Establishing Significant Climate Drivers of Vegetation Variability during the Arctic Growing Season

Studies have shown increasing green vegetation in the Northern Hemisphere high latitudes. Climate change is believed to be a leading factor in the evolving landscape in Arctic areas, but which individual drivers contribute variations in the vegetation during the growing season? Variations at subseasonal to interannual scales can provide insights. We explore that question within the framework of land-atmosphere interactions, incorporating satellite Normalized Difference Vegetation Index (NDVI), solar induced fluorescence (SIF) and vegetation optical depth (VOD) information with environmental data from flux tower sites and across the Arctic to determine how strongly specific climate drivers affect vegetation properties locally. Locally we apply a univariate Granger causality Test, selecting anomalies in air temperature, vapor pressure deficit, incoming shortwave radiation, soil temperature, and soil water content from flux tower data as the predictors, designating anomalies in NDVI, SIF, and VOD as the predictands, each representing different aspects and timescales of vegetation response to subseasonal-interannual climate variations. Results are compared locally to those using co-located reanalysis data, verifying accuracy, before scaling to the pan-Arctic analysis. Field significance suggests multiple potential drivers with strong spatial variability across stations, supporting the broader analysis. Pan-Arctic results show multiple significant drivers of vegetation variability, spatial and time-lag variations for all three predictands. Granger causality tests show the significant and dominant climate drivers for vegetation variability and their large-scale patterns across the Arctic region. Air and soil temperature are dominant drivers for subseasonal-interannual SIF and NDVI variations, while moisture variables tend to drive VOD, but with lower significance.