Search

Climate warming has induced significant shifts in spring phenology over both temperate and boreal forests. The timing of bud growth resuming from dormancy is crucial for predicting spring phenology. However, the mechanisms by which environmental cues, other than chilling accumulation, initiate bud growth remains unclear. By constructing a revised process-based spring phenology model incorporating photoperiod and temperature triggers of bud growth, we simulated the first leaf unfolding and first flowering dates of four deciduous forest trees during 1981-2014 at 102 stations across China’s monsoon regions. Then, we revealed spatial patterns of the two triggers. Moreover, we compared fitting precision and robustness of the revised model with three mainstream models. Results show that the revised models can effectively simulate all spring phenology time series. Growth initiation of foliar and floral buds was induced by photoperiod lengthening in 80.8% and 77.7% of time series, and by temperature increasing in remaining 19.2% and 22.3% of time series, respectively. The proportions of time series with photoperiod- and temperature-initiated bud growth significantly increase and decrease from northern to southern climatic zones, respectively. Chilling exposure controls the predominant bud growth triggers in different climate zones. Specifically, in regions with long and severe winters where chilling requirement is easily fulfilled, rising temperature in spring alleviates the cold constraint and initiate bud growth. Conversely, in regions with short and mild winters, prolonged daylength in spring compensates the lack of chilling exposure to initiate bud growth. These findings suggest that photoperiod may limit spring phenology response to temperature in low-latitudes. Overall, our model slightly outperforms other models in terms of efficiency, accuracy, and robustness in modeling leaf unfolding and flowering dates. Therefore, this study deepens our understanding of the mechanisms of spring phenology, and improves the predicting capability of spring phenology models in the face of ongoing global warming.

The phenology of alpine grassland on the Qinghai–Tibet Plateau (QTP) is critical to regional climate change through climate–vegetation feedback. Although many studies have examined QTP vegetation dynamics and their climate sensitivities, the interspecific difference in the phenology response to climate change between alpine species is poorly understood. Here, we used a 30-year (1989–2018) record of in situ phenological observation for five typical alpine herbs (Elymus nutans, Kobresia pygmaea, Plantago asiatica, Puccinellia tenuiflora, and Scirpus distigmaticus) and associated climatic records at Henan Station in the eastern QTP to examine the species-level difference in spring and autumn phenology and then quantify their climate sensitivities. Our results show that with significantly warming, the green-up dates of herbs were insignificantly shifted, while the brown-off dates in four out of the five herbs were significantly delayed. Meanwhile, the interspecific difference in brown-off dates significantly increased at a rate of 0.62 days/annual from 1989 to 2016, which was three times larger than that in green-up dates (0.20 days/annual). These diverse rates were attributed to the different climate controls on spring and autumn phenology. In particular, green-up dates in most herbs were sensitive to mean surface temperature, while brown-off dates were sensitive to the night surface temperature. Furthermore, brown-off dates are less sensitive to the warming in high ecological niche (with higher herb height and aboveground biomass) herbs than low niche herbs (with lower herb height and aboveground biomass). The increased phenology interspecific difference highlights the complex responses of herbs to future climate change even under the same alpine environment and indicates a potential alternation in the plants community of alpine QTP, which may further influence the regional climate–vegetation feedback.

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.

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.