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Following a Granger causality framework based on a random forest predictive model, we exploit the current wealth of multi-decadal satellite data records to uncover the main spatiotemporal drivers of monthly vegetation variability globally. Results based on 1981–2010 indicate that water availability is the most dominant factor driving vegetation globally. This overall dependency of vegetation on water...
Cross-correlations provide a useful technique to analyse the similarities between vegetation indices and time series of climatic variables. However, correlation analyses are not sufficient to unveil changes in the co-variability of vegetation and climate as a function of time or for different temporal scales. Here, we introduce the use of wavelet coherence to evaluate the relationship between vegetation...
Intra-annual and multi-decadal variations in the Earth's climate are to a large extent driven by periodic oscillations in the coupled state of atmosphere and ocean. Because changes in climate impact terrestrial ecosystems, these ocean-atmospheric oscillations are expected to influence land evaporation as well. In this study, we investigate the control of ocean-atmospheric oscillations over global...
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