Convergence and divergence emerging in climatic controls of polynomial trends for northern ecosystem productivity over 2000–2018

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Dokumenter

  • Fulltext

    Forlagets udgivne version, 3,74 MB, PDF-dokument

  • Wenxin Zhang
  • Hongxiao Jin
  • Sadegh Jamali
  • Zheng Duan
  • Mousong Wu
  • Youhua Ran
  • Jonas Ardö
  • Lars Eklundh
  • Anna Maria Jönsson
  • Huaiwei Sun
  • Guojie Hu
  • Xiaodong Wu
  • Hanbo Yun
  • Qingbai Wu
  • Ziteng Fu
  • Kailiang Yu
  • Feng Tian
  • Tagesson, Håkan Torbern
  • Xing Li
  • Jingfeng Xiao

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.

OriginalsprogEngelsk
Artikelnummer162425
TidsskriftScience of the Total Environment
Vol/bind874
Antal sider12
ISSN0048-9697
DOI
StatusUdgivet - 2023

Bibliografisk note

Funding Information:
This research work is funded by the Swedish National Space Agency (SNSA) project (209/19) and the joint National Science Foundation of China (NSFC)-the Swedish Foundation for International Cooperation in Research and Higher Education (STINT) project (202100-3211 and CH2019-8281). H.S. acknowledges funding from the NSFC projects (52079055 and 52011530128). Y.R. acknowledges funding from NSFC (42071421). F.T. acknowledges funding from NSFC (42001299) and the Seed Fund Program for Sino-Foreign Joint Scientific Research Platform of Wuhan University (WHUZZJJ202205). X.W. acknowledges funding from the NSFC projects (41941015 and 32061143032). T.T. acknowledges funding from SNSA (95/16 and 2021-00144) and FORMAS (2021-00644). The authors thank the Center for Scientific and Technical Computing at Lund University (LUNARC) for proving resources of computation and storage within the Swedish National Infrastructure for Computing project (LU 2021/2-115, SNIC 2020/6-29 and SNIC 2019/3-588). The authors also thank Dr. Youngryel Ryu for providing photosynthetically active radiation datasets. This study is a contribution to the strategic research areas Modeling the Regional and Global Earth System (MERGE) and Biodiversity and Ecosystem Services in a Changing Climate (BECC) at Lund University. All the codes used for processing the data can be provided by the corresponding author upon reasonable request.

Funding Information:
This research work is funded by the Swedish National Space Agency (SNSA) project ( 209/19 ) and the joint National Science Foundation of China (NSFC) -the Swedish Foundation for International Cooperation in Research and Higher Education (STINT) project ( 202100-3211 and CH2019-8281 ). H.S. acknowledges funding from the NSFC projects ( 52079055 and 52011530128 ). Y.R. acknowledges funding from NSFC ( 42071421 ). F.T. acknowledges funding from NSFC ( 42001299 ) and the Seed Fund Program for Sino-Foreign Joint Scientific Research Platform of Wuhan University ( WHUZZJJ202205 ). X.W. acknowledges funding from the NSFC projects ( 41941015 and 32061143032 ). T.T. acknowledges funding from SNSA ( 95/16 and 2021-00144 ) and FORMAS ( 2021-00644 ). The authors thank the Center for Scientific and Technical Computing at Lund University (LUNARC) for proving resources of computation and storage within the Swedish National Infrastructure for Computing project ( LU 2021/2-115 , SNIC 2020/6-29 and SNIC 2019/3-588 ). The authors also thank Dr. Youngryel Ryu for providing photosynthetically active radiation datasets. This study is a contribution to the strategic research areas Modeling the Regional and Global Earth System (MERGE) and Biodiversity and Ecosystem Services in a Changing Climate (BECC) at Lund University.

Publisher Copyright:
© 2023 The Authors

ID: 347812101