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Three‐dimensional change in temperature sensitivity of northern vegetation phenology
Author(s) -
Gao Mengdi,
Wang Xuhui,
Meng Fandong,
Liu Qiang,
Li Xiangyi,
Zhang Yuan,
Piao Shilong
Publication year - 2020
Publication title -
global change biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.146
H-Index - 255
eISSN - 1365-2486
pISSN - 1354-1013
DOI - 10.1111/gcb.15200
Subject(s) - phenology , altitude (triangle) , latitude , longitude , climate change , climatology , temperate climate , northern hemisphere , environmental science , precipitation , physical geography , boreal , atmospheric sciences , geography , ecology , geology , meteorology , biology , geometry , mathematics , geodesy , archaeology
Understanding how the temperature sensitivity of phenology changes with three spatial dimensions (altitude, latitude, and longitude) is critical for the prediction of future phenological synchronization. Here we investigate the spatial pattern of temperature sensitivity of spring and autumn phenology with altitude, latitude, and longitude during 1982–2016 across mid‐ and high‐latitude Northern Hemisphere (north of 30°N). We find distinct spatial patterns of temperature sensitivity of spring phenology (hereafter “spring S T ”) among altitudinal, latitudinal, and longitudinal gradient. Spring S T decreased with altitude mostly over eastern Europe, whereas the opposite occurs in eastern North America and the north China plain. Spring S T decreased with latitude mainly in the boreal regions of North America, temperate Eurasia, and the arid/semi‐arid regions of Central Asia. This distribution may be related to the increased temperature variance, decreased precipitation, and radiation with latitude. Compared to spring S T , the spatial pattern of temperature sensitivity of autumn phenology (hereafter “autumn S T ”) is more heterogeneous, only showing a clear spatial pattern of autumn S T along the latitudinal gradient. Our results highlight the three‐dimensional view to understand the phenological response to climate change and provide new metrics for evaluating phenological models. Accordingly, establishing a dense, high‐quality three‐dimensional observation system of phenology data is necessary for enhancing our ability to both predict phenological changes under changing climatic conditions and to facilitate sustainable management of ecosystems.