As global warming intensifies, the greening of vegetation in the mid–high latitudes of Eurasia has been accelerating. Vegetation greening not only affects regional climate but is also closely linked to global warming. Therefore, scientifically understanding and predicting the climatic variability patterns of vegetation growth in these regions holds crucial scientific significance.
Recently, a research team led by Professor Ke Fan from Sun Yat-sen University, China, revealed the principal modes of the interannual variations of summer vegetation growth in eastern Siberia, and developed efficient climate prediction models based on the year-to-year increment method. The results have been published in Atmospheric and Oceanic Science Letters .
This research identified three principal modes of interannual variations in summer normalized difference vegetation index (NDVI) across eastern Siberia: the regionally consistent mode, the western–eastern dipole mode, and the northern–southern dipole mode. These patterns are found to be influenced by various factors, including preceding soil moisture conditions in western Siberia, sea surface temperature anomalies in both tropical and mid–high latitudes, and Arctic sea-ice anomalies. The study further elucidates the underlying processes and mechanisms.
Meanwhile, this study utilized the year-to-year increment method proposed by Fan et al. (2008) to develop climate prediction models targeting the principal modes of interannual variations in summer NDVI across eastern Siberia. Both independent hindcasts and cross-validation results demonstrate the model's superior predictive capability.
This study provides critical insights for understanding climatic variability patterns and advancing predictive capabilities regarding Eurasian mid–high latitude vegetation.
