The global spatiotemporal heterogeneity of land surface-air temperature difference and its influencing factors

Jiang, Kang; Pan, Zhihua; Pan, Feifei; Wang, Jialin; Han, Guolin; Song, Yu; Zhang, Ziyuan; Huang, Na; Ma, Shen; Xiao, Chen; Zhang, Zhenzhen; Men, Jingyu

doi:10.1016/j.scitotenv.2022.156214

Cited by 8 publications

(4 citation statements)

References 45 publications

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“…Therefore, the meteorological data considered herein are the ERA5‐land reanalysis data, which were obtained from the Copernicus climate data website (https://cds.climate.copernicus.eu). ERA5 is the fifth generation atmospheric reanalysis dataset produced by the ECMWF (Jiang et al, 2022; Muñoz‐Sabater et al, 2021). ERA5‐land monthly data from 1981 to 2022 with a spatial resolution of 0.1° (approx.…”

Section: Methodsmentioning

confidence: 99%

Comparison of aeolian desertification between the Moltsog Dune Field in Mongolia and the Ujimqin Dune Field in China

Hasi

Wulantuya

et al. 2023

Land Degrad Dev

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Aeolian desertification is a severe ecological and environmental problem in arid regions. Research on its spatio-temporal distribution, modelling and driving force is necessary to prevent the development of aeolian desertification. In this study, the Moltsog Dune Field in Mongolia and the Ujimqin Dune Field in China were selected as the study areas, as both contain dunes under similar physical conditions. Using

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Section: Methodsmentioning

confidence: 99%

Comparison of aeolian desertification between the Moltsog Dune Field in Mongolia and the Ujimqin Dune Field in China

Hasi

Wulantuya

et al. 2023

Land Degrad Dev

Self Cite

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“…Fo ample, the effect of soil moisture on GST-SAT shows a non-linear relationship due t different background values of soil moisture and temperature [32]. The variation o radiation with latitude mainly determines the global spatial heterogeneity of GST-As the snowpack increases, the high albedo of the snowpack leads to a decrease in and thus to a decrease in GST-SAT [33].…”

Section: Datamentioning

confidence: 99%

“…Therefore, studies on GST-SAT have mainly focused on the Chinese region. Jiang et al [32,33] used ERA-5 reanalysis data to discuss global land surface-air temperature differences. They found that the mean land surface-air temperature difference showed an increasing trend on the eastern QTP and in northern China and eastern Inner Mongolia.…”

Section: Gst-satmentioning

confidence: 99%

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Spatio-Temporal Patterns of Warm-Season Ground Surface Temperature—Surface Air Temperature Difference over China Mainland

Long

Ren

2023

Land

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Examining large-scale characteristics of the difference between ground surface temperature (GST) and surface air temperature (SAT) and its long-term trend will help understand land surface energy exchange and the effect of land-atmosphere interaction on climate change and variability. Based on a homogenized monthly dataset of GST and SAT from 1961 to 2018, this study analyzes the spatial distribution and long-term trend of the difference between ground surface temperature and surface air temperature (GST–SAT) in the warm season (April to October) over China mainland. The results show that the warm-season mean GST–SAT in the Qinghai-Tibet Plateau and the northwestern deserts have the largest GST–SAT. On average, the GST–SAT in China is the greatest in summer, with the maximum monthly value occurring in July. During 1961–2018, the warm-season mean GST–SAT undergoes a significant increasing trend (0.04 °C/10yr, p < 0.01), with the largest increase seen in mid-late spring (April and May), and the smallest increase in August. Spatially, the GST–SAT increases significantly in the northern region, decreases slightly in the southern region, and remains unchanged in the Qinghai-Tibet Plateau. The warm-season mean GST–SAT is significantly positively correlated with altitude and sunshine duration (R = 0.50, 0.40; p < 0.05), and significantly negatively correlated with relative humidity and precipitation (R = 0.48, −0.42; p < 0.05), in the country on a whole in the analysis period.

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Global land degradation hotspots based on multiple methods and indicators

Jiang,

Teuling,

Chen

et al. 2024

Ecological Indicators

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The global spatiotemporal heterogeneity of land surface-air temperature difference and its influencing factors

Cited by 8 publications

References 45 publications

Comparison of aeolian desertification between the Moltsog Dune Field in Mongolia and the Ujimqin Dune Field in China

Comparison of aeolian desertification between the Moltsog Dune Field in Mongolia and the Ujimqin Dune Field in China

Spatio-Temporal Patterns of Warm-Season Ground Surface Temperature—Surface Air Temperature Difference over China Mainland

Global land degradation hotspots based on multiple methods and indicators

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