scholarly journals Snow cover variations across China from 1951–2018

2020 ◽  
Author(s):  
Xiaodong Huang ◽  
Changyu Liu ◽  
Zhaojun Zheng ◽  
Yunlong Wang ◽  
Xubing Li ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Snow Cover ◽  
Snow Depth ◽  
Northeast China ◽  
Regional Differences ◽  
Meteorological Factors ◽  
Onset Date ◽  
The Tibetan Plateau ◽  
Maximum Snow Depth ◽  
The Postponement

Abstract. Based on a snow depth dataset retrieved from meteorological stations, this experiment explored snow indices, including snow depth (SD), snow covered days (SCDs), and snow phenology variations, across China from 1951 to 2018. The results indicated that the snow cover in China exhibits regional differences. The annual mean SD tended to increase, and the increases in mean and maximum snow depth were 0.04 cm and 0.1 cm per decade, respectively. SCDs tended to increase by approximately 0.5 days per decade. The significant increases were concentrated at latitudes higher than 40° N, especially in Northeast China. However, in the Tibetan Plateau, the SD and SCDs tended to decrease but not significantly. Regarding the snow phenology variations, the snow duration days in China decreased, and 25.2 % of the meteorological stations showed significant decreasing trends. This result was mainly caused by the postponement of the snow onset date and the advancement of the snow end date. Geographical and meteorological factors are closely related to snow cover, especially the change in temperature, which will lead to significant changes in snow depth and phenology.

scholarly journals Snow cover variations across China from 1952–2012

2019 ◽  
Author(s):  
Xiaodong Huang ◽  
Changyu Liu ◽  
Yunlong Wang ◽  
Qisheng Feng ◽  
Tiangang Liang
Keyword(s):  
Tibetan Plateau ◽  
Snow Cover ◽  
Snow Depth ◽  
Northeast China ◽  
Regional Differences ◽  
Onset Date ◽  
The Tibetan Plateau ◽  
Increasing Trend ◽  
Main Period ◽  
The Postponement

Abstract. Based on a snow depth (SD) dataset retrieved from meteorological stations, this experiment explored snow indices including SD, snow covered days (SCDs), and snow phenology variations in China from 1952 to 2012. The results indicated that the snow in China exhibits regional differences, and the snow cover is mainly concentrated in three snow cover areas in Northeast China, northern Xinjiang and the Tibetan Plateau. In China, the annual average SD showed an increasing trend, and the increases in the average snow depth (SDaverage), cumulative snow depth (SDoverall) and maximum snow depth (SDmax) reached 0.04 cm, 0.05 cm and 0.07 cm per decade, respectively. The significant increases were mainly concentrated in areas higher than 40° N latitude, especially in Northeast China. The SDaverage, SDoveralland SDmax jump points are mainly in 1956, 1957, 1978, and 1987. In the first main period, the SDoverall oscillation in China is relatively stable, and its average period is approximately 13 years. The SCDs showed an increasing trend, with an increase of 0.5 days per decade. The significant increases in SCDs were also concentrated in areas higher than 40° N latitude, especially in Northeast China.However, in the Tibetan Plateau, the decrease in the SCDs reached 0.1 days per decade. In snow phenology, the snow duration days (SDDs) of China decreased, and 17.4 % of the meteorological stations showed significant decreasing trends. This result is mainly caused by the postponement of the snow onset date (SOD) and the advancement of the snow end date (SED). Geographical factors, including latitude, longitude and altitude, affect snow cover distribution directly and indirectly. The squared multiple correlations of SDDs and SCDs are greater than 0.9. Among the effects of SDDs and SCDs, the largest standardized total effect is from altitude on the SDDs, and the effect reaches 0.8.

scholarly journals Spatio-Temporal Variation Characteristics of Snow Depth and Snow Cover Days over the Tibetan Plateau

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 307
Author(s):  
Chi Zhang ◽  
Naixia Mou ◽  
Jiqiang Niu ◽  
Lingxian Zhang ◽  
Feng Liu
Keyword(s):  
Tibetan Plateau ◽  
Snow Cover ◽  
Snow Depth ◽  
Feedback Effect ◽  
Effect Of Temperature ◽  
The Tibetan Plateau ◽  
Reanalysis Dataset ◽  
Spatio Temporal ◽  
The Impact ◽  
Main Factor

Changes in snow cover over the Tibetan Plateau (TP) have a significant impact on agriculture, hydrology, and ecological environment of surrounding areas. This study investigates the spatio-temporal pattern of snow depth (SD) and snow cover days (SCD), as well as the impact of temperature and precipitation on snow cover over TP from 1979 to 2018 by using the ERA5 reanalysis dataset, and uses the Mann–Kendall test for significance. The results indicate that (1) the average annual SD and SCD in the southern and western edge areas of TP are relatively high, reaching 10 cm and 120 d or more, respectively. (2) In the past 40 years, SD (s = 0.04 cm decade−1, p = 0.81) and SCD (s = −2.3 d decade−1, p = 0.10) over TP did not change significantly. (3) The positive feedback effect of precipitation is the main factor affecting SD, while the negative feedback effect of temperature is the main factor affecting SCD. This study improves the understanding of snow cover change and is conducive to the further study of climate change on TP.

scholarly journals Observed Relationship of Spring and Summer East Asian Rainfall with Winter and Spring Eurasian Snow

2007 ◽  
Vol 20 (7) ◽  
pp. 1285-1304 ◽  
Author(s):  
Renguang Wu ◽  
Ben P. Kirtman
Keyword(s):  
Tibetan Plateau ◽  
Snow Cover ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Southern Oscillation ◽  
Southern China ◽  
Summer Rainfall ◽  
Wave Pattern ◽  
Summer Monsoon Rainfall ◽  
The Tibetan Plateau

Abstract This study investigates the relationship between spring and summer rainfall in East Asia and the preceding winter and spring snow cover/depth over Eurasia, using station rainfall observations, satellite-observed snow cover, satellite-derived snow water equivalent, and station observations of the number of days of snow cover and snow depth. Correlation analysis shows that snow-depth anomalies can persist from winter to spring whereas snow cover anomalies cannot in most regions of Eurasia. Locally, snow cover and snow-depth anomalies in February are not related in most regions to the north of 50°N, but those anomalies in April display consistent year-to-year variations. The results suggest that the winter snow cover cannot properly represent all the effects of snow and it is necessary to separate the winter and spring snow cover in addressing the snow–monsoon relationship. Spring snow cover in western Siberia is positively correlated with spring rainfall in southern China. The circulation anomalies associated with the western Siberian spring snow cover variations show an apparent wave pattern over the eastern Atlantic through Europe and midlatitude Asia. Spring snow cover over the Tibetan Plateau shows a moderate positive correlation with spring rainfall in southern China. Analysis shows that this correlation includes El Niño–Southern Oscillation (ENSO) effects. In contrast to the Indian summer monsoon rainfall for which the ENSO interferes with the snow effects, the Tibetan Plateau snow cover and ENSO work cooperatively to enhance spring rainfall anomalies in southern China. In comparison, ENSO has larger impacts than the snow on spring rainfall in southern China.

scholarly journals Evaluation of snow depth and snow cover over the Tibetan Plateau in global reanalyses using in situ and satellite remote sensing observations

2019 ◽  
Vol 13 (8) ◽  
pp. 2221-2239 ◽  
Author(s):  
Yvan Orsolini ◽  
Martin Wegmann ◽  
Emanuel Dutra ◽  
Boqi Liu ◽  
Gianpaolo Balsamo ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Snow Cover ◽  
Snow Depth ◽  
Operating Conditions ◽  
The Tibetan Plateau ◽  
Critical Surface ◽  
High Altitudes ◽  
Medium Range ◽  
In Situ Observations

Abstract. The Tibetan Plateau (TP) region, often referred to as the Third Pole, is the world's highest plateau and exerts a considerable influence on regional and global climate. The state of the snowpack over the TP is a major research focus due to its great impact on the headwaters of a dozen major Asian rivers. While many studies have attempted to validate atmospheric reanalyses over the TP area in terms of temperature or precipitation, there have been – remarkably – no studies aimed at systematically comparing the snow depth or snow cover in global reanalyses with satellite and in situ data. Yet, snow in reanalyses provides critical surface information for forecast systems from the medium to sub-seasonal timescales. Here, snow depth and snow cover from four recent global reanalysis products, namely the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 and ERA-Interim reanalyses, the Japanese 55-year Reanalysis (JRA-55) and the NASA Modern-Era Retrospective analysis for Research and Applications (MERRA-2), are inter-compared over the TP region. The reanalyses are evaluated against a set of 33 in situ station observations, as well as against the Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover and a satellite microwave snow depth dataset. The high temporal correlation coefficient (0.78) between the IMS snow cover and the in situ observations provides confidence in the station data despite the relative paucity of in situ measurement sites and the harsh operating conditions. While several reanalyses show a systematic overestimation of the snow depth or snow cover, the reanalyses that assimilate local in situ observations or IMS snow cover are better capable of representing the shallow, transient snowpack over the TP region. The latter point is clearly demonstrated by examining the family of reanalyses from the ECMWF, of which only the older ERA-Interim assimilated IMS snow cover at high altitudes, while ERA5 did not consider IMS snow cover for high altitudes. We further tested the sensitivity of the ERA5-Land model in offline experiments, assessing the impact of blown snow sublimation, snow cover to snow depth conversion and, more importantly, excessive snowfall. These results suggest that excessive snowfall might be the primary factor for the large overestimation of snow depth and cover in ERA5 reanalysis. Pending a solution for this common model precipitation bias over the Himalayas and the TP, future snow reanalyses that optimally combine the use of satellite snow cover and in situ snow depth observations in the assimilation and analysis cycles have the potential to improve medium-range to sub-seasonal forecasts for water resources applications.

scholarly journals Evaluation of snow depth and snow-cover over the Tibetan Plateau in global reanalyses using in-situ and satellite remote sensing observations

2019 ◽  
Author(s):  
Yvan Orsolini ◽  
Martin Wegmann ◽  
Emanuel Dutra ◽  
Boqi Liu ◽  
Gianpaolo Balsamo ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Snow Cover ◽  
Snow Depth ◽  
Global Climate ◽  
Operating Conditions ◽  
The Tibetan Plateau ◽  
Critical Surface ◽  
High Altitudes ◽  
In Situ Observations

Abstract. The Tibetan Plateau (TP) region, often referred to as the Third Pole and, is the world highest plateau and exerts a considerable influence on regional and global climate. The state of the snowpack over the TP is a major research focus due to its great impacts on the headwaters of a dozen major Asian rivers. While many studies have attempted to validate atmospheric re-analyses over the TP area in terms of temperature or precipitation, there have been – remarkably – no studies aimed at systematically comparing the snow depth or snow cover in global re-analyses with satellite and in-situ data. Yet, snow in re-analyses provides critical surface information for forecast systems from the medium to sub-seasonal time scales. Here, snow depth and snow cover from 5 recent global reanalysis products are inter-compared over the TP region, and evaluated against a set of 33 in-situ station observations, as well as against the Interactive Multi-sensor Snow and Ice Mapping System (or IMS) snow cover and a satellite microwave snow depth dataset. The high temporal correlation coefficient (0.78) between the IMS snow cover and the in-situ observations provides confidence in the station data despite the relative paucity of in-situ measurement sites and the harsh operating conditions. While several re-analyses show a systematic over-estimation of the snow depth or snow cover, the reanalyses that assimilate local in-situ observations or IMS snow-cover are better capable of representing the shallow, transient snowpack over the TP region. The later point is clearly demonstrated by examining the family of re-analyses from the European Centre for Medium-Range Weather Forecasts (ECMWF), of which only the older ERA-Interim assimilated IMS snow cover at high altitudes, while ERA5 did not consider IMS snow cover for high altitudes. One missing process in the re-analyses is the blown snow sublimation, which seems important in the dry, windy and cold conditions of the TP. By incorporating a simple parametrisation of this process in the ECMWF land re-analysis, the positive snow bias is somewhat alleviated. Future snow reanalyses that optimally combine the use of satellite snow cover and in-situ snow-depth observations over the Tibetan Plateau region in the assimilation and analysis cycles, along with improved representation of snow processes, have the potential to substantially improve weather and climate prediction and water resources applications.

scholarly journals Trends and variability in snowmelt in China under climate change

2021 ◽  
Author(s):  
Yong Yang ◽  
Rensheng Chen ◽  
Guohua Liu ◽  
Zhangwen Liu ◽  
Xiqiang Wang
Keyword(s):  
Climate Change ◽  
Tibetan Plateau ◽  
Snow Cover ◽  
Northeast China ◽  
Cmip5 Models ◽  
Historical Period ◽  
The Tibetan Plateau ◽  
Snowmelt Runoff ◽  
The Mean ◽  
Precipitation And Temperature

Abstract. Snowmelt is a major fresh water resource, and quantifying snowmelt and its variability under climate change is necessary for planning and management of water resources. Spatiotemporal changes in snow properties in China have drawn wide attention in recent decades; however, country-wide assessments of snowmelt are lacking. Using precipitation and temperature data with a high spatial resolution (0.5 seconds, approximately 1 km), this study calculated the monthly snowmelt in China for the 1951–2017 period using a simple temperature index model, and the model outputs were validated using snowfall, snow depth, snow cover extent and snow water equivalent. Precipitation and temperature scenarios developed from five CMIP5 models were used to predict future snowmelt in China under three different representative concentration pathways (RCP) scenarios (RCP2.6, RCP4.5 and RCP8.5). The results showed that the mean annual snowmelt in China from 1951 to 2017 was 2.41 × 1011 m3. The mean annual snowmelts in Northern Xinjiang, Northeast China, and the Tibetan Plateau – China’s three main stable snow cover regions – were 0.18 × 1011 m3, 0.42 × 1011 m3 and 1.15 × 1011 m3, respectively. From 1951 to 2017, the snowmelt increased significantly in the Tibetan Plateau and decreased significantly in North, Central and Southeast China. In the whole of China, there was a decreasing trend in snowmelt, but this was not statistically significant. The mean annual snowmelt runoff ratios were generally more than 10 % in almost all third-level basins in West China, more than 5 % in third-level basins in North and Northeast China, and less than 2 % in third-level basins in South China. From 1951 to 2017, the annual snowmelt runoff ratios decreased in most third-level basins in China. Under RCP2.6, RCP4.5 and RCP8.5, the projected snowmelt in China in 2030s (2050s, 2090s) may decrease by 13.4 % (16.3 %, 13.8 %), 19.1 % (19.8 %, 22.5 %), 17.1 % (24.7 %, 42.8 %) compared with the historical period (1951–2017), respectively. Most of the projected mean annual snowmelt runoff ratios in third-level basins in different decades (2030s, 2050s and 2090s) were lower than those in the historical period. Low temperature regions can tolerate more warming, and the snowmelt change in these regions is mainly influenced by precipitation; however, the snowmelt change in warm regions is more sensitive to temperature increases. The spatial variability of snowmelt changes may lead to regional differences in the impact of snowmelt on water supply.

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