Spring Phenological Sensitivity to Climate Change in the Northern Hemisphere: Comprehensive Evaluation and Driving Force Analysis
Plant phenology depends largely on temperature, but temperature alone cannot explain the Northern Hemisphere shifts in the start of the growing season (SOS). The spatio–temporal distribution of SOS sensitivity to climate variability has also changed in recent years. We applied the partial least squa...
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doaj-5fe229804c45436cbd0afb2fcfaeafca2021-06-01T00:22:56ZengMDPI AGRemote Sensing2072-42922021-05-01131972197210.3390/rs13101972Spring Phenological Sensitivity to Climate Change in the Northern Hemisphere: Comprehensive Evaluation and Driving Force AnalysisKaiwei Li0Chunyi Wang1Qing Sun2Guangzhi Rong3Zhijun Tong4Xingpeng Liu5Jiquan Zhang6School of Environment, Northeast Normal University, Changchun 130024, ChinaChinese Academy of Meteorological Sciences, Beijing 100081, ChinaChinese Academy of Meteorological Sciences, Beijing 100081, ChinaSchool of Environment, Northeast Normal University, Changchun 130024, ChinaSchool of Environment, Northeast Normal University, Changchun 130024, ChinaSchool of Environment, Northeast Normal University, Changchun 130024, ChinaSchool of Environment, Northeast Normal University, Changchun 130024, ChinaPlant phenology depends largely on temperature, but temperature alone cannot explain the Northern Hemisphere shifts in the start of the growing season (SOS). The spatio–temporal distribution of SOS sensitivity to climate variability has also changed in recent years. We applied the partial least squares regression (PLSR) method to construct a standardized SOS sensitivity evaluation index and analyzed the combined effects of air temperature (Tem), water balance (Wbi), radiation (Srad), and previous year’s phenology on SOS. The spatial and temporal distributions of SOS sensitivity to Northern Hemisphere climate change from 1982 to 2014 were analyzed using time windows of 33 and 15 years; the dominant biological and environmental drivers were also assessed. The results showed that the combined sensitivity of SOS to climate change (S<sub>Com</sub>) is most influenced by preseason temperature sensitivity. However, because of the asymmetric response of SOS to daytime/night temperature (Tmax/Tmin) and non-negligible moderating of Wbi and Srad on SOS, S<sub>Com</sub> was more effective in expressing the effect of climate change on SOS than any single climatic factor. Vegetation cover (or type) was the dominant factor influencing the spatial pattern of SOS sensitivity, followed by spring temperature (Tmin > Tmax), and the weakest was water balance. Forests had the highest S<sub>Com</sub> absolute values. A significant decrease in the sensitivity of some vegetation (22.2%) led to a decreasing trend in sensitivity in the Northern Hemisphere. Although temperature remains the main climatic factor driving temporal changes in S<sub>Com</sub>, the temperature effects were asymmetric between spring and winter (Tems/Temw). More moisture might mitigate the asymmetric response of S<sub>Com</sub> to spring/winter warming. Vegetation adaptation has a greater influence on the temporal variability of SOS sensitivity relative to each climatic factor (Tems, Temw, Wbi, Srad). More moisture might mitigate the asymmetric response of S<sub>Com</sub> to spring/winter warming. This study provides a basis for vegetation phenology sensitivity assessment and prediction.https://www.mdpi.com/2072-4292/13/10/1972vegetation phenologyclimate changephenological sensitivitystart of the growing seasondriving forcepartial least squares regression |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Kaiwei Li Chunyi Wang Qing Sun Guangzhi Rong Zhijun Tong Xingpeng Liu Jiquan Zhang |
spellingShingle |
Kaiwei Li Chunyi Wang Qing Sun Guangzhi Rong Zhijun Tong Xingpeng Liu Jiquan Zhang Spring Phenological Sensitivity to Climate Change in the Northern Hemisphere: Comprehensive Evaluation and Driving Force Analysis Remote Sensing vegetation phenology climate change phenological sensitivity start of the growing season driving force partial least squares regression |
author_facet |
Kaiwei Li Chunyi Wang Qing Sun Guangzhi Rong Zhijun Tong Xingpeng Liu Jiquan Zhang |
author_sort |
Kaiwei Li |
title |
Spring Phenological Sensitivity to Climate Change in the Northern Hemisphere: Comprehensive Evaluation and Driving Force Analysis |
title_short |
Spring Phenological Sensitivity to Climate Change in the Northern Hemisphere: Comprehensive Evaluation and Driving Force Analysis |
title_full |
Spring Phenological Sensitivity to Climate Change in the Northern Hemisphere: Comprehensive Evaluation and Driving Force Analysis |
title_fullStr |
Spring Phenological Sensitivity to Climate Change in the Northern Hemisphere: Comprehensive Evaluation and Driving Force Analysis |
title_full_unstemmed |
Spring Phenological Sensitivity to Climate Change in the Northern Hemisphere: Comprehensive Evaluation and Driving Force Analysis |
title_sort |
spring phenological sensitivity to climate change in the northern hemisphere: comprehensive evaluation and driving force analysis |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2021-05-01 |
description |
Plant phenology depends largely on temperature, but temperature alone cannot explain the Northern Hemisphere shifts in the start of the growing season (SOS). The spatio–temporal distribution of SOS sensitivity to climate variability has also changed in recent years. We applied the partial least squares regression (PLSR) method to construct a standardized SOS sensitivity evaluation index and analyzed the combined effects of air temperature (Tem), water balance (Wbi), radiation (Srad), and previous year’s phenology on SOS. The spatial and temporal distributions of SOS sensitivity to Northern Hemisphere climate change from 1982 to 2014 were analyzed using time windows of 33 and 15 years; the dominant biological and environmental drivers were also assessed. The results showed that the combined sensitivity of SOS to climate change (S<sub>Com</sub>) is most influenced by preseason temperature sensitivity. However, because of the asymmetric response of SOS to daytime/night temperature (Tmax/Tmin) and non-negligible moderating of Wbi and Srad on SOS, S<sub>Com</sub> was more effective in expressing the effect of climate change on SOS than any single climatic factor. Vegetation cover (or type) was the dominant factor influencing the spatial pattern of SOS sensitivity, followed by spring temperature (Tmin > Tmax), and the weakest was water balance. Forests had the highest S<sub>Com</sub> absolute values. A significant decrease in the sensitivity of some vegetation (22.2%) led to a decreasing trend in sensitivity in the Northern Hemisphere. Although temperature remains the main climatic factor driving temporal changes in S<sub>Com</sub>, the temperature effects were asymmetric between spring and winter (Tems/Temw). More moisture might mitigate the asymmetric response of S<sub>Com</sub> to spring/winter warming. Vegetation adaptation has a greater influence on the temporal variability of SOS sensitivity relative to each climatic factor (Tems, Temw, Wbi, Srad). More moisture might mitigate the asymmetric response of S<sub>Com</sub> to spring/winter warming. This study provides a basis for vegetation phenology sensitivity assessment and prediction. |
topic |
vegetation phenology climate change phenological sensitivity start of the growing season driving force partial least squares regression |
url |
https://www.mdpi.com/2072-4292/13/10/1972 |
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