Change in fresh snow density in Tianshan Mountains, China

Xia, Chen; Wang, Wei; Liu, Mingzhe

doi:10.1007/s11769-010-0434-0

Cited by 12 publications

(12 citation statements)

References 21 publications

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“…Chen et al . () observed that the fresh snow density in the Tianshan Mountain region increases drastically with the time with a quadruple increase in about 10 days. The weight of the upper layers of snow acts to densify the lower part of the deposit (Judson and Doesken, ).…”

Section: Summary and Discussionmentioning

confidence: 98%

“…Based on observations at Colorado, Judson and Doesken (2000) showed that the snow density declines largely with the temperature drop. Chen et al (2011) observed that the fresh snow density in the Tianshan Mountain region increases drastically with the time with a quadruple increase in about 10 days. The weight of the upper layers of snow acts to densify the lower part of the deposit (Judson and Doesken, 2000).…”

Section: Discussionmentioning

confidence: 99%

“…The snow density is a quantity that may subject to large changes with time (Judson and Doesken, 2000;Chen et al, 2011). Based on observations at Colorado, Judson and Doesken (2000) showed that the snow density declines largely with the temperature drop.…”

Section: Discussionmentioning

confidence: 99%

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Low‐frequency snow changes over the Tibetan Plateau

Wang

Huang

2017

Intl Journal of Climatology

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Snow change over the Tibetan Plateau may exert a large influence on climate variability in the surrounding regions. However, the characteristics of snow changes at different time scales and the factors for these changes are still not clear. The present study documents linear trends in snow cover and snow water equivalent over the Tibetan Plateau and their relationship to surface air temperature changes during 1979-2006 based on satellite data. The long-term snow variations display a remarkable regional difference and an obvious seasonal dependence. A significant decreasing trend is observed in the western part for snow cover and snow water equivalent in summer and fall and in the southern part for snow cover in all the four seasons. A significant increasing trend is identified in the central-eastern part for snow cover in fall, winter, and spring and in the eastern and far western parts for snow water equivalent in winter and spring. The relationship between snow and surface air temperature changes features regional disparity. The temperature increase is accompanied by snow cover decrease in the southern and western parts, but by snow cover and snow water equivalent increase in the central-eastern part. The reasons for the snow changes vary with the season. The increase in snowmelt following the temperature increase may be the reason for the snow cover decrease in the western and southern parts in summer. The snowfall change induced by vertical motion change appears to be a factor for the snow water equivalent increase in the far western part and the snow cover decrease in the southern part in winter. The increase in snowfall induced by the increase in atmospheric moisture following the temperature increase and the enhanced upward motion may contribute to the snow increase in the eastern part in winter.

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Section: Summary and Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

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Low‐frequency snow changes over the Tibetan Plateau

Wang

Huang

2017

Intl Journal of Climatology

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“…Our main innovation is to separate the observed negative correlation between depth and density over short timescales from their positive correlation at longer timescales. The importance of different governing processes at separate timescales has been observed in field studies of bulk densities (e.g., Chen et al, 2011). Incorporating behavior at distinct timescales into our new model provides more realistic daily time series of bulk density.…”

Section: Introductionmentioning

confidence: 85%

Modeling bulk density and snow water equivalent using daily snow depth observations

McCreight

Small

2014

The Cryosphere

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Abstract. Bulk density is a fundamental property of snow relating its depth and mass. Previously, two simple models of bulk density (depending on snow depth, date, and location) have been developed to convert snow depth observations to snow water equivalent (SWE) estimates. However, these models were not intended for application at the daily time step. We develop a new model of bulk density for the daily time step and demonstrate its improved skill over the existing models.Snow depth and density are negatively correlated at short (10 days) timescales while positively correlated at longer (90 days) timescales. We separate these scales of variability by modeling smoothed, daily snow depth (long timescales) and the observed positive and negative anomalies from the smoothed time series (short timescales) as separate terms. A climatology of fit is also included as a predictor variable.Over half a million daily observations of depth and SWE at 345 snowpack telemetry (SNOTEL) sites are used to fit models and evaluate their performance. For each location, we train the three models to the neighboring stations within 70 km, transfer the parameters to the location to be modeled, and evaluate modeled time series against the observations at that site. Our model exhibits improved statistics and qualitatively more-realistic behavior at the daily time step when sufficient local training data are available. We reduce density root mean square error (RMSE) by 9.9 and 4.5 % compared to previous models while increasing R 2 from 0.46 to 0.52 to 0.56 across models. Focusing on the 21-day window around peak SWE in each water year, our model reduces density RMSE by 24 and 17.4 % relative to the previous models, with R 2 increasing from 0.55 to 0.58 to 0.71 across models. Removing the challenge of parameter transfer over the full observational record increases R 2 scores for both the existing and new models, but the gain is greatest for the new model (R 2 = 0.75). Our model shows general improvement over existing models when data are more frequent than once every 5 days and at least 3 stations are available for training.

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“…natural seasonality and regional temperature changes on snow densities, which could also modify passive microwave SWE estimates over the course of our time series, and thus are part of the trends that we present as changes in SWE in this study (Judson and Doesken, 2000;Chen et al, 2011;Dai et al, 2012). The impact of seasonal oscillations in snow density is somewhat mitigated by removing the seasonal cycle from our data before trend fitting, as some of the seasonality in SWE estimates will be driven by changes in snow density.…”

Section: Data Caveatsmentioning

confidence: 92%

Assessing Multi-Temporal Snow-Volume Trends in High Mountain Asia From 1987 to 2016 Using High-Resolution Passive Microwave Data

Smith

Bookhagen

2020

Front. Earth Sci.

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High Mountain Asia (HMA) is dependent upon both the amount and timing of snow and glacier meltwater. Previous model studies and coarse resolution (0.25°× 0.25°, ∼25 km × 25 km) passive microwave assessments of trends in the volume and timing of snowfall, snowmelt, and glacier melt in HMA have identified key spatial and seasonal heterogeneities in the response of snow to changes in regional climate. Here we use recently developed, continuous, internally consistent, and high-resolution passive microwave data (3.125 km × 3.125 km, 1987-2016) from the special sensor microwave imager instrument family to refine and extend previous estimates of changes in the snow regime of HMA. We find an overall decline in snow volume across HMA; however, there exist spatially contiguous regions of increasing snow volume-particularly during the winter season in the Pamir, Karakoram, Hindu Kush, and Kunlun Shan. Detailed analysis of changes in snow-volume trends through time reveal a large step change from negative trends during the period 1987-1997, to much more positive trends across large regions of HMA during the periods 1997-2007 and 2007-2016. We also find that changes in high percentile monthly snowwater volume exhibit steeper trends than changes in low percentile snow-water volume, which suggests a reduction in the frequency of high snow-water volumes in much of HMA. Regions with positive snow-water storage trends generally correspond to regions of positive glacier mass balances.

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Change in fresh snow density in Tianshan Mountains, China

Cited by 12 publications

References 21 publications

Low‐frequency snow changes over the Tibetan Plateau

Low‐frequency snow changes over the Tibetan Plateau

Modeling bulk density and snow water equivalent using daily snow depth observations

Assessing Multi-Temporal Snow-Volume Trends in High Mountain Asia From 1987 to 2016 Using High-Resolution Passive Microwave Data

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