Divergent Response of Leaf Coloring Seasons to Temperature Change in Northern China over the Past 50 Years

Autumn phenology plays a critical role in terrestrial ecosystem circulations. However, the changes in autumn phenology and their correlation with temperature remain uncertain because mean temperature alone was not able to determine the changes in autumn phenology at various sites. Here, the leaf coloring season (LCS) was defined as the period when the leaves of more than half of the species had recognized changes in color. We systematically studied the changes in peak, start, end, and duration of LCS and their correlations with five temperature parameters (mean temperature, accumulated cold temperature, day temperature, night temperature, and temperature difference between day and night) in four periods. Similarly to previous findings, the start date of LCS advanced and the end of LCS delayed over the past 50 years, which consequently led to a lengthened duration of LCS in Xi’an, Harbin, Minqin, and Shenyang. In general, the rise in mean temperature, day temperature, and night temperature would delay the peak, start, and end of LCS and lengthen the duration of LCS in most cases. We also proved that the changes in LCS metrics not only could completely be explained by mean temperature but also were influenced by day temperature, night temperature, temperature difference, and even other climatic factors such as precipitation, at different sites.


Introduction
Phenological study is critical for understanding the interactions between climate and terrestrial biosphere. e shifts in leaf unfolding date and leaf senescence date in the Northern Hemisphere caused by global warming profoundly influenced growing season length and altered many ecosystem biogeochemical circulations, such as the carbon cycle, nitrogen cycle, and water cycle [1][2][3][4][5][6]. e phenological changes in spring, such as earlier leaf-out and flowering, have been well documented on a global or national scale [7][8][9]. e changes in autumn phenology, by contrast, are more complex and have received much fewer concerns [10,11]. Although some studies have used remotely sensed data to study the spatiotemporal patterns of autumn phenology, the results are sometimes inconsistent due to different data sources, data preprocessing, and phenophase extraction methods [12][13][14]. e autumn phenological trends derived from in situ observations were reliable but highly spatially heterogeneous. In spite of the overall delaying trend of autumn phenophases, there were still about 1/3 of the sites in which the autumn phenology evidently advanced [15]. e question has arisen that what leads to the spatial difference in autumn phenological changes? With respect to the coherent environmental controls of autumn leaf phenology, some studies noted that mean temperature or accumulated temperature could be a reasonable determinant of autumn phenology [10,[16][17][18]. Low temperature, on the one hand, would force deciduous trees to shed their leaves to avoid freezing damage [19] and, on the other hand, accelerate the resolution of chlorophyll and formation of anthocyanin, leading to changes in leaf color [20,21]. However, temperature changes, which tended to increase in almost all the areas of northern China over the past few decades, ignoring the difference in magnitude, could not perfectly agree with the trends of autumn phenology [22,23]. e mismatch demonstrated that the mean temperature or accumulated temperature alone was not sufficient to explain all the changes in autumn phenology at different sites [24,25].
Some studies argued that the effect of temperature difference between day and night on autumn phenology was underestimated, since temperature difference above 15°C was propitious to color presentation of leaves [20,26]. In addition, minimum temperature was also suggested as an influencing factor of autumn phenology that lower autumn minimum temperature would result in earlier leaf coloring date [27][28][29][30]. Although other environmental factors, such as precipitation, photoperiod, and insolation, would also influence autumn phenology through affecting the formation of leaf abscission meristems and plant carboxylation reaction [31], they were more likely to only constrain the temperature-induced changes. For example, photoperiod was thought to affect the tolerance of plants to low temperature [32,33].
In addition, temperature in different periods might have divergent effects on autumn phenological changes [34]. e growing season temperatures were announced to play a greater role than the autumn temperatures in leaf senescence [27]. Regarding different phenophases, the August temperature could account for the start of leaf coloration, while the end of leaf coloration was likely determined by temperature in September and October [35].
In this study, we defined leaf coloring season (LCS) and investigated the change in peak, start, end, and duration of LCS at six sites in northern China. Subsequently, the correlations between LCS metrics and five temperature parameters in four periods were analyzed. e objectives of the present study are (1) to reveal the temporal changes of the leaf coloring season in northern China, (2) to identify the correlation between LCS metrics and different temperature parameters and how it is influenced by time intervals, and (3) to determine the main factor influencing the changes in LCS metrics.

Data Source.
e start of leaf coloring date (LCD) and full-leaf coloring date (FLCD) during 1963-2010 was observed by the China Phenological Network (CNPN), which could be downloaded from the National Earth System Science Data Sharing Infrastructure (http://www.geodata.cn). Most of the observation sites were located at the edge of the city or in the suburbs; thus, the plant phenology was less susceptible to urbanization. e sites and species in the study had to meet the following criteria: (1) all the species were woody plants; (2) the leaves of the species shifted apparently in color; (3) in order to detect the interannual trends through the whole period, all the species needed to contain LCD and FLCD data of at least 5 years before and after 1980, respectively; and (4) each site included more than 20 species. After the filtering, our study consisted of six sites (Figure 1). e time series of LCD and FLCD were almost consecutive except for two periods (Figure 2). e numbers of records ranged from 429 (21 species) in Shenyang to 1903 (49 species) in Beijing for each phenophase. e meteorological data were downloaded from the China Meteorological Data Service Center (http://data.cma.cn/). We used the daily mean, maximum, and minimum air temperature at the above-mentioned six sites from 1963 to 2010 to study the relationship between temperature and phenology of the leaf coloring season. In this study, the maximum and minimum temperature was used to represent day and night temperature, respectively.

Methods.
e leaf coloring season (LCS) was defined as the period when the leaves of more than half of the species had recognized changes in color. us, the LCS at a specific site was determined by the following steps ( Figure 3): (1) we counted the daily numbers of species in leaf coloring (after the LCD and before the FLCD); (2) during the period when more than one plant had changed in color, a 5-day moving average was calculated to smooth daily fluctuations; and (3) a threshold of 0.5 was determined as the midpoint between the minimum and the maximum number of species in leaf coloring. e peak of the LCS was defined as the date with the maximum number of species in leaf coloring. If there were more than one date with the maximum number of species within a year (e.g., double peaks, or consecutive peaks), the peak of the LCS was identified as the average of the peak dates. e start and end of the LCS were the first and the last date when the number of species exceeded the threshold, respectively. e duration was the time span between the start and end of the LCS. rough these steps, the LCS metrics in each year at the six sites were decided.
We computed the annual mean peak, start, end, and duration of the LCS for the six sites. e duration of the LCS was depicted by days, and other LCS metrics were depicted by day of year (DOY). e trends of the LCS metrics for each site were also calculated through linear regression between the peak, start, end, and duration of the LCS and year.
Finally, we use Pearson's correlation analysis to examine the influences of five different temperature parameters (mean temperature, accumulated daily mean temperature less than 25°C, day temperature, night temperature, and temperature difference between day and night) in four periods (2 months before the start of LCS (2MS), 1 month before the start of LCS (1MS), 2 months before the end of LCS (2ME), and during the duration of LCS (D)) on the changes in start, end, peak, and duration of the LCS.
rough the comparison of the correlation coefficient and significant level (p), we were able to identify which period and what type of temperature most influenced the start, end, peak, and duration of the LCS. e temperature sensitivity (days/°C) was also calculated as the slope of the peak, start, end, and duration of LCS against its most correlated temperature parameters by the least squares method.    With respect to the durations, the LCS in two sites (Beijing and Mudanjiang) experienced a shortening trend over the past 50 years with one signi cant site (Beijing: −1.12 d/decade; p < 0.05), while the LCS in other three sites (Xi'an, Harbin, and Shenyang) was signi cantly lengthened by 4.01 to 6.96 d/ decade (p < 0.01). We also noticed that the lengthening trends in these three sites were all caused by an earlier start and a later end of LCS.

Correlation between LCS and Temperature Changes.
In most cases, the rise in mean temperature, day temperature, and night temperature would lead to a delayed peak, start, and end of LCS and a lengthened duration of LCS ( Figure 7). Inversely, the temperature below 25°C and the temperature difference were negatively correlated with the peak, start, and end of LCS and positively correlated with the duration of LCS. For Beijing, the peak of LCS was most correlated with the mean temperature one month before the start of LCS (r � −0.46, p < 0.01), and the start, end, and duration of LCS were all mainly influenced by night temperature (p < 0.01). With respect to Xi'an, the temperature difference between day and night 2 months before the start of LCS was suggested to be the most significant factor determining the peak, start, and duration of LCS. e end of LCS in Xi'an was mostly influenced by the mean temperature 2 months before the end of LCS (r � 0.63, p < 0.01). e correlations between LCS and temperature were similar in Harbin and Mudanjiang. In both sites, the start of LCS was mainly driven by the day temperature, and the end of LCS was positively and significantly correlated with the night temperature. However, the correlation between the start of LCS and day temperature was positive in Mudanjiang but negative in Harbin. Regarding Minqin, the significant correlation was only found between the peak of LCS and the mean temperature 2 months before the end of LCS (r � 0.48, p < 0.05). e peak, start, end, and duration in Shenyang showed no significant correlations with mean temperature, temperature below 25°C, and night temperature. Instead, the day temperature was exhibited to be the most important factor influencing the start and duration of LCS in Shenyang (p < 0.05). e sensitivity of the peak of LCS to temperature ranged from −2.45 d/°C (R 2 � 0.12, p > 0.05) in Harbin to 5.39 d/°C in Minqin (R 2 � 0.12, p < 0.05), with an average of 0.99 d/°C (Figure 8). e sensitivity of the start of LCS to temperature presented the maximum value in Xi'an (4.57 d/°C; R 2 � 0.25, p < 0.05) and the minimum value in Shenyang (−6.31 d/°C; R 2 � 0.24, p < 0.05). In terms of the sensitivity of the end of LCS, it showed a small difference among different sites (standard deviation: 1.49 d/°C). On the contrary, the response of duration of LCS to temperature differed evidently in space, with the sensitivity ranging from −4.77 d/°C in Xi'an (R 2 � 0.25, p < 0.01) to 7.79 d/°C in Shenyang (R 2 � 0.32, p < 0.05).

Discussion
Autumn phenology has great influence on the ecosystem and receives more and more concerns in recent decades. However, the change in autumn phenology still remains uncertain. In our study, we investigated the changes in the start, peak, end, and duration of the leaf coloring season (LCS) at six sites in China. We found an evident spatial difference in the changes in the start of LCS, with half of the sites showing an advancing trend and the others showing a delaying trend. is result is consistent with the study of Ge et al. [15], which suggested a large difference in autumn phenology across China during 1960-2011. According to the definition of LCS, the changes in the start of LCS may show the overall phenological trend of species with earlier leaf coloration. We also noticed a delayed end of LCS for majority of the sites, indicating a unified delaying trend for the species with later leaf coloration. e delaying trend of 0.19 d/year on average is larger than the trend of leaf senescence (0.11 d/year) of deciduous trees in Eurasia and smaller than the trend (0.28 d/year) of that in North America over the last two decades [10]. In addition, the extending duration of LCS represents a more dispersed period of leaf coloration for different species, or vice versa. We found that the duration of LCS prolonged significantly in Xi'an, Harbin, and Shenyang, which was induced by an advanced start of LCS and a delayed end of LCS. On the contrary, the duration of LCS shortened in the other sites. Both the lengthening and shortening trend may indicate that the changes in leaf coloring are not synchronized among different species [11].
Our study exhibited an overall positive correlation between mean temperature and peak, start, and end of LCS; that is, increasing temperature would delay autumn phenology. is is in accordance with previous studies [16,36]. However, the contribution of mean temperature to LCS variability is low when compared to spring phenology [30], with nonsignificant correlation existing between the mean temperature and LCS variability in most cases, particularly at Mudanjiang, Minqin, and Shenyang. As previous studies suggested, the night temperature has a predominant effect on the leaf coloring date for some deciduous trees [27,37]. In this study, we found the largest correlation coefficient between night temperature and the end and duration of LCS in three sites, respectively. In Mudanjiang and Harbin, the changes in the LCS were quite different. e possible reason was that the night temperature two months before the LCS decreased in Mudanjiang (−0.86°C/decade) but significantly increased in Harbin (0.39°C/decade; p < 0.01) during 1963-2010. It is also noteworthy that the rise in temperature difference, rather than other factors, significantly accounted for the advance in the peak of LCS in Mudanjiang, demonstrating that the fluctuation in temperature would benefit for leaf coloration [20].
Many studies showed that the influence of temperature on autumn phenology varies in different periods. For example, Liu et al. [27] announced antagonistic effects of the growing season and autumn temperature on the leaf coloring date for four deciduous trees in China. Similarly, we notice that the correlation coefficient between temperature and autumn phenology is not completely consistent in the four periods. For instance, the end of LCS in Shenyang was positively correlated with mean temperature during the duration of LCS but negatively influenced by mean temperature 2 months before the start of LCS.
Furthermore, it is notable that, in Beijing and Xi'an, although the preseason mean temperature, day temperature, night temperature, and temperature difference had a similar trend, the changes in the start of LCS in the sites were opposite. It suggests that other climatic factors, besides temperature, may contribute to the changes in LCS. To verify this assumption, we further calculated the trend of 6 Advances in Meteorology Peak * * * * * * * * * * * * potential in uencing factors, i.e., precipitation, relative humidity, wind velocity, and sunshine duration two months before LCS in Beijing and Xi'an. Subsequently, partial correlation analysis between the start of LCS and one of the potential factors, controlling mean temperature, day temperature, night temperature, temperature di erence, and other potential factors was applied. e partial correlation coe cient clearly showed that increasing precipitation would delay the coloration of leaves (Table 1) probably because precipitation prolonged the growing season of plants and slowed down leaf senescence [31]. us, the increased precipitation in Beijing would lead to a delayed start of LCS. Meanwhile, the decreased precipitation would cause an advance of the start of LCS in Xi'an. In addition, the inconsistency of species types and numbers may also be one of the reasons for various trends between di erent sites.
Our study can not only provide better understanding on the response of autumn phenology to climate change but also be meaningful to guide tourism activities, since foliage coloration has become an important attraction for recreation and vacations in autumn [38]. e duration of LCS in this study represents the best period for foliage sightseeing in a particular area. It can be pointed out that tourists may enjoy a longer view of the coloration of the leaves in most sites with the global warming in the future.

Conclusions
In this study, we de ned leaf coloring season (LCS) and investigated the changes in peak, start, end, and duration of LCS as well as their correlations with di erent temperature parameters. e results showed the following: (1) for most of the six sites studied, the start date of the leaf coloring season (LCS) advanced and the end of LCS delayed over the past 50 years. As a result, a lengthened duration of LCS was found in Xi'an, Harbin, Minqin, and Shenyang. (2) In general, the rise in mean temperature, day temperature, and night temperature would lead to a delayed peak, start, and end of LCS and a lengthened duration of LCS in most cases. (3) e changes in LCS were not only determined by mean temperature but also in uenced by day temperature, night temperature, and temperature di erence in di erent sites. Other factors, such as precipitation, could explain the changes in LCS to some extent.
Data Availability e phenological data could be downloaded from the National Earth System Science Data Sharing Infrastructure (http://www.geodata.cn). e meteorological data were  available from the China Meteorological Data Service Center (http://data.cma.cn/).

Conflicts of Interest
e authors declare that there are no conflicts of interest.