# Climate change-induced greening on the Tibetan Plateau modulated by mountainous characteristics

Hongfen Teng, Zhongkui Luo, Jinfeng Chang, Zhou Shi, Songchao Chen, Yin

Zhou, Philippe Ciais, Hanqin Tian

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Hongfen Teng, Zhongkui Luo, Jinfeng Chang, Zhou Shi, Songchao Chen, et al. Climate change-induced greening on the Tibetan Plateau modulated by mountainous characteristics. Environmental Research Letters, 2021, 16 (6), pp.064064. 10.1088/1748-9326/abfeeb. hal-03277518

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# Climate change-induced greening on the Tibetan Plateau modulated by mountainous characteristics

To cite this article: Hongfen Teng et al 2021 *Environ. Res. Lett.* **16** 064064

View the [article online](#) for updates and enhancements.
ENVIRONMENTAL RESEARCH
LETTERS

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LETTER

Climate change-induced greening on the Tibetan Plateau
modulated by mountainous characteristics

Hongfen Teng<fnref n="1" />,<fnref n="2" /> ID, Zhongkui Luo<fnref n="2" /> ID, Jinfeng Chang<fnref n="2" /> ID, Zhou Shi<fnref n="2" />,<fnref n="3" />,* ID, Songchao Chen<fnref n="4" />,<fnref n="5" /> ID, Yin Zhou<fnref n="2" />, Philippe Ciais<fnref n="5" /> ID and Hanqin Tian<fnref n="7" /> ID

E-mail: shizhou@zju.edu.cn

**Keywords:** vegetation greenness, spatial variability, climate change, mountainous effects, Tibetan Plateau

Supplementary material for this article is available [online](#)

**Abstract**

Global terrestrial vegetation is greening, particularly in mountain areas, providing strong feedbacks to a series of ecosystem processes. This greening has been primarily attributed to climate change. However, the spatial variability and magnitude of such greening do not synchronize with those of climate change in mountain areas. By integrating two data sets of satellite-derived normalized difference vegetation index (NDVI) values, which are indicators of vegetation greenness, in the period 1982–2015 across the Tibetan Plateau (TP), we test the hypothesis that climate-change-induced greening is regulated by terrain, baseline climate and soil properties. We find a widespread greening trend over 91% of the TP vegetated areas, with an average greening rate (i.e. increase in NDVI) of 0.011 per decade. The linear mixed-effects model suggests that climate change alone can explain only 26% of the variation in the observed greening. Additionally, 58% of the variability can be explained by the combination of the mountainous characteristics of terrain, baseline climate and soil properties, and 32% of this variability was explained by terrain. Path analysis identified the interconnections of climate change, terrain, baseline climate and soil in determining greening. Our results demonstrate the important role of mountainous effects in greening in response to climate change.

**1. Introduction**

Understanding changes in vegetation greenness in
mountainous areas is of great importance because
these changes are associated with a series of ecosys-
tem processes and services, such as carbon and water
cycling (Gottfried *et al* 2012, Hagedorn *et al* 2019).
Changes in vegetation greenness over time usually
consist of an alternating sequence of greening and/or
browning, which are defined as statistically significant

increases and decreases in vegetation greenness over a
period of several years (De Jong *et al* 2012, Chen *et al*
2019a). The effects of climate change, notably global
warming or wetting, on vegetation greenness have
been widely recognized (Zhu *et al* 2016, Du *et al*
2018, Park *et al* 2020). However, the effects of cli-
mate change on greening trends are inconsistent
depending on the mountainous characteristics, such
as topography, climate and soil properties, which may
interact with climate change to regulate vegetation

<footnote n="1">¹ School of Environmental Ecology and Biological Engineering, Wuhan Institute of Technology, Wuhan 430205, People's Republic of China</footnote>

<footnote n="2">² College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, People's Republic of China</footnote>

<footnote n="3">³ State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, People's Republic of China</footnote>

<footnote n="4">⁴ INRA, Unité Infosol, 45075 Orléans, France</footnote>

<footnote n="5">⁵ UMR SAS, INRA, Agrocampus Ouest, 35042 Rennes, France</footnote>

<footnote n="6">⁶ Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ/IPSL, Université Paris Saclay, 91191 Gif sur Yvette, France</footnote>

<footnote n="7">⁷ International Center for Climate and Global Change Research, School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL 36849, United States of America</footnote>

<footnote>* Author to whom any correspondence should be addressed.</footnote>
greenness dynamics (Pugnaire et al 2019, Xu et al 2020). Identifying the processes underlying the spatial variability of greening under climate change is challenging, particularly in mountainous areas.

The Tibetan Plateau (TP), which is called the ‘Third Pole’ of the earth with an average elevation of 4000 m (Qiu 2008), has responded more sensitively to climate change than other zones at the same latitude (Duan et al 2017, Teng et al 2018). Over the past decades, satellite-derived vegetation indices have indicated widespread change on the TP (Shen et al 2015a, Zhu et al 2016, An et al 2018). The long-term changes in vegetation greenness over the TP are recognized to be driven primarily by climate change (Piao et al 2014, Zhu et al 2016), specifically warming (Qiu 2008) and wetting (Chen et al 2013). However, the trend of greening shows great variability on the TP due to the strong vertical zonality of the topography (Anderson et al 2020), baseline climate (Shen et al 2011), and soil conditions (Kato et al 2006). The terrain is a significant regulator of energy and water availability even at fine scales and is very complex on the TP; thus, the terrain may play a vital role in regulating vegetation dynamics (Bolton et al 2018, Gao et al 2019). The baseline climate plays a key role in the surface energy balance and in modulating the land-climate interactions (Forzieri et al 2017), and may therefore impact the widespread greening of the TP. In addition, the heterogeneity of soil nutrient availability and the abiotic soil conditions may lead to profound changes in plant species diversity and community structure (Pugnaire et al 2019), ultimately driving diverse greenness dynamics on the TP. The comprehensive impact of climate change, terrain, baseline climate and soil, which influence the permafrost and groundwater, the flow of rainwater into or off the soil, and the heat absorbed by soil, induce extreme local variability of the vegetation composition and structure on the TP (Cheng and Jin 2013, Mu et al 2016). Thus, investigating the greening pattern on the TP and the underlying drivers is important for sustainably managing the TP ecosystems and is also important for the land surface energy balance of the region (Zhang et al 2013, An et al 2018). However, it is still challenging to understand the underlying mechanisms underpinning the spatial variability of greening on the TP.

Long-term remotely sensed measurements of the normalized difference vegetation index (NDVI) have been widely used to indicate changes in large-scale vegetation dynamics and greenness (Huete 2016, Myers-Smith et al 2020). Here, we investigate the trend of the growing season NDVI across the TP for the period 1982–2015 using remotely sensed data sets. We present a new method to spatially resample the NDVI record and thus provide a new growing season NDVI data set at a 1 km resolution. Combined with climate records from meteorological stations across the TP, we also disentangle the relative importance

of, and interactions among, climate change variables, terrain attributes, baseline climatic characteristics, and soil properties in controlling the spatial variability of the greening trend.

## 2. Materials and methods

### 2.1. Satellite-derived NDVI data sets

Two remote sensing data sets, AVHRR GIMMS NDVI3g (hereafter NDVI<sub>GIMMS</sub>) during the period from 1982 to 2015 and SPOT-VGT NDVI (hereafter NDVI<sub>SPOT</sub>) covering the period from 1999 to 2013, were used in this study. NDVI<sub>GIMMS</sub> is the longest time series NDVI data set and provides information on terrestrial vegetation changes with a spatial resolution of 1/12° (~8 km) (Tucker et al 2005). This data set was compiled by merging segments (data strips) during a half-month period using the maximum value composite (MVC) method. The NDVI<sub>SPOT</sub> product has been pre-processed by the VEGETATION Processing Centre at the Flemish Institute for Technological Research in Belgium. The spatial resolution of NDVI<sub>SPOT</sub> is 1 km, and the temporal resolution is 10 days (Maisongrande et al 2004). To ensure temporal overlap and equal treatment with NDVI<sub>GIMMS</sub>, the 10 day images were aggregated into monthly MVC.

In this study, we used an empirical orthogonal teleconnections (EOT) model (Appelhans et al 2015, Detsch et al 2016) to assess the dynamics of the NDVI during 1982–2015 at a 1 km spatial resolution (SI appendix, Processing of NDVI data sets (available online at stacks.iop.org/ERL/16/064064/mmedia)). After that, the new NDVI data set (hereafter NDVI<sub>EOT</sub>) was used in the following analysis. In this study, the EOT analysis was performed using the package remote in R 3.6.3 (R Core Team 2020). To minimize the impacts of snow and ice, we considered NDVI<sub>EOT</sub> within the plant growing season from May to September during 1982–2015 (Shen et al 2015b). The mean values of the monthly NDVI<sub>EOT</sub> (hereafter NDVI<sub>mean</sub>) during the growing season over 1982–2015 were calculated based on the corresponding NDVI<sub>EOT</sub> values to represent the average of growing season NDVI. To reduce the noise from non-vegetation signals, grid cells with long-term NDVI<sub>mean</sub> values less than 0.1 over the period 1982–2015 were excluded in this study (Liu et al 2016).

### 2.2. Estimation of long-term NDVI trend

To quantify the long-term trend of growing season vegetation growth, we performed least squares linear regression analysis using the annual growing season NDVI<sub>mean</sub> as the dependent variable and the year as the independent variable over the period 1982–2015. The slope of the regression (hereafter Tr<sub>mean</sub>) was then defined as the linear trend of the vegetation
that explained 97.25% of the baseline climate variance and six PCcs (PCc1 to PCc6) that explained 96.08% of the variance in the assessed climate change factors were used in the following analysis (figure S2).

### 2.3.2. Terrain

At each of the 60 meteorological stations, seven terrain attributes, including the digital elevation model (DEM), slope, aspect, curvature (Curv), topographic wetness index (TWI), multiresolution index of valley bottom flatness (MrVBF), and terrain ruggedness index (TRI), were extracted from the 3 arcsecond (approximately 90 m) grid Shuttle Radar Topography Mission DEM (SI appendix, Explanation of terrain attributes).

### 2.3.3. Soil attributes

Maps of bulk density (BD), total nitrogen (TOTN), soil content (sand, silt and clay), effective cation exchange capacity (ECEC), total exchangeable bases (TEB), and available water capacity (TAWC) were obtained from a 30 arcsecond raster database of the harmonized soil property values for the world (WISE30sec) (Batjes 2016). Soil maps of pH (Chen et al 2019b) and soil organic matter (SOM) (Liang et al 2019) were produced using soil samples, environmental variables, and digital soil mapping techniques. In this study, the ten soil attributes mentioned above were extracted at the 60 meteorological station locations for the following analysis.

### 2.3.4. Ecosystem types

The ecosystem types were identified according to the vegetation map of the TP that was obtained from the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC) (www.resdc.cn). The vegetation map was produced from a digitized 1:1000 000 Vegetation Atlas of China (Chinese Academy of Sciences 2001). In the vegetation map, four vegetation types related to forest (coniferous forest, coniferous and broad-leaved mixed forest, broad-leaved forest, and shrub) were classified as woodland ecosystems; three vegetation types belonging to grassland (steppe, herbs and meadow) were classified as woodland ecosystems. Other vegetation types, including alpine vegetation, cultivated vegetation desert and others, were classified as other ecosystems (figure 1).

## 2.4. Disentangling the contributions of driving factors on the spatial variability of the greening trends

We explored the variability of the relationships between indicators (i.e. climate change, terrain, baseline climate and soil) and the response variable ($T_{rmean}$) by using a linear mixed-effects model (LMM) (Qie et al 2017) at the 60 meteorological stations. The LMM contains two components: a fixed

effect (the explanatory variables) and random effects. Hypotheses were made prior to assessing the contributions of climate change, terrain, baseline climate and soil to the greening variation. First, we assumed that climate change impacted greening. Second, we assumed that the impact of climate change on the spatial variability of the greening trend was modulated by terrain, baseline climate and soil attributes. Third, we assumed that terrain characteristics amplified the spatial variability of the greening.

We built three sets of LMMs to examine these hypotheses. The first set of LMMs used the six climate change variables (PCc1 to PCc6, figure S2) as explanatory variables and $T_{rmean}$ as the response variable to describe the response variable of the greening trend. The second set of LMMs used the variables of climate change (PCc1 to PCc6), terrain, baseline climate (PCb1 to PCb3) and soil as explanatory variables. The third set of LMMs was used similarly to the second set of LMMs, but without the terrain attribute.

Ecosystem types were treated as a random effect, allowing each ecosystem type to have a separate intercept in all sets of LMMs. The predicted greening rates by the three sets of LMMs driven by the identified variables mentioned above were compared with the corresponding modeled greening rates. The LMM enabled the estimation of the assessed RMSE of an explanatory variable (i.e. climate change, terrain, baseline climate and soil in this study) compared with other variables that were considered. In this study, we used the RMSE value to evaluate the relative importance of a given variable in the LMM (SI appendix, Relative importance of variables), and also used the resulting coefficient of determination ($R^2$) to present the explanation rate of the model. The LMMs were used at the locations of the 60 meteorological stations using the package *nlme* in R 3.6.3 (R Core Team 2020).

## 2.5. Partial least squares—path modeling (PLS-PM)

We applied PLS-PM with four latent variables, namely, climate change, terrain, baseline climate and soil, to assess their direct/indirect effects on the variability of $T_{rmean}$. The latent variables were reflected by indicators. For example, for the latent variable ‘climate change,’ we considered six indicators: PCc1 to PCc6. In the PLS-PM, the loading of each latent variable was the key to estimating the variable scores and was calculated as the correlation between a latent variable and its indicators. An iterative algorithm was used to estimate the loadings until the convergence of the loadings was reached to maximize the explained variance of the dependent variables (both latent and indicator variables) (Luo et al 2019). The $R^2$ values of the endogenous latent variables indicated the amount of variance in the endogenous latent variable explained by its independent latent variables. The
result of the path coefficient can be positive or nega-
tive. A negative path coefficient means that increasing
the causal variable causes a decrease in the dependent
variable if all other causal variables are held constant.

Using PLS-PM, we explored how the greening
trend responded to climate change, terrain, baseline
climate and soil, and we also investigated the interac-
tions among these variables. We first assumed that cli-
mate change, terrain, baseline climate and soil directly
impact the spatial variability of the greening trend.
Second, we assumed that these variables have indir-
ect impacts on the spatial variability of the green-
ing trend through their interactions with each other.
We also tested the assumptions based on the variables
without terrain (climate change, baseline climate and
soil). The PLS-PM was performed using the package
plspm in R 3.6.3 (R Core Team 2020).

3. Results

**3.1. Climate change over the past decades on the TP**

Figure 2 shows the long-term annual total precip-
itation, average mean temperature and annual total
evaporation as well as their corresponding trends dur-
ing 1982–2015. During the past three decades, the TP
has experienced a warming trend based on the com-
parison of time series annual average temperature
on the whole TP (0.05 °C yr⁻¹, *p* < 0.0001), wood-
land ecosystem (0.04 °C yr⁻¹, *p* < 0.0001) and grass-
land ecosystem (0.07 °C yr⁻¹, *p* < 0.0001) (figure 2).
Based on the records of the 60 meteorological sta-
tions, the total annual precipitation showed inter-
annual variation with an insignificant trend during
1982–2015 (0.13 mm yr⁻¹, *p* = 0.8206). Woodland
ecosystem, however, showed a significant decreas-
ing trend of annual total precipitation over the study
period (−1.67 mm yr⁻¹, *p* = 0.0415). The annual
total evaporation showed a universal decreasing trend
over the whole TP, woodland ecosystem, and grass-
land ecosystem (figure 2).

**3.2. Greening on the TP**

The growing season NDVI<sub>EOT</sub> of the time series grid
cells at a 1 km resolution shows a mean value of
NDVI<sub>mean</sub> of 0.343, with an increasing trend of 0.011
per decade (i.e. greening, *p* < 0.0001) across the
whole TP during the period 1982–2015 (figure 3(c)).
In general, the NDVI increases from the very dry
western TP to the wetter eastern TP. At the pixel
level, NDVI<sub>mean</sub> shows great spatial variability, rang-
ing from 0.10 to 0.83 (figure 3(a)). A 91% pro-
portion of the vegetated land over the TP exhibits
greening (i.e. Tr<sub>mean</sub> > 0, *p* < 0.0001), and only 9%
experiences browning (*p* < 0.0001), that is, a decrease
in NDVI<sub>mean</sub> (figure 3(b)). Woodland ecosystems,
which grow at lower altitudes (1706 ~ 4440 m),
have a higher NDVI<sub>mean</sub> value (0.514) (figure 3(d))
and show more rapid and significant greening rates
(0.013 per decade, *p* = 0.0003) than the average

value for the whole TP. Grassland ecosystems, which
account for 45% of the total TP (figure 1(c)), show
a lower greening rate than the whole TP (0.010 per
decade, *p* < 0.0001), with an NDVI<sub>mean</sub> value of 0.286
(figure 3(e)).

**3.3. Association of greening with climate change only**

The LMM results suggested that climate change
explains only 26% of the spatial variability of greening
trends (R² = 0.26) (figure 4(a)). The random inter-
cepts for woodland and grassland ecosystems were
0.08 (×10⁻¹⁰) and 0.04 (×10⁻¹⁰), respectively (table
S2). Among the indicators of climate change, PCc4,
which had a high correlation with Tr<sub>Tmx gs</sub> over 1982–
2015 based on the PCA results (correlation = 0.55,
figure S2(f)), showed significant correlation with the
greening trend (coefficient = -3.23, table S2).

**3.4. Greening modulated by terrain, baseline
climate and soil properties**

In addition to climate change, we introduce terrain,
baseline climate and soil variables (table S1) into the
LMM to test the hypothesis that these factors modu-
late the impact of climate change on the spatial vari-
ability of greening rates. We find that variables of ter-
rain, baseline climate and soil can explain another
58% of the greening trend variability (R² = 0.26 for
climate change alone versus R² = 0.84 for all variables
together). Among these variables, terrain can help
explain more than 32% of the variability (R² = 0.52
with all variables except terrain) (figure 4(a)).

The results regarding the relative influence of
the LMM, which includes all environmental vari-
ables, suggest that terrain is the most important vari-
able in terms of explaining the spatial variation in
greening trends (relative importance = 42%). Alti-
tude has been found to be the most important
indicator within terrain variables in driving the spa-
tial variation in greening trends (relative import-
ance = 14%) (figure 4(b)). PCb1, which was mainly
correlated with temperature (figure S2(c)), showed
the highest contribution in the baseline climate to
explaining the spatial variance of greening trends
(relative importance = 13%). Overall, the contribu-
tion of soil to explaining the variance of greening
was comparable to that of other variables (relative
importance = 28%). According to our results, the
ECEC showed the highest relative importance value
(8%) for the greening trend among the soil attributes
(figure 4(b)).

**3.5. Interconnections between greening trend and its drivers**

PLS-PM suggests that climate change is directly and
negatively correlated with the spatial variability of
greening trends over the whole TP (correlation coef-
ficients = -0.40) (figure 5(a)). However, the cor-
relation between climate change and greening could
<figure><img src="image_4.png" /></figure>

be different among ecosystems, which is 0.57 in the
woodland and -0.11 in the grassland (figures 5(b)
and (c)). According to the loading score of each indic-
ator to the corresponding variable, PCc1, which was
mainly correlated with temperature (figure S2(c)),
was a more powerful indicator of climate change for

the model over the whole TP and woodland (loading
score = 0.76 and 0.89, respectively). However, PCc2,
which was mainly correlated with evaporation (figure
S2(c)), was a more powerful indicator of climate
change for the grassland (loading score = -0.96)
(table S3). Climate change also indirectly affects the
<figure><img src="image_5.png" /><figcaption>**Figure 3.** Spatial pattern of (a) average annual mean growing season NDVI (NDVImean) and (b) the trend in NDVImean (Trmean) for 1982–2015. White pattern indicates areas with a multiyear average growing season NDVI less than 0.1. Comparisons of long-term trends in annual mean growing season NDVI over the different ecosystem types across the TP based on the zonal statistics for (c) the whole TP, (d) woodland ecosystem, and (e) grassland ecosystem over the growing season (May to September) during 1982–2015. The statistically insignificant regions (*p* > 0.05) are masked out from the statistics. The black and solid regression lines are overlaid and corresponding regression equations are reported in the labels. The average values of mean growing season NDVI for each ecosystem are shown by gray and dashed lines.</figcaption></figure>

greening trend through its impact on soils (correla-
tion coefficients = 0.19) (figure 5(a)). Baseline cli-
mate, which was mainly derived by temperature on
PCb2 (loading scores = −0.77, figure S2(f), table S3),
was shown to have a higher direct contribution
than climate change in terms of explaining the vari-
ance of greening (correlation coefficient = −0.72)
(figure 5(a)).

Terrain, which is mainly reflected by the TRI and
TWI (loading scores in the path analysis are −0.90
and 0.90, respectively; table S4), had direct and neg-
ative control on the variability of greenness (correla-
tion coefficient = −0.24) (figure 5(d)). This finding
could be amplified over woodland ecosystems, with
a direct correlation coefficient of −0.65 between ter-
rain and greening (figure 5(e)). The terrain also had
an indirect relationship with the greening rate via all
hypothesized pathways, including those involving cli-
mate change, baseline climate and soil (figure 5). The

direct impact of climate change on greening could be
strengthened over woodland and grassland when the
terrain attributes were included in the path analysis
(figures 5(e) and (f)).

4. Discussion

As expected, climate change has a significant direct
effect on the spatial variability of greening trends. The
finding of a high correlation between temperature
and vegetation dynamics (table S2 and figure S2(f))
was in line with previous research showing that the
vegetation phenology at high latitudes is primar-
ily determined by temperature (Shen et al 2016).
However, the temperature increase may have vari-
able effects on greening among ecosystems. In this
study, woodland ecosystem experienced a relatively
rapid greening trend during the warming period
over 1982–2015. The increasing temperature, which
<figure><img src="image_6.png" /></figure>

primarily contributed to the indicator of PCc1 in the climate change variable, promoted the greening over the woodland ecosystem (figure 5(b), table S3, figure S2(f)), indicating that warmer temperature may lead to an extended growing season for temperate forests due to earlier greenup and delayed senescence (Hwang et al 2014). However, increasing temperature will have a negative impact on the greening over the grassland ecosystem. Warming over the TP induced an increased preseason precipitation and corresponding reduced precipitation during the growing season. The deficient sunshine intensity and duration of preseason, and the drought stress induced by dramatic increases in temperature and inadequate precipitation during the growing season, could counteract early spring carbon assimilation (Angert et al 2005) and increase vegetation mortality (Adams et al 2009). Accelerated leaf senescence associated with drought stress along with warming has been studied specifically in grasslands (Rivero et al 2007), for which chemical mechanisms are largely known.

Moreover, climate change not only favors the rapid growth of plants and controls the structure and function of ecosystems but also accelerates permafrost thawing throughout the TP (Chen et al 2013). In past decades, the mean annual permafrost temperatures at a depth of 6.0 m increased by 0.12 °C–0.67 °C (Wu and Zhang 2008). Such permafrost degradation caused rapid soil carbon loss through the efflux of CH₄ and CO₂. In turn, the decomposition of soil organic carbon over these areas has accelerated with climate warming (Chen et al 2013). On the other hand, a changing climate has the potential to alter the composition of plant and soil communities and the

interactions between them. The interactions among plants and their associated abiotic soils, which are termed plant–soil feedbacks, can lead to complex feedbacks that regulate plant community dynamics and ecosystem processes (Pugnaire et al 2019).

There is growing evidence that the impact of climate change on the trend of vegetation greenness can be amplified by terrain attributes (Liu et al 2013, An et al 2018, Radula et al 2018). The influence of elevation-dependent warming on the vegetation dynamics in mountainous environments has also been documented, revealing that warming is more rapid at higher elevations (Mountain Research Initiative EDW Working Group 2015, Xu et al 2020). For instance, warming promotes seedling establishment and vegetation infilling (Du et al 2018) and induces the upward migration of the treeline on the TP. The terrain also causes localized changes in soil moisture and soil temperature. The steepness, shape and slope of an area affect the flow of rainwater into or off the soil as well as the amount of heat absorbed by the soil. Moreover, the structure and function of ecosystems vary significantly with climate driven by fine-scale variations in topography. It is usually assumed that the spatial variability of the greening trend over the mid- and high-latitude forest phenology is primarily determined by temperature, while seasonal rainfall is a more important factor for the interannual vegetation greenness dynamics in arid and semiarid grassland regions (figure 2) (Hwang et al 2014).

Baseline climate also has an impact on the greenness trend through its relationship with climate change, soil, and vegetation type (figure 5). At higher latitudes, plants slow or postpone developmental
Path analysis for the trend in growing season NDVI based on all variables but terrain

<figure><img src="image_7.png" /></figure>

Path analysis for the trend in growing season NDVI based on all variables

<figure><img src="image_8.png" /><figcaption>**Figure 5.** Results of path analysis on the direction and magnitude of the effects of latent variable climate change and climate, and soil for $T_{rmean}$ during 1982–2015 for (a) the whole TP, (b) woodland ecosystem, and (c) grassland ecosystem. Path analysis on the direction and magnitude of the effects of latent variable climate change, climate, terrain and soil for $T_{rmean}$ during 1982–2015 for (d) the whole TP, (e) woodland ecosystem, and (f) grassland ecosystem. Continuous arrows indicate positive relationships. Dashed arrows indicate negative relationships. Numbers in parentheses show the direct correlation coefficients between the latent variables. Path analysis was performed based on the values at 60 meteorological stations.</figcaption></figure>

processes to mitigate the high risk of freezing injury at very low temperatures (Shen et al 2016). Frozen soil water under low temperatures could limit water absorption by alpine vegetation roots. The dominant enhancing effect of the increasing minimum temperature was likely caused by the reduction in low-temperature constraints on alpine plants, which promoted vegetation growth. In this study, the grassland with a higher average temperature (i.e. meadow) showed a higher greening rate than that in a colder environment (i.e. steppe) (figures 2 and 4). Moreover, the combined effects of changes in snow depth and cloud cover in response to the increase in CO₂ concentrations result in greater surface heat storage in

high-mountain regions and therefore amplify the warming rate with elevation on the TP (Palazzi et al 2017). The implications for the combination of cloud radiation and snow albedo feedbacks and the increases in water vapor content in the atmosphere can thus affect the greening trend (Mountain Research Initiative EDW Working Group 2015, Yan et al 2016).

**5. Conclusion**

This study used multiple data sources and approaches to evaluate the greening trend of the TP vegetation and revealed the drivers of the variation in this
greening. The findings of this study have several important implications. First, our results suggest an overall greening trend over the TP in recent decades. Second, the variability of greening in recent decades has been driven not only by climate change but also by terrain, baseline climate and soil. Finally, the interconnections of climate change, terrain, baseline climate and soil need to be considered in understanding and predicting the vegetation dynamics over the TP. The methods and logic developed here could aid understanding of the influence of climate change on terrestrial vegetation and toward improving the simulation of future vegetation dynamics.

## Data availability statement

All data that support the findings of this study are included within the article (and any supplementary files).

## Acknowledgments

This research was supported by the National Natural Science Foundation of China (No. 41901055, 41930754). We thank all the people involved in climate analysis and vegetation modeling related to this study.

## ORCID iDs

Hongfen Teng https://orcid.org/0000-0003-4384-9402

Zhongkui Luo https://orcid.org/0000-0002-6744-6491

Jinfeng Chang https://orcid.org/0000-0003-4463-7778

Zhou Shi https://orcid.org/0000-0003-3914-5402

Songchao Chen https://orcid.org/0000-0003-1245-0482

Philippe Ciais https://orcid.org/0000-0001-8560-4943

Hanqin Tian https://orcid.org/0000-0002-1806-4091

## References

Adams H D, Guardiola-Claramonte M, Barron-Gafford G A, Villegas J C, Breshears D D, Zou C B, Troch P A and Huxman T E 2009 Temperature sensitivity of drought-induced tree mortality portends increased regional die-off under global-change-type drought *Proc. Natl Acad. Sci. USA* **106** 7063–6

An S, Zhu X, Shen M, Wang Y, Cao R, Chen X, Yang W, Chen J and Tang Y 2018 Mismatch in elevational shifts between satellite observed vegetation greenness and temperature isolines during 2000–2016 on the Tibetan Plateau *Glob. Change Biol.* **24** 5411–25

Anderson K, Fawcett D, Cugulliere A, Benford S, Jones D and Leng R 2020 Vegetation expansion in the subnival Hindu Kush Himalaya *Glob. Change Biol.* **26** 1608–25

Angert A, Biraud S, Bonfils C, Henning C C, Buermann W, Pinzon J, Tucker C J and Fung I 2005 Drier summers cancel out the CO₂ uptake enhancement induced by warmer springs *Proc. Natl Acad. Sci. USA* **102** 10823–7

Appelhans T, Detsch F and Nauss T 2015 Remote: empirical orthogonal teleconnections in *R J. Stat. Softw.* **65** 1–19

Batjes N H 2016 Harmonized soil property values for broad-scale modelling (WISE30sec) with estimates of global soil carbon stocks *Geoderma* **269** 61–8

Bolton D K, Coops N C, Hermosilla T, Wulder M A and White J C 2018 Evidence of vegetation greening at alpine treeline ecotones: three decades of Landsat spectral trends informed by lidar-derived vertical structure *Environ. Res. Lett.* **13** 084022

Chen C *et al* 2019a China and India lead in greening of the world through land-use management *Nat. Sustain.* **2** 122–9

Chen H *et al* 2013 The impacts of climate change and human activities on biogeochemical cycles on the Qinghai-Tibetan Plateau *Glob. Change Biol.* **19** 2940–55

Chen S C, Liang Z, Webster R, Zhang G, Zhou Y, Teng H, Hu B, Arrouays D and Shi Z 2019b A high-resolution map of soil pH in China made by hybrid modelling of sparse soil data and environmental covariates and its implications for pollution *Sci. Total Environ.* **655** 273–83

Cheng G D and Jin H J 2013 Permafrost e águas subterrâneas no planalto de Qinghai-Tibete e no nordeste da China *Hydrogeol. J.* **21** 5–23

Chinese Academy of Sciences 2001 1:1 000 000 Vegetation Atlas of China. (Beijing: Science Press)

De Jong R, Verbesselt J, Schaepman M E and De Bruin S 2012 Trend changes in global greening and browning: contribution of short-term trends to longer-term change *Glob. Change Biol.* **18** 642–55

Detsch F, Otte I, Appelhans T, Hemp A and Nauss T 2016 Seasonal and long-term vegetation dynamics from 1-km GIMMS-based NDVI time series at Mt. Kilimanjaro, Tanzania *Remote Sens. Environ.* **178** 70–83

Du H B, Liu J, Li M-H, Büntgen U, Yang Y, Wang L, Wu Z and He H S 2018 Warming-induced upward migration of the alpine treeline in the Changbai Mountains, northeast China *Glob. Change Biol.* **24** 1256–66

Duan J P, Esper J, Büntgen U, Li L, Xoplaki E, Zhang H, Wang L, Fang Y and Luterbacher J 2017 Weakening of annual temperature cycle over the Tibetan Plateau since the 1870s *Nat. Commun.* **8** 14008

Forzieri G, Alkama R, Miralles D G and Cescatti A 2017 Satellites reveal contrasting responses of regional climate to the widespread greening of Earth *Science* **356** 1140–4

Gao M D, Piao S, Chen A, Yang H, Liu Q, Fu Y H and Janssens I A 2019 Divergent changes in the elevational gradient of vegetation activities over the last 30 years *Nat. Commun.* **10** 10

Gottfried M *et al* 2012 Continent-wide response of mountain vegetation to climate change *Nat. Clim. Change* **2** 111–5

Hagedorn F, Gavazov K and Alexander J M 2019 Above- and belowground linkages shape responses of mountain vegetation to climate change *Science* **365** 1119–23

Huete A 2016 Vegetation's responses to climate variability *Nature* **531** 181–2

Hwang T, Band L E, Miniat C F, Song C, Bolstad P V, Vose J M and Love J P 2014 Divergent phenological response to hydroclimate variability in forested mountain watersheds *Glob. Change Biol.* **20** 2580–95

Kato T, Tang Y, Gu S, Hirota M, Du M, Li Y and Zhao X 2006 Temperature and biomass influences on interannual changes in CO₂ exchange in an alpine meadow on the Qinghai-Tibetan Plateau *Glob. Change Biol.* **12** 1285–98

Liang Z Z, Chen S, Yang Y, Zhao R, Shi Z and Viscarra Rossel R A 2019 National digital soil map of organic matter in topsoil and its associated uncertainty in 1980's China *Geoderma* **335** 47–56

Liu H, Tian F, Hu H C, Hu H P and Sivapalan M 2013 Soil moisture controls on patterns of grass green-up in Inner
Mongolia: an index based approach *Hydrol. Earth Syst. Sci.*
17 805–15

Liu Q, Fu Y H, Zeng Z, Huang M, Li X and Piao S 2016
Temperature, precipitation, and insolation effects on
autumn vegetation phenology in temperate China *Glob.
Change Biol.* **22** 644–55

Luo Z K, Wang G C and Wang E L 2019 Global subsoil organic carbon turnover times dominantly controlled by soil properties rather than climate *Nat. Commun.*
10 3688

Maisongrande P, Duchemin B and Dedieu G 2004
VEGETATION/SPOT: an operational mission for the Earth
monitoring; presentation of new standard products *Int. J.
Remote Sens.* **25** 9–14

Mountain Research Initiative EDW Working Group 2015
Elevation-dependent warming in mountain regions of the
world *Nat. Clim. Change* **5** 424–30

Mu C C, Zhang T, Zhang X, Li L, Guo H, Zhao Q, Cao L, Wu Q and Cheng G 2016 Carbon loss and chemical changes from permafrost collapse in the northern Tibetan Plateau *J. Geophys. Res. Biogeosci.* **121** 1781–91

Myers-Smith I H et al 2020 Complexity revealed in the greening of
the Arctic *Nat. Clim. Change* **10** 106–17

Palazzi E, Filippi L and Von Hardenberg J 2017 Insights into
elevation-dependent warming in the Tibetan
Plateau-Himalayas from CMIP5 model simulations *Clim.
Dyn.* **48** 3991–4008

Park H, Jeong S and Penuelas J 2020 Accelerated rate of vegetation green-up related to warming at northern high latitudes *Glob. Change Biol.* **26** 6190–202

Piao S L et al 2014 Evidence for a weakening relationship between interannual temperature variability and northern vegetation activity *Nat. Commun.* **5** 5018

Pugnaire F I, Morillo J A, Peñuelas J, Reich P B, Bardgett R D, Gaxiola A, Wardle D A and Van Der Putten W H 2019 Climate change effects on plant-soil feedbacks and consequences for biodiversity and functioning of terrestrial ecosystems *Sci. Adv.* **5** eaaz1834

Qie L et al 2017 Long-term carbon sink in Borneo's forests halted by drought and vulnerable to edge effects *Nat. Commun.*
8 1966

Qiu J 2008 China: the third pole *Nature* **454** 393–6

R Core Team 2020 R: a language and environment for statistical computing *R Foundation for Statistical Computing*
(Vienna)

Radula M W, Szymura T H and Szymura M 2018 Topographic wetness index explains soil moisture better than

bioindication with Ellenberg's indicator values *Ecol. Indic.*
85 172–9

Rivero R M, Kojima M, Gepstein A, Sakakibara H, Mittler R, Gepstein S and Blumwald E 2007 Delayed leaf senescence induces extreme drought tolerance in a flowering plant *Proc. Natl Acad. Sci. USA* **104** 19631–6

Shen M G et al 2015b Evaporative cooling over the Tibetan Plateau induced by vegetation growth *Proc. Natl Acad. Sci. USA* **112** 9299–304

Shen M G, Piao S, Chen X, An S, Fu Y H, Wang S, Cong N and Janssens I A 2016 Strong impacts of daily minimum temperature on the green-up date and summer greenness of the Tibetan Plateau *Glob. Change Biol.* **22** 3057–66

Shen M G, Piao S, Dorji T, Liu Q, Cong N, Chen X, An S, Wang S, Wang T and Zhang G 2015a Plant phenological responses to climate change on the Tibetan Plateau: research status and challenges *Natl Sci. Rev.* **2** 454–67

Shen M G, Tang Y H, Chen J, Zhu X L and Zheng Y H 2011
Influences of temperature and precipitation before the
growing season on spring phenology in grasslands of the
central and eastern Qinghai-Tibetan Plateau *Agric. Forest
Meteorol.* **151** 1711–22

Teng H F, Liang Z, Chen S, Liu Y, Viscarra Rossel R A, Chappell A, Yu W and Shi Z 2018 Current and future assessments of soil erosion by water on the Tibetan Plateau based on RUSLE and CMIP5 climate models *Sci. Total Environ.*
**635** 673–86

Tucker C J, Pinzon J E, Brown M E, Slayback D A, Pak E W, Mahoney R, Vermote E F and El Saleous N 2005 An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data *Int. J. Remote Sens.*
**26** 4485–98

Wu Q B and Zhang T J 2008 Recent permafrost warming on the Qinghai-Tibetan plateau *J. Geophys. Res. Atmos.* **113** D13108

Xu S, Yu Z, Lettenmaier D P, McVicar T R and Ji X 2020
Elevation-dependent response of vegetation dynamics to
climate change in a cold mountainous region *Environ. Res.
Lett.* **15** 094005

Yan L, Liu Z, Chen G, Kutzbach J E and Liu X 2016 Mechanisms of elevation-dependent warming over the Tibetan plateau in quadrupled CO₂ experiments *Clim. Change* **135** 509–19

Zhang G L, Zhang Y J, Dong J W and Xiao X M 2013 Green-up dates in the Tibetan Plateau have continuously advanced from 1982 to 2011 *Proc. Natl Acad. Sci. USA*
110 4309–14

Zhu Z C et al 2016 Greening of the Earth and its drivers *Nat. Clim. Change* **6** 791–6