Weakening Relationship Between Vegetation Growth Over the Tibetan Plateau and Large‐Scale Climate Variability

Shi, Fangzhong; Wu, Xiuchen; Li, Xiao Yan; Chen, Deliang; Liu, Hongyan; Liu, Shaomin; Hu, Xia; He, Bin; Shi, Chunming; Wang, Pei; Mao, Rui; Ma, Yujun; Huang, Yongmei

doi:10.1002/2017jg004134

Cited by 22 publications

(12 citation statements)

References 82 publications

(98 reference statements)

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“…However, a few studies have assessed the direct correlation between the growth of vegetation in the TP and large-scale climate oscillations (Shi et al, 2018;Yu et al, 2018;Cheng et al, 2019). For example, Cheng et al (2019) have calculated the correlation coefficient between four ENSO indices and tree growth in the southern TP and have reported that tree growth has been sensitive to the ENSO since the 1970s.…”

Section: Introductionmentioning

confidence: 99%

Local Climatic Factors Mediated Impacts of Large-Scale Climate Oscillations on the Growth of Vegetation Across the Tibetan Plateau

Zhang

Shen

Shi

et al. 2021

Front. Environ. Sci.

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Large-scale climate oscillations, particularly the Atlantic Multidecadal Oscillation (AMO) and the Pacific Decadal Oscillation (PDO), have widespread influences on climate systems across the Tibetan Plateau (TP). It is understudied how the temporal changes in AMO and PDO affected growth of vegetation through modifying the local climatic factors in different areas across the TP. We used the AMO and PDO indices, gridded growing season mean temperature (TGS), cumulative precipitation (PGS), and normalized difference vegetation index (NDVIGS) data from 1982 to 2015 to investigate the temporal trends of these variables and the correlations of the TGS and PGS with each of the AMO and PDO indices as well as their correlations with the NDVIGS. The results showed that the warming of the TGS over the TP and the increases of the PGS in western, central, and northeastern areas of the TP may have been related to an increase of the AMO index and a decrease of the PDO index. Combining those relationships with the spatial patterns of the TGS-NDVIGS and PGS-NDVIGS correlations suggested that the changes of the AMO and PDO may have indirectly increased the NDVIGS in the central and northeastern areas of the TP by increasing TGS and PGS, in most parts of the southwestern TP by increasing PGS, and in the eastern and south-central regions of the TP by increasing TGS. In contrast, the decrease of the NDVIGS in some areas of the southeastern and southwestern TP may have been associated with a negative effect of warming as a result of changes in the AMO and PDO. These results highlight the indirect impacts of changes in large-scale climate oscillations on the growth of vegetation through modification of local climatic factors across the TP, and they suggest the substantial spatial heterogeneity of these impacts largely depends on the responses of vegetation to local climatic factors.

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Section: Introductionmentioning

confidence: 99%

Local Climatic Factors Mediated Impacts of Large-Scale Climate Oscillations on the Growth of Vegetation Across the Tibetan Plateau

Zhang

Shen

Shi

et al. 2021

Front. Environ. Sci.

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“…All variables were linearly detrended prior to partial correlation and GLM analyses. In our study, we consistently defined the growing season for tree growth as April to October, with spring (SP), summer (SU) and autumn (AU) correspondingly defined as April to May, June to August, and September to October, respectively [33].…”

Section: Discussionmentioning

confidence: 99%

“…This dataset has been comprehensively corrected for atmospheric effects, sensor degradation, solar zenith angle, cloud contamination, and intersensor difference [30]. The GIMMS NDVI3g dataset has been widely used to investigate and evaluate spatio-temporal patterns in vegetation growth/productivity across diverse bioclimatic regions [30][31][32][33]. In this paper, we limited our analyses to forested regions, and resampled the NDVI of these regions into a spatial resolution of 0.1 • to match the gridded precipitation and temperature data.…”

Section: Tree Growth Proxies and Land Cover Datamentioning

confidence: 99%

Seasonal Divergent Tree Growth Trends and Growth Variability along Drought Gradient over Northeastern China

Shi

et al. 2019

Forests

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With the increasing temperature and intensified drought, global climate change has profound impacts on tree growth in temperate regions, which consequently regulates terrestrial-atmosphere biogeochemical processes and biophysical feedbacks. Thus, increasing numbers of studies have addressed the long-term annual trends in tree growth and their response to climate change at diverse spatial scales. However, the potential divergence in tree growth trends and growth variability (represented by coefficient of variance) in different seasons across large-scale climate gradients remains poorly understood. Here, we investigated the tree growth trends and growth variability in different seasons across diverse drought conditions in forested regions over northeastern China during the period 1982–2015, using both remote sensing observations and in situ tree-ring measurements. We found clear seasonal divergence in tree growth trends during 1982–2015, and the apparent increase was mainly observed in spring and autumn, attributed mainly to the increase in spring temperature and autumn solar radiation, respectively, but not in summer. The magnitudes of increasing trends in tree growth decrease with the increase of the multi-year average dryness index (MAI) in semi-arid areas (1.5 < MAI < 4.0) in all seasons. We further revealed that the interannual variability in tree growth was much larger in the semi-arid regions than in the humid and semi-humid regions in all seasons, and tree growth variability was significantly and negatively correlated with the variations in temperature and water deficit. Our findings improve our understanding of seasonal divergence in tree growth trends and provide new insights into spatial patterns in forest vulnerability in a warmer and drier climate.

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“…More detailed information about ERA-Interim can be found in Berrisford et al (2009). ERA-Interim has long been utilized as reference temperature in the TP (Xu et al, 2017;Shi et al, 2018;Yuan et al, 2018). 6-hourly 2m temperature at resolution of 0.75 × 0.75 was collected from ECMWF portal (https://www.ecmwf.int) in our study.…”

Section: Era-interim Reanalysismentioning

confidence: 99%

How well do the ERA‐Interim, ERA‐5, GLDAS‐2.1 and NCEP‐R2 reanalysis datasets represent daily air temperature over the Tibetan Plateau?

Liu

Xie

et al. 2020

Intl Journal of Climatology

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Snow and glacier are important components in the hydrological cycle of the Tibetan Plateau (TP). Air temperature, as the main driver in freezing and thawing processes, becomes vital for hydrological modelling and prediction in this region. Due to a sparse ground gauging network, spatial density of air temperature measurement is insufficient for hydro‐meteorological studies. Therefore, the aim of this study is to identify the best representative temperature data for hydrological applications from four widely used reanalysis products, including ERA‐Interim, ERA‐5, GLDAS‐2.1 and NCEP‐R2, with reference to in situ measurements and gridded snow depth from the year 2008–2017 over the entire TP. To reduce errors, Bayesian Joint Probability (BJP) approach based on K‐Nearest Neighbour (KNN) classification algorithm (KNN‐BJP) is proposed to post‐process gridded reanalyses. The results indicate that all the reanalysis datasets provide highly correlated but cold biased air temperature. The correlation ecoefficiency is greater than 0.85. The cold biases are near −3 ° C and mainly distributed in the southeastern TP. Bias in daily maximum temperature during Spring is greater than −8 ° C for most stations. ERA‐Interim is found to have the closest agreement with in situ measurements, closely followed by GLDAS‐2.1. KNN‐BJP is found to be effective within a distance smaller than 5°. After post‐processing, the prominent underestimation is efficiently corrected with Bias near 0. RMSE is markedly reduced to be smaller than 2.5 ° C. The post‐processed ERA‐5 and GLDAS‐2.1 are as accurate as ERA‐Interim, but able to provide more detailed information for extreme events due to their finer spatial resolution. Thus, ERA‐5 and GLDAS‐2.1 are more recommended to represent air temperature in the TP. Snow depth as complementary reference data is able to present spatial variance of air temperature. Our study can help alleviate the problem of sparse air temperature data over the TP.

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Weakening Relationship Between Vegetation Growth Over the Tibetan Plateau and Large‐Scale Climate Variability

Cited by 22 publications

References 82 publications

Local Climatic Factors Mediated Impacts of Large-Scale Climate Oscillations on the Growth of Vegetation Across the Tibetan Plateau

Local Climatic Factors Mediated Impacts of Large-Scale Climate Oscillations on the Growth of Vegetation Across the Tibetan Plateau

Seasonal Divergent Tree Growth Trends and Growth Variability along Drought Gradient over Northeastern China

How well do the ERA‐Interim, ERA‐5, GLDAS‐2.1 and NCEP‐R2 reanalysis datasets represent daily air temperature over the Tibetan Plateau?

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