. Positive changes (blue) indicate net AGC gains, whereas negative changes (brown) denote net AGC losses. e--h, Global and biome-specific net changes and trends in AGC stocks for the corresponding time periods. Net changes represent the annual mean AGC difference over each period, while trends are derived from Theil–Sen slope estimates of the AGC stock time series over the same corresponding period. For trend calculation, we consider only grid cells with valid data across all years, and AGC stock values for 1991 and 1992 are ignored. Error bars indicate the 95% uncertainty range (see Methods).
• Figure 5: Tropical AGC fluxes and stock changes over time.a, Annual AGC flux (black) over pan-tropical forests, spanning approximately 23.5°N to 23.5°S, compared with atmospheric CO2 growth rate (blue), both with long-term linear trends removed to highlight interannual variability (IAV). Asterisks (*) indicate statistical significance at $p < 0.05$, using the two-tailed t-test. b, IAV contribution of different sub-regions to the overall interannual variability of pan-tropical AGC fluxes across tropical America, Africa, Asia during four time periods (D1: 1989--2000, D2: 2001--2010, D3: 2011--2021, All: 1989--2021). Sub-regions include the Amazon, Congo, and Indonesian rainforests, and the remaining non-rainforest areas. c, Time series of AGC stock changes with respect to 1988 for all pan-tropical forests. d--i, Corresponding AGC stock changes for tropical America (d), Amazon rainforests (e), tropical Africa (f), Congo rainforests (g), tropical Asia (h), and Indonesian rainforests (i). Only grid cells with valid data across all years are considered. Grey-shaded regions in each panel represent the 95% uncertainty intervals (see Methods). Blue-shaded regions represent the uncertainties in CO2 growth rates. The vertical grey band is the period of the Mt. Pinatubo eruption, not included in the time series analysis. Vertical red shading represents the drought-affected area fraction across tropical forest grid cells, based on the three-month Standardized Precipitation Evapotranspiration Index (SPEI3). Grid cells with SPEI3$\leq -1$ are classified as drought-affected.