Accelerated decline of snow cover in China from 1979 to 2018 observed from space

Zhu, Linglong; Ma, Guangyi; Zhang, Yongyong; Wang, Jiangeng; Tian, Wei; Kan, Xi

doi:10.1016/j.scitotenv.2021.152491

Cited by 21 publications

(8 citation statements)

References 38 publications

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“…For example, for the Plateau, decreases in spring snow depth starting in the 2000s (Zhu et al, 2015), increases in winter but decreases in fall snow depth after the 1980s were reported by Lü et al (2008) and You et al (2011), which are consistent with our results. Nevertheless, owing to differences in data sources, trends in snow depth we obtained, using ground observations, differ from trends in a recent study, obtained from satellite-born passive microwave retrieval of snow depth (Zhu et al, 2022). Unlike the reported increasing trends in snow depth across the northeast and northwest of China, they reported a recent accelerated reduction in these regions.…”

Section: Discussioncontrasting

confidence: 98%

“…(2011), which are consistent with our results. Nevertheless, owing to differences in data sources, trends in snow depth we obtained, using ground observations, differ from trends in a recent study, obtained from satellite‐born passive microwave retrieval of snow depth (Zhu et al ., 2022). Unlike the reported increasing trends in snow depth across the northeast and northwest of China, they reported a recent accelerated reduction in these regions.…”

Section: Discussionmentioning

confidence: 99%

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Snow depth alteration and its relation with climate variability in China

Saydi

Tang

Ding

et al. 2023

Intl Journal of Climatology

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Various climatic factors and their interactions affect China's snow depth alterations. However, a measure of the relative importance of climatic factors regarding the variance in snow depth is lacking. Here, we use a heretofore largest dataset, quality‐controlled daily observations from 1,665 meteorological stations, and statistical analyses to identify proxy drivers and their contributions regarding regional variability and trends in China's snow depth and snow depth‐climate relationships from 1951 to 2019. Results show that seasonal snow depth alterations are substantial in China and caused by various climatic drivers. In winter, observed positive trends in the northwest and northeast of China mainly respond to augmentation in solid precipitation and snow accumulation in fall. For the Tibetan Plateau, the negative snow depth anomaly seems to respond to the rising air temperature below the freezing point along with some secular changes in solid precipitation and shallower fall snowpacks. All these trends in winter snow depth over the northwest, northeast, and Tibetan Plateau of China account approximately for 30% of the total interannual variance in spring snow depth in these regions. In the milder central north and south of China, snow accumulation is not evident. Besides solid precipitation and rising air temperature below the freezing point, atmospheric humidity is also notable for its effect on the interannual variance in winter snow depth in these regions. In spring and fall, however, the negative effect of warming climate on snow depth becomes remarkably more important. It seems that about 13–33% (12–50%) of the total interannual variance in the widespread decrease (insignificant perturbation), except for some weak increases in the northeast region (decreases in the Tibetan Plateau), of China's spring (fall) snow depth are caused by the rising air temperature below the freezing point.

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Section: Discussioncontrasting

confidence: 98%

Section: Discussionmentioning

confidence: 99%

Snow depth alteration and its relation with climate variability in China

Saydi

Tang

Ding

et al. 2023

Intl Journal of Climatology

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“…In the summer and autumn of 2007, the precipitation in the dust source regions of northwestern China and western Inner Mongolia was higher than the that for the same period during other years of the study period, which caused the vegetation to grow well in northwestern China [65,66]. In addition, East Asia received significant rain and snow, and the snow cover was large in the spring of 2008 [67]. Meanwhile, the zonal circulation was dominant in northern China in the spring of 2008 [68], which was not conductive to the activity of cold air in the spring, and provided a circulation background for the weak dust events.…”

Section: Discussionmentioning

confidence: 93%

Spatial and Temporal Variations in Spring Dust Concentrations from 2000 to 2020 in China: Simulations with WRF-Chem

Wang

Kong

et al. 2022

Remote Sensing

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Dust emitted from arid and semi-arid areas of China is a main contributor to the global atmospheric aerosols. However, the long-term spatial and temporal variations in dust concentrations in China is still unknown. Here, we simulated the spatial and temporal variations in spring dust concentrations in China from 2000 to 2020 using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). The results showed that the configured WRF-Chem model in this study reproduced the spatial patterns and temporal variations of dust aerosols. The annual mean spring dust concentration at the country level was 26.95 g kg−1-dry air and showed a slightly increasing trend in China during 2000–2020. There were clear spatial differences and inter-annual variations in dust concentrations. The dust concentration generally decreased from the dust source regions of the northwest to the southeast regions of China. Obvious increasing and decreasing trends in spring dust concentrations were identified in the regions of northern Xinjiang and Gansu and in the regions of southern Xinjiang and western Inner Mongolia, respectively. In May, the dust concentration showed an increasing trend in most regions of northwestern China. This provided the basic information for insight into the long-term spatial and temporal variations in spring dust concentrations in China.

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“…The Mann-Kendall test is a non-parametric test method to detect the time series trend, and is not disturbed by outliers in the time series. This method has been successfully applied in meteorological, hydrological, and ecological-related studies (e.g., Wang et al, 2020b; Wei et al, 2022; Zhu et al, 2022). Definable statistic S :…”

Section: Methodsmentioning

confidence: 99%

A comparative study on the spatio-temporal patterns and drivers of net primary productivity in three basins across different climate gradients in China

Lai,

Xu,

Yin

et al. 2023

Transactions in Earth, Environment, and Sustainability

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The comparative study on terrestrial net primary productivity in regions across climate gradients may help to better understand ecosystem response to climate change, yet is relatively understudied. Therefore, in this study, we compared the spatio-temporal patterns of net primary productivity during 2010-2019 and identify the different controls in three basins with typical climates in China: the Heihe River Basin with arid and semi-arid climate, Weihe River Basin with semi-arid and semi-humid climate, and Lower Yangtze River Basin with humid climate. Results indicated that the net primary productivity followed the order: Lower Yangtze River Basin (480.3 g C m−2 yr−1) > Weihe River Basin (346.7 g C m−2 yr−1) > Heihe River Basin (98.1 g C m−2 yr−1). The vegetation in the Heihe River Basin had severel hydrothermal limitation as the greatest importance of temperature and precipitation to the net primary productivity. In contrast, land cover type instead of climate became the most important variable on net primary productivity in the Weihe River Basin, and topography and land cover type were critical controls in the Lower Yangtze River Basin. In addition, the annual net primary productivity in all three areas exhibited an increasing trend during 2010-2019. However, the increase of net primary productivity in the Heihe River Basin was primarily driven by the increase of both temperature and precipitation due to the warming and humification in the northwestern of China. In contrast, the increase of temperature (especially spring warming) dominated the increase of net primary productivity in the Weihe River Basin and Lower Yangtze River Basin, due to the relatively sufficient precipitation. Furthermore, monthly net primary productivity exhibited a unimodal curve throughout the year. However, the increasing trend nearly stopped in June and July in the Lower Yangtze River Basin. This is possibly due to the light deficiency with abundant precipitation for plant photosynthesis in the natural ecosystem like forest and shrubland, and the rotation from wheat harvesting to rice planting in the cropland in these months.

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Accelerated decline of snow cover in China from 1979 to 2018 observed from space

Cited by 21 publications

References 38 publications

Snow depth alteration and its relation with climate variability in China

Snow depth alteration and its relation with climate variability in China

Spatial and Temporal Variations in Spring Dust Concentrations from 2000 to 2020 in China: Simulations with WRF-Chem

A comparative study on the spatio-temporal patterns and drivers of net primary productivity in three basins across different climate gradients in China

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