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

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; Torbern Tagesson; Xing Li; Jingfeng Xiao

doi:10.1016/j.scitotenv.2023.162425

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

Sci Total Environ. 2023 May 20:874:162425. doi: 10.1016/j.scitotenv.2023.162425. Epub 2023 Mar 3.

Authors

Wenxin Zhang¹, Hongxiao Jin², Sadegh Jamali³, Zheng Duan², Mousong Wu⁴, Youhua Ran⁵, Jonas Ardö², Lars Eklundh², Anna Maria Jönsson², Huaiwei Sun⁶, Guojie Hu⁷, Xiaodong Wu⁸, Hanbo Yun⁹, Qingbai Wu¹⁰, Ziteng Fu¹⁰, Kailiang Yu¹¹, Feng Tian¹², Torbern Tagesson¹³, Xing Li¹⁴, Jingfeng Xiao¹⁵

Affiliations

¹ Department of Physical Geography and Ecosystem Science, Lund University, SE-22362 Lund, Sweden. Electronic address: wenxin.zhang@nateko.lu.se.
² Department of Physical Geography and Ecosystem Science, Lund University, SE-22362 Lund, Sweden.
³ Department of Technology and Society, Lund University, SE-221 00 Lund, Sweden.
⁴ International Institute for Earth System Sciences, Nanjing University, Nanjing, China; Jiangsu Provincial Key Laboratory of Geographic Information Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu 210023, China.
⁵ Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, China; University of the Chinese Academy Sciences, No. 19A Yuquan Road, Shijingshan District, Beijing 100049, China.
⁶ School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
⁷ Cryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, 320 West Donggang Road, Lanzhou 730000, China.
⁸ University of the Chinese Academy Sciences, No. 19A Yuquan Road, Shijingshan District, Beijing 100049, China; Cryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, 320 West Donggang Road, Lanzhou 730000, China.
⁹ Beiluhe Observation Station of Frozen Soil Environment and Engineering, State Key Laboratory of Frozen Soil Engineering, Northwest Institute of Eco-environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China.
¹⁰ University of the Chinese Academy Sciences, No. 19A Yuquan Road, Shijingshan District, Beijing 100049, China; Beiluhe Observation Station of Frozen Soil Environment and Engineering, State Key Laboratory of Frozen Soil Engineering, Northwest Institute of Eco-environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China.
¹¹ High Meadows Environmental Institute, Princeton University, Princeton, NJ 08544, USA.
¹² School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China.
¹³ Department of Physical Geography and Ecosystem Science, Lund University, SE-22362 Lund, Sweden; Department of Geosciences and Natural Resource Management, University of Copenhagen, Øster Voldgade 10, DK-1350 Copenhagen, Denmark.
¹⁴ Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, South Korea.
¹⁵ Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH 03824, USA.

PMID: 36870485
DOI: 10.1016/j.scitotenv.2023.162425

Abstract

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 (PPI_INT) 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 PPI_INT 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, PPI_INT 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 PPI_INT, 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.

Keywords: Climate sensitivity; Gross primary productivity; Non-linear; Northern hemisphere; Plant phenology index; Terrestrial ecosystems; Vegetation dynamics.

MeSH terms

Arctic Regions
Climate Change
Ecosystem*
Forests*
Plants
Seasons
Temperature