Land use and land cover changes have altered terrestrial ecosystem carbon storage, but their impacts on ecosystem sensitivity to drought and temperature fluctuations have not been evaluated spatially over the globe. We estimate drought and temperature sensitivities of ecosystems using vegetation greenness from satellite observations and vegetation biomass from dynamic global vegetation model (DGVM) simulations. Using a space-for-time substitution with satellite data, we first illustrate the effects of vegetation cover changes on drought and temperature sensitivity and compare them with the effects estimated from DGVMs. We also compare simulations forced by scenarios with and without land cover changes to estimate the historical land cover change effects. Satellite data and vegetation models both show that converting forests to grasslands results in a more negative or decreased positive sensitivity of vegetation greenness or biomass to drought. Significant variability exists among models for other types of land cover transitions. We identify substantial effects of historical land cover changes on drought sensitivity from model simulations with a generally positive direction globally. Deforestation can lead to either an increased negative sensitivity, as drought-tolerant forests are replaced by grasslands or croplands, or a decreased negative sensitivity since forests under current land cover are predicted to exhibit greater drought resistance compared to those under pre-industrial land cover. Overall, our findings emphasize the critical role of forests in maintaining ecosystem stability and resistance to drought and temperature fluctuations, thereby implying their importance in stabilizing the carbon stock under increasingly extreme climate conditions.

Vegetation productivity in India varies at intraseasonal to interannual time scales, influenced by meteorological factors sensitive to large-scale climate teleconnections. While the impact of global climate variability on Indian monsoon and its extremes is well known, their effects on Indian vegetation productivity are relatively less understood. This study addresses this gap by decomposing dominant modes of spatio-temporal variability of gross primary productivity (GPP) over India and examining their dependence on climate teleconnections. We found that El-Niño Southern Oscillation (ENSO) and Pacific Meridional Mode (PMM) significantly impact GPP, especially in western and southern peninsular India during the monsoon and post-monsoon seasons. However, there is an east-west asymmetry in the PMM-GPP correlation. The western region and southern peninsula are negatively correlated, while northeast India positively correlates with PMM. Using wavelet decomposition, we show that more than half of temporal variability in the GPP comprises low-frequency components. These low-frequency signals primarily drive the relationship between GPP and climate teleconnections. Next, we identify the dominant spatial modes of low-frequency signals of GPP. We tested the predictability of the principal components of GPP using teleconnections and hydrometeorological variables. While most of the predictive skill of GPP comes from its past (memory up to 5 months, R2 score of up to 0.5), adding teleconnection indices as predictors improves the prediction skill at lead times (with an increase of 0.1-0.2 in R2 values). Our results underscore the utility of using hydrometeorological and distant climate teleconnection in GPP prediction for longer lead times.