Long-Term Impact of Extreme Weather Events on Grassland Growing Season Length on the Mongolian Plateau

Understanding how extreme weather events (EWEs) impact vegetation phenology is crucial for assessing ecosystem stability under climate change. This study systematically investigated the ecosystem growing season length (GL) response to four types of EWEs- extreme heat, extreme cold, extreme wetness (surplus precipitation), and extreme drought (lack of precipitation). The EWEs extremity thresholds were found statistically using detrended long time series (2000-2022) ERA5 meteorological data through z-score transformation. The analysis was based on grassland ecosystem in the Mongolian Plateau (MP) from 2000 to 2022. Using solar-induced chlorophyll fluorescence data and event coincidence analysis, we evaluated the probability of GL anomalies coinciding with EWEs and assessed vegetation sensitivity to climate variability. Our results showed negative GL anomalies exhibited a stronger association with EWEs than positive anomalies, particularly in arid and cold regions of the MP, where extreme drought and extreme cold events predominantly shortened the GL. Conversely, extreme heat and extreme wet events played dominant role in warmer and wetter regions, influencing both GL lengthening and shortening. The regional background hydrothermal conditions modulated vegetation sensitivity, with warmer regions being more susceptible to extreme heat stress and drier regions exhibiting higher vulnerability to extreme drought conditions. Furthermore, grassland ecosystems demonstrated lower resilience to extreme drought than forest ecosystems likely due to differences in root structure and water use efficiency. These findings emphasize the importance of regional weather variability and climate characteristics in shaping vegetation phenology and provide new insights into how weather extremes impact ecosystem stability in semi-arid and arid regions. Future research should explore extreme weather events and the role of human activities to enhance predictions of vegetation-climate interactions in grassland ecosystems of the MP.