The Widely Increasing Sensitivity of Vegetation Productivity to Phenology in Northern Middle and High Latitudes

Although vegetation phenology generally alters productivity, spatiotemporal variations in this effect and its potential drivers remain unclear. We used satellite-based vegetation phenology and gross primary productivity (GPP) data sets to analyze trends in the sensitivity of spring GPP to spring phenology (spring S-GP) and autumn GPP to autumn phenology (autumn S-GP). We also explored potential drivers across the northern middle and high latitudes (>30 degrees N) from 2001 to 2019. Our analysis revealed significant increases in spring and autumn S-GP (P < 0.05), with pronounced increases in boreal forests and tundra biomes. In contrast, spring S-GP significantly declined in deserts and xeric shrublands (P < 0.05). Spring temperatures and leaf area index (LAI) were key factors influencing spring S-GP, while autumn LAI and downward surface solar radiation drove the variation in autumn S-GP. Our findings highlight the critical role of phenology-productivity interactions in achieving carbon goals and the need for future research on climate feedback mechanisms. Plain Language Summary Earlier spring greenness and delayed autumn dormancy typically lead to higher vegetation growth, but the spatiotemporal variations in these effects and the drivers behind these changes are poorly understood. We used satellite data to examine the sensitivity of spring and autumn vegetation growth (measured as gross primary productivity) to the timing of spring greenness and autumn dormancy across northern regions from 2001 to 2019. Our study found that earlier spring greenness and delayed autumn dormancy generally boost vegetation growth. We also observed that this effect has intensified over the past two decades, particularly in boreal forests and tundra biomes. Temperature, leaf area index and downward surface solar radiation were key factors in enhancing this effect. Our findings highlight the importance of understanding how these changes influence climate feedback processes, which is crucial for predicting future ecosystem responses and meeting global carbon reduction goals.