Evidence for satellite observed changes in the relative influence of climate indicators on autumn phenology over the Northern Hemisphere
Introduction
The Earth is experiencing substantial warming, which has a measurable influence on the timing and rate of plant processes (Kharouba et al., 2018; Richardson et al., 2018). Vegetation phenology is particularly sensitive to climate change and has played a crucial role in regulating the interannual variability of carbon exchange of terrestrial ecosystems (Park et al., 2018; Richardson et al., 2013; Visser, 2016; Wu et al., 2013). The influence of climate on plant phenology, particularly at the beginning of the growing season, has received increased attention over the past decade as many factors have a profound impact on triggering the advance or delay of phenology (Seyednasrollah et al., 2018; Yang et al., 2017). However, for different biome types, the responses of the end of growing season (EOS) to climate change are not as well documented as the start growth of season (SOS) (Fu et al., 2014; Peng et al., 2017; Vitasse et al., 2018; White et al., 2014).
Large variability exists in the influences of climate factors on phenology, resulting in substantial uncertainty in predicting future EOS and the shifting climate impacts on phenology over time (Estiarte and Penuelas, 2015; Graven et al., 2013; Ovaskainen et al., 2013). Vegetation phenological shifts in recent years are mainly caused by feedback of vegetation to climate change (Ibanez et al., 2010; Miller-Rushing and Primack, 2008; Parmesan and Yohe, 2003; Peñuelas and Filella, 2001). The mechanisms of ongoing climate change on the start growth of season (SOS; i.e., spring vegetation green-up, leaf unfold) are well documented (Liu et al., 2016b). For example, Piao et al. (2006) found that SOS occurred earlier in China during 1982–1999 as a result of warming temperatures. Polgar and Primack (2011) further validated that leaf-out woody plants were highly sensitive to temperature and proposed that onset leaf green-up will continue to advance but at a low rate. Fu et al. (2014) highlighted the importance of winter precipitation in regulating spring vegetation green-up phenology during 1982–2009. Basler and Korner (2012) demonstrated that photoperiod was a reliable environmental signal to the start of growing season (SOS), especially in high latitude. Other studies documented that the earlier flowering in a grassland with increasing spring temperature and decreasing winter precipitation (Lesica and Kittelson, 2010).
Unlike SOS, responses of EOS to shifting climate appear more dynamic and variable, making environmental drivers challenging to identify and model. There are some efforts on examining the influence of autumn phenology. For example, Yang et al. (2015b) found that under the influence of temperature and precipitation EOS experienced a weakened delayed change rate or even present advanced trend during 1982–2010. Gill et al. (2015) conducted meta-analysis on published studies of the strongest influence predictors (i.e., temperature, precipitation, latitude, and photoperiod et al.) on site autumn phenology during 1931–2010 and found that the extension of EOS was higher in low latitudes (25°~ 49° N) than high latitudes (50°~ 70° N), which was primarily driven by temperature and photoperiod, respectively. Liu et al. (2016a) related autumn phenology to both climate and spring phenology during 1982–2011, concluding that the spring phenology as well as environmental factors played a significant role in modeling EOS. Tylewicz et al. (2018) through physiology experiments demonstrated that photoperiodic has an important influence on seasonal growth. A recent study showed that the low explanation of mean temperature of EOS is that autumn leaf senescence responded oppositely to increases in daytime and nighttime temperatures (Wu et al., 2018).
All these findings suggest that autumn phenology is driven by more complex and dynamic physical mechanisms than spring phenology, and temperature, precipitation and photoperiod are the most important factors in indicting fluctuation of EOS (Pastor-Guzman et al., 2018; Wu et al., 2014). Most of previous phenological studies have focused on overall specific autumn phenology, but how climate factors impact EOS over time, and in diverse biome types are not well investigated over the large Northern Hemisphere (Ackerly and Monson, 2003; Melaas et al., 2016; Stevens and Carson, 2001; Yang et al., 2017). To address these, we used EOS from the Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI) and explored drivers separately of EOS for the first period (FP: 1982–2000) and second period (SP: 2001–2015), two phases during which EOS exhibited different changes over time.
Section snippets
Study area
We selected natural vegetation surface in Northern Hemisphere (NH > 30°N) as the study area (Fig. 1), since satellite-derived NDVI for these regions were less contaminated by solar zenith angle effects at middle and high latitude (Cong et al., 2013; Piao et al., 2011; Slayback et al., 2003; Yang et al., 2015a). Cultivated areas (i.e., American's central plains, northeast China plain et al.) were also eliminated to better study the diverse influences of natural climate variables on EOS (White et
Spatial distribution and trends of EOS in 1982–2000 and 2001–2015
Significant difference in EOS trends was observed before and after the 21st century and among biomes. Though both of two periods had more than 90.5% of the study area with EOS occurred during September to November (from DOY 244 to 335), 84.0% occurred during September to October for 1982–2000, while 96.3% for 2001–2015 (Fig. 2a, b). Overall, the latitudinal mean values of EOS during 2001–2015 were higher than 1982–2000 in 30–60°N, while the result reversed when the latitude exceeded 60°N. In
The spatial and temporal of vegetation phenology
Our analysis revealed an overall delayed trend in EOS from 1982 to 2000 (8.5% of significant delayed trend in study area, 0.6 ± 0.7 day yr−1), followed by a reversal (27.6% of significant advanced trend in study area, −2.1 ± 1.5 day yr−1) during 2001–2015 over Northern Hemisphere (NH > 30°) (Fig. 2). The spatial variation of EOS trends appears variable across biomes (Cleland et al., 2007) and function of abiotic factors including temperature, precipitation, light availability (Lesica and
Conclusions
Based on GIMMS-3 g 1/12° spatial resolution NDVI data and CRU-TS climate data during 1982–2000 and 2001–2015, we examined the trends in EOS of different biomes and further investigated the likely causes of changes in EOS using partial correlation between EOS and climate variables (i.e. temperature, precipitation and light availability). The overall trend was an EOS delay (i.e., more than 8.5% of study area, significant trendmean = 0.6 ± 0.7 day yr−1) during 1982–2000, but the trend reversed and
Acknowledgments
This work was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA19040103), National Natural Science Foundation of China (41871255) and the key Research Program of Frontier Sciences, CAS (QYZDB-SSW-DQC011).
Declaration of Competing Interest
The authors declare that there is no Conflict of Interest with this study.
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