Spatiotemporal variability of sunshine duration and influential climatic factors in mainland China during 1959–2017

Xiong, Jinghua; Lai, Chengguang; Liao, Yong; Wu, Xushu

doi:10.1002/joc.6580

Cited by 20 publications

(13 citation statements)

References 88 publications

(116 reference statements)

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“…The ET 0 was potentially affected by these changes. Nationwide, U and SSD all turned around 1990 (Wild, 2009;Xiong et al, 2020). In terms of time, the mutation point of ET 0 in 1994 in this study is synchronous with the changes of these two meteorological factors.…”

Section: Contributions Of Meteorological Factors To Et 0 Changessupporting

confidence: 50%

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Spatiotemporal variations and driving factors of reference evapotranspiration in the Yiluo river basin

Wang²,

Zhang³

et al. 2023

Front. Earth Sci.

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The variations in the reference evapotranspiration (ET0) are closely related to meteorological factors. The purpose of this study is to explore the relationships between the meteorological factors and the ET0. Based on meteorological data from 26 meteorological stations in the Yiluo River Basin (YLRB) and its surrounding areas from 1958 to 2020, in this study, the temporal and spatial variations and driving factors of the ET0 in the YLRB are investigated. The results are as follows. Spatially, the annual ET0 decreases from the northeast to the southwest in the YLRB. Temporally, the annual ET0 exhibits a fluctuating decreasing trend rather than a monotonic decreasing trend during the entire period. The trend of the ET0 contains two mutation points, in 1972 and 1994. Thus, the research period can be divided into three periods. It is concluded that the variations in the ET0 are the most sensitive to the relative humidity, but the driving factor that contributes the most to the variations in the ET0 is the wind speed. The driving factors are closely related to the rates of relative change of the meteorological factors.

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Section: Contributions Of Meteorological Factors To Et 0 Changessupporting

confidence: 50%

“…This phenomenon is found all over the world, which also known as 'wind stilling' (Roderick and Farquhar, 2002). A decrease in U also tends to lead to the accumulation of clouds, leading to a decrease in solar radiation (Xiong et al, 2020). The ET 0 was potentially affected by these changes.…”

Section: Contributions Of Meteorological Factors To Et 0 Changesmentioning

confidence: 98%

Spatiotemporal variations and driving factors of reference evapotranspiration in the Yiluo river basin

Wang²,

Zhang³

et al. 2023

Front. Earth Sci.

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“…The random forest algorithm is a machine learning algorithm based on training samples and feature sets with decision trees as the basic classifier (Breiman 2001). The algorithm is characterized by high accuracy, high efficiency, and stable performance, and is widely used in assessing the importance of independent variables Xiong et al 2020;Yang et al 2020). The random forest algorithm draws training samples by bagging and constructs multiple cart decision trees and forms a random forest by randomly selecting a subset of each node variable after splitting within N decision trees according to the principle of minimization of Gini coefficients.…”

Section: Estimation Of the Relative Influential Rates Of Driving Factorsmentioning

confidence: 99%

Fractional contribution of global warming and regional urbanization to intensifying regional heatwaves across Eurasia

et al. 2022

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Increasing frequency and intensity of heatwaves (HWs) in a warming climate exert catastrophic impacts on human society and natural environment. However, spatiotemporal variations of HW and their driving factors still remain obscure, especially for HW changes over Eurasia, the region with the largest population of the world. Here we provide a systematic investigation of the HW changes over Eurasia and quantify the contributions of different natural and anthropogenic factors to these changes. Increasing frequency, duration and intensity of HW are observed in most parts of Eurasia, and the occurrence of the first HW event tends to be earlier as well, especially in Europe, East Asia, Central Asia, Southwest Asia, and the Mediterranean region. These intensified HW activities are particularly stronger and more widespread after 1990s. The spatial pattern of the increasing HW trend is closely tied to the interdecadal changes of sea surface temperature in the North Pacific. More intense hot airmass convection, atmospheric circulation obstruction over the Mediterranean region and the enhanced Mongolian high hinders the southward movement of cold air and cold and wet airmass exchange. Further analyses suggest that the intensifying Eurasian HW tendency is a combined result of both climate change and human activities. Overall, the fractional contributions of climate warming, urbanization, standardized precipitation evaporation index, and Atlantic Multi-decadal Oscillation to the frequency of Eurasian HWs are 30%, 25%, 21% and 24%, respectively. It is also suggested that the relative influential rate of different driving factors for HW varies over time and differs in different areas.

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“…Due to its reliability for variable selection and determination of variable importance, RF was used in this study to identify the contributions of five associated large‐scale circulation influencing factors on the changing properties of the PC. The PCI was averaged with monthly rainfall events to keep in step with teleconnection indicators, with PCI as dependent variable and the five indices as independent variables in the RF model (Xiong et al ., 2020). The contribution degree (CD) of each indicator to the PC is calculated by:

CD goodbreak= \frac{\sum_{i = 1}^{h} \sum_{j = 1}^{s} D_{ij}}{\sum_{k = 1}^{t} \sum_{i = 1}^{h} D_{ij}}

where t , h and s are the total number of indices, classification trees and nodes, respectively.…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal variability of event‐based rainstorm: The perspective of rainfall pattern and concentration

Wang

et al. 2022

Intl Journal of Climatology

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Rainfall pattern (RP) and precipitation concentration (PC) are two critical indices for measuring rainfall. Detecting their changes under global warming helps to understand better the rainfall variability and the flooding formation. Using the Guangdong Province, China, as a study case, five criteria were used to determine the independent precipitation events for hourly precipitation data from 1967 to 2012. The RP and PC of the events were identified, and then, their spatiotemporal variability was investigated further. The results show that (a) the occurrence frequency during 46 years, average rainfall amount and duration of independent rainfall in the coastal areas were higher than in other regions. (b) The dominant RPs in Guangdong are unimodal (more than 70%), especially the pattern with early peak (mode I; 39.5%); however, the number of stations with mode I as dominant RP decreased over time, while those with mode III (the pattern with late peak) increased. (c) Precipitation concentration index can be used for measuring the concentration of independent rainfall events, and its fixed minimum inter-event time has significant impacts on RP and PC. The PC of the events with early peak is higher, and the concentration in the west is generally higher than those in the east under different RPs.

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Spatiotemporal variability of sunshine duration and influential climatic factors in mainland China during 1959–2017

Cited by 20 publications

References 88 publications

Spatiotemporal variations and driving factors of reference evapotranspiration in the Yiluo river basin

Spatiotemporal variations and driving factors of reference evapotranspiration in the Yiluo river basin

Fractional contribution of global warming and regional urbanization to intensifying regional heatwaves across Eurasia

Spatiotemporal variability of event‐based rainstorm: The perspective of rainfall pattern and concentration

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