Asymmetric effects of daytime and nighttime warming on spring phenology in the temperate grasslands of China

Shen, Xiangjin; Liu, Binhui; Henderson, Mark; Wang, Lei; Wu, Zhengfang; Wu, Haitao; Jiang, Ming; Lü, Xianguo

doi:10.1016/j.agrformet.2018.05.006

Cited by 103 publications

(70 citation statements)

References 72 publications

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“…Our result showed that the mean sensitivity of SOS to preseason temperature is −1.23 days • C −1 , which is lower than the temperate region (−2.5 days • C −1 ) [79]. Symmetric effects of daytime and nighttime warming also have different sensitivity to phenology metrics, for example, an increase of 1 • C Tmin and Tmax in spring would advance SOS 1.34 days and 0.64 days, respectively [80]. In contrast to the extensive research efforts on the sensitivity of SOS, the sensitivity of EOS to temperature is more challenging to understand [22,81].…”

Section: Relationships Between Phenology Metrics and Climatic Factorsmentioning

confidence: 64%

Forest Phenology Dynamics to Climate Change and Topography in a Geographic and Climate Transition Zone: The Qinling Mountains in Central China

Xia

Qin

Feng

et al. 2019

Forests

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Forest ecosystems in an ecotone and their dynamics to climate change are growing ecological and environmental concerns. Phenology is one of the most critical biological indicators of climate change impacts on forest dynamics. In this study, we estimated and visualized the spatiotemporal patterns of forest phenology from 2001 to 2017 in the Qinling Mountains (QMs) based on the enhanced vegetation index (EVI) from MODerate-resolution Imaging Spectroradiometer (MODIS). We further analyzed this data to reveal the impacts of climate change and topography on the start of the growing season (SOS), end of the growing season (EOS), and the length of growing season (LOS). Our results showed that forest phenology metrics were very sensitive to changes in elevation, with a 2.4 days delayed SOS, 1.4 days advanced EOS, and 3.8 days shortened LOS for every 100 m increase in altitude. During the study period, on average, SOS advanced by 0.13 days year−1, EOS was delayed by 0.22 days year−1, and LOS increased by 0.35 day year−1. The phenological advanced and delayed speed across different elevation is not consistent. The speed of elevation-induced advanced SOS increased slightly with elevation, and the speed of elevation-induced delayed EOS shift reached a maximum value of 1500 m from 2001 to 2017. The sensitivity of SOS and EOS to preseason temperature displays that an increase of 1 °C in the regionally averaged preseason temperature would advance the average SOS by 1.23 days and delay the average EOS by 0.72 days, respectively. This study improved our understanding of the recent variability of forest phenology in mountain ecotones and explored the correlation between forest phenology and climate variables in the context of the ongoing climate warming.

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Section: Relationships Between Phenology Metrics and Climatic Factorsmentioning

confidence: 64%

Forest Phenology Dynamics to Climate Change and Topography in a Geographic and Climate Transition Zone: The Qinling Mountains in Central China

Xia

Qin

Feng

et al. 2019

Forests

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“…The new generation of the NDVI dataset added percentile data and recovered NDVI negative values of snow‐covered regions in the winter at high latitudes in the Northern Hemisphere. The NDVI 3g dataset has been widely applied for the quantification of long‐term changes in vegetation growth (Piao et al, ; Shen et al, ; Wu & Liu, ; Xia et al, ; Yu et al, ; Yu, Liu, et al, ).…”

Section: Methodsmentioning

confidence: 99%

“…The study used the latest and longest release of GIMMS NDVI 3g version 1 dataset, which was provided by the Global Inventory The NDVI 3g dataset has been widely applied for the quantification of long-term changes in vegetation growth Shen et al, 2018;Wu & Liu, 2013;Xia et al, 2018;Yu et al, 2017;Yu, Liu, et al, 2013a).…”

Section: Data Collection and Generationmentioning

confidence: 99%

“…Therefore, the trend of vegetation phenology and its response to climate change should be further investigated with the accumulation of satellite observation data. At present, a new generation of GIMMS NDVI 3g dataset has been extended to 2015 (Liu et al, ; Shen et al, ; Xia et al, ), and its quality is better than the previous version (Dai et al, ; Zhou et al, ). Longer‐term data are more useful for us to understand the relationship between phenology and climate change.…”

Section: Introductionmentioning

confidence: 99%

“…Traditional ground-based observations are widely used for the species level and for small-scale areas because detailed information can be documented at the observation; however, these observations are limited by the location and number of observation sites and spatial scopes (Cleland et al, 2007;Studer, Stöckli, Appenzeller, & Vidale, 2007;Wu & Liu, 2013;Zhu et al, 2012). Remote sensing, in another way, provides continuously spatial and temporal informations on a variety of surface vegetation at regional or global scales, and numerous studies have investigated the variations of vegetation phenology in different regions over different time periods based on satellite-measured Normalized Difference Vegetation Index (NDVI) datasets (Fu et al, 2014;Myneni, Keeling, Tucker, Asrar, & Nemani 1997;Shen et al, 2018;Wang et al, 2016;White et al, 2009;Wu & Liu, 2013;Zhang, Tarpley, & Sullivan 2007).…”

Section: Introductionmentioning

confidence: 99%

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Discrepancies in vegetation phenology trends and shift patterns in different climatic zones in middle and eastern Eurasia between 1982 and 2015

Zhang

et al. 2019

Ecology and Evolution

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Changes in vegetation phenology directly reflect the response of vegetation growth to climate change. In this study, using the Normalized Difference Vegetation Index dataset from 1982 to 2015, we extracted start date of vegetation growing season (SOS), end date of vegetation growing season (EOS), and length of vegetation growing season (LOS) in the middle and eastern Eurasia region and evaluated linear trends in SOS, EOS, and LOS for the entire study area, as well as for four climatic zones. The results show that the LOS has significantly increased by 0.27 days/year, mostly due to a significantly advanced SOS (−0.20 days/year) and a slightly delayed EOS (0.07 days/year) over the entire study area from 1982 to 2015. The vegetation phenology trends in the four climatic zones are not continuous throughout the 34‐year period. Furthermore, discrepancies in the shifting patterns of vegetation phenology trend existed among different climatic zones. Turning points (TP) of SOS trends in the Cold zone, Temperate zone, and Tibetan Plateau zone occurred in the mid‐ or late 1990s. The advanced trends of SOS in the Cold zone, Temperate zone, and Tibetan Plateau zone exhibited accelerated, stalled, and reversed patterns after the corresponding TP, respectively. The TP did not occurred in Cold‐Temperate zone, where the SOS showed a consistent and continuous advance. TPs of EOS trends in the Cold zone, Cold‐Temperate zone, Temperate zone, and Tibetan Plateau zone occurred in the late 1980s or mid‐1990s. The EOS in the Cold zone, Cold‐Temperate zone, Temperate zone, and Tibetan Plateau zone showed weak advanced or delayed trends after the corresponding TP, which were comparable with the delayed trends before the corresponding TP. The shift patterns of LOS trends were primarily influenced by the shift patterns of SOS trends and were also heterogeneous within climatic zones.

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Spatiotemporal variation in vegetation phenology and its response to climate change in marshes of Sanjiang Plain, China

Liu

Shen

Zhang

et al. 2023

Ecology and Evolution

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Asymmetric effects of daytime and nighttime warming on spring phenology in the temperate grasslands of China

Cited by 103 publications

References 72 publications

Forest Phenology Dynamics to Climate Change and Topography in a Geographic and Climate Transition Zone: The Qinling Mountains in Central China

Forest Phenology Dynamics to Climate Change and Topography in a Geographic and Climate Transition Zone: The Qinling Mountains in Central China

Discrepancies in vegetation phenology trends and shift patterns in different climatic zones in middle and eastern Eurasia between 1982 and 2015

Spatiotemporal variation in vegetation phenology and its response to climate change in marshes of Sanjiang Plain, China

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