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Autumn phenology remains a relatively neglected aspect in climate change research, which hinders an accurate assessment of the global carbon cycle and its sensitivity to climate change. Leaf coloration, a key indicator of the growing season end, is thought to be triggered mainly by high or low temperature and drought. However, how the control of leaf coloration is split between temperature and drought is not known for many species. Moreover, whether growing season and autumn temperatures interact in influencing the timing of leaf coloration is not clear. Here, we revealed major climate drivers of leaf coloration dates and their interactions using 154 phenological datasets for four winter deciduous tree species at 89 stations, and the corresponding daily mean/minimum air temperature and precipitation data across China's temperate zone from 1981 to 2012. Results show that temperature is more decisive than drought in causing leaf coloration, and the growing season mean temperature plays a more important role than the autumn mean minimum temperature. Higher growing season temperature and lower autumn minimum temperature would induce earlier leaf coloration date. Moreover, the mean temperature over the growing season correlates positively with the autumn minimum temperature. This implies that growing season mean temperature may offset the requirement of autumn minimum temperature in triggering leaf coloration. Our findings deepen the understanding of leaf coloration mechanisms in winter deciduous trees and suggest that leaf life-span control depended on growing season mean temperature and autumn low temperature control and their interaction are major environmental cues. In the context of climate change, whether leaf coloration date advances or is delayed may depend on intensity of the offset effect of growing season temperature on autumn low temperature.

Autumn phenology is a crucial indicator for identifying the alpine grassland growing season’s end date on the Qinghai-Tibet Plateau (QTP), which intensely controls biogeochemical cycles in this ecosystem. Although autumn phenology is thought to be mainly influenced by the preseason temperature, precipitation, and insolation in alpine grasslands, the relative contributions of these climatic factors on the QTP remain uncertain. To quantify the impacts of climatic factors on autumn phenology, we built stepwise linear regression models for 91 meteorological stations on the QTP using in situ herb brown-off dates, remotely sensed autumn phenological metrics, and a multi-factor climate dataset during an optimum length period. The results show that autumn precipitation has the most extensive influence on interannual variation in alpine grassland autumn phenology. On average, a 10 mm increase in autumn precipitation during the optimum length period may lead to a delay of 0.2 to 4 days in the middle senescence date (P < 0.05) across the alpine grasslands. The daily minimum air temperature is the second most important controlling factor, namely, a 1 °C increase in the mean autumn minimum temperature during the optimum length period may induce a delay of 1.6 to 9.3 days in the middle senescence date (P < 0.05) across the alpine grasslands. Sunshine duration is the third extensive controlling factor. However, its influence is spatially limited. Moreover, the relative humidity and wind speed also have strong influences at a few stations. Further analysis indicates that the autumn phenology at stations with less autumn precipitation is more sensitive to precipitation variation than at stations with more autumn precipitation. This implies that autumn drought in arid regions would intensely accelerate the leaf senescence of alpine grasslands. This study suggests that precipitation should be considered for improving process-based autumn phenology models in QTP alpine grasslands.

Revealing grassland growing season spatial patterns and their climatic controls is crucial for estimating the spatial heterogeneity of grassland productivity and carbon sequestration. In this study, we first used satellite‐derived normalized difference vegetation index data and a double logistic function to extract the start (SOS), end (EOS), and length of the growing season in midlatitude grasslands of the Northern Hemisphere during 1981–2014. Then, we verified the accuracy of satellite‐derived SOS and EOS using ground‐observed phenological records and gross primary production data at some locations. Moreover, we analyzed the spatial patterns of growing season indicators and their climatic controls. Results show that both SOS and EOS appear first in cool semidesert grasslands (CG), then in temperate grasslands (TG) and alpine grasslands (AG), and finally in warm semidesert grasslands (WG). A delaying tendency of SOS and EOS from north to south was identified in TG of North America. In contrast, an advancing tendency of SOS and EOS from north to south was detected in CG of Central and Western Asia. Further analysis indicates that a spatial opposite effect of spring temperature and precipitation triggers SOS in TG, whereas a spatial synergy effect of spring temperature and precipitation triggers SOS in CG of Asia, WG, and AG. Meanwhile, a spatial synergy effect of autumn temperature and precipitation triggers EOS for TG of North America and AG, whereas a spatial opposite effect of autumn temperature and precipitation determines EOS for CG.

Autumn vegetation phenology plays a critical role in identifying the end of the growing season and its response to climate change. Using the six vegetation indices retrieved from moderate resolution imaging spectroradiometer data, we extracted an end date of the growing season (EOS) in the temperate deciduous broadleaf forest (TDBF) area of China. Then, we validated EOS with the ground-observed leaf fall date (LF) of dominant tree species at 27 sites and selected the best vegetation index. Moreover, we analyzed the spatial pattern of EOS based on the best vegetation index and its dependency on geo-location indicators and seasonal temperature/precipitation. Results show that the plant senescence reflectance index-based EOS agrees most closely with LF. Multi-year averaged EOS display latitudinal, longitudinal and altitudinal gradients. The altitudinal sensitivity of EOS became weaker from 2000 to 2012. Temperature-based spatial phenology modeling indicated that a 1 K spatial shift in seasonal mean temperature can cause a spatial shift of 2.4–3.6 days in EOS. The models explain between 54% and 73% of the variance in the EOS timing. However, the influence of seasonal precipitation on spatial variations of EOS was much weaker. Thus, spatial temperature variation controls the spatial patterns of EOS in TDBF of China, and future temperature increase might lead to more uniform autumn phenology across elevations.