State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China.
Collaborative Innovation Centre on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China.
PLoS One. 2020 Oct 28;15(10):e0239465. doi: 10.1371/journal.pone.0239465. eCollection 2020.
The Tibetan Plateau and Siberia are both crucial regions in which the vegetation dynamics are sensitive to climate change. The variabilities in the normalized difference vegetation index (NDVI) over the two regions have been explored previously, but there have been few studies on the relationship of the NDVI in the two regions. Using the GIMMS-NDVI, GHCN-CAMS and NCEP reanalysis datasets and statistical and physical diagnostic methods, we show that the summer (June, July and August) NDVI over the eastern Tibetan Plateau and Lake Baikal and its adjacent eastern region of Siberia have an in-phase co-variability, especially on an interannual timescale (with a correlation coefficient of 0.69 during the time period 1982-2014). Further analyses show that precipitation and the related cloud cover and solar radiation are responsible for the variability in the NDVI over the eastern Tibetan Plateau, whereas temperature has the more important role in modulating the variability in the NDVI over the Lake Baikal region. A dipole pattern prevails over the Tibetan Plateau-Lake Baikal region and reflects the anomalies in the intensity and location of the South Asian high and the northeast Asian blocking high. This dipole pattern simultaneously modulates precipitation over the eastern Tibetan Plateau and the temperature over the Lake Baikal region and leads to the co-variability of the NDVI between the two regions. A synergistic sea surface temperature index, which reflects sea surface temperature anomalies in the eastern tropical Pacific Ocean, the northwest Pacific Ocean, the northern Indian Ocean and the subtropical north Atlantic Ocean, appears to adjust this Tibetan Plateau-Lake Baikal dipole pattern and is therefore closely related to the co-variability of the NDVI between the eastern Tibetan Plateau and the Lake Baikal region. Our results suggest that vegetation dynamics may not be only a local phenomenon in some areas, but are also likely to remotely link with variations in vegetation over other regions.
青藏高原和西伯利亚都是植被动态对气候变化敏感的关键区域。先前已经研究了这两个区域归一化差异植被指数（NDVI）的变化，但对两个区域之间 NDVI 的关系研究较少。利用 GIMMS-NDVI、GHCN-CAMS 和 NCEP 再分析数据集以及统计和物理诊断方法，我们表明，青藏高原东部和贝加尔湖及其相邻的西伯利亚东部地区夏季（6、7 和 8 月）NDVI 具有同相共变特征，尤其是在年际时间尺度上（1982-2014 年期间的相关系数为 0.69）。进一步的分析表明，降水及其相关的云量和太阳辐射是导致青藏高原东部 NDVI 变化的原因，而温度在调节贝加尔湖地区 NDVI 变化方面发挥着更重要的作用。在青藏高原-贝加尔湖地区存在偶极子模式，反映了南亚高压和东北亚阻塞高压强度和位置的异常。该偶极子模式同时调制了青藏高原东部的降水和贝加尔湖地区的温度，导致了两个区域 NDVI 的共变。一个协同的海表温度指数，反映了东热带太平洋、西北太平洋、北印度洋和亚热带北大西洋的海表温度异常，似乎调节了这个青藏高原-贝加尔湖偶极子模式，因此与青藏高原东部和贝加尔湖地区 NDVI 的共变密切相关。我们的结果表明，植被动态可能不仅是某些地区的局部现象，而且还可能与其他地区的植被变化远程相关。
