TY - JOUR
T1 - Convergence and divergence emerging in climatic controls of polynomial trends for northern ecosystem productivity over 2000–2018
AU - Zhang, Wenxin
AU - Jin, Hongxiao
AU - Jamali, Sadegh
AU - Duan, Zheng
AU - Wu, Mousong
AU - Ran, Youhua
AU - Ardö, Jonas
AU - Eklundh, Lars
AU - Jönsson, Anna Maria
AU - Sun, Huaiwei
AU - Hu, Guojie
AU - Wu, Xiaodong
AU - Yun, Hanbo
AU - Wu, Qingbai
AU - Fu, Ziteng
AU - Yu, Kailiang
AU - Tian, Feng
AU - Tagesson, Torbern
AU - Li, Xing
AU - Xiao, Jingfeng
N1 - Publisher Copyright:
© 2023 The Authors
PY - 2023
Y1 - 2023
N2 - Recent rapid warming has caused uneven impacts on the composition, structure, and functioning of northern ecosystems. It remains unknown how climatic drivers control linear and non-linear trends in ecosystem productivity. Based on a plant phenology index (PPI) product at a spatial resolution of 0.05° over 2000–2018, we used an automated polynomial fitting scheme to detect and characterize trend types (i.e., polynomial trends and no-trends) in the yearly-integrated PPI (PPIINT) for northern (> 30°N) ecosystems and their dependence on climatic drivers and ecosystem types. The averaged slope for the linear trends (p < 0.05) of PPIINT was positive across all the ecosystems, among which deciduous broadleaved forests and evergreen needle-leaved forests (ENF) showed the highest and lowest mean slopes, respectively. More than 50% of the pixels in ENF, arctic and boreal shrublands, and permanent wetlands (PW) had linear trends. A large fraction of PW also showed quadratic and cubic trends. These trend patterns agreed well with estimates of global vegetation productivity based on solar-induced chlorophyll fluorescence. Across all the biomes, PPIINT in pixels with linear trends showed lower mean values and higher partial correlation coefficients with temperature or precipitation than in pixels without linear trends. Overall, our study revealed the emergence of latitudinal convergence and divergence in climatic controls on the linear and non-linear trends of PPIINT, implying that northern shifts of vegetation and climate change may potentially increase the non-linear nature of climatic controls on ecosystem productivity. These results can improve our understanding and prediction of climate-induced changes in plant phenology and productivity and facilitate sustainable management of ecosystems by accounting for their resilience and vulnerability to future climate change.
AB - Recent rapid warming has caused uneven impacts on the composition, structure, and functioning of northern ecosystems. It remains unknown how climatic drivers control linear and non-linear trends in ecosystem productivity. Based on a plant phenology index (PPI) product at a spatial resolution of 0.05° over 2000–2018, we used an automated polynomial fitting scheme to detect and characterize trend types (i.e., polynomial trends and no-trends) in the yearly-integrated PPI (PPIINT) for northern (> 30°N) ecosystems and their dependence on climatic drivers and ecosystem types. The averaged slope for the linear trends (p < 0.05) of PPIINT was positive across all the ecosystems, among which deciduous broadleaved forests and evergreen needle-leaved forests (ENF) showed the highest and lowest mean slopes, respectively. More than 50% of the pixels in ENF, arctic and boreal shrublands, and permanent wetlands (PW) had linear trends. A large fraction of PW also showed quadratic and cubic trends. These trend patterns agreed well with estimates of global vegetation productivity based on solar-induced chlorophyll fluorescence. Across all the biomes, PPIINT in pixels with linear trends showed lower mean values and higher partial correlation coefficients with temperature or precipitation than in pixels without linear trends. Overall, our study revealed the emergence of latitudinal convergence and divergence in climatic controls on the linear and non-linear trends of PPIINT, implying that northern shifts of vegetation and climate change may potentially increase the non-linear nature of climatic controls on ecosystem productivity. These results can improve our understanding and prediction of climate-induced changes in plant phenology and productivity and facilitate sustainable management of ecosystems by accounting for their resilience and vulnerability to future climate change.
KW - Climate sensitivity
KW - Gross primary productivity
KW - Non-linear
KW - Northern hemisphere
KW - Plant phenology index
KW - Terrestrial ecosystems
KW - Vegetation dynamics
U2 - 10.1016/j.scitotenv.2023.162425
DO - 10.1016/j.scitotenv.2023.162425
M3 - Journal article
C2 - 36870485
AN - SCOPUS:85149724021
VL - 874
JO - Science of the Total Environment
JF - Science of the Total Environment
SN - 0048-9697
M1 - 162425
ER -