Divergent seasonal responses of carbon fluxes to extreme droughts over China

Droughts affect the interannual variability of global land carbon fluxes and are expected to exert widespread impacts on the carbon cycle given a future climate with more intense and frequent droughts. Evidence indicates that the impacts of droughts on the carbon cycle vary in different seasons, but a quantitative examination of the seasonal differences is lacking. Here we ensemble multiple data streams for greenness indices and carbon fluxes, including those from remote sensing observations, flux tower upscaling, atmospheric inversions, and dynamic global vegetation models (DGVMs), to quantify the seasonal responses of vegetation to extreme droughts in China. We find that summer droughts cause the largest negative responses of leaf area index (LAI, with median standardized anomalies of -0.40), gross primary productivity (GPP, -0.55), and net ecosystem productivity (NEP, -0.74); notably, droughts in autumn largely suppress carbon uptake. The response patterns show a high degree of heterogeneity, and we identify the factors driving these spatial variations using the extreme gradient boosting (XGBoost) machine learning approach. Climate is the dominant driver of spring and autumn GPP responses while LAI predominantly drives summer GPP loss. Looking into the biotic factors, carry-over effects (previous-season vegetation growth affecting current-season growth) contribute substantially to the alleviation of drought stress, in that previous-season greening compensate vegetation loss during droughts. Our results not only quantify the seasonal response differences in carbon fluxes and greenness, but suggest that carbon fluxes respond more sensitively to drought than greenness. Also, we show seasonal differences in the degree to which factors contribute to drought impacts, which highlight that annual-scale drought analyses may mask spring and autumn vegetation response to droughts.