scholarly journals Nonuniform Time-Lag Effects of Asymmetric Warming on Net Primary Productivity across Global Terrestrial Biomes

2018 ◽  
Vol 22 (8) ◽  
pp. 1-26 ◽  
Author(s):  
Youyue Wen ◽  
Xiaoping Liu ◽  
Guoming Du
Keyword(s):  
Partial Correlation ◽  
Net Primary Productivity ◽  
Net Primary Production ◽  
Time Lag ◽  
Correlation Coefficients ◽  
Terrestrial Ecosystem ◽  
Terrestrial Vegetation ◽  
Vegetation Growth ◽  
Lag Effects ◽  
Terrestrial Biomes

Abstract Climate warming exhibits asymmetric patterns over a diel time, with the trend of nighttime warming exceeding that of daytime warming, a phenomenon commonly known as asymmetric warming. Recently, increasing studies have documented the significant instantaneous impacts of asymmetric warming on terrestrial vegetation growth, but the indirect effects of asymmetric warming carrying over vegetation growth (referred to here as time-lag effects) remain unknown. Here, we quantitatively studied the time-lag effects (within 1 year) of asymmetric warming on global plant biomes by using terrestrial vegetation net primary production (NPP) derived by the Carnegie–Ames–Stanford Approach (CASA) model and accumulated daytime and nighttime temperature (ATmax and ATmin) from 1982 to 2013. Partial correlation and time-lag analyses were conducted at a monthly scale to obtain the partial correlation coefficients between NPP and ATmax/ATmin and the lagged durations of NPP responses to ATmax/ATmin. The results showed that (i) asymmetric warming has nonuniform time-lag effects on single plant biomes, and distinguishing correlations exist in different vegetation biomes’ associations to asymmetric warming; (ii) terrestrial biomes respond to ATmax (4.63 ± 3.92 months) with a shorter protracted duration than to ATmin (6.06 ± 4.27 months); (iii) forest biomes exhibit longer prolonged duration in responding to asymmetric warming than nonforest biomes do; (iv) mosses and lichens (Mosses), evergreen needleleaf forests (ENF), deciduous needleleaf forests (DNF), and mixed forests (MF) tend to positively correlate with ATmax, whereas the other biomes associate with ATmax with near-equal splits of positive and negative correlation; and (v) ATmin has a predominantly positive influence on terrestrial biomes, except for Mosses and DNF. This study provides a new perspective on terrestrial ecosystem responses to asymmetric warming and highlights the importance of including such nonuniform time-lag effects into currently used terrestrial ecosystem models during future investigations of vegetation–climate interactions.

scholarly journals Periodic Relations between Terrestrial Vegetation and Climate Factors across the Globe

2020 ◽  
Vol 12 (11) ◽  
pp. 1805
Author(s):  
Boyi Liang ◽  
Hongyan Liu ◽  
Xiaoqiu Chen ◽  
Xinrong Zhu ◽  
Elizabeth L. Cressey ◽  
...  
Keyword(s):  
Time Lag ◽  
Climate Factors ◽  
Vegetation Types ◽  
Terrestrial Vegetation ◽  
Area Index ◽  
Vegetation Growth ◽  
Annual Cycles ◽  
Vegetation Models ◽  
Global Vegetation ◽  
The Impact

In this paper, cross-spectrum analysis was used to verify the agreement of periodicity between the global LAI (leaf area index) and climate factors. The results demonstrated that the LAI of deciduous forests and permanent wetlands have high agreement with temperature, rainfall and radiation over annual cycles. A low agreement between the LAI and seasonal climate variables was observed for some of the temperate and tropical vegetation types including shrublands and evergreen broadleaf forests, possibly due to the diversity of vegetation and human activities. Across all vegetation types, the LAI demonstrated a large time lag following variation in radiation (>1 month), whereas relatively short lag periods were observed between the LAI and annual temperature (around 2 weeks)/rainfall patterns (less than 10 days), suggesting that the impact of radiation on global vegetation growth is relatively slow, which is in accord with the results of previous studies. This work can provide a benchmark of the phenological drivers in global vegetation, from the perspective of periodicity, as well as helping to parameterize and refine the DGVMs (Dynamic Global Vegetation Models) for different vegetation types.

Non-uniform time-lag effects of terrestrial vegetation responses to asymmetric warming

2018 ◽  
Vol 252 ◽  
pp. 130-143 ◽  
Author(s):  
Youyue Wen ◽  
Xiaoping Liu ◽  
Fengsong Pei ◽  
Xia Li ◽  
Guoming Du
Keyword(s):  
Time Lag ◽  
Terrestrial Vegetation ◽  
Lag Effects ◽  
Vegetation Responses

A CLASSIFICATION INDICES-BASED MODEL FOR NET PRIMARY PRODUCTIVITY (NPP) AND POTENTIAL PRODUCTIVITY OF VEGETATION IN CHINA

2012 ◽  
Vol 05 (03) ◽  
pp. 1260009 ◽  
Author(s):  
HUILONG LIN ◽  
JUN ZHAO ◽  
TIANGANG LIANG ◽  
JAN BOGAERT ◽  
ZHENQING LI
Keyword(s):  
Primary Productivity ◽  
Large Scale ◽  
Net Primary Productivity ◽  
Global Climate ◽  
Terrestrial Ecosystem ◽  
Terrestrial Vegetation ◽  
Moisture Index ◽  
Potential Productivity ◽  
Climate Conditions ◽  
Factors Affecting

Net Primary Productivity (NPP) is an important parameter, which is closely connected with global climate change, the global carbon balance and cycle. The study of climate-vegetation interaction is the basis for research on the responses of terrestrial ecosystem to global change and mainly comprises two important components: climate vegetation classification and the NPP of the natural vegetation. Comparing NPP estimated from the classification indices-based model with NPP derived from measurements at 3767 sites in China indicated that the classification indices-based model was capable of estimating large scale NPP. Annual cumulative temperature above 0°C and a moisture index, two main factors affecting NPP, were spatially plotted with the ArcGIS grid tool based on measured data in 2348 meteorological stations from 1961 to 2006. The distribution of NPP for potential vegetation classes under present climate conditions was simulated by the classification indices-based model. The model estimated the total NPP of potential terrestrial vegetation of China to fluctuate between 1.93 and 4.54 Pg C year-1. It provides a reliable means for scaling-up from site to regional scales, and the findings could potentially favor China's position in reducing global warming gases as outlined in the Kyoto Protocol in order to fulfill China's commitment of reducing greenhouse gases.

scholarly journals A Decade’s Change in Vegetation Productivity and Its Response to Climate Change over Northeast China

Plants ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 821
Author(s):  
Min Yan ◽  
Mei Xue ◽  
Li Zhang ◽  
Xin Tian ◽  
Bowei Chen ◽  
...  
Keyword(s):  
Climate Change ◽  
Northeast China ◽  
Partial Correlation ◽  
Net Primary Productivity ◽  
Climatic Factors ◽  
Average Rate ◽  
Vegetation Growth ◽  
Tree Ring Data ◽  
Predominant Factor ◽  
Boreal Ecosystem

In this study, we simulated vegetation net primary productivity (NPP) using the boreal ecosystem productivity simulator (BEPS) between 2003 and 2012 over Northeast China, a region that is significantly affected by climate change. The NPP was then validated against the measurements that were calculated from tree ring data, with a determination coefficient (R2) = 0.84 and the root mean square error (RMSE) = 42.73 gC/m2·a. Overall, the NPP showed an increasing trend over Northeast China, with the average rate being 4.48 gC/m2·a. Subsequently, partial correlation and lag analysis were conducted between the NPP and climatic factors. The partial correlation analysis suggested that temperature was the predominant factor that accounted for changes in the forest NPP. Solar radiation was the main factor that affected the forest NPP, and the grass NPP was the most closely associated with precipitation. The relative humidity substantially affected the annual variability of the shrub and crop NPPs. The lag time of the NPP related to precipitation increased with the vegetation growth, and it was found that the lag period of the forest was longer than that of grass and crops, whereas the cumulative lag month of the forest was shorter. This comprehensive analysis of the response of the vegetation NPP to climate change can provide scientific references for the managing departments that oversee relevant resources.

scholarly journals The First National Survey of Cadmium in Cacao Farm Soil in Colombia

Agronomy ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 761
Author(s):  
Daniel Bravo ◽  
Clara Leon-Moreno ◽  
Carlos Alberto Martínez ◽  
Viviana Marcela Varón-Ramírez ◽  
Gustavo Alfonso Araujo-Carrillo ◽  
...  
Keyword(s):  
Graphical Models ◽  
Partial Correlation ◽  
National Level ◽  
Prediction Method ◽  
Correlation Coefficients ◽  
Predictive Performance ◽  
Nationwide Survey ◽  
Critical Discussion ◽  
Cadmium Content ◽  
Soil Variables

This study represents the first nationwide survey regarding the distribution of Cd content in cacao-growing soils in Colombia. The soil Cd distribution was analyzed using a cold/hotspots model. Moreover, both descriptive and predictive analytical tools were used to assess the key factors regulating the Cd concentration, considering Cd content and eight soil variables in the cacao systems. A critical discussion was performed in four main cacao-growing districts. Our results suggest that the performance of a model using all the variables will always be superior to the one using Zn alone. The analyzed variables featured an appropriate predictive performance, nonetheless, that performance has to be improved to develop a prediction method that might be used nationwide. Results from the fitted graphical models showed that the largest associations (as measured by the partial correlation coefficients) were those between Cd and Zn. Ca had the second-largest partial correlation with Cd and its predictive performance ranked second. Interestingly, it was found that there was a high variability in the factors correlated with Cd in cacao growing soils at a national level. Therefore, this study constitutes a baseline for the forthcoming studies in the country and should be reinforced with an analysis of cadmium content in cacao beans.

scholarly journals Reduced resilience of terrestrial ecosystems locally is not reflected on a global scale

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Yuhao Feng ◽  
Haojie Su ◽  
Zhiyao Tang ◽  
Shaopeng Wang ◽  
Xia Zhao ◽  
...  
Keyword(s):  
Climate Change ◽  
Global Climate Change ◽  
Vegetation Index ◽  
Global Climate ◽  
Terrestrial Ecosystem ◽  
Terrestrial Ecosystems ◽  
Global Scale ◽  
Ecological Resilience ◽  
Terrestrial Vegetation ◽  
Ecosystem Resilience

AbstractGlobal climate change likely alters the structure and function of vegetation and the stability of terrestrial ecosystems. It is therefore important to assess the factors controlling ecosystem resilience from local to global scales. Here we assess terrestrial vegetation resilience over the past 35 years using early warning indicators calculated from normalized difference vegetation index data. On a local scale we find that climate change reduced the resilience of ecosystems in 64.5% of the global terrestrial vegetated area. Temperature had a greater influence on vegetation resilience than precipitation, while climate mean state had a greater influence than climate variability. However, there is no evidence for decreased ecological resilience on larger scales. Instead, climate warming increased spatial asynchrony of vegetation which buffered the global-scale impacts on resilience. We suggest that the response of terrestrial ecosystem resilience to global climate change is scale-dependent and influenced by spatial asynchrony on the global scale.

scholarly journals A Weighted-Time-Lag Method to Detect Lag Vegetation Response to Climate Variation: A Case Study in Loess Plateau, China, 1982–2013

2021 ◽  
Vol 13 (5) ◽  
pp. 923
Author(s):  
Qianqian Sun ◽  
Chao Liu ◽  
Tianyang Chen ◽  
Anbing Zhang
Keyword(s):  
Climate Change ◽  
Loess Plateau ◽  
Time Lag ◽  
New Method ◽  
Climate Factors ◽  
The Loess Plateau ◽  
Climate Conditions ◽  
Vegetation Growth ◽  
Lag Effect

Vegetation fluctuation is sensitive to climate change, and this response exhibits a time lag. Traditionally, scholars estimated this lag effect by considering the immediate prior lag (e.g., where vegetation in the current month is impacted by the climate in a certain prior month) or the lag accumulation (e.g., where vegetation in the current month is impacted by the last several months). The essence of these two methods is that vegetation growth is impacted by climate conditions in the prior period or several consecutive previous periods, which fails to consider the different impacts coming from each of those prior periods. Therefore, this study proposed a new approach, the weighted time-lag method, in detecting the lag effect of climate conditions coming from different prior periods. Essentially, the new method is a generalized extension of the lag-accumulation method. However, the new method detects how many prior periods need to be considered and, most importantly, the differentiated climate impact on vegetation growth in each of the determined prior periods. We tested the performance of the new method in the Loess Plateau by comparing various lag detection methods by using the linear model between the climate factors and the normalized difference vegetation index (NDVI). The case study confirmed four main findings: (1) the response of vegetation growth exhibits time lag to both precipitation and temperature; (2) there are apparent differences in the time lag effect detected by various methods, but the weighted time-lag method produced the highest determination coefficient (R2) in the linear model and provided the most specific lag pattern over the determined prior periods; (3) the vegetation growth is most sensitive to climate factors in the current month and the last month in the Loess Plateau but reflects a varied of responses to other prior months; and (4) the impact of temperature on vegetation growth is higher than that of precipitation. The new method provides a much more precise detection of the lag effect of climate change on vegetation growth and makes a smart decision about soil conservation and ecological restoration after severe climate events, such as long-lasting drought or flooding.

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