Global Change Biology Global Change Biology (2015) 21, 652–665, doi: 10.1111/gcb.12778

Changes in autumn vegetation dormancy onset date and the climate controls across temperate ecosystems in China from 1982 to 2010 Y U T I N G Y A N G 1 , 2 , 3 , H U A D E G U A N 1 , 2 , M I A O G E N S H E N 4 , 5 , W E I L I A N G 6 and L E I J I A N G 3 1 National Centre for Groundwater Research and Training, Adelaide, South Australia, Australia, 2School of the Environment, Flinders University, Adelaide, South Australia, Australia, 3State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing, China, 4Key Laboratory of Alpine Ecology and Biodiversity, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China, 5CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing, China, 6 Department of Tourism and Environmental Sciences, Shaanxi Normal University, Xi’an, China

Abstract Vegetation phenology is a sensitive indicator of the dynamic response of terrestrial ecosystems to climate change. In this study, the spatiotemporal pattern of vegetation dormancy onset date (DOD) and its climate controls over temperate China were examined by analysing the satellite-derived normalized difference vegetation index and concurrent climate data from 1982 to 2010. Results show that preseason (May through October) air temperature is the primary climatic control of the DOD spatial pattern across temperate China, whereas preseason cumulative precipitation is dominantly associated with the DOD spatial pattern in relatively cold regions. Temporally, the average DOD over China’s temperate ecosystems has delayed by 0.13 days per year during the past three decades. However, the delay trends are not continuous throughout the 29-year period. The DOD experienced the largest delay during the 1980s, but the delay trend slowed down or even reversed during the 1990s and 2000s. Our results also show that interannual variations in DOD are most significantly related with preseason mean temperature in most ecosystems, except for the desert ecosystem for which the variations in DOD are mainly regulated by preseason cumulative precipitation. Moreover, temperature also determines the spatial pattern of temperature sensitivity of DOD, which became significantly lower as temperature increased. On the other hand, the temperature sensitivity of DOD increases with increasing precipitation, especially in relatively dry areas (e.g. temperate grassland). This finding stresses the importance of hydrological control on the response of autumn phenology to changes in temperature, which must be accounted in current temperature-driven phenological models. Keywords: China, climate change, dormancy onset date, NDVI, remote sensing, temperate zone, vegetation phenology Received 12 May 2014; revised version received 4 October 2014 and accepted 16 October 2014

Introduction Surface vegetation is a sensitive indicator of global climate change and plays an essential role in regulating climate system through its influence on the exchange of energy, water and carbon at the land–atmospheric interface (Zeng et al., 1999; Cornelissen et al., 2007; Piao et al., 2008; Jeong et al., 2009; Shen et al., 2014a). Vegetation phenology, which refers to the seasonal timing of life cycle events of plants, has been found to be strongly regulated by climate factors, and plays an important role in determining the carbon budget of the terrestrial ecosystem (Piao et al., 2007; Richardson et al., 2010). It provides valuable information for understanding dynamic responses of terrestrial ecosystems to climate change (Piao et al., 2006; Shen, 2011; Shen et al., 2011; Cong et al., 2013; Wu & Liu, 2013; Che et al., 2014). For Correspondence: Yuting Yang, tel. +61 (8) 82012020, fax +61 (8) 82012676, e-mail: [email protected]

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example, advance in spring green-up onset date (GOD) and delay in autumn dormancy onset date (DOD) in response to rising air temperature would result in a longer vegetation growing season, which provides potential for more photosynthetic carbon uptake (Richardson et al., 2010). As a result, studies on vegetation phenology have been gaining an ever-increasing attention in global ecological and environmental communities (e.g. Menzel, 2002; Badeck et al., 2004; Schwartz et al., 2006). In comparison with spring phenology (i.e. GOD), which has been relatively well studied (e.g. Shen et al., 2013, 2014a), knowledge on autumn phenology (i.e. DOD) is limited, particularly at regional and continental scales (See Jeong et al. (2011) and Zeng et al. (2011) for reviews). However, during vegetation growing cycles, the DOD (or known as the end date of the growing season) is also an important variable that determines the duration of the growing season and controls variations in carbon, energy and water cycles (e.g. Zhu © 2014 John Wiley & Sons Ltd

A U T U M N P H E N O L O G Y I N T E M P E R A T E C H I N A 1 9 8 2 – 2 0 1 0 653 et al., 2012; Wu et al., 2013; Garonna et al., 2014). Zhu et al. (2012) examined the phenological metrics in North America during 1982–2006 and found that the extension of the growing season was primarily driven by delayed autumn over mid- and high latitudes. Similar results were obtained by Garonna et al. (2014) over Europe based on data from 1982 to 2011 and by Jeong et al. (2011) over the temperate and boreal Northern Hemisphere from 1982 to 2008. From simulations and observations, Piao et al. (2008) found that ~90% of warming-induced increases in CO2 uptake during spring were offset by increased CO2 loss in response to warming in autumn, implying that spring and autumn phenology plays an equally important role in determining net carbon uptake at the annual scale. In addition, a recent study by Wu et al. (2013) has reported that the interannual variability in net ecosystem productivity in forests can be mainly explained by carbon flux phenology in autumn. All these findings suggest a compelling need to better understand the autumn phenology and its changes to permit the forecasting of potential biosphere feedback to changes in the climate system. Ground-based observations provide detailed phenological information for individual species with high accuracy; however, such information is often limited by spatial and temporal factors, and thus prevents it from being broadly utilized to examine vegetation responses to changing climate at larger scales (Zhu et al., 2012; Cong et al., 2013; Wu & Liu, 2013; Shen et al., 2014b). Satellite remote sensing has provided an unprecedented opportunity for capturing vegetation information across a variety of spatial and temporal scales that are not attainable by conventional techniques (White et al., 1997; Pettorelli et al., 2005). Specifically, the Global Inventory Modelling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) data set, which represents the longest NDVI time series to date, has been widely used in large-scale phenological studies (Tucker et al., 2005; Piao et al., 2006; Jeong et al., 2011; Cong et al., 2013; Yu et al., 2013). In this paper, we presented a study on examining the variation of satellite-derived DOD and its relation to climate variability in China’s temperate vegetation from 1982 to 2010. China has a vast land area, encompassing a wide range of ecosystems and climates. It also encompasses the ‘third pole’ of the Earth (i.e. the QinghaiTibetan Plateau), which is considered to be one of the most sensitive zones to climate change around the world (Yu et al., 2010; Piao et al., 2011; Shen et al., 2011; Zhang et al., 2013). The great diversity of bio-climate zones in China provides a good opportunity for identifying effects of climate change on vegetation activity and their variations. Previous studies have reported some analysis results on autumn phenology in China. © 2014 John Wiley & Sons Ltd, Global Change Biology, 21, 652–665

However, most of these studies were limited at specific regions or within shorter periods. For example, Che et al. (2014) investigated the spatial and temporal variations in DOD over the Qinghai-Tibetan Plateau, whereas the study by Yu et al. (2013) focused on two transects in eastern China. Piao et al. (2006) provided a more detailed analysis on autumn phenology over the entire temperate zone of China, but their study only covered a relatively short time period of 1982–1999. Recent studies have suggested that the warming trend and greening trend may have slowed down during the first decade of the 21st century in comparison to that in 1980s and 1990s (Buermann et al., 2007; Cane, 2010). To that end, a consecutive study on autumn phenology covering the whole period of the past three decades is imperative, which would potentially provide a greater insight into the trajectories for both climate and vegetation. We hypothesize that temperature would play a dominant role in determining the spatial and temporal patterns of autumn phenology in the study area, and the retarded warming trends would have led to a shift in DOD trends in more recent decades. As a result, the objectives of this study were (i) to examine the spatial pattern of DOD over temperate China; (ii) to investigate the trend of DOD during the past three decades; (iii) to understand the climate control on the spatial and interannual variations in DOD; and (iv) to quantify the DOD sensitivity to climate change.

Materials and methods

Data The Global Inventory Modelling and Mapping Studies (GIMMS)-3g NDVI data with a spatial resolution of 8 9 8 km2 and a temporal resolution of 15 days derived from the National Oceanic and Atmospheric Administration (NOAA)Advanced Very High Resolution Radiometer (AVHRR) from 1982 to 2010 were used in this study to detect the onset date of vegetation dormancy. The GIMMS-3g NDVI data set has been corrected to minimize various deleterious effects, such as calibration loss, orbital drift and volcanic eruptions, and has been used widely for identifying long-term trends in vegetation activities (e.g. Piao et al., 2006, 2011; Jeong et al., 2011; Cong et al., 2013; Wu & Liu, 2013). The land cover classification map was obtained from the Joint Research Center (JRC) of European Commission under the project of Global Land Cover 2000 (http://bioval.jrc.ec.europa.eu/products/ glc2000/glc2000.php). Gridded meteorological data, including air temperature and precipitation at the monthly scale, were generated from more than 800 weather stations throughout the country (http://cdc.cma.gov.cn/) based on Kriging interpolation technique (Goovaerts, 1997). The land use map and climate data were resampled to a spatial resolution of 8 9 8 km2 in accordance with the NDVI data.

A U T U M N P H E N O L O G Y I N T E M P E R A T E C H I N A 1 9 8 2 – 2 0 1 0 655 China occur between Julian days 228 and 359, with the mean value of Julian day 294 corresponding to October 21st (Fig. 2a). DODs at more than 70% of pixels occur in October, from Julian day 274 through 304. The spatial patterns of DOD appear to be elevation and latitude dependent, suggesting temperature being the primary controlling factor (Fig. 2b). It shows that, in general, locations with one degree warmer in preseason (May through October) mean air temperature are associated with a later DOD by 1.06 days (R2 = 0.53, P < 0.001). Vegetation growth in the Qinghai-Tibetan Plateau (i.e. the south-western part of the study area) and the northeast China ends a little earlier than in other areas,

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whereas later DODs are generally constrained in two regions, i.e. the middle and the northwest parts of China (Fig. 2). Eastward from the Qinghai-Tibetan Plateau, the DOD postpones from early October to December. Besides, with the increase of latitude, DOD advances from the central to northeast China. Similar temperature-determined DOD spatial pattern is also supported at the ecosystem level that deciduous needle-leaf forests which distribute in the far northeast China has the earliest DOD, whereas shrublands scattered in the central part of the country show the latest DOD (Fig. 2c). However, our analysis also shows that the DOD spatial pattern tends to be less determined by temperature in places where preseason mean air temperature is less than ~13 °C (Fig. 2b). This phenomenon suggests that there might be a minimal temperature threshold, below which temperature becomes less important in determining the spatial pattern of DOD. In such areas, other factors such as water availability may play a dominant role in shaping the DOD spatial pattern. We further examined the relationship between DOD and preseason (May through October) cumulative precipitation for areas where preseason mean temperature is less 13 °C and found a very good relationship between the two that 100 mm higher preseason cumulative precipitation corresponds to 2.84 days later in DOD (R2 = 0.76, P < 0.001, Fig. 3), implying a strong hydrological control on the spatial pattern of DOD in relatively cold areas. This may explain the late DOD in the northwest corner of China (Fig. 2a) where annual mean temperature is low (i.e. 0.1), except for a significant advance trend for alpine grasslands in the 1990s (Fig. 4g). Besides alpine grasslands, desert vegetation also shows an advance trend of DOD during the 1990s, although it is not statistically significant (Fig. 4e). For the entire period of 1982–2010, no consistence can be found in the DOD trend among vegetation types. Although most of vegetation types show significant delay trends in DOD, alpine and desert ecosystems have experienced a slight advance trend in DOD, which (a)

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was mainly driven by the advanced DOD during the 1990s. For different vegetation types, forest ecosystems generally show longer delays in DOD than nonforest ecosystems. The largest delay occurs in deciduous broadleaf forests (0.25 day yr1), whereas the smallest delay is found in temperate grasslands (0.08 day yr1). The spatial distributions of DOD trends for the entire period of 1982–2010 and for each subperiod are illustrated in Fig. 5. Over 1982–2010, 68% of natural ecosystem pixels show a delay trend in DOD, of which 82% are significant at a significance level of 0.1 and 60% are significant at a significance level of 0.05. Longer delays are generally concentrated in the central and northeast parts of the country, whereas pixels with an advance trend are scatted in the northern part of the Taklamakan Desert (i.e. the north-western part of the study area), the eastern part of the Inner Mongolia and the central part of the Qinghai-Tibetan Plateau (Fig. 5a). Consistent with the spatial averaged results shown in Fig. 4, the DOD shows the largest and more uniform delay trend in the 1980s throughout the country, with more than 85% of pixels being significant (P < 0.1). However, the delay trend in DOD has slowed down or even reversed in some regions during the 1990s and 2000s. In the 1990s, only 29% of pixels show a significant delay trend in DOD, and this number comes to 38% in the 2000s. Compared with the 1980s, a large proportion of areas in the Qinghai-Tibetan Plateau and northwest China show reversed trends in DOD from delay to advance in the 1990s (Fig. 5c). In 2000s, this advance trend further extends to the northeast part of China (Fig. 5d). (b)

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Fig. 5 Spatial distribution of trends in the onset date of vegetation dormancy over temperate China from (a) 1982–2010, (b) 1982–1990, (c) 1991–2000 and (d) 2001–2010. © 2014 John Wiley & Sons Ltd, Global Change Biology, 21, 652–665

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Fig. 6 Partial correlation coefficients between the onset date of vegetation dormancy and preseason mean temperature (circle) and cumulative precipitation (triangle) for different preseason periods by vegetation types: (a) all ecosystems, (b) deciduous broadleaf forests, (c) deciduous needle-leaf forests, (d) shrublands, (e) desert, (f) temperate grasslands and (g) alpine grasslands. The horizontal solid and dashed lines correspond to the 10% and 5% significance level respectively.

© 2014 John Wiley & Sons Ltd, Global Change Biology, 21, 652–665

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DOD in relation to climate To further investigate possible causes of the changes in DOD, we performed a partial correlation analysis between DOD series and preseason (i.e. predormancy) climate conditions for each ecosystem type during 1982–2010 (Fig. 6). The results show that the onset dates of vegetation dormancy are significantly and positively correlated with the preseason mean temperature (i.e. delay in DOD as temperature increased) for all averaging preseason periods averaged over the whole temperate China (Fig. 6a), suggesting that delay/advance in DOD is mainly driven by increased/decreased temperature during the preceding growing season at the regional scale. In contrast, the onset dates of vegetation dormancy are generally not significantly correlated with preseason cumulative precipitation (Fig. 6a). This result is consistent with Piao et al. (2006), who did similar analysis over temperate China for the period of 1982–1999. At the ecosystem type level, changes in preseason mean temperatures also significantly and positively correlated with those in DOD for almost all vegetation types, consistent with many other studies (e.g. Piao et al., 2006; Jeong et al., 2011; Zeng et al., 2011; Yu et al., 2013). The only exception is the desert ecosystem in which interannual variations in DOD are mainly controlled by changes in preseason cumulative precipitation, with higher rainfall amount corresponding to later DOD, while the temperature effect on DOD appears to be less important. For other ecosystems, preseason cumulative precipitation and DOD are negatively correlated in deciduous broadleaf forests, deciduous needle-leaf forests and alpine grasslands (Fig. 6b, c and g), suggesting that a larger amount of rainfall before DOD appears to be correlated with an earlier onset date of vegetation dormancy in these ecosystems. To further examine areas where changes in DOD are mainly controlled by temperature or precipitation, or neither of them, we plotted the controlling-factor map based on the maximum partial correlation coefficient between DOD and climate variables averaged (for temperature) or accumulated (for precipitation) over different preseason periods. For example, if the maximum partial correlation coefficient between DOD and mean preseason temperature for any averaging preseason periods (i.e. 1–6 months) is significant and higher than that between DOD and cumulative preseason precipitation for any cumulating preseason periods, the trend in DOD for the corresponding pixel is considered to be controlled by temperature, and vice versa. The result shows that preseason temperature is the major driven factor for DOD interannual variation at more than 61% of the examined pixels, whereas preseason precipita© 2014 John Wiley & Sons Ltd, Global Change Biology, 21, 652–665

Fig. 7 Spatial distribution of major climate controls on the onset date of vegetation dormancy over temperate China from 1982 to 2010.

tion controls DOD interannual variation for only 15% of the grid cells (Fig. 7). The precipitation-controlled zones are mainly located in the eastern part of Inner Mongolia, the northeast part to the North China Plain and the Loess Plateau, where desert ecosystems are the primary land use type. The remaining pixels show nonsignificant relationship between DOD and either temperature or precipitation, which are scattered throughout the temperate zone of China, but with fewer occurring in the Qinghai-Tibetan Plateau. To understand to what degree a change in preseason climate conditions affects that in DOD, the slope relating DOD to preseason climate (temperature or precipitation) as a surrogate of autumn phenology sensitivity to climate variation is shown in Fig. 8. According to Fig. 6, the correlation between DOD and temperature for all ecosystem types (except for desert) is generally most significant (P < 0.05) when the preseason mean temperature is averaged over 6 months before DOD (i.e. May through October). Thus, we used the 6-month

Fig. 8 Sensitivity of onset date of vegetation dormancy to preseason mean temperature for deciduous broadleaf forests (DBF), deciduous needle-leaf forest (DNF), shrublands (Shrub), temperate grasslands (temperate grass), alpine grasslands (alpine), and to preseason cumulative precipitation for the desert ecosystem. Error bars indicate the standard deviation among pixels. Only pixels with the relationship between dormancy onset dates and climate variables being significant (P < 0.1) were used in the analysis.

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Fig. 9 Spatial distribution of the sensitivity of dormancy onset date to (a) preseason mean temperature (May through October) in deciduous broadleaf forests, deciduous needle-leaf forest, shrublands, temperate grasslands, alpine grasslands and (b) preseason cumulative precipitation (May through October) in desert. Only pixels with the relationship between dormancy onset dates and climate variables being significant (P < 0.1) are shown.

preseason period to examine DOD sensitivity to temperature in the following analysis. Similarly, the 6-month preseason period was used to quantify DOD sensitivity to precipitation for desert ecosystems (Fig. 6e). As shown in Fig. 8, for all ecosystems except for desert, an increase of mean air temperature from early May through late October by 1 °C will induce a delay in vegetation dormancy by 3.4 days. At the level of vegetation type, Alpine grasslands have the highest temperature sensitivity of DOD (4.3 days °C1), followed in a descending order by deciduous needle-leaf forests (3.9 days °C1), shrublands (3.6 days °C1), deciduous broadleaf forests (2.2 days °C1), and temperate grassland (1.5 days °C1). Regarding precipitation sensitivity of DOD in desert ecosystem, an increase in cumulative precipitation from early May through late October by 100 mm will lead to a delay of 4.7 days in its DOD. However, the desert ecosystem shows the largest spatial variability in the autumn phenology sensitivity to climate variations (Fig. 8). The spatial distribution of autumn phenology sensitivities to climate variables is shown in Fig. 9. Higher temperature sensitivities of DOD are generally concentrated in two regions, i.e. the QinghaiTibetan Plateau and the Northeast China, where annual mean air temperature is low and the surfaces have relatively abundant soil moisture (Fig. 9a). This is consistent with the results in Fig. 8 that Alpine grasslands and deciduous needle-leaf forests, which

are mainly located in the above two regions, have higher temperature sensitivity of DOD, whereas DOD of deciduous broadleaf forests and temperature grasslands in warmer areas responds much slower to changes in temperature. In addition, DOD in shrublands that are located in warmer central China responds almost insensitively to changes in temperature, whereas shrublands on the Qinghai-Tibetan Plateau have a high temperature sensitivity of DOD, which results in an overall high temperature sensitivity of DOD as shown in Fig. 8. For precipitation sensitivity in desert ecosystems, higher values are observed in the eastern part of Inner Mongolia and northern part of the Xinjiang Province (i.e. Northwest China), whereas deserts scattered in other parts of the country show less autumn phenology sensitivity to preseason cumulative precipitation (Fig. 9b). To further understand how climate sensitivity of DOD will change under different climate scenarios, we performed correlation analysis between temperature/ precipitation sensitivity of DOD and preseason (May through October) mean temperature/cumulative precipitation across all pixels over the study region and pixels within each vegetation type (Tables 1 and 2). Please note that we only considered precipitation sensitivity of DOD in desert ecosystem (Table 2) and temperature sensitivity of DOD in other ecosystems (Table 1). We found that temperature responses of DOD significantly became stronger with increasing preseason cumulative precipitation (r = 0.17, P < 0.001), © 2014 John Wiley & Sons Ltd, Global Change Biology, 21, 652–665

A U T U M N P H E N O L O G Y I N T E M P E R A T E C H I N A 1 9 8 2 – 2 0 1 0 661 Table 1 Summary of relationship between temperature sensitivity of autumn phenology and mean preseason temperature [as indicated by (T) following each statistic] or cumulative preseason precipitation [as indicated by (P) following each statistic] for different ecosystems. The statistics shown are the slope of linear regression function between DOD climate sensitivity and climate variables [i.e. Slope (P) (days °C1 100 mm1) and Slope (T) (days °C1 °C1)], the Pearson’s correlation coefficient [i.e. r (P) and r (T)] and the significance level of the relationship [i.e. Significance (P) and Significance (T)]. Note that NS indicates that the relationship is not statistically significant at a significance level of 0.05 Ecosystem type

Slope (P)

r (P)

Significance (P)

Slope (T)

r (T)

Significance (T)

All except desert DBF DNF Shrub Temperate grass Alpine grass

0.4 0.07 0.41 0.26 0.69 0.28

0.17 0.08 0.29 0.03 0.3 0.14

P< NS P< NS P< P
0.05) in deciduous broadleaf forests and shrublands. With respect to the relationship between temperature sensitivity of DOD and temperature within a vegetation type, three ecosystems (deciduous broadleaf forests, shrublands and temperate grasslands) showed a significant negative relationship, whereas positive relationship was found in deciduous needle-leaf forest (although not significant) and alpine grassland. For desert ecosystem, precipitation sensitivity of DOD increases significantly with decreasing precipitation and increasing temperature across sites (P < 0.001), which are in contrast to relationships between temperature sensitivity of DOD and precipitation or temperature in other ecosystems.

Discussion

Variation in autumn dormancy onset date Our analysis of the long-term remote sensing NDVI series has revealed an overall delay trend in DOD over temperate China during the past three decades (0.13 day yr1)(Fig. 6a). However, this estimated delay trend is smaller than the result of Piao et al. (2006), who detected a delay trend in DOD by 0.37 days yr1 over © 2014 John Wiley & Sons Ltd, Global Change Biology, 21, 652–665

temperate China from 1982 to 1999. This discrepancy can be mainly ascribed to the difference in study periods and land use types being considered (agricultural lands were considered in Piao et al. (2006), but are excluded in this study). When the analysis results over the same period are compared, the difference between the two studies becomes much smaller (0.32 day yr1 in this study vs. 0.37 day yr1 in Piao et al. (2006)). Compared with other regions around the world, this delay trend in DOD is larger than that in North America (0.22 day yr1 from 1981 to 1999) (Zhou et al., 2001), but is smaller than that in Europe (0.47 day yr1 from 1982 to 2000) (Stockli & Vidale, 2004). Despite the overall delay trend during the past three decades, our study based on an extended period over Piao et al. (2006) also show that changes in DOD were not spatially and temporally consistent over the whole period. By separating the entire study period into three subperiods (i.e. 1980s, 1990s and 2000s), we found that the largest delay in DOD occurred in the 1980s, whereas this delay trend has been markedly decelerated or even reversed in the latter two subperiods. Especially in the 1990s, a large area in the Qinghai-Tibetan Plateau showed an advance DOD trend, which was mainly driven by a steep advance trend in DOD after 1994. This result is consistent with that of Che et al. (2014), who also detected a significant advance trend in growing season ending date on the Qinghai-Tibetan Plateau during 1994–1999 based on remotely sensed leaf area index data series. A similar

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Fig. 10 Spatial distribution of the time corresponding to the turning point in the autumn phenology trend estimated by the piecewise linear regression approach. NT indicates that no significant turning point (P > 0.05) can be found during the whole period.

shift in DOD trend from delay to advance in the 1990s is also observed in desert ecosystems, which agrees with the finding by Piao et al. (2006). Besides alpine and desert ecosystems, this shifting pattern of DOD trend also exists in other ecosystem types. Results of a piecewise linear regression analysis (Toms & Lesperance, 2003; Wu & Liu, 2013) show that the turning point in DOD trend generally occurred from the mid- to late 1990s (i.e. 1994–2000), with more than 25% of pixels experiencing a reversed trend in DOD from delay before the turning point to advance after the turning point and about 34% of grids showing a significant decelerated delay trend in DOD (Fig. 10). These results imply that the delay trend in DOD in temperate China has disappeared for recent decades, particularly since the mid to late 1990s. Interestingly, previous study has reported an analogous shifting pattern in spring phenology during the same period in temperate China (Wu & Liu, 2013), suggesting the prominent role of the climate system in the phenological dynamics. The observed obvious and generally consistent shifting trends in autumn phenology over China’s temperate ecosystems in such a short time demonstrate a rapid and possibly nonlinear response of vegetation dormancy to changing climate (Zheng et al., 2002). Similar shifting trends in autumn phenology were noticed for other parts of the Eastern Asia, but they have not been observed in North American and Europe, where autumn phenology in the recent decade exhibited even longer delays than that during 1982– 1999 (Jeong et al., 2011). Although our results correspond reasonably well with previous studies, it is worthwhile to mention that the estimated DOD based on remote sensing data series is less accurate than field-based measurements. Nevertheless, it is the only operational way to examine variations in phenological metrics over larger spatial and longer temporal scales (Piao et al., 2006; Cong et al., 2013; Wu & Liu, 2013). In addition, previous studies

have compared remotely sensed phenological metrics with in situ observation and reported good validation results (e.g. Cong et al., 2012). Here, we did not validate our results with in situ data, mainly due to the scale mismatch between coarse GIMMS NDVI data and fine ground-based observations. A recent study by Cong et al. (2013) showed phenological metrics from five different remote sensing-based methods sharing similar interannual variations despite large differences in the estimated values of phenological metrics. This suggests that even though large errors might be associated with the predicted DOD, the trend in DOD can still be reasonably well detected (Zhu et al., 2012).

Autumn phenology and climate change Changes in climate conditions are considered to be responsible for changes in phenological events. Our results show that the interannual variations in DOD over temperate China are mainly controlled by changes in preseason mean air temperature, which is consistent with previous findings (Piao et al., 2006; Jeong et al., 2011; Yu et al., 2013; Che et al., 2014). In general, autumn phenology was most significantly correlated with mean air temperature occurring 6 months before the mean dormancy date and 1 °C increase in temperature leads to an average delay of 3.4 days in DOD, which agrees very well with the findings by Piao et al. (2006) (3.8 days’ delay in DOD in response to 1 °C increase in temperature). The rates of physiological activities (i.e. photosynthesis) are temperature dependent and increase with increasing temperature (Badeck et al., 2004). Therefore, warmer preseason temperature favours vegetation growth in autumn and thus postpones the onset date of vegetation dormancy. During the 1980s, a marked growing season warming in temperate China resulted in a significant delay in DOD. However, this delay trend in DOD has been significantly slowed down or reversed when the warming trend decelerated or even reversed in more recent decades (Cane, 2010; Jin et al., 2013; You et al., 2013; Yu et al., 2013). Nevertheless, we did observe some pixels with advanced DOD to increasing temperature, which is possibly due to a few cold snaps that cause leaf senescence at the end of the growing season (Ceccherini et al., 2014). Besides, this phenomenon is also probably associated with drought stress at the autumn dormancy onset date, which is probably site- or vegetation dependent (e.g. desert ecosystem) (Cong et al., 2013). In addition, our analysis indicates that temperature also predominantly controls the spatial variability in temperature sensitivity of DOD (Figs 8 and 9), which leads to the highest and lowest temperature sensitivities of DOD in Alpine grasslands and temperate © 2014 John Wiley & Sons Ltd, Global Change Biology, 21, 652–665

A U T U M N P H E N O L O G Y I N T E M P E R A T E C H I N A 1 9 8 2 – 2 0 1 0 663 grasslands respectively. Our cross-pixel analysis between temperature sensitivity of DOD and preseason (May through October) mean temperature also supports this finding that temperature sensitivity of DOD decreases significantly with increasing preseason mean temperature (Table 1). In contrast, autumn dormancy onset shows increasing delay in response to preseason temperature as preseason precipitation increases. This is probably related to the fact that temperature control of plant growth became stronger if the soil moisture constraints were released, especially for relatively dry environments (e.g. temperate grasslands) (Table 1). If we transfer this information from across-space to across-time, future climate warming coupled with shifted patterns in precipitation (precipitation is predicted to decrease in dry areas and increase in humid areas (Held & Soden, 2006)) would potentially dampen the temperature response of DOD in more arid ecosystems, e.g. temperate grasslands. However, the response of temperature sensitivity of DOD to climate change (both temperature and precipitation) in humid regions can be more complicated. Although temperature plays a major role in regulating autumn phenology for most vegetation types, preseason precipitation also affects the autumn phenology, especially in water-limited ecosystems. Our results show that in the desert ecosystem, changes in DOD are significantly and positively (delay of DOD with increasing precipitation) correlated with preseason (May through October) cumulative precipitation, suggesting that higher preseason precipitation benefits the vegetation growth in autumn. An increase in preseason precipitation would mitigate drought stress in autumn in desert ecosystems and therefore results in later vegetation dormancy. In addition, we found a significant positive relationship between precipitation sensitivity of DOD and preseason mean temperature across pixels in desert ecosystems, which further highlights the importance of hydrological control on DOD under the context of future climate warming (more severe atmospheric drought) in the area. A similar delay effect of preseason cumulative precipitation on DOD is also observed in temperate grasslands, which represents another typical dry environment in the study region (Fig. 6f). Although the relationship between DOD and precipitation is not as strong as that between DOD and temperature, hydrological control on DOD may have greatly reduced the temperature sensitivity of DOD in temperate grasslands (Fig. 8 and Table 1). It is also worthwhile to stress that even though we rarely observed significant influence of preseason cumulative precipitation on autumn dormancy onset date in other ecosystems, this insensitivity to precipitation amount did not negate impacts of other precipita© 2014 John Wiley & Sons Ltd, Global Change Biology, 21, 652–665

tion attributes (e.g. timing and intensity of precipitation) on autumn phenology. For example, we noticed that the cumulative precipitation 2–4 months and 1–2 months preceding the DOD is also significantly and negatively (advance in DOD as precipitation increases) correlated with DOD in deciduous needleleaf forests and alpine grasslands respectively (Fig. 6c and g). Two reasons may account for this phenomenon. First, both deciduous needle-leaf forests and alpine grasslands have relatively high soil moisture content. Further water input would build an anaerobic environment within the plant root zone and hence prevent vegetation growth. Second, increased precipitation is usually accompanied by an increase in cloud cover, which leads to a reduction in incoming solar radiation (Piao et al., 2006). However, this advancing effect of precipitation on DOD tends to decrease with increasing cumulative time, demonstrating the importance of the effect of precipitation timing on vegetation autumn phenology. Apart from air temperature and precipitation, autumn phenology has been shown to be also regulated by many other environmental factors. Che et al. (2014) investigated the relationship between DOD and sunshine duration in the Qinghai-Tibetan Plateau and reported a negative correlation between the two. Jin et al. (2013) examined the relationship between soil temperature and DOD in the same region, and found that 1 °C increase in soil temperature would result in a delay of 7.3 to 10.5 days in DOD. Moreover, recent study by Reyes-Fox et al. (2014) showed that the elevated atmospheric CO2 concentration may further postpone DOD under warming conditions. In addition, variations in photoperiod length and the first frost day are also found to be responsible for changes in autumn phenology (Huang et al., 2001; Schwartz, 2003). A comprehensive understanding of the combined environmental control on autumn phenology remains a great challenge for further studies. Shift in autumn phenology has significant implications on ecosystem carbon budget. On the one hand, a delayed autumn corresponds to a longer period for photosynthetic carbon uptake; on the other hand, more carbon may release via ecosystem respiration as a result of autumn warming. Piao et al. (2008) found that the increase in respiration is much greater than that in photosynthesis during autumn warming in northern ecosystems, resulting in a net carbon loss despite the delaying autumn. Similar conclusion was also made in Xia et al. (2014). If this finding were true, our results that the delay trend in DOD has been significantly slowed down during the 1990s and 2000s caused by decelerated warming trend imply that the negative effect of delayed autumn on net carbon uptake (i.e. increased

664 Y . Y A N G et al. net carbon loss with delaying autumn) may have been attenuated during the past two decades in comparison with that in the 1980s over the temperate zone of China. However, future ground-based and more specific modelling studies are needed to examine the effects of changes in autumn phenology on ecosystem carbon balance.

Acknowledgement This work is financially supported by the National Centre for Groundwater Research and Training, Australia. This work is also funded by Open Research Fund Program of State key Laboratory of Hydroscience and Engineering, China (SKLHSE2014-A-01) and the National Natural Science Foundation of China (No. 41201459). The authors thank the two reviewers whose comments helped to improve the manuscript considerably.

References Badeck FW, Bondeau A, Bottcher K, Doktor D, Lucht W, Schaber J, Sitch S (2004) Response of spring phenology to climate change. New Phytologist, 162, 295–309. Buermann W, Lintner BR, Koven CD, Angert A, Prizon JE, Tucker CJ, Fung IY (2007) The changing carbon cycle at Mauna Loa observatory. Proceedings of the National Academy of Science of the United States of America, 104, 4249–4254. Cane MA (2010) Climate science: decadal predications in demand. Nature Geoscience, 3, 231–232. Ceccherini G, Gobron N, Migliavacca M (2014) On the response of European vegetation phenology to hydroclimatic anomalies. Remote Sensing, 6, 3143–3169. Che ML, Chen BZ, Innes JL et al. (2014) Spatial and temporal variations in the end date of the vegetation growing season throughout the Qianhai-Tibetan Plateau from 1982 to 2011. Agricultural and Forest Meteorology, 189–190, 81–90. Cong N, Piao SL, Chen AP et al. (2012) Spring vegetation green-up in China inferred from SPOT NDVI data: a multiple model analysis. Agricultural and Forest Meteorology, 165, 104–113. Cong N, Wang T, Nan HJ et al. (2013) Changes in satellite-derived spring vegetation green-up date and its linkage to climate in China from 1982 to 2010: a multimethod analysis. Global Change Biology, 19, 881–891. Cornelissen JHC, Bodegon PM, Aerts R et al. (2007) Global negative vegetation feedback to climate warming responses of leaf litter decomposition rates in cold biomes. Ecology Letters, 10, 619–627. Garonna I, Jong RD, Wit AJWD, Mucher CA, Schmid B, Schaepman M (2014) Strong contribution of autumn phenology to changes in satellite-derived growing season length estimates across Europe (1982–2011). Global Change Biology, 20, 3457–3470. Goovaerts P (1997) Geostatistics for Nature Resources Evaluation. Oxford University Press, New York. Held IM, Soden BJ (2006) Robust responses of the hydrological cycle to global warming. Journal of Climate, 19, 5686–5699. Huang JZ, Shrestha A, Tollenaar M, Deen W, Rajcan I, Rahimian H, Swanton CJ

Pettorelli N, Vik JO, Mysterud A, Gaillard JM, Tucker CJ, Stenseth NC (2005) Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in Ecology & Evolution, 20, 503–510. Piao SL, Fang JY, Zhou LM, Ciais P, Zhu B (2006) Variations in satellite-derived phenology in China’s temperate vegetation. Global Change Biology, 12, 672–685. Piao SL, Friedlingstein P, Ciais P, Viovy N, Demarty J (2007) Growing season extension and its effects on terrestrial carbon flux over the last two decades. Global Biogeochemical Cycles, 21, GB3018. Piao SL, Ciais P, Friedlingstein P et al. (2008) Net carbon dioxide losses of northern ecosystems in response to autumn warming. Nature, 451, 49–53. Piao SL, Cui MD, Chen AP, Wang XH, Ciais P, Liu J, Yang YH (2011) Altitude and temperature dependence of change in the spring vegetation green-up date from 1982 to 2006 in the Qianhai-Xizang Plateau. Agricultural and Forest Meteorology, 151, 1599–1608. Reyes-Fox M, Steltzer H, Trlica MJ, McMaster GS, Andales AA, LeCain DR, Morgan JA (2014) Elevated CO2 further lengthens growing season under warming conditions. Nature, 510, 259–262. Richardson AD, Black TA, Ciais P et al. (2010) Influence of spring and autumn phenological transitions on forest ecosystem productivity. Philosophical Transactions of the Royal Society B: Biological Sciences, 365, 3227–3246. Schwartz M (2003) Phenology: An Integrative Environmental Sciences. Kluwer Academic Publishers, The Netherlands. Schwartz MD, Ahas R, Aasa A (2006) Onset of spring starting earlier across the northern hemisphere. Global Change Biology, 12, 343–351. Shen M (2011) Spring phenology was not consistently related to winter warming on the Tibetan Plateau. Proceedings of the National Academy of Science of the United States of America, 108, E91–E92. Shen M, Tang Y, Chen J, Zhu X, Zheng Y (2011) Influences of temperature and precipitation before the growing season on spring phenology in grasslands of the central and eastern Qinghai-Tibetan Plateau. Agricultural and Forest Meteorology, 151, 1711–1722. Shen M, Sun Z, Wang S, Zhang G, Kong W, Chen A, Piao S (2013) No evidence of continuously advanced green-up dates in the Tibetan Plateau over the last decade. Proceedings of the National Academy of Science of the United States of America, 110, E2329. Shen M, Tang Y, Chen J et al. (2014a) Earlier-season vegetation has greater temperature sensitivity of spring phenology in Northern Hemisphere. PLoS ONE, 9, e88178. Shen M, Tang Y, Desai AR, Gough C, Chen J (2014b) Can EVI-derived land surface phenology be used as a surrogate for phenology of canopy photosynthesis? International Journal of Remote Sensing, 35, 1162–1174. Slayback TH, Pinzon JE, Los SO, Tucker CJ (2003) Northern hemisphere photosynthetic trends 1982–1999. Global Change Biology, 9, 1–15. Stockli R, Vidale PL (2004) European plant phenology as seen in a 20-year AVHRR land-surface parameter dataset. International Journal of Remote Sensing, 25, 3303– 3330. Toms JD, Lesperance ML (2003) Piecewise regression: a tool for identifying ecological thresholds. Ecology, 84, 2034–2041. Tucker CJ, Pinzon JE, Brown ME et al. (2005) An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. International Journal of Remote Sensing, 26, 4485–4498. White MA, Thornton PE, Running SW (1997) A continental phenology model for monitoring vegetation response to interannual climatic variability. Global Biogeochemical Cycles, 11, 217–234.

(2001) Effect of temperature and photoperiod on the phenological development of wild mustard. Field Crops Research, 70, 75–86. Jeong SJ, Ho CH, Jeong JH (2009) Increase in vegetation greenness and decrease in springtime warming over east Asia. Geophysical Research Letters, 36, L02710. Jeong SJ, Ho CH, Gim HJ, Brown ME (2011) Phenology shifts at start vs. end of growing season in temperate vegetation over the Northern Hemisphere for the period

Wu XC, Liu HY (2013) Consistent shifts in spring vegetation green-up date across temperate biomes in China, 1982–2006. Global Change Biology, 19, 870–880. Wu BF, Xu WT, Yang CZ, Xu WB (2004) Combining Spot4-vegetation and meteorological data derived land cover map in China. IEEE Geoscience and Remote Sensing Symposium, 4, 2672–2675. Wu CY, Chen JM, Black TA et al. (2013) Interannual variability of net ecosystem productivity in forests is explained by carbon flux phenology in autumn. Global Ecol-

1982–2008. Global Change Biology, 17, 2385–2399. Jin ZN, Zhuang QL, He JS, Luo TX, Shi Y (2013) Phenology shift from 1989 to 2008 on the Tibetan Plateau: an analysis with a process-based soil physical model and remote sensing data. Climatic Change, 119, 435–449. Lowry R (1999–2009) Concepts and application of inferential statistics, http://factuly. vassar.edu/lowry/webtext.html, Vassar College, Poughkeepsie, NY, USA.

ogy and Biogeography, 22, 994–1006. Xia JY, Chen JQ, Piao SL, Ciais P, Luo YQ, Wan SQ (2014) Terrestrial carbon cycle affected by non-uniform climate warming. Nature Geoscience, 7, 173–180. Xiao XM, Melillo JM, Kicklighter DW, Pan Y, McGuire AD, Helfrich J (1996) Net primary production of terrestrial ecosystems in China and its equilibrium responses to climate and atmospheric CO2 concentration. Report #12 in Joint program of the

Menzel A (2002) Phenology, its important to the global change community editorial comment. Climatic Change, 54, 379–285. Peng SS, Piao SS, Ciais P et al. (2013) Asymmetric effects of daytime and night-time warming on Northern Hemisphere vegetation. Nature, 501, 88–92.

Science and Policy of Global Change, MIT, Cambridge, MA. You QL, Fraedrich K, Ren GY, Pepin N, Kang SC (2013) Variability of temperature in Tibetan Plateau based on homogenized surface stations and reanalysis data. International Journal of Climatology, 33, 1337–1347.

© 2014 John Wiley & Sons Ltd, Global Change Biology, 21, 652–665

A U T U M N P H E N O L O G Y I N T E M P E R A T E C H I N A 1 9 8 2 – 2 0 1 0 665 Yu HY, Luedeling E, Xu JC (2010) Winter and spring warming result in delayed spring phenology on the Tibetan Plateau. Proceedings of the National Academy of

Zhang GL, Zhang YJ, Dong JW, Xiao XM (2013) Green-up dates in the Tibetan Plateau have continuously advanced from 1982 to 2011. Proceedings of the National Academy

Sciences of the United States of America, 107, 22151–22156. Yu Z, Sun P, Liu S, Wang J, Everman A (2013) Sensitivity of large-scale vegetation greenup and dormancy dates to climate change in the north-south transect of eastern China. International Journal of Remote Sensing, 34, 7312–7328. Zeng N, Neelin JD, Lau KM, Tucker CJ (1999) Enhancement of interdecadal climate variability in the Sahel by vegetation interaction. Science, 286, 1537–1540. Zeng HQ, Jia GS, Epstein H (2011) Recent changes in phenology over the northern

of Science of the United States of America, 110, 4309–4314. Zheng JY, Ge QS, Hao ZX (2002) Impacts of climate warming on plants phenophases in China for the last 40 years. Chinese Science Bulletin, 47, 1826–131. Zhou LM, Tucker CJ, Kaufmann RK, Slayback D, Shabanov NV, Myneni RB (2001) Variations in northern vegetation activity inferred from satellite data of vegetation index during 1982–1999. Journal of Geophysical Research, 106, 20069–20083. Zhu WQ, Tian HQ, Xu XF, Pan YZ, Chen GS, Lin WP (2012) Extension of the growing

high latitudes detected from multi-satellite data. Environmental Research Letters, 6, 045508.

season due to delayed autumn over mid and high latitudes in North America during 1982–2006. Global Ecology and Biogeography, 21, 260–271.

© 2014 John Wiley & Sons Ltd, Global Change Biology, 21, 652–665