Climatology of snow phenology over the Tibetan plateau for the period 2001–2014 using multisource data

Chen, Xiaona; Long, Di; Hong, Yang; Hao, Xiaohua; Hou, Aizhong

doi:10.1002/joc.5455

Cited by 19 publications

(11 citation statements)

References 47 publications

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“…De dominated the snow season variations due to the higher Tm across the TS, which was coincidental with the studies in the Northern Hemisphere [6,69,79]. However, the Do in the Tibetan Plateau controlled the variability of snow phenology [27]. In contrast, decreased Ta, which was driven , and (f) sensitivity of D e to SD max (days cm −1 ).…”

Section: Factors Driving Snow Phenology Changes Over the Tssupporting

confidence: 70%

“…D e dominated the snow season variations due to the higher T m across the TS, which was coincidental with the studies in the Northern Hemisphere [6,69,79]. However, the D o in the Tibetan Plateau controlled the variability of snow phenology [27]. In contrast, decreased T a , which was driven by the Pacific Ocean's surface cooling in winter and amplified Arctic warming effects [80][81][82], resulted in earlier D o in the Tibetan Plateau (2001-2014) [27,79].…”

Section: Factors Driving Snow Phenology Changes Over the Tssupporting

confidence: 69%

“…This daily database was developed through a modified Chang algorithm, which was based on the in situ snow data of China, taking the impact of forest cover, liquid water content in snow layer, and surface water body into consideration [11,30,64,65]. Compared with the snow depth from SSM/I and SSMI/S, this dataset has high accuracy and low biases in China [65] and allows the successful investigation of snow depth in China, the Tibetan Plateau, northeastern China, and Xinjiang [11,27,30,31].…”

Section: Remote Sensing Snow Depth Productsmentioning

confidence: 99%

“…To investigate the variability of snow phenology over the TS, this study defined the period from September to the following August as a snow hydrological year. The snow cover onset day (D o ) for the hydrological year was defined as the first day of the first five consecutive snow cover days on each grid to reduce the influence of instantaneous snow on the snow phenology computations [27]. The snow cover end day (D e ) for the hydrological year was defined as the last day of the last five consecutive snow cover days on each grid.…”

Section: Snow Phenology Calculationmentioning

confidence: 99%

“…Increasing temperatures have shortened the snow cover duration (D d ) [6,22], delayed the snow onset day (D o ) [23], and resulted in an earlier snow end day (D e ) [24]. In contrast, enhanced mid-latitude westerlies have caused an enlarged snow cover extent in High Asia and Tibet with prolonged D d , earlier D o , and later D e [25][26][27]. However, the cloud mask and lack of snow depth detection methods limit the application of snow products from optical remote sensing [28][29][30][31].…”

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confidence: 99%

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Changes in Snow Phenology from 1979 to 2016 over the Tianshan Mountains, Central Asia

Yang

Ahmad

et al. 2019

Remote Sensing

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Snowmelt from the Tianshan Mountains (TS) is a major contributor to the water resources of the Central Asian region. Thus, changes in snow phenology over the TS have significant implications for regional water supplies and ecosystem services. However, the characteristics of changes in snow phenology and their influences on the climate are poorly understood throughout the entire TS due to the lack of in situ observations, limitations of optical remote sensing due to clouds, and decentralized political landscapes. Using passive microwave remote sensing snow data from 1979 to 2016 across the TS, this study investigates the spatiotemporal variations of snow phenology and their attributes and implications. The results show that the mean snow onset day (Do), snow end day (De), snow cover duration days (Dd), and maximum snow depth (SDmax) from 1979 to 2016 were the 78.2nd day of hydrological year (DOY), 222.4th DOY, 146.2 days, and 16.1 cm over the TS, respectively. Dd exhibited a spatial distribution of days with a temperature of <0 °C derived from meteorological station observations. Anomalies of snow phenology displayed the regional diversities over the TS, with shortened Dd in high-altitude regions and the Fergana Valley but increased Dd in the Ili Valley and upper reaches of the Chu and Aksu Rivers. Increased SDmax was exhibited in the central part of the TS, and decreased SDmax was observed in the western and eastern parts of the TS. Changes in Dd were dominated by earlier De, which was caused by increased melt-season temperatures (Tm). Earlier De with increased accumulation of seasonal precipitation (Pa) influenced the hydrological processes in the snowmelt recharge basin, increasing runoff and earlier peak runoff in the spring, which intensified the regional water crisis.

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Section: Factors Driving Snow Phenology Changes Over the Tssupporting

confidence: 70%

“…D e dominated the snow season variations due to the higher T m across the TS, which was coincidental with the studies in the Northern Hemisphere [6,69,79]. However, the D o in the Tibetan Plateau controlled the variability of snow phenology [27]. In contrast, decreased T a , which was driven by the Pacific Ocean's surface cooling in winter and amplified Arctic warming effects [80][81][82], resulted in earlier D o in the Tibetan Plateau (2001-2014) [27,79].…”

Section: Factors Driving Snow Phenology Changes Over the Tssupporting

confidence: 69%

Section: Remote Sensing Snow Depth Productsmentioning

confidence: 99%

Section: Snow Phenology Calculationmentioning

confidence: 99%

mentioning

confidence: 99%

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Changes in Snow Phenology from 1979 to 2016 over the Tianshan Mountains, Central Asia

Yang

Ahmad

et al. 2019

Remote Sensing

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Spring snow depth changes and feedback to surface air temperature across the Tibetan plateau from 1961 to 2013

Chen

Yang

2021

Intl Journal of Climatology

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Spring snow depth (SSD) in the Tibetan Plateau (TP) plays an essential role in snow water estimation, vegetation growth, and climate projections for Southwest China and the surrounding Asian countries. Previous studies have reported a decline in SSD that is associated with increasing surface air temperature (Ts) across the TP over the past decades. However, only a few studies have explored the potential feedback of SSD to Ts, which has hindered the elucidation of the vulnerability of snow cover in the TP against the backdrop of rapid climate change. Based on data from 69 meteorological stations in the central and eastern parts of the TP, this study analyses changes in SSD and its feedback to Ts from 1961 to 2013. Overall, a slightly negative trend in SSD was observed from 1961 to 2013, during which two contrasting trends were detected using a piecewise regression approach: an initial increase from 1961 to 1981 (0.12 cm decades−1, p < .01) followed by a decrease from 1982 to 2013 (−0.36 cm decades−1, p < .01). Sensitivity analysis revealed that the SSD decline was systematically correlated with a positive Ts anomaly, with sensitivities of −0.050 cm °C−1 (p < .01) and −0.069 cm °C−1 (p < .01) for 1961–2013 and 1982–2013, respectively. However, following the accelerated SSD decline during 1982–2013, its potential feedback to Ts was attenuated, with averaged feedback values from −1.328°C cm−1 during 1961–2013 to −0.936°C cm−1 during 1982–2013 in latitudes below 3,500 m, distributed in the Qaidam and Yellow River basins, and the upper reaches of the Yangtze River basin. The decrease in feedback from SSD to Ts may lead to changes in the mechanism controlling Ts dynamics across the TP, which should be considered in climate change projections in future studies.

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The sensitivity and evolutionary trajectory of the mountain cryosphere: Implications for mountain geomorphic systems and hazards

Knight

Harrison

2023

Land Degrad Dev

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Global climate change gives rise to changing spatial patterns of snow and ice, especially over mountain blocks where orographic and synoptic circulation effects play significant roles in creating patterns of precipitation and glacier development. The presence of snow and ice results in heat balance changes and other land surface feedbacks that have implications for patterns of mountain glacier retreat and the dynamics of mountain geomorphic systems. This study considers the sensitivity of the mountain cryosphere (snow, ice, permafrost) to global climate change, and the implications of this sensitivity analysis for evaluating mountain surface stability, geomorphic change, and the generation of mountain geohazards. Consideration of these issues is informed by evidence from case studies reported in the literature and by field observations of mountain system dynamics worldwide. Results show that ‘sensitivity’ to climate forcing has been interpreted and defined in different ways in mountain snow, ice, and permafrost systems, with respect to properties such as albedo, mass balance or rapidity of system change. There are also significant spatial differences in sensitivity between different mountain blocks for snow, ice and permafrost, and these regions are therefore likely to follow different trajectories of geomorphic change in response to climate forcing, related to their physiographic properties and the extent of cryospheric coverage. Within glaciated mountains in particular, the relative timing of different geomorphic events, and the interplay between slope, glacier front, and proglacial sediment sources and environments, may vary depending on glacier size, geomorphic setting, and microclimate. By contrast, responses to permafrost warming (increased surface instability and mass movements) and changes in snow patterns (avalanche risk, floods) may have quite different spatial and temporal patterns and influenced by different environmental controls. An integrated evolutionary model for mountain system development under a changing cryosphere is proposed, highlighting the critical role of energy balance as a forcing factor that then triggers downstream mountain system responses. This suggests that different elements of mountain systems exhibit different sensitivities, and furthermore that these sensitivities change over time and space through the period of anthropogenic global warming and paraglacial relaxation.

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Climatology of snow phenology over the Tibetan plateau for the period 2001–2014 using multisource data

Cited by 19 publications

References 47 publications

Changes in Snow Phenology from 1979 to 2016 over the Tianshan Mountains, Central Asia

Changes in Snow Phenology from 1979 to 2016 over the Tianshan Mountains, Central Asia

Spring snow depth changes and feedback to surface air temperature across the Tibetan plateau from 1961 to 2013

The sensitivity and evolutionary trajectory of the mountain cryosphere: Implications for mountain geomorphic systems and hazards

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