Articles | Volume 17, issue 7
https://doi.org/10.5194/tc-17-2625-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-17-2625-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Brief communication
| Highlight paper
| 
06 Jul 2023
Brief communication | Highlight paper |
| 06 Jul 2023
| 06 Jul 2023
Brief communication: How deep is the snow on Mount Everest?
Wei Yang
State Key Laboratory of Tibetan Plateau Earth System, Environment and
Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy
of Sciences, Beijing 100101, China
Huabiao Zhao
CORRESPONDING AUTHOR
State Key Laboratory of Tibetan Plateau Earth System, Environment and
Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy
of Sciences, Beijing 100101, China
Baiqing Xu
State Key Laboratory of Tibetan Plateau Earth System, Environment and
Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy
of Sciences, Beijing 100101, China
Jiule Li
State Key Laboratory of Tibetan Plateau Earth System, Environment and
Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy
of Sciences, Beijing 100101, China
Weicai Wang
State Key Laboratory of Tibetan Plateau Earth System, Environment and
Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy
of Sciences, Beijing 100101, China
Guangjian Wu
State Key Laboratory of Tibetan Plateau Earth System, Environment and
Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy
of Sciences, Beijing 100101, China
Zhongyan Wang
State Key Laboratory of Tibetan Plateau Earth System, Environment and
Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy
of Sciences, Beijing 100101, China
Tandong Yao
State Key Laboratory of Tibetan Plateau Earth System, Environment and
Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy
of Sciences, Beijing 100101, China
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Yongkang Xue, Tandong Yao, Aaron A. Boone, Ismaila Diallo, Ye Liu, Xubin Zeng, William K. M. Lau, Shiori Sugimoto, Qi Tang, Xiaoduo Pan, Peter J. van Oevelen, Daniel Klocke, Myung-Seo Koo, Tomonori Sato, Zhaohui Lin, Yuhei Takaya, Constantin Ardilouze, Stefano Materia, Subodh K. Saha, Retish Senan, Tetsu Nakamura, Hailan Wang, Jing Yang, Hongliang Zhang, Mei Zhao, Xin-Zhong Liang, J. David Neelin, Frederic Vitart, Xin Li, Ping Zhao, Chunxiang Shi, Weidong Guo, Jianping Tang, Miao Yu, Yun Qian, Samuel S. P. Shen, Yang Zhang, Kun Yang, Ruby Leung, Yuan Qiu, Daniele Peano, Xin Qi, Yanling Zhan, Michael A. Brunke, Sin Chan Chou, Michael Ek, Tianyi Fan, Hong Guan, Hai Lin, Shunlin Liang, Helin Wei, Shaocheng Xie, Haoran Xu, Weiping Li, Xueli Shi, Paulo Nobre, Yan Pan, Yi Qin, Jeff Dozier, Craig R. Ferguson, Gianpaolo Balsamo, Qing Bao, Jinming Feng, Jinkyu Hong, Songyou Hong, Huilin Huang, Duoying Ji, Zhenming Ji, Shichang Kang, Yanluan Lin, Weiguang Liu, Ryan Muncaster, Patricia de Rosnay, Hiroshi G. Takahashi, Guiling Wang, Shuyu Wang, Weicai Wang, Xu Zhou, and Yuejian Zhu
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Thomas E. Shaw, Wei Yang, Álvaro Ayala, Claudio Bravo, Chuanxi Zhao, and Francesca Pellicciotti
The Cryosphere, 15, 595–614, https://doi.org/10.5194/tc-15-595-2021, https://doi.org/10.5194/tc-15-595-2021, 2021
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In comparison with other terrestrial regions of the world, meteorological observations are scarce over the Tibetan Plateau.
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Third Pole of the world.
Siqi Ma, Xuelei Zhang, Chao Gao, Daniel Q. Tong, Aijun Xiu, Guangjian Wu, Xinyuan Cao, Ling Huang, Hongmei Zhao, Shichun Zhang, Sergio Ibarra-Espinosa, Xin Wang, Xiaolan Li, and Mo Dan
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Yanbin Lei, Tandong Yao, Kun Yang, Zhu La, Yaoming Ma, and Broxton W. Bird
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-421, https://doi.org/10.5194/hess-2019-421, 2019
Revised manuscript not accepted
Xin Wang, Hailun Wei, Jun Liu, Baiqing Xu, Mo Wang, Mingxia Ji, and Hongchun Jin
The Cryosphere, 13, 309–324, https://doi.org/10.5194/tc-13-309-2019, https://doi.org/10.5194/tc-13-309-2019, 2019
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Jiamao Zhou, Xuexi Tie, Baiqing Xu, Shuyu Zhao, Mo Wang, Guohui Li, Ting Zhang, Zhuzi Zhao, Suixin Liu, Song Yang, Luyu Chang, and Junji Cao
Atmos. Chem. Phys., 18, 13673–13685, https://doi.org/10.5194/acp-18-13673-2018, https://doi.org/10.5194/acp-18-13673-2018, 2018
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Adrien Gilbert, Silvan Leinss, Jeffrey Kargel, Andreas Kääb, Simon Gascoin, Gregory Leonard, Etienne Berthier, Alina Karki, and Tandong Yao
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Balram Pokhrel, Ping Gong, Xiaoping Wang, Sanjay Nath Khanal, Jiao Ren, Chuanfei Wang, Shaopeng Gao, and Tandong Yao
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As Nepal is a tropical country close to the Himalayas, it is essential to investigate concentration levels and long-range transport potential of persistent organic pollutants (POPs) in its cities to assess whether these pollutants can contaminate the high Himalaya. We found high concentration and long travel distance (> 1000 km) of dichlorodiphenyltrichloroethane and hexachlorocyclohexane in the atmosphere of Nepalese cities, suggesting Nepal can be an important regional source region for POPs.
Jiao Ren, Xiaoping Wang, Chuanfei Wang, Ping Gong, and Tandong Yao
Atmos. Chem. Phys., 17, 1401–1415, https://doi.org/10.5194/acp-17-1401-2017, https://doi.org/10.5194/acp-17-1401-2017, 2017
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Do the water bodies in the Tibetan Plateau (TP) act as a sink or secondary source of organic pollutants (OPs)? To answer this question, atmospheric processes of OPs over a large lake on the TP were quantified. We found that the lake was a net sink of hexachlorocyclohexanes (HCHs) and most polycyclic aromatic hydrocarbons (PAHs), but it turned into a secondary source of phenanthrene, coinciding with the melting of lake ice.
Bin Liu, Zhiyuan Cong, Yuesi Wang, Jinyuan Xin, Xin Wan, Yuepeng Pan, Zirui Liu, Yonghong Wang, Guoshuai Zhang, Zhongyan Wang, Yongjie Wang, and Shichang Kang
Atmos. Chem. Phys., 17, 449–463, https://doi.org/10.5194/acp-17-449-2017, https://doi.org/10.5194/acp-17-449-2017, 2017
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The first observation net of background atmospheric aerosols of the Himalayas and Tibetan Plateau were conducted in 2011–2013, and the aerosol mass loadings were especially illustrated in this paper. Consequently, these terrestrial aerosol masses were strongly ecosystem-dependent, with various seasonality and diurnal cycles at these sites. These findings implicate that regional characteristics and fine-particle emissions need to be treated sensitively when assessing their climatic effects.
Xiaoxin Yang, Sunil Acharya, and Tandong Yao
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2016-876, https://doi.org/10.5194/acp-2016-876, 2016
Revised manuscript has not been submitted
Xuelei Zhang, Daniel Q. Tong, Guangjian Wu, Xin Wang, Aijun Xiu, Yongxiang Han, Tianli Xu, Shichun Zhang, and Hongmei Zhao
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2016-681, https://doi.org/10.5194/acp-2016-681, 2016
Revised manuscript has not been submitted
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More detailed knowledge regarding recent variations in the characteristics of East Asian dust events and dust sources can effectively improve regional dust modeling and forecasts. Here we reassess the accuracy of previous predictions of trends in dust variations in East Asia, and establish a relatively detailed inventory of dust events based on satellite observations from 2000 to 2015.
Xiaoping Wang, Jiao Ren, Ping Gong, Chuanfei Wang, Yonggang Xue, Tandong Yao, and Rainer Lohmann
Atmos. Chem. Phys., 16, 6901–6911, https://doi.org/10.5194/acp-16-6901-2016, https://doi.org/10.5194/acp-16-6901-2016, 2016
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Is there any linkage between climate interactions and spatial distribution of persistent organic pollutants (POPs)? To answer this question, we conducted long-term passive air monitoring across the Tibetan Plateau. We found that there are three graphical zones over the Tibetan Plateau that could be classified as a function of POP fingerprints. This study highlights validity of using POP fingerprints as chemical tracers to track the interactions of climate systems.
N. Holzer, S. Vijay, T. Yao, B. Xu, M. Buchroithner, and T. Bolch
The Cryosphere, 9, 2071–2088, https://doi.org/10.5194/tc-9-2071-2015, https://doi.org/10.5194/tc-9-2071-2015, 2015
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Investigations of glacier mass-balance and area changes at Muztagh Ata (eastern Pamir) are based on Hexagon KH-9 (1973), ALOS-PRISM (2009), Pléiades (2013) and Landsat 7 ETM+/SRTM-3 (2000). Surface velocities of Kekesayi Glacier are derived by TerraSAR-X (2011) amplitude tracking. Glacier variations differ spatially and temporally, but on average not significantly for the entire massif. Stagnant Kekesayi and other debris-covered glaciers indicate no visual length changes, but clear down-wasting.
W. Yu, L. Tian, Y. Ma, B. Xu, and D. Qu
Atmos. Chem. Phys., 15, 10251–10262, https://doi.org/10.5194/acp-15-10251-2015, https://doi.org/10.5194/acp-15-10251-2015, 2015
S. Kang, F. Wang, U. Morgenstern, Y. Zhang, B. Grigholm, S. Kaspari, M. Schwikowski, J. Ren, T. Yao, D. Qin, and P. A. Mayewski
The Cryosphere, 9, 1213–1222, https://doi.org/10.5194/tc-9-1213-2015, https://doi.org/10.5194/tc-9-1213-2015, 2015
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M. Wang, B. Xu, J. Cao, X. Tie, H. Wang, R. Zhang, Y. Qian, P. J. Rasch, S. Zhao, G. Wu, H. Zhao, D. R. Joswiak, J. Li, and Y. Xie
Atmos. Chem. Phys., 15, 1191–1204, https://doi.org/10.5194/acp-15-1191-2015, https://doi.org/10.5194/acp-15-1191-2015, 2015
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Carbonaceous aerosols recorded in a Tibetan glacier present a distinct seasonal dependence and an increasing trend after 1980, which has important implications for the accelerated glacier melting. We use a global aerosol--climate model to quantify the aerosol source--receptor relationships, showing that emissions in South Asia had the largest contribution. The emission inventories and historical fuel consumption in South Asia are consistent with our ice-core analysis and model results.
Related subject area
Discipline: Snow | Subject: Mountain Processes
Subgridding high-resolution numerical weather forecast in the Canadian Selkirk mountain range for local snow modeling in a remote sensing perspective
Improving climate model skill over High Mountain Asia by adapting snow cover parameterization to complex-topography areas
Snow sensitivity to temperature and precipitation change during compound cold–hot and wet–dry seasons in the Pyrenees
Multi-scale snowdrift-permitting modelling of mountain snowpack
How much snow falls in the world's mountains? A first look at mountain snowfall estimates in A-train observations and reanalyses
Paul Billecocq, Alexandre Langlois, and Benoit Montpetit
The Cryosphere, 18, 2765–2782, https://doi.org/10.5194/tc-18-2765-2024, https://doi.org/10.5194/tc-18-2765-2024, 2024
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Snow covers a vast part of the globe, making snow water equivalent (SWE) crucial for climate science and hydrology. SWE can be inversed from satellite data, but the snow's complex structure highly affects the signal, and thus an educated first guess is mandatory. In this study, a subgridding framework was developed to model snow at the local scale from model weather data. The framework enhanced snow parameter modeling, paving the way for SWE inversion algorithms from satellite data.
Mickaël Lalande, Martin Ménégoz, Gerhard Krinner, Catherine Ottlé, and Frédérique Cheruy
The Cryosphere, 17, 5095–5130, https://doi.org/10.5194/tc-17-5095-2023, https://doi.org/10.5194/tc-17-5095-2023, 2023
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This study investigates the impact of topography on snow cover parameterizations using models and observations. Parameterizations without topography-based considerations overestimate snow cover. Incorporating topography reduces snow overestimation by 5–10 % in mountains, in turn reducing cold biases. However, some biases remain, requiring further calibration and more data. Assessing snow cover parameterizations is challenging due to limited and uncertain data in mountainous regions.
Josep Bonsoms, Juan Ignacio López-Moreno, and Esteban Alonso-González
The Cryosphere, 17, 1307–1326, https://doi.org/10.5194/tc-17-1307-2023, https://doi.org/10.5194/tc-17-1307-2023, 2023
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This work analyzes the snow response to temperature and precipitation in the Pyrenees. During warm and wet seasons, seasonal snow depth is expected to be reduced by −37 %, −34 %, and −27 % per degree Celsius at low-, mid-, and high-elevation areas, respectively. The largest snow reductions are anticipated at low elevations of the eastern Pyrenees. Results anticipate important impacts on the nearby ecological and socioeconomic systems.
Vincent Vionnet, Christopher B. Marsh, Brian Menounos, Simon Gascoin, Nicholas E. Wayand, Joseph Shea, Kriti Mukherjee, and John W. Pomeroy
The Cryosphere, 15, 743–769, https://doi.org/10.5194/tc-15-743-2021, https://doi.org/10.5194/tc-15-743-2021, 2021
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Mountain snow cover provides critical supplies of fresh water to downstream users. Its accurate prediction requires inclusion of often-ignored processes. A multi-scale modelling strategy is presented that efficiently accounts for snow redistribution. Model accuracy is assessed via airborne lidar and optical satellite imagery. With redistribution the model captures the elevation–snow depth relation. Redistribution processes are required to reproduce spatial variability, such as around ridges.
Anne Sophie Daloz, Marian Mateling, Tristan L'Ecuyer, Mark Kulie, Norm B. Wood, Mikael Durand, Melissa Wrzesien, Camilla W. Stjern, and Ashok P. Dimri
The Cryosphere, 14, 3195–3207, https://doi.org/10.5194/tc-14-3195-2020, https://doi.org/10.5194/tc-14-3195-2020, 2020
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The total of snow that falls globally is a critical factor governing freshwater availability. To better understand how this resource is impacted by climate change, we need to know how reliable the current observational datasets for snow are. Here, we compare five datasets looking at the snow falling over the mountains versus the other continents. We show that there is a large consensus when looking at fractional contributions but strong dissimilarities when comparing magnitudes.
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Co-editor-in-chief
This manuscript describes a very unique set of measurements made in extreme conditions at the summit of Mount Everest. This accomplishment provides a new perspective on snow conditions at an iconic location, and will be of broad public interest.
This manuscript describes a very unique set of measurements made in extreme conditions at the...
Short summary
There is very strong scientific and public interest regarding the snow thickness on Mountain Everest. Previously reported snow depths derived by different methods and instruments ranged from 0.92 to 3.5 m. Our measurements in 2022 provide the first clear radar image of the snowpack at the top of Mount Everest. The snow thickness at Earth's summit was averaged to be 9.5 ± 1.2 m. This updated snow thickness is considerably deeper than values reported during the past 5 decades.
There is very strong scientific and public interest regarding the snow thickness on Mountain...
