Spatial–Temporal Variability of Snow Cover and Depth in the Qinghai–Tibetan Plateau

Xu, Wenfang; Ma, Lijuan; Ma, Mengqing; Zhang, Haicheng; Yuan, Wenping

doi:10.1175/jcli-d-15-0732.1

Cited by 120 publications

(140 citation statements)

References 47 publications

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“…However, a direct comparison with their results is difficult due to differences in the temporal and spatial resolution of source data, filtering methods, and statistical treatment of SWE trends. Negative snowmelt onset trends have also been previously observed in Central Asia (Lioubimtseva and Henebry, 2009;Dietz et al, 2014), the Himalaya (Lau et al, 2010;Panday et al, 2011), and the Tibetan Plateau (Xu et al, 2017).…”

Section: Spatial Melt Patterns From Hierarchical Clusteringsupporting

confidence: 54%

Spatiotemporal patterns of High Mountain Asia's snowmelt season identified with an automated snowmelt detection algorithm, 1987–2016

2017

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Abstract. High Mountain Asia (HMA) -encompassing the Tibetan Plateau and surrounding mountain ranges -is the primary water source for much of Asia, serving more than a billion downstream users. Many catchments receive the majority of their yearly water budget in the form of snow, which is poorly monitored by sparse in situ weather networks. Both the timing and volume of snowmelt play critical roles in downstream water provision, as many applications -such as agriculture, drinking-water generation, and hydropower -rely on consistent and predictable snowmelt runoff. Here, we examine passive microwave data across HMA with five sensors (SSMI, SSMIS, AMSR-E, AMSR2, and GPM) from 1987 to 2016 to track the timing of the snowmelt season -defined here as the time between maximum passive microwave signal separation and snow clearance. We validated our method against climate model surface temperatures, optical remote-sensing snow-cover data, and a manual control dataset (n = 2100, 3 variables at 25 locations over 28 years); our algorithm is generally accurate within 3-5 days. Using the algorithm-generated snowmelt dates, we examine the spatiotemporal patterns of the snowmelt season across HMA. The climatically short (29-year) time series, along with complex interannual snowfall variations, makes determining trends in snowmelt dates at a single point difficult.We instead identify trends in snowmelt timing by using hierarchical clustering of the passive microwave data to determine trends in self-similar regions. We make the following four key observations. (1) The end of the snowmelt season is trending almost universally earlier in HMA (negative trends). Changes in the end of the snowmelt season are generally between 2 and 8 days decade −1 over the 29-year study period (5-25 days total). The length of the snowmelt season is thus shrinking in many, though not all, regions of HMA. Some areas exhibit later peak signal separation (positive trends), but with generally smaller magnitudes than trends in snowmelt end. (2) Areas with long snowmelt periods, such as the Tibetan Plateau, show the strongest compression of the snowmelt season (negative trends). These trends are apparent regardless of the time period over which the regression is performed. (3) While trends averaged over 3 decades indicate generally earlier snowmelt seasons, data from the last 14 years (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016) exhibit positive trends in many regions, such as parts of the Pamir and Kunlun Shan. Due to the short nature of the time series, it is not clear whether this change is a reversal of a long-term trend or simply interannual variability. (4) Some regions with stable or growing glaciers -such as the Karakoram and Kunlun Shan -see slightly later snowmelt seasons and longer snowmelt periods. It is likely that changes in the snowmelt regime of HMA account for some of the observed heterogeneity in glacier response to climate change. While the decadal increases in regional temperature have in ge...

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Section: Spatial Melt Patterns From Hierarchical Clusteringsupporting

confidence: 54%

Spatiotemporal patterns of High Mountain Asia's snowmelt season identified with an automated snowmelt detection algorithm, 1987–2016

2017

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“…The decadal change in the spring snow depth over the TP can impact the Asian summer monsoon and a close relationship exists between the interdecadal increase of snow depth over the TP during March-April, increased summer rainfall over the Yangtze River valley, and a drier summer along the southeast coast of China and the Indochinese Peninsula (Zhang et al, 2004). Based on observations from 103 meteorological stations across the TP, the duration of snow cover exhibited a significant decreasing trend (1.2 days decade −1 ), which was jointly controlled by a later snow starting time (1.6 ± 0.8 days decade −1 ) and an earlier snow ending time (−1.9 ± 0.8 days decade −1 ) consistent with a response to climate change (Xu et al, 2017).…”

Section: Study Areamentioning

confidence: 90%

“…(4), we can estimate changes of snow cover duration ( D, days) based on the observed snow depth (mm w.e.) during sampling (Table S1) and previous studies (Che et al, 2012;Xu et al, 2017;Zhong et al, 2018). We expressed the results for each case as low, medium, and high scenarios, based on albedo variations.…”

Section: Estimates Of Changes In Snow Cover Durationmentioning

confidence: 99%

“…Covered by a large volume of snow/ice in the mid-low latitudes, the TP is considered to be a sensitive and readily visible indicator of climate change (Yao et al, 2012a). Recently, the TP cryosphere has undergone rapid changes (Kang et al, 2010), including glacial shrinkage (Xu et al, 2009;Yao et al, 2012b), permafrost degradation (Cheng and Wu, 2007), and reduction in the annual duration of snow cover days (Ménégoz et al, 2014;Xu et al, 2017). Studies on the TP indicate that BC has been a significant contributing factor to the observed cryospheric change Ming et al, 2009;Niu et al, 2017;Xu et al, 2009;Yang et al, 2015;Zhang et al, 2017) and a major forcer of climate change Ji, 2016).…”

mentioning

confidence: 99%

“…Changes in snow cover over the TP have attracted much attention in recent years owing to climate change (Xiao and Duan, 2016;Xu et al, 2017). Snow cover on the TP plays an important role in the Asian summer monsoon, and serves as a crucial water source for several major Asian rivers (Bai and Feng, 1994;Lau et al, 2010;Vernekar et al, 1995;Yao et al, 2012a;Zhao and Moore, 2004).…”

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confidence: 99%

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Black carbon and mineral dust in snow cover on the Tibetan Plateau

et al. 2018

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Abstract. Snow cover plays a key role for sustaining ecology and society in mountainous regions. Light-absorbing particulates (including black carbon, organic carbon, and mineral dust) deposited on snow can reduce surface albedo and contribute to the near-worldwide melting of snow and ice. This study focused on understanding the role of black carbon and other water-insoluble light-absorbing particulates in the snow cover of the Tibetan Plateau (TP). The results found that the black carbon, organic carbon, and dust concentrations in snow cover generally ranged from 202 to 17 468 ng g −1 , 491 to 13 880 ng g −1 , and 22 to 846 µg g −1 , respectively, with higher concentrations in the central to northern areas of the TP. Back trajectory analysis suggested that the northern TP was influenced mainly by air masses from Central Asia with some Eurasian influence, and air masses in the central and Himalayan region originated mainly from Central and South Asia. The relative biomassburning-sourced black carbon contributions decreased from ∼ 50 % in the southern TP to ∼ 30 % in the northern TP. The relative contribution of black carbon and dust to snow albedo reduction reached approximately 37 and 15 %, respectively. The effect of black carbon and dust reduced the snow cover duration by 3.1 ± 0.1 to 4.4 ± 0.2 days. Meanwhile, the black carbon and dust had important implications for snowmelt water loss over the TP. The findings indicate that the impacts of black carbon and mineral dust need to be properly accounted for in future regional climate projections, particularly in the high-altitude cryosphere.

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Changes in snow depth under elevation‐dependent warming over the Tibetan Plateau

Shen

Zhang

Ullah

et al. 2021

Atmospheric Science Letters

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Snow plays an essential role in regulating climate change, the hydrological cycle, and various biological processes. Passive microwave snow depth data and gridded data from the Climate Research Unit (CRU_TS4.04) are utilized in this study to investigate spatiotemporal variations of snow depth over the Tibetan Plateau (TP), with special focus on the vertical dimension. The response of snow to elevationdependent warming (EDW) is determined accordingly. High mountains experience more rapid warming than lower elevations. During 1980-2014, the total snow depth over the TP decreased; areas with the most significant decreasing trends are mainly concentrated in the northwestern and southwestern parts of the TP. The plateauwide decrease in snow depth (À0.24 cm/decade) is mainly affected by increasing temperature (0.30 C/decade). The reduction in snow depth trend intensifies as subregional mean elevation increases from 3,332 m (IID2) to 5,074 m (ID1). A stronger snow depth decrease in high-elevation sub-regions generally corresponds to higher warming rates, which demonstrates EDW. The most pronounced correlation between snow depth decrease rate and elevation occurs in the southeastern TP, which covers the largest elevation range on the plateau (from 2,000 to 6,000 m).

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Spatial–Temporal Variability of Snow Cover and Depth in the Qinghai–Tibetan Plateau

Cited by 120 publications

References 47 publications

Spatiotemporal patterns of High Mountain Asia's snowmelt season identified with an automated snowmelt detection algorithm, 1987–2016

Spatiotemporal patterns of High Mountain Asia's snowmelt season identified with an automated snowmelt detection algorithm, 1987–2016

Black carbon and mineral dust in snow cover on the Tibetan Plateau

Changes in snow depth under elevation‐dependent warming over the Tibetan Plateau

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