# Warming does not delay the start of autumnal leaf coloration but slows its progress rate

Nan Jiang, Miaogen Shen, Philippe Ciais, Matteo Campioli, Josep Peñuelas, Christian Körner, Ruyin Cao, Shilong Piao, Licong Liu, Shiping Wang, et al.

► To cite this version:

Nan Jiang, Miaogen Shen, Philippe Ciais, Matteo Campioli, Josep Peñuelas, et al. Warming does not delay the start of autumnal leaf coloration but slows its progress rate. Global Ecology and Biogeography, 2022, 31 (11), pp.2297-2313. 10.1111/geb.13581. hal-03763268

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# Warming does not delay the start of autumnal leaf coloration but slows its progress rate

Nan Jiang<fnref n="1" />,<fnref n="2" /> ID

Miaogen Shen<fnref n="2" /> ID

Philippe Ciais<fnref n="3" />

Matteo Campioli<fnref n="4" />

Josep Peñuelas<fnref n="5" />,<fnref n="6" />

Christian Körner<fnref n="7" />

Ruyin Cao<fnref n="8" />

Shilong Piao<fnref n="1" />,<fnref n="9" /> ID

Licong Liu<fnref n="2" />

Shiping Wang<fnref n="1" />

Eryuan Liang<fnref n="1" /> ID

Nicolas Delpierre<fnref n="10" />

Kamel Soudani<fnref n="10" />

Yuhan Rao<fnref n="11" />

Leonardo Montagnani<fnref n="12" />,<fnref n="13" />

Lukas Hörtnagl<fnref n="14" />

Eugénie Paul-Limoges<fnref n="15" />

Ranga Myneni<fnref n="16" />

Georg Wohlfahrt<fnref n="17" />

Yongshuo Fu<fnref n="18" />

Ladislav Šigut<fnref n="19" />

Andrej Varlagin<fnref n="20" />

Jin Chen<fnref n="2" />

Yanhong Tang<fnref n="9" />

Wenwu Zhao<fnref n="2" />

## Correspondence

Miaogen Shen, State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.

Email: shen.miaogen@gmail.com

Handling Editor: Stephanie Pau

## Abstract

**Aim:** Initiation of autumnal leaf senescence is crucial for plant overwintering and eco-system dynamics. Previous studies have focused on the advanced stages of autumnal leaf senescence and reported that climatic warming delayed senescence, despite the fundamental differences among the stages of senescence. However, the timing of onset of leaf coloration ($D_{LCO}$), the earliest visual sign of senescence, has rarely been studied. Here, we assessed the response of $D_{LCO}$ to temperature.

**Location:** 30–75° N in the Northern Hemisphere.

**Time period:** 2000–2018.

<footnote n="1">¹State Key Laboratory of Tibetan Plateau Earth System, Resources and Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China</footnote>

<footnote n="2">²State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, China</footnote>

<footnote n="3">³Laboratoire des Sciences du Climat et de l'Environnement (LSCE), CEA CNRS UVSQ, Gif Sur Yvette, France</footnote>

<footnote n="4">⁴Centre of Excellence PLECO (Plants and Ecosystems), Department of Biology, University of Antwerp, Wilrijk, Belgium</footnote>

<footnote n="5">⁵CSIC, Global Ecology Unit CREAF-CEAB-UAB, Bellaterra, Spain</footnote>

<footnote n="6">⁶CREAF, Cerdanyola del Vallès, Spain</footnote>

<footnote n="7">⁷Institute of Botany, University of Basel, Basel, Switzerland</footnote>

<footnote n="8">⁸School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, China</footnote>

<footnote n="9">⁹College of Urban and Environmental Sciences, Peking University, Beijing, China</footnote>

<footnote n="10">¹⁰CNRS, AgroParisTech, Ecologie Systématique et Evolution, Université Paris-Saclay, Orsay, France</footnote>

<footnote n="11">¹¹North Carolina Institute for Climate Studies, North Carolina State University, Asheville, North Carolina, USA</footnote>

<footnote n="12">¹²Forest Service, Autonomous Province of Bolzano-Bozen, Bolzano-Bozen, Italy</footnote>

<footnote n="13">¹³Faculty of Science and Technology, Free University of Bolzano, Bolzano, Italy</footnote>

<footnote n="14">¹⁴Department of Environmental Systems Science, Institute of Agricultural Sciences, ETH Zurich, Zurich, Switzerland</footnote>

<footnote n="15">¹⁵Department of Geography, University of Zurich, Zurich, Switzerland</footnote>

<footnote n="16">¹⁶Department of Earth and Environment, Boston University, Boston, Massachusetts, USA</footnote>

<footnote n="17">¹⁷Department of Ecology, University of Innsbruck, Innsbruck, Austria</footnote>

<footnote n="18">¹⁸College of Water Sciences, Beijing Normal University, Beijing, China</footnote>

<footnote n="19">¹⁹Department of Matter and Energy Fluxes, Global Change Research Institute of the Czech Academy of Sciences, Brno, Czech Republic</footnote>

<footnote n="20">²⁰A.N. Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, Moscow, Russia</footnote>
**Major taxa studied:** Deciduous vegetation.

**Methods:** We retrieved $D_{LCO}$ from high-temporal-resolution satellite data, which were then validated by PhenoCam observations. We investigated the temporal changes in $D_{LCO}$ and the relationship between $D_{LCO}$ and temperature by using satellite and ground observations.

**Results:** $D_{LCO}$ was not significantly ($p$ > .05) delayed between 2000 and 2018 in 94% of the area. $D_{LCO}$ was positively ($p$ < .05) correlated with pre-$D_{LCO}$ mean daily minimum temperature ($T_{min}$) in only 9% of the area, whereas the end of leaf coloration ($D_{LCE}$) was positively correlated with pre-$D_{LCE}$ mean $T_{min}$ over a larger area (34%). Further analyses showed that warming slowed the progress of leaf coloration. Interestingly, $D_{LCO}$ was less responsive to pre-$D_{LCO}$ mean $T_{min}$ in areas where daylength was longer across the Northern Hemisphere, particularly for woody vegetation.

**Main conclusions:** The rate of progress of coloration is more sensitive to temperature than its start date, resulting in an extension of the duration of leaf senescence under warming. The dependence of $D_{LCO}$ response to temperature on daylength indicates stronger photoperiodic control on initiation of leaf senescence in areas with longer daylength (i.e., shorter nights), possibly because plants respond to the length of uninterrupted darkness rather than daylength. This study indicates that the onset of leaf coloration was not responsive to climate warming and provides observational evidence of photoperiod control of autumnal leaf senescence at biome and continental scales.

KEYWORDS

autumnal leaf senescence, global warming, leaf coloration onset, Northern Hemisphere, photoperiod

1 | INTRODUCTION

In contrast to the leaves of evergreen conifers, those of northern deciduous plants are not sufficiently tolerant of freezing to survive cold periods and, therefore, are shed before the onset of winter. This autumnal senescence process is controlled by changes in gene expression and metabolic adjustments that include the degradation of macromolecules (e.g., chlorophyll), a decrease in photosynthesis and, importantly, the recycling and re-allocation of nutrients (Gan & Amasino, 1997; Thomas & Stoddart, 1980). In parallel to leaf senescence, carbon sink activity ceases progressively, and plants switch to nutrient recovery and resorption processes (Estiarte & Peñuelas, 2015; Keskitalo et al., 2005). Without timely leaf senescence and abscission, early frost would reduce nutrient resorption, leading to a loss of leaf resources. Changes in the timing of key steps of leaf senescence extensively influence ecosystem structure and functions, such as vegetation activity, trophic interaction, carbon and nutrient cycling, land-atmosphere moisture and energy fluxes (Keenan et al., 2014; Morisette et al., 2009), which could further affect the climate system (Peñuelas et al., 2009; Richardson et al., 2013).

Senescence starts as a cryptic phenological process before any visible symptoms become apparent (Körner & Basler, 2010). The timing of the start of the leaf coloration following senescence varies, depending on the rate of the senescence process, which is

related to environmental conditions (e.g., temperature) (Fracheboud et al., 2009). Hence, the process of autumnal leaf senescence has two phases (Figure 1a): (1) a visually indistinguishable ontogenetic stage that precedes (2) a visible change in leaf colour (Tang et al., 2016). The timings of the middle and end of leaf coloration are the focus of *in situ* phenological observations and have been the main concern of most autumnal phenological studies to date.

Satellite and ground-based observations indicate that climate warming in the last several decades has substantially advanced the onset of spring green-up and the peak of the growing season, and it has slightly delayed the timing of the end of leaf coloration [$D_{LCE}$, the time when the normalized difference vegetation index (NDVI) decreases by 50% of its annual amplitude in the second half of a year in satellite-based studies (Ganguly et al., 2010; Lukasová et al., 2019; Melaas et al., 2013; Nagai et al., 2010; White et al., 1997; Yu et al., 2010)] in the Northern Hemisphere (Figure 1b) (Fu et al., 2015, 2019; Gill et al., 2015; Jeganathan et al., 2014; Menzel et al., 2020; Xu et al., 2016). In addition to temperature, an increase in precipitation also delays $D_{LCE}$ in temperate dry grasslands in the northern middle latitudes (Liu et al., 2016). Besides these abiotic factors, temporal changes in $D_{LCE}$ are also associated with the onset of green-up in some temperate tree species (Keenan & Richardson, 2015) and in boreal ecosystems (Liu et al., 2016). In contrast to $D_{LCE}$, the timing of onset of leaf coloration ($D_{LCO}$; Figure 1b) has been studied inadequately. In
<figure><img src="image_2.png" /></figure>

<figure><img src="image_3.png" /><figcaption>FIGURE 1 Conceptual graphs illustrating (a) the developmental processes in pigments during leaf senescence that are related to photosynthetic capacity and leaf colour; and (b) phenological changes retrieved from the normalized difference vegetation index (NDVI) in the last few decades. In (a), $D_{\text{PDO}}$ and $D_{\text{LCO}}$ are the timings of the onsets of the decrease in maximum canopy photosynthetic capacity and leaf coloration in autumn, respectively; $D_{\text{LCE}}$ is the timing of the end of leaf coloration. In (b), the onset of green-up corresponds to a 20% increase in NDVI in spring, the peak of the season corresponds to the maximum NDVI, and $D_{\text{LCE}}$ corresponds to a 50% decrease in NDVI in autumn. $D_{\text{LCO}}$ was defined by two methods, corresponding to a 10% decrease in NDVI (orange point) and the inflection point at which NDVI begins to decline (red point), respectively (for details, see Section 2). The leftward and rightward arrows indicate advances of onset of green-up and peak of season and delay of $D_{\text{LCE}}$, respectively, over the past few decades. The question mark indicates a research gap regarding temporal changes in $D_{\text{LCO}}$ and their drivers.</figcaption></figure>

particular, it is not known whether $D_{\text{LCO}}$ is sensitive to climate and whether it has been responsive to recent climate change. $D_{\text{LCO}}$ is of key importance because it indicates when leaf senescence becomes detectable from NDVI and its progress accelerates (Figure 1b). As shown by experiments on young trees, some temperate and boreal woody species use the shortening of the photoperiod as a signal for the onset of leaf senescence (Supporting Information Table S1), but many in situ and satellite observations indicate that increased temperature induces delays in the advanced stages of senescence, such as $D_{\text{LCE}}$ (Delpierre et al., 2009; Estrella & Menzel, 2006; Ge et al., 2015; Gill et al., 2015; Jeong et al., 2011; Liu et al., 2016).

A dominant photoperiodic control of early senescence implies that $D_{\text{LCO}}$ should not be delayed, even if the temperature increases, because its timing is controlled only by daylength (hypothesis 1). Moreover, because $D_{\text{LCE}}$ delays with warmer temperature, we may further hypothesize that earlier stages of leaf senescence are less sensitive to temperature than are more advanced stages and expect an extension of the period between $D_{\text{LCE}}$ and $D_{\text{LCO}}$ under warming. In contrast, without photoperiodic control, shifts in $D_{\text{LCO}}$ are expected in the case of climatic warming (hypothesis 2). Alternatively, if $D_{\text{LCO}}$ is influenced by both photoperiod and temperature, the relationships between $D_{\text{LCO}}$ and temperature should vary among different areas, because the strength of the photoperiod signal varies (hypothesis 3).

To test these hypotheses, we initially investigated the temporal changes in $D_{\text{LCO}}$ and the interannual relationships between $D_{\text{LCO}}$ and pre-$D_{\text{LCO}}$ $T_{\text{min}}$ (the mean of monthly average daily minimum temperature for an optimized period preceding $D_{\text{LCO}}$) for northern vegetation (30–75° N, with cropland pixels excluded) during the period 2000–2018. We then examined whether the timings of earlier stages of leaf coloration are less responsive to temperature and show fewer delays and assessed the impacts of temperature on the progress of leaf coloration. Given that only a few in situ observational programs

or networks have monitored $D_{\text{LCO}}$, we determined $D_{\text{LCO}}$ from a 5-day composite time series of the NDVI derived from daily surface spectral reflectance (MOD09CMG) at a spatial resolution of .05°, provided by the spaceborne Moderate Resolution Imaging Spectroradiometer (MODIS) (Vermote, 2015). To complement the NDVI data, we also used 332 time series of $D_{\text{LCO}}$ observed by professional observers according to standard observation guidelines (China Meteorological Administration, 1993) in the field in China (Supporting Information Figure S1a; Table S2) and the timing of onset of autumnal decline in maximum canopy photosynthetic capacity ($D_{\text{PDO}}$) derived from eddy covariance $CO_2$ flux observations (Gu et al., 2009; Shen, Tang, et al., 2014) at 36 sites from within the FLUXNET2015 dataset (Pastorello et al., 2017) (Supporting Information Figure S1b; Table S3).

## 2 | MATERIALS AND METHODS

### 2.1 | Estimating timings of stages of leaf coloration from satellite observations of NDVI time series

#### 2.1.1 | Dataset and preprocessing

The NDVI is a proxy for vegetation greenness and has been widely used for phenological studies at large spatial scales (Buitenwerf et al., 2015; Gao et al., 2019; Keenan et al., 2014; Myneni et al., 1997; Wu et al., 2018). NDVI has also been proved capable of detecting the onset of leaf coloration (Mariën et al., 2019; Soudani et al., 2012, 2021; Yang et al., 2014; Zhao et al., 2020). Previous studies have usually used half-month/16-day composite NDVI time series to retrieve phenological metrics. However, because the duration of leaf coloration could be as short as 4 weeks in some areas (Ye & Zhang, 2021), NDVI time-series data with higher temporal resolution are required.
We estimated phenological metrics (i.e., the timings of the onset and the advanced stages of leaf coloration and the onset of green-up) for 2000–2018 from a 5-day composite NDVI time series produced from the MODIS reflectance product (MOD09CMG Collection 6, available at: https://ladsweb.modaps.eosdis.nasa.gov, accessed on 29 January 2019) (Vermote, 2015). MOD09CMG provides an estimate of daily surface spectral reflectance at a spatial resolution of .05°. The quality of the daily surface reflectance data from MOD09CMG is unsatisfactory owing to cloud and snow contamination (Vermote, 2015); therefore, we used the 5-day maximum value composite approach (Zhang, 2015), combined with a Savitzky–Golay filter (Cao et al., 2018), to produce a high-quality NDVI time series before determining $D_{LCO}$. Initially, NDVI values that were lower than the uncontaminated winter (December–February) mean NDVI were replaced by the latter (Beck et al., 2006; Zhang et al., 2007). After that, cloud-contaminated and irregularly high and low NDVI values were identified and reconstructed by using a Savitzky–Golay filter (Cao et al., 2018). Details for preparation of the high-quality NDVI time series are given in the Supporting Information (Section 1 of the Supplementary Methods).

We focused on natural vegetation by excluding pixels dominated by cropland, artificial surfaces, permanent snow or ice, and water bodies, on the basis of the MODIS land-cover map (MCD12C1 Version 6, available at: https://ladsweb.modaps.eosdis.nasa.gov, accessed on 20 August 2018) (Friedl & Sulla-Menashe, 2015) for the middle year of the time series (2009). Some pixels were also excluded from analysis because of sparse vegetation coverage, weak seasonality, or NDVI peaking in October–April. We adopted three criteria for pixel inclusion: mean annual NDVI must be >.10 (Jeong et al., 2011); NDVI should peak between May and September in the multiyear mean NDVI time series (Shen et al., 2020); and mean NDVI for July and August must be >1.15 times the mean NDVI for December and for January–February in every year (Shen, Zhang, et al., 2014).

## 2.1.2 | Estimation of timings of leaf coloration

Two methods can generally be used to estimate the parameters of vegetation phenology (Chen et al., 2016; Shang et al., 2017), including $D_{LCO}$ from annual NDVI profiles. One is based on thresholds (White et al., 1997), whereas the other is based on inflection points (Zhang et al., 2003). We applied the threshold-based method by initially using a generalized sigmoid function to fit the NDVI annual profile [Equation (7) in the paper by Klosterman et al., 2014] and then determining $D_{LCO}$ as the first date when NDVI decreased by 10% of its annual amplitude in the descending period (Leblans et al., 2017; Richardson et al., 2018). Although a smaller decrease in NDVI corresponds to an earlier stage of leaf coloration, consideration of it would introduce more uncertainty. We also determined $D_{LCO}$ by using the algorithm based on inflection point. In this method, $D_{LCO}$ was defined as the date when the rate of change of the curvature of a double logistic function (Beck et al., 2006; Elmore et al., 2012) fitted to the NDVI time series

reached its first local minimum in the descending period (Zhang et al., 2003). Theoretically, the $D_{LCO}$ defined by the inflection method is close to the date when NDVI decreases by c. 9% of its annual magnitude (Shang et al., 2017).

The advanced stages of leaf coloration were determined as the dates when NDVI decreases by 20, 30, 40 and 50% (corresponding to the timing of the end of leaf coloration, $D_{LCE}$) of its annual amplitude, respectively. In addition, given that in a few studies (Berman et al., 2020; Ren et al., 2017) the end of leaf coloration was defined as the dates when NDVI drops by 60 or 90% of its annual amplitude, we also included these definitions in analysis. We defined the timing of the onset of green-up as the date when NDVI increased by 20% (Yu et al., 2010).

### 2.1.3 | Evaluation of satellite $D_{LCO}$ using PhenoCam

It is unreasonable to validate the satellite-derived $D_{LCO}$ by comparing it with the $D_{LCO}$ of a few plant individuals from ground observation, because of mismatch in spatial coverage, different definitions of phenological metrics, and the spatial heterogeneity in phenological phases among individuals for a pixel. Fortunately, pairs of field observations of NDVI and leaf coloration showed good consistency between the start of NDVI decrease and the onset of leaf coloration (Soudani et al., 2012, 2021). Moreover, the comparison between the start of autumn from satellite-observed NDVI and field observations of leaf coloration onset for the entire area covered by the pixel also showed little difference between them (Zhao et al., 2020). Those studies suggest that NDVI is capable of detecting the onset of leaf coloration if the observed leaves or individuals are identical between ground and satellite observations. However, there are very limited pairs of compatible observations of NDVI and leaf coloration that can be used for validation.

Considering the high capability of PhenoCam in capturing the variations in leaf coloration onset at the landscape scale (Klosterman et al., 2014; Klosterman & Richardson, 2017; Nezval et al., 2020; Wingate et al., 2015), we used the PhenoCam dataset v.2.0 (Richardson et al., 2018; Seyednasrollah, Young, Hufkens, Milliman, Friedl, Frolking, & Richardson, 2019; Seyednasrollah, Young, Hufkens, Milliman, Friedl, Frolking, Richardson, Abraha, et al., 2019) to assess the relationships between satellite-derived $D_{LCO}$ and the $D_{LCO}$ derived from time series of the green chromatic coordinate (GCC) and vegetation contrast index (VCI) observed by PhenoCam. The GCC and VCI were determined from the digital numbers (DN) in red (R), green (G) and blue (B) channels. Specifically, GCC and VCI were calculated as $DN_G/(DN_R + DN_G + DN_B)$ and $DN_G/(DN_R + DN_B)$, respectively. Details for the determinations of $D_{LCO}$ from time series of GCC and VCI are given in the Supporting Information (Section 2 of the Supplementary Methods).

## 2.2 | $D_{LCO}$ from *in situ* phenological observations

$D_{LCO}$ was extracted at the species level from datasets of *in situ* phenological observations in China provided by the Chinese Academy of
The data for $T_{min}$ and precipitation were extracted from the Climatic Research Unit (CRU) Time-Series (TS) 4.03 dataset (http://data.ceda.ac.uk, accessed on 11 June 2019), which provided monthly data at a spatial resolution of .5°×.5° until 2018. It should be noted that $T_{min}$ in the dataset is an approximation of the mean of daily minimum temperature for a calendar month, which is calculated arithmetically from the gridded monthly mean temperature and the diurnal temperature range (Harris et al., 2014) and does not exactly reflect the interannual variations in the absolute minimum temperature (Körner & Hiltbrunner, 2018) experienced by plants before $D_{LCO}$. The CRU data were resampled at .05°×.05° by replication to match the $D_{LCO}$ data.

Complementary to the pre-$D_{LCO}$ period in which $T_{min}$ had the strongest interannual partial correlation with $D_{LCO}$, we also used fixed durations (1 month and 15 days preceding multiyear mean $D_{LCO}$, respectively) as the pre-$D_{LCO}$ periods. We calculated the partial correlation between $D_{LCO}$ and pre-$D_{LCO}$ $T_{min}$, with concurrent total precipitation as the control variable. Moreover, we investigated the partial correlation coefficient between $D_{LCO}$ and the lowest $T_{min}$ during the 15 days before the multiyear mean $D_{LCO}$, with the concurrent mean $T_{min}$ (mean of the remaining 14 $T_{min}$ values after removal of the lowest $T_{min}$ during the period) and total precipitation as control variables. Note that when the pre-$D_{LCO}$ period was defined as the 15 days preceding $D_{LCO}$ and when we analysed the relationship between the lowest $T_{min}$ and $D_{LCO}$, daily $T_{min}$ and precipitation were extracted from the CRU-NCEP dataset (v.7.2, available at: https://vesg.ipsl.upmc.fr, assessed on 10 January 2019), which provides 6-hourly data at a spatial resolution of .5°×.5° until 2016 (Viovy, 2018). The CRU-NCEP v.7.2 is a combination of two datasets: the CRU TS 3.2 .5°×.5° monthly data covering the period 1901–2002 and the NCEP re-analysis 2.5°×2.5° 6-hourly data covering the period 1948–2016. We determined daily $T_{min}$ as the minimum value of the four 6-hourly minimum temperature values for each day. The CRU-NCEP data were resampled at .05°×.05° by replication to match the $D_{LCO}$ data.

We also investigated the impact of $T_{min}$ and precipitation on $D_{LCO}$ from ground-based observations in China and on $D_{PDO}$ from eddy-covariance sites as complementary to satellite-derived $D_{LCO}$. Climatic data for in situ observations in China were extracted from the “Daily Surface Climate Variables of China” catalogue (a dataset named SURF_cli_CHN_MUL_DAY_V3.0), which includes daily climate data for 2,474 sites in China from January 1951 to July 2014, provided by the Chinese Meteorological Administration. The distance between phenological and meteorological stations was <25 km. Climatic data for $D_{PDO}$ were calculated from the half-hourly temperature dataset provided by FLUXNET2015.

### 2.4.3 | Relationships between the progress of leaf coloration and temperature

The impacts of temperature on the progress of leaf coloration were assessed in four ways.

First, we calculated the partial correlation coefficient between each of the timings of different stages in leaf coloration (determined

as NDVI decreases by 20, 30, 40, 50, 60 and 90%) and preceding $T_{min}$ using the approach described in Section 2.4.2. We then compared the percentage of area corresponding to the partial correlation coefficient among the different timings.

Second, the difference in temperature sensitivity between the $D_{LCE}$ and $D_{LCO}$ was used to assess the differential responses to $T_{min}$ between $D_{LCE}$ and $D_{LCO}$. The temperature sensitivity of $D_{LCO}$ was defined as the coefficient for pre-$D_{LCO}$ $T_{min}$ in a linear regression in which $D_{LCO}$ was set as the dependent variable, and pre-$D_{LCO}$ $T_{min}$ and pre-$D_{LCO}$ total precipitation were independent variables. The temperature sensitivity of $D_{LCE}$ was calculated in a similar manner. See Section 2.4.2 for the details of the determination of pre-$D_{LCO}$ (or pre-$D_{LCE}$) $T_{min}$ and total precipitation.

Third, the temperature sensitivity of the duration of leaf coloration was used to assess the impact of temperature on the duration of leaf coloration. The duration of leaf coloration was defined as the difference between $D_{LCE}$ and $D_{LCO}$. Its temperature sensitivity was estimated as the coefficient for mean $T_{min}$ in the linear regression in which the duration was set as the dependent variable and the mean $T_{min}$ and total precipitation in the period between $D_{LCE}$ and $D_{LCO}$ were independent variables.

Fourth, the temperature sensitivity of the speed of leaf coloration was used to assess the impact of temperature on the speed of leaf coloration within a season. The speed of leaf coloration within a season was defined as the normalized decreasing speed of NDVI between $D_{LCE}$ and $D_{LCO}$, calculated as $-(NDVI_{DLCE} - NDVI_{DLCO})/(D_{LCE} - D_{LCO})/AMP_{NDVI}$, where AMP$_{NDVI}$ is the annual amplitude of NDVI for a given pixel and given year. The temperature sensitivity of the speed of leaf coloration was then calculated as the coefficient for mean $T_{min}$ when regressing the speed of leaf coloration against mean $T_{min}$ and total precipitation between $D_{LCE}$ and $D_{LCO}$. Here, $T_{min}$ and precipitation were extracted from the CRU TS 4.03 monthly data.

### 2.4.4 | Dependence of $D_{LCO}$ on daylength

Previous experimental findings suggest that daylength is a signal for the start of autumn leaf senescence (Supporting Information Table S1), indicating a photoperiodic control of $D_{LCO}$. However, it is difficult to assess the role of daylength by using interannual correlations between $D_{LCO}$ and daylength in natural conditions, because the daylength on a given date does not vary among years. Alternatively, because control of photoperiod on autumn leaf phenology might vary with daylength across different regions (Howe et al., 1995; Pau et al., 2011; Paus et al., 1986; Saikkonen et al., 2012), we examined the variabilities in the correlation between $D_{LCO}$ and $T_{min}$ and in temporal changes in $D_{LCO}$ against the spatial gradient of daylength to explore the dependence of $D_{LCO}$ on daylength. Meanwhile, the spatial variations in the response of autumn leaf phenology to temperature might be associated with local background temperature conditions (Ford et al., 2017; Zohner et al., 2016). Hence, the spatial variations in background
temperature should be minimized when assessing the dependence of $D_{LCO}$ on daylength. To do this, we first calculated the daylength for each pixel at the date of multiyear mean $D_{LCO}$ over the period 2000–2018 and the mean $T_{min}$ of the period before multiyear mean $D_{LCO}$. The period before multiyear mean $D_{LCO}$ was the month preceding the multiyear mean $D_{LCO}$ if the multiyear mean $D_{LCO}$ was in the first half of a month; otherwise, the period was the month of the multiyear mean $D_{LCO}$. Next, for each cell of 1.5-h daylength and 4 °C mean $T_{min}$ in the space of the daylength and mean $T_{min}$ (for a graphical illustration, see Figure 5), we calculated the percentage of area with significant ($p$ < .05, t test) $D_{LCO}$ delays, the average of positive correlation, and the percentage of area with a positive correlation between $D_{LCO}$ and $T_{min}$ (or precipitation).

In addition, there is more experimental evidence of photoperiodic control on the onset of leaf senescence for woody plants than for herbaceous plants (Supporting Information Table S1), indicating that woody and herbaceous vegetation might respond to photoperiod in different ways. Therefore, the above exploration was also performed separately for woody and herbaceous vegetation. The woody and herbaceous vegetation were merged from Classes 1–6 and Class 10, respectively, in the MODIS land-cover product (MCD12C1, v.6) for 2009 (Friedl & Sulla-Menashe, 2015).

## 2.4.5 | Possible effect of summer NDVI

In some deciduous forests, NDVI can decline in early summer (i.e., late May–July) before leaf coloration, and this might potentially interfere to some extent with the determination of $D_{LCO}$ (Elmore et al., 2012) and its relationship with temperature. To address this, for the pixels classified as deciduous broadleaf forest in the MODIS land-cover product (Friedl & Sulla-Menashe, 2015), we redefined $D_{LCO}$ considering the possible effect of summer NDVI decline on $D_{LCO}$ and then re-analysed the trends in $D_{LCO}$ and the relationship between $D_{LCO}$ and temperature as described in Sections 2.4.1 and 2.4.2.

For the sake of robustness, the possible effect of summer NDVI decline on $D_{LCO}$ was considered in three different ways. First, we used a modified double logistic model that considers early summer NDVI decline (Elmore et al., 2012) to fit the NDVI time series instead of the original double logistic function for the pixels classified as deciduous broadleaf forest. $D_{LCO}$ was then determined as the date when the rate of change of the curvature of a double logistic function fitted to the NDVI time series reached its first local minimum in the descending period. Second, $D_{LCO}$ was defined as the date when NDVI decreased by 10% of its annual amplitude from 1 August. The maximum value used to determine the annual amplitude was the mean value of the upper quartile of the fitted NDVI values in August. Third, $D_{LCO}$ was defined as the date when NDVI decreased by 10% of its annual amplitude from 16 August. The maximum value used to determine the annual amplitude was the mean value of the upper quartile of the fitted NDVI values in the second half of August.

## 2.4.6 | Possible cold events before $D_{LCO}$ (or $D_{PDO}$)

A sudden drop of night-time temperature to the freezing point can induce leaf coloration in a few days (Körner, 2007), and this might interfere with our partial correlation analysis between $D_{LCO}$ (or $D_{PDO}$) and temperature. Hence, we re-examined the temporal changes in $D_{LCO}$ and the correlation between $D_{LCO}$ and temperature as described in Sections 2.4.1 and 2.4.2, after excluding possible cold events estimated using an empirical approach as follows (taking satellite-derived $D_{LCO}$ as an example).

First, we determined the $T_{min}$ threshold below which there could potentially be a cold event for each pixel. Given that a cold event that induces rapid leaf senescence should happen 1–5 days before $D_{LCO}$, the lowest $T_{min}$ during the 6–35 days before $D_{LCO}$ for all years was set as the $T_{min}$ threshold. A temperature higher than such a threshold will not induce a cold event. For vegetation in middle and high latitudes, a temperature higher than freezing (0°C) does not cause frost damage (Körner, 2021; Lenz et al., 2013; Sakai & Larcher, 1987; Taschler & Neuner, 2004). Therefore, if the lowest $T_{min}$ was >0°C, the $T_{min}$ threshold was set to 0°C.

Second, for a given pixel, a year was determined as a candidate cold event year if the lowest $T_{min}$ in the period 1–5 days before $D_{LCO}$ was lower than the above-mentioned $T_{min}$ threshold. Then, from the years in which there was no candidate cold event, we determined the latest $D_{LCO}$ that was not caused by a cold event for that pixel.

Finally, a $D_{LCO}$ was recognized as possibly being caused by a cold event if it was in the candidate cold event years and also earlier than the latest $D_{LCO}$ that was not caused by a cold event. For a $D_{LCO}$ (referred to as $D'_{LCO}$) from the candidate cold event years and later than the latest $D_{LCO}$ that was not caused by a cold event, it ($D'_{LCO}$) would be recognized as a $D_{LCO}$ possibly caused by a cold event if one of the following two conditions was met: (1) the decreasing rate of $T_{min}$ in the period 1–5 days before $D'_{LCO}$ was faster than the maximum decreasing rate of $T_{min}$ among the years in which there was no candidate cold event; or (2) the decrease (absolute value) in $T_{min}$ in the period 1–5 days before $D'_{LCO}$ was greater than the maximum decrease in $T_{min}$ among the years with no candidate cold event. Here, for a given year, the decreasing rate of $T_{min}$ in the period 1–5 days before $D_{LCO}$ (or $D'_{LCO}$) was calculated as the minimum of the slopes of $T_{min}$ against calendar date. A slope of $T_{min}$ against calendar date was calculated as $[T_{min}(time2) - T_{min}(time1)]/(time2 - time1)$, where time2 = $D_{LCO} - 1$, $D_{LCO} - 2$, $D_{LCO} - 3$, $D_{LCO} - 4$ or $D_{LCO} - 5$ and time1 = $D_{LCO} - 2$, $D_{LCO} - 3$, $D_{LCO} - 4$ or $D_{LCO} - 5$, and time2 is later than time1. The decrease in $T_{min}$ in the period 1–5 days before $D_{LCO}$ (or $D'_{LCO}$) for a given year was the maximum value of magnitudes of $[T_{min}(time2) - T_{min}(time1)]$.

This empirical approach might have overestimated the number of years with cold events before $D_{LCO}$ (hereafter, these identified events are referred to as possible cold events), but our objective here was to exclude as many cold events as possible, then to examine whether the main findings of our study were caused by cold events. In addition, under clear skies, the temperature of the canopy surface could be lower than the air temperature; therefore, we also
determined the possible cold events by using 2°C as the $T_{min}$ thresh-
old (Körner, 2021).

Here, the daily $T_{min}$ used to determine possible cold events for
satellite-derived $D_{LCO}$ was extracted from the CRU-NCEP v.7.2 data-
set. Daily $T_{min}$ for ground-based observations in China was derived
from the nearest meteorological station (<25 km), provided by the
Chinese Meteorological Administration. Daily $T_{min}$ for $D_{PDO}$ was
calculated from the half-hourly temperature dataset provided by
FLUXNET2015.

# 3 | RESULTS

## 3.1 | Comparison of satellite $D_{LCO}$ with PhenoCam $D_{LCO}$

The satellite $D_{LCO}$ explained >80% of the variations in PhenoCam-
derived $D_{LCO}$ ($n = 378$ and 377 for GCC and VCI, respectively;
Figure 2). The differences between the satellite-derived $D_{LCO}$
and the PhenoCam-derived $D_{LCO}$ are caused by the mismatch be-
tween the annual NDVI and GCC (or VCI) trajectories owing to
the difference in spatial coverage between the PhenoCam and
satellite pixels in the case of phenologically heterogeneous land
surface (see Supporting Information Supplementary Methods
Section 2).

## 3.2 | Proportion of possible cold events before $D_{LCO}$ (or $D_{PDO}$)

Possible cold events occurred before $D_{LCO}$ or $D_{PDO}$ in very small
fractions of pixel-years/site-years with phenological data (1.6, 1.7
and .6% for satellite-derived $D_{LCO}$, ground-based observations in
China, and $D_{PDO}$ from eddy-covariance sites, respectively; Table 1).
The proportion of years possibly affected by cold events was slightly
higher when using the method based on a temperature threshold of
2°C than that of 0°C.

<figure><img src="image_4.png" /><figcaption>FIGURE 2 Comparison between satellite timing of the onset of leaf coloration in autumn ($D_{LCO}$) and PhenoCam-derived $D_{LCO}$. The PhenoCam-derived $D_{LCO}$ was determined from the green chromatic coordinate (GCC; a) and vegetation contrast index (VCI; b), respectively. AAD = average absolute difference; bias is defined as the difference between the mean of satellite-derived $D_{LCO}$ and the mean of PhenoCam-derived $D_{LCO}$, and negative bias means that the PhenoCam-derived $D_{LCO}$ is earlier than satellite-derived $D_{LCO}$; R = Pearson's correlation coefficient; RMSE = root mean square error.</figcaption></figure>

## 3.3 | Temporal trends in $D_{LCO}$ and the advanced stages of leaf coloration

$D_{LCO}$ was not significantly delayed in 94% of the area during the study period, as assessed by OLSR. The few pixels with a significant delay trend (6%; $p < .05$, t test) were scattered across the Northern Hemisphere (Figure 3a). Excluding years with possible cold events before $D_{LCO}$ produced similar results (Supporting Information Figure S3; Table S4). The Theil-Sen estimator generated results supporting the lack of changes in $D_{LCO}$ (no significant delay in 96% of the area; $p < .05$, Mann-Kendall test; Supporting Information Figure S4a; Table S5). When we defined $D_{LCO}$ as the inflection point at which NDVI begins to decline, we obtained similar results (Supporting Information Figure S4b,c). Considering early summer NDVI decline produced similar results (Supporting Information Figures S5-S7).

Complementary to satellite-derived $D_{LCO}$, we also examined the
temporal changes of $D_{LCO}$ by using ground-based leaf coloration
data from China. $D_{LCO}$ was not significantly delayed for 90 and 94%
of the 332 time series as shown by OLSR (Figure 3b) and the Theil-
Sen method (Supporting Information Table S5), respectively. Similar
results were produced when possible cold events were excluded
(Supporting Information Table S4).

The timings of earlier stages of leaf coloration exhibited delay-
ing trends in fewer areas. The leaf coloration stages determined as
the dates when NDVI decreases by 50% (i.e., $D_{LCO}$), 40, 30, 20 and
10% (i.e., $D_{LCO}$) were significantly ($p < .05$, t test) delayed for 14,
14, 12, 9 and 6% of the area, respectively (Supporting Information
Figure S8).

## 3.4 | Correlation between $D_{LCO}$ and temperature or precipitation

$D_{LCO}$ was not consistently correlated with pre-$D_{LCO}$ $T_{min}$, with only 9%
of the area in scattered pixels showing a significant positive correla-
tion and 5% showing a significant negative correlation (Figure 4a).
$D_{LCO}$ was positively correlated with pre-$D_{LCO}$ total precipitation in
<table><caption>TABLE 1 Proportions of years with possible cold events before $D_{LCO}$ (for satellite and *in situ* observations) and before $D_{PDO}$ (for FLUXNET2015)</caption><thead><tr><th>Metrics</th><th>Satellite D<sub>LCO</sub> (2000-2016)</th><th>In situ D<sub>LCO</sub> China</th><th>FLUXNET2015 D<sub>PDO</sub></th></tr></thead><tbody><tr><td>Proportion (%) of years with possible cold events (0°C)</td><td>1.6</td><td>1.7</td><td>.6</td></tr><tr><td>Proportion (%) of years with possible cold events (2°C)</td><td>2.1</td><td>3.5</td><td>1.0</td></tr></tbody></table>

Note: Possible cold events were determined mainly by using a threshold-based method with a daily minimum temperature of 0 or 2°C (for identification of possible cold events, see Section 2.4.5).

Abbreviations: $D_{LCO}$, timing of onset of leaf coloration in autumn; $D_{PDO}$, timing of onset of the decrease in maximum canopy photosynthetic capacity in autumn.

13% of the area, mainly in the temperate grassland of North America and in the middle latitudes of Eurasia, sub-arctic grassland, and alpine steppe of the Tibetan Plateau (Supporting Information Figure S9). Therefore, neither pre-$D_{LCO}$ $T_{min}$ nor precipitation was a useful predictor of $D_{LCO}$ in most areas. We obtained similar results when using the month preceding $D_{LCO}$ (Supporting Information Figure S10) or 15 days preceding $D_{LCO}$ (Supporting Information Figure S11) as the pre-$D_{LCO}$ period, with only 6 and 5%, respectively, of the area showing a significant positive correlation between $D_{LCO}$ and pre-$D_{LCO}$ $T_{min}$. We also investigated the relationship between $D_{LCO}$ and the lowest daily minimum temperature during the 15 days before the multiyear mean $D_{LCO}$, and only 3% of the area showed a significant positive correlation (Supporting Information Figure S12). Moreover, including the date of onset of green-up as an extra control variable in the partial correlation analyses did not affect the results (Supporting Information Figure S13). The *in situ* phenological records in China indicated that ground-observed $D_{LCO}$ was positively correlated with pre-$D_{LCO}$ $T_{min}$ for 13% of the time series and was not correlated with pre-$D_{LCO}$ $T_{min}$ for 82% of the time series (Table 2). Excluding $D_{LCO}$ possibly caused by cold events produced similar results (Supporting Information Figure S14; Table S6). Overall, these results suggest that an increase in pre-$D_{LCO}$ $T_{min}$ is not likely to delay $D_{LCO}$ in most areas in the middle and high northern latitudes.

## 3.5 | Impacts of temperature on the progress of leaf coloration

We first examined whether the timings of earlier stages of leaf coloration are less closely related to temperature than later stages. The fact that the earlier stages of leaf coloration had fewer areas with a significantly delayed trend (Supporting Information Figure S8) is in agreement with the finding that the significantly positive correlations between the timings of earlier stages of leaf coloration and $T_{min}$ were observed in fewer areas (Figure 4c). The timings of leaf coloration stage corresponding to NDVI decreases by 40, 30, 20 and

<figure><img src="image_5.png" /><figcaption>FIGURE 3 Temporal trends in the timing of the onset of leaf coloration ($D_{LCO}$), as retrieved from satellite and *in situ* observations. (a) Satellite-derived $D_{LCO}$ trends over 2000–2018. The bar chart in the bottom-left corner shows the percentage of area within each interval of the significant temporal trends and the percentage of area with non-significant trends, indicated by the colour scale at the bottom. Positive and negative trend values refer to significantly delayed and advanced $D_{LCO}$, respectively. $D_{LCO}$ corresponds to a 10% decrease in normalized difference vegetation index. (b) Ground-observed $D_{LCO}$ trends derived over 1971–1997 from observations of *in situ* leaf coloration in China. Significant temporal trends were determined using t tests at $p < .05$ and ordinary least squares regression.</figcaption></figure>

10% (i.e., $D_{LCO}$) exhibited a significant positive correlation with $T_{min}$ in 30, 25, 17 and 9% of the area, respectively (Figure 4a,c; Supporting Information Figure S15). In particular, $D_{LCE}$ exhibited a significant
positive correlation with pre-$D_{LCE}$ $T_{min}$ in 34% of the area (Figure 4b), substantially more than that for the $D_{LCO}$-$T_{min}$ correlations (9%; Figure 4a). The proportion increased to 38 and 41% for the timings of leaf coloration stage corresponding to a 60 and 90% decrease in NDVI, respectively (Supporting Information Figure S16).

These results show decreasing correlations with temperature of earlier stages of leaf senescence. To verify this, we examined the correlation between $D_{PDO}$, an indicator of leaf senescence earlier than $D_{LCO}$, and pre-$D_{PDO}$ $T_{min}$. $D_{PDO}$ and pre-$D_{PDO}$ $T_{min}$ exhibited a less positive correlation than NDVI-derived $D_{LCO}$ and pre-$D_{LCO}$ $T_{min}$ at the same sites during the same periods (3 and 6% of the sites for $D_{PDO}$ and $D_{LCO}$, respectively; Supporting Information Table S7). Among all the eddy-covariance towers, $D_{LCO}$ exhibited a significant positive correlation with pre-$D_{LCO}$ $T_{min}$ in 3% of the 36 and was not correlated with pre-$D_{PDO}$ $T_{min}$ in 89% of the eddy-covariance records (Table 2). Moreover, excluding $D_{PDO}$ possibly caused by cold events produced similar results (Supporting Information Table S6).

We then examined whether $D_{LCO}$ is less sensitive to temperature than $D_{LCE}$. In most regions (66%) of the middle and high northern latitudes, the temperature sensitivity of $D_{LCO}$ was less than that of $D_{LCE}$ (Figure 4d). The temperature sensitivity of $D_{LCO}$ was less than $D_{LCE}$ by ≥4 days/°C in 39% of the study area, mainly in northern Europe, the eastern USA, eastern Canada and western Russia. In 14% of the area, the temperature sensitivity of $D_{LCO}$ was >4 days/°C greater than $D_{LCE}$, mainly distributed in the Tibetan Plateau, western North America, areas in Europe near 60° N, northern Kazakhstan, and between 45 and 65° N in Russia.

As can be expected from the lower temperature sensitivity of $D_{LCO}$ relative to that of $D_{LCE}$, warming could extend the duration of leaf coloration in 71% of the area (Figure 4e). In 42% of the area, the temperature sensitivity of the duration of leaf coloration was >3 days/°C, mainly in Russia, eastern North America and northern Europe. The area with a temperature sensitivity <-3 days/°C accounted for 11% of the study area, scattered in the Tibetan Plateau, central USA, western North America, between 45 and 60° N in Europe, northern Kazakhstan and south-eastern Russia.

Moreover, warming could slow the progress of leaf coloration. In 69% of the area, the speed of leaf coloration could be reduced by higher temperature (Figure 4f), particularly in the region north of

60° N. The temperature sensitivity of the speed of leaf coloration was <-1‰/day/°C in 34% of the study area (negative values of temperature sensitivity indicate that warming reduces the speed of leaf coloration), mainly in eastern and northern Canada, northern Europe and northern Russia. Only 13% of the area showed a large increase in the speed of leaf coloration under increasing temperature (>1‰/day/°C), scattered in Mongolia, the Tibetan Plateau, western Canada, central and western USA, and central and south-eastern Russia.

When considering early summer NDVI decline, we also found that more advanced stages of leaf coloration were more responsive to temperature (Supporting Information Figures S17c,d and S18c,d), and that warming could slow the progress of coloration (Supporting Information Figures S17f and S18f) and extend the duration of leaf coloration (Supporting Information Figures S17e and S18e).

## 3.6 | Dependence of $D_{LCO}$ on daylength

We attempted to explore the dependence of $D_{LCO}$ on daylength by examining the variabilities in the correlation between $D_{LCO}$ and $T_{min}$ and in temporal changes in $D_{LCO}$ against the spatial gradient of daylength. In the areas with longer daylengths at multiyear mean $D_{LCO}$, there were proportionally fewer significant $D_{LCO}$ delays during 2000–2018 (Figure 5a; Supporting Information Figure S19a), and the positive relationship between $D_{LCO}$ and pre-$D_{LCO}$ $T_{min}$ was slightly weaker, as indicated by the lower partial correlation coefficient between them (Figure 5d). Such patterns were more prominent for woody vegetation than for herbaceous vegetation (Figure 5b,c,e,f; Supporting Information Figure S19b,c). For vegetation with a daylength at $D_{LCO}$ of >13.5 h, $D_{LCO}$ was more positively correlated with pre-$D_{LCO}$ $T_{min}$ in colder areas at a given daylength (Figure 5d–f). The dependences of $D_{LCO}$ trends on daylength and of the correlation between $D_{LCO}$ and pre-$D_{LCO}$ $T_{min}$ on daylength were also found when years with possible cold events before $D_{LCO}$ were excluded (Supporting Information Figure S20) and when we considered summer decline in NDVI (Supporting Information Figures S21 and S22). The correlation between $D_{LCO}$ and pre-$D_{LCO}$ total precipitation was independent of daylength and was slightly stronger for the areas with a higher temperature before $D_{LCO}$, mostly because of the

**FIGURE 4** Impacts of temperature on the timing of different stages of leaf coloration and on the progress of leaf coloration over the period 2000–2018. (a) Spatial pattern of the partial correlation coefficient ($R_{TN}$) between the onset of leaf coloration [$D_{LCO}$, 10% decrease in normalized difference vegetation index (NDVI)] and pre-$D_{LCO}$ mean daily minimum temperature ($T_{min}$). (b) Spatial pattern of $R_{TN}$ between timing of the end of leaf coloration ($D_{LCE}$, 50% decrease in NDVI) and pre-$D_{LCE}$ $T_{min}$. The bar charts inset in (a) and (b) show the percentage of area for each interval of the partial correlation coefficient ($p$ < .05), with the coefficient indicated by the colour scale on the right. Non-significant correlations ($p$ > .05) are in grey. (c) Percentage of area for which $R_{TN}$ between the timing of a given stage of leaf coloration and preceding $T_{min}$ is higher than a given threshold indicated by the horizontal axis. For example, $R_{TN}$ for the onset of leaf coloration is >.2 in c. 40% of the area. (d) Difference in temperature sensitivity between $D_{LCE}$ and $D_{LCO}$. Positive values indicate that $D_{LCE}$ is more sensitive to temperature than $D_{LCO}$, whereas negative values indicate that $D_{LCO}$ is more sensitive to temperature than $D_{LCE}$. (e) Temperature sensitivity of the duration of leaf coloration. Positive values indicate that warming extends the duration of leaf coloration, whereas negative values indicate that warming shortens the leaf coloration duration. (f) Temperature sensitivity of the speed of leaf coloration. Positive values indicate that warming increases the speed of leaf coloration, whereas negative values indicate that warming reduces the speed of leaf coloration. The bar charts inset in (d–f) show the percentage of area for each interval of the temperature sensitivity indicated by the colour scale on the right.
<figure><img src="image_6.png" /><figcaption>(c) $R_{TN}$ between different stages of leaf coloration and preceding $T_{min}$</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>(e) Temperature sensitivity of the length of duration of leaf coloration</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>(b) $R_{TN}$ between and $D_{LCE}$ and pre-$D_{LCE}$ $T_{min}$</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>(d) Difference in temperature sensitivity between $D_{LCE}$ and $D_{LCO}$</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>(f) Temperature sensitivity of the speed of leaf coloration</figcaption></figure>

<figure><img src="image_11.png" /></figure>
<table><caption>TABLE 2 Percentage of correlations between $D_{LCO}$ or $D_{PDO}$ and each climate factor for each interval of the partial correlation coefficient</caption><thead><tr><th rowspan="2">Metric</th><th rowspan="2">Number of time series</th><th rowspan="2">Climate factor</th><th colspan="7">Interval of the partial correlation coefficient (p < .05)</th></tr><tr><th>(-1.0, -.8)</th><th>(-.8, -.6)</th><th>(-.6, 0)</th><th>(0, .6)</th><th>(.6, .8)</th><th>(.8, 1.0)</th><th>p > .05</th></tr></thead><tbody><tr><td rowspan="2">In situ D<sub>LCO</sub> China</td><td rowspan="2">332</td><td>Temperature</td><td>0</td><td>2</td><td>3</td><td>4</td><td>8</td><td>1</td><td>82</td></tr><tr><td>Precipitation</td><td>0</td><td>3</td><td>3</td><td>4</td><td>5</td><td>0</td><td>85</td></tr><tr><td rowspan="2">FLUXNET2015 D<sub>PDO</sub></td><td rowspan="2">36</td><td>Temperature</td><td>0</td><td>5</td><td>3</td><td>0</td><td>3</td><td>0</td><td>89</td></tr><tr><td>Precipitation</td><td>0</td><td>6</td><td>0</td><td>8</td><td>3</td><td>5</td><td>78</td></tr></tbody></table>

Note: The data in the rightmost column indicate the percentages of area or time series with non-significant correlations.

Abbreviations: $D_{LCO}$, timing of the onset of leaf coloration in autumn; $D_{PDO}$, timing of the onset of the decrease in maximum canopy photosynthetic capacity in autumn.

<figure><img src="image_12.png" /><figcaption>FIGURE 5 Dependence of temporal trends in the timing of the onset of leaf coloration [$D_{LCO}$, 10% decrease in normalized difference vegetation index (NDVI); a-c] and of the partial correlation coefficient ($R_{TN}$; d-f) between $D_{LCO}$ and pre-$D_{LCO}$ mean daily minimum temperature ($T_{min}$) on daylength and temperature over the period 2000-2018. (a) All vegetation. Colour indicates the percentage of area with significant (p < .05) $D_{LCO}$ delays in each cell (i.e., a specific temperature × daylength combination), as indicated in the scale at the bottom. The number in each cell indicates the ratio (unit: %) of the area in each cell to the total area with $D_{LCO}$ retrieval. The temporal trends and their significances were determined with ordinary least squares regression and t tests. (b,c) The same as (a), but for woody and herbaceous vegetation, respectively. (d) All vegetation. Colour indicates the average of the positive $R_{TN}$, as indicated in the scale at the bottom. The number in each cell indicates the percentage of area with a positive correlation in each cell. (e,f) The same as (d), but for woody and herbaceous vegetation, respectively. Only cells where the ratio of the area of the cell to the total area is >1% are represented.</figcaption></figure>
stronger effect of precipitation in delaying $D_{LCO}$ in herbaceous vegetation (Supporting Information Figures S9 and S23).

4 | DISCUSSION

In previous analyses of *in situ* and satellite observations (Garonna et al., 2014; Gill et al., 2015; Liu et al., 2016), the advanced stage of autumnal leaf senescence, indicated by $D_{LCE}$, was significantly delayed in a larger proportion of areas, or time series, than was $D_{LCO}$ in our study. In the present study, $D_{LCE}$ was also significantly delayed in more areas than $D_{LCO}$ (Supporting Information Figure S8), probably because the timings of the earlier stages of leaf coloration determined from satellite data were less affected by $T_{min}$ than the later stages (Figure 4a-c; Supporting Information Figure S15). Evidence for photoperiodic control of the start of leaf senescence (Fracheboud et al., 2009; Keskitalo et al., 2005) suggests that the early phases of leaf senescence are insensitive to warming, in contrast to the later phases. Given that the degradation of chlorophyll starts earlier than leaf coloration (Lim et al., 2007; Tang et al., 2016), the timing of autumnal phenological metrics that closely follow chlorophyll degradation before $D_{LCO}$ should be less delayed by temperature increase than $D_{LCO}$ if chlorophyll degradation is triggered by the photoperiod. In our analysis, we verified that $D_{PDO}$ was less positively correlated with temperature than $D_{LCO}$ (Supporting Information Table S7), probably because the start of autumnal chlorophyll degradation was controlled by photoperiod and was not delayed by higher temperature (Bauerle et al., 2012; Fracheboud et al., 2009; Keskitalo et al., 2005).

Overall, our results suggest that temperature does not initiate senescence in autumn in most areas; instead, it influences the speed of change in coloration after it starts (Figure 4f) (Fracheboud et al., 2009). The lack of a positive correlation between $D_{LCO}$ (or $D_{PDO}$) and pre-$D_{LCO}$ (or pre-$D_{PDO}$) temperature suggests an overriding photoperiodic control that makes the timing of the onset of leaf senescence stable. In the areas with longer daylengths (calculated for each pixel/ location at multiyear mean $D_{LCO}$ over 2000–2018), there were proportionally fewer significant $D_{LCO}$ delays during 2000–2018 (Figure 5a; Supporting Information Figure S19a), and the positive relationship between $D_{LCO}$ and pre-$D_{LCO}$ $T_{min}$ was slightly weaker, as indicated by the smaller partial correlation coefficient between them (Figure 5d). Such dependences on daylength were more prominent for woody vegetation than for herbaceous vegetation (Figure 5b,c,e,f; Supporting Information Figure S19b,c), in agreement with experimental findings suggesting that the initiation of leaf senescence in woody plants is likely to be controlled by photoperiod (Fracheboud et al., 2009; Keskitalo et al., 2005). These findings indicate stronger photoperiodic control in areas where daylength at $D_{LCO}$ is longer (i.e., shorter nights), possibly because plants respond to the duration of uninterrupted darkness rather than daylength (Borthwick & Hendricks, 1960; Hamner, 1940; Howe et al., 1995; Paus et al., 1986). Interestingly, for vegetation with a daylength at $D_{LCO}$ of >13.5 h, $D_{LCO}$ was more positively correlated with pre-$D_{LCO}$ $T_{min}$ in colder areas (Figure 5d-f), indicating a stronger effect of temperature in areas with harsh

temperature conditions, consistent with experimental studies (Ford et al., 2017; Zohner et al., 2016). Therefore, although for these types of vegetation the correlation between $D_{LCO}$ and temperature is weak, probably because of stronger photoperiodic control, there is still a signal of temperature influence on $D_{LCO}$, reflecting a stronger selection pressure in harsher temperature environments.

Although observational evidence is limited, experimental results have been reported for the photoperiodic induction of leaf senescence in several, mostly woody, species (Supporting Information Table S1). However, in those manipulative experiments, daylength was altered by several hours (>4 h; Supporting Information Table S1), which is more extreme than the natural conditions plants are likely to experience. Daylength depends only on the day of year and location. Owing to the interannual limited variations in the timings of leaf coloration or senescence onset, the fluctuation in daylength in natural conditions is far less than that in manipulative experiments. Therefore, the role of photoperiod in leaf senescence identified in such experimental conditions does not necessarily apply to plants in natural conditions. The results in the present study support experimental findings in wild plants at the biome and continental scales and show that photoperiod influences the onset of leaf coloration, which closely follows the initiation of leaf senescence.

Autumnal leaf senescence in preparation for overwintering is an evolutionary trade-off between the re-allocation of leaf nutrients before leaf shed to reduce the risk of frost damage and the assimilation of carbon (Estiarte & Peñuelas, 2015). The response of leaf senescence to an increase in temperature in autumn influences this trade-off. The absence of delays over time in the onset of leaf coloration and in the onset of decrease in maximum canopy photosynthetic capacity in response to climate warming, as observed in our study, might limit the detrimental effects of frost in autumn (Liu et al., 2018) and might also have limited impacts on the start of the remobilization and resorption of nutrients (Estiarte & Peñuelas, 2015). The slower rate of progress of leaf senescence (Figure 4f) and extended duration of leaf coloration (Figure 4e) under warming might increase the efficiency of nitrogen resorption (Rennenberg et al., 2010) and increase the vegetation greenness in this period, which will modify the surface energy balance through biophysical processes (Shen et al., 2015). The extended period of leaf coloration might also prolong the plant transpiration time and increase soil water consumption. The impact of autumn warming on net ecosystem productivity is dual, increasing both respiratory flux to the atmosphere (Piao et al., 2008) and forest gross primary photosynthesis (Keenan et al., 2014). The relatively static onset date of leaf coloration and its weak response to temperature would preclude the vegetation from fully using the potential increase in CO₂ assimilation in early autumn induced by warming (Stinziano & Way, 2017). Combined with a delay in the end of the season and an increase in respiration attributable to warming, this suggests that additional warming will probably not result in a continuous increase in autumn CO₂ assimilation.

In summary, satellite NDVI time series and ground-based physiological observations indicated no significant delay in the start of
autumnal leaf coloration for most areas covered by natural vegetation over middle and high northern latitudes. Neither pre-D$_{LCO}$ temperature nor pre-D$_{LCO}$ precipitation significantly affected the interannual variations of the start of leaf coloration in most areas, indicating that the start of leaf senescence is triggered by photoperiod. Interestingly, there was a weaker positive correlation between the start of autumnal leaf coloration and pre-D$_{LCO}$ T$_{min}$ for vegetation in regions with longer daylength, indicating strong photoperiodic control of the start of leaf senescence. For vegetation with a given daylength at D$_{LCO}$ >13.5 h, the positive correlation between D$_{LCO}$ and pre-D$_{LCO}$ T$_{min}$ was slightly stronger in colder areas, suggesting that there is strong selection pressure in harsher temperature environments on the timing of leaf coloration onset, and that autumn warming could have a stronger delaying effect on leaf coloration onset in colder areas than in warmer areas. This study suggests that autumnal warming will not change the start date of leaf senescence, but it might slow the rate of senescence. A slower senescence speed might extend the period of senescence and provide more time to re-allocate nutrients and prepare for overwintering. Such changes could substantially affect carbon and nutrient cycles. Our study provides a foundation for understanding the complex relationships among nutrient cycling, vegetation growth, energy exchange and climate change in autumn in temperate and boreal regions dominated by winter deciduous vegetation.

## AUTHOR CONTRIBUTIONS

M.S. designed the research and conceived the study; N.J. and M.S. performed the research; N.J. analysed the data; and all the authors interpreted the results and wrote the paper.

## ACKNOWLEDGMENTS

This work was funded by the Second Scientific Expedition to the Qinghai-Tibet Plateau (no. 2019QZKK0307) and the Fundamental Research Funds for the Central Universities. M.C. wishes to acknowledge the ERC Starting Grant LEAF-FALL (714916). J.P. acknowledges financial support from the European Research Council Synergy (grant no. ERC-SyG-2013-610028 IMBALANCE-P). L.Š. was supported by the Ministry of Education, Youth and Sports of the Czech Republic within the CzeCOS programme (grant no. LM2015061) and by SustES—Adaptation Strategies for Sustainable Ecosystem Services and Food Security Under Adverse Environmental Conditions (project no. CZ.02.1.01/0.0/0.0/16_019/0000797).

## DATA AVAILABILITY STATEMENT

All data used for this study are publicly available online. The satellite reflectance products at .05° resolution (MOD09CMG) used to estimate phenological metrics and the global land cover map (MCD12C1-2009) used to identify natural vegetation are freely available online at: https://ladsweb.modaps.eosdis.nasa.gov. The PhenoCam data (PhenoCam Dataset v.2.0) used to evaluate the satellite-derived phenological metrics are available at: https://doi.org/10.3334/ORNLD AAC/1674; and the subsets of satellite reflectance products at 500-m resolution (MOD09A1) at PhenoCam sites are downloaded from: https://modis.ornl.gov/globalssubset/. The climatic data for the region

are publicly available: CRU TS 4.03 monthly climatic data are available via http://data.ceda.ac.uk; and CRU-NCEP v.7.2 6-hourly climatic data are available via https://vesg.ipsl.upmc.fr. In situ phenological observations in China are available from the National Earth System Science Data Sharing Infrastructure, National Science and Technology Infrastructure of China (http://www.geodata.cn). The climatic data of in situ observations in China are available from National Meteorological Information Center (http://data.tpcdc.ac.cn). The site-based gross primary productivity products used to estimate phenological metrics and the corresponding half-hourly climatic data are extracted from the FLUXNET2015 Dataset (http://fluxnet.fluxdata.org/data/fluxnet2015-dataset/). The source code of the spatial-temporal Savitzky-Golay filter is available at: https://github.com/cao-sre/STSG_IDL Program. The codes for analyses are available from figshare (https://figshare.com/s/be760555bb74ef0e6bf2).

## ORCID

Nan Jiang https://orcid.org/0000-0001-5151-5715

Miaogen Shen https://orcid.org/0000-0001-5742-8807

Shilong Piao https://orcid.org/0000-0001-8057-2292

Eryuan Liang https://orcid.org/0000-0002-8003-4264

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## BIOSKETCH

Miaogen Shen is a professor at the Beijing Normal University, China. He is interested in interactions between vegetation and climate. More information is available at: https://www.resea.rchgate.net/profile/Miaogen-Shen.

## SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

**How to cite this article:** Jiang, N., Shen, M., Ciais, P., Campioli, M., Peñuelas, J., Körner, C., Cao, R., Piao, S., Liu, L., Wang, S., Liang, E., Delpierre, N., Soudani, K., Rao, Y., Montagnani, L., Hörtnagl, L., Paul-Limoges, E., Myneni, R., Wohlfahrt, G. ... Zhao, W. (2022). Warming does not delay the start of autumnal leaf coloration but slows its progress rate. *Global Ecology and Biogeography*, 31, 2297-2313. https://doi.org/10.1111/geb.13581