Articles | Volume 18, issue 9
https://doi.org/10.5194/tc-18-4089-2024
© Author(s) 2024. 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-18-4089-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Sep 2024
Research article |
| 10 Sep 2024
| 10 Sep 2024
Which global reanalysis dataset has better representativeness in snow cover on the Tibetan Plateau?
Shirui Yan
Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
Yang Chen
Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
Yaliang Hou
Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
Kexin Liu
Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
Xuejing Li
Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
Yuxuan Xing
Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
Dongyou Wu
Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
Jiecan Cui
Zhejiang Development & Planning Institute, Hangzhou 310030, China
Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
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This study investigated the impact of dust storms from the Taklamakan Desert on surrounding high mountains and regional radiation balance. Using satellite data and simulations, researchers found that dust storms significantly darken the snow surface in the Tien Shan, Kunlun, and Qilian mountains, reaching mountains up to 1000 km away. This darkening occurs not only in spring but also during summer and autumn, leading to increased absorption of solar radiation.
Yuxuan Xing, Yang Chen, Shirui Yan, Xiaoyi Cao, Yong Zhou, Xueying Zhang, Tenglong Shi, Xiaoying Niu, Dongyou Wu, Jiecan Cui, Yue Zhou, Xin Wang, and Wei Pu
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In this study, we do the first investigation of WSOC in seasonal snow of northeastern China. The results revealed the regional-specific compositions and sources of WSOC due to different natural environments and anthropogenic activities. The abundant concentrations of WSOC and its absorption properties contributed to a crucial impact on the snow albedo and radiative effect. We established that our study could raise awareness of carbon cycling processes, hydrological processes, and climate change.
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Atmos. Chem. Phys., 21, 8531–8555, https://doi.org/10.5194/acp-21-8531-2021, https://doi.org/10.5194/acp-21-8531-2021, 2021
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We present a comprehensive characterization of water-soluble organic carbon (WSOC) in seasonal snow of northwestern China. We applied complementary multimodal analytical techniques to investigate bulk and molecular-level composition, optical properties, and sources of WSOC. For the first time, we estimated the extent of radiative forcing due to WSOC in snow using a model simulation and showed the profound influences of WSOC on the energy budget of midlatitude seasonal snowpack.
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The Cryosphere, 15, 2255–2272, https://doi.org/10.5194/tc-15-2255-2021, https://doi.org/10.5194/tc-15-2255-2021, 2021
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We have explicitly resolved optical properties of coated BC in snow for explaining complex enhancement of snow albedo reduction due to coating effect in real environments. The parameterizations are developed for climate models to improve the understanding of BC in snow on global climate. We demonstrated that the contribution of BC coating effect to snow light absorption has exceeded dust over north China and will significantly contribute to the retreat of Arctic sea ice and Tibetan glaciers.
Tenglong Shi, Jiecan Cui, Yang Chen, Yue Zhou, Wei Pu, Xuanye Xu, Quanliang Chen, Xuelei Zhang, and Xin Wang
Atmos. Chem. Phys., 21, 6035–6051, https://doi.org/10.5194/acp-21-6035-2021, https://doi.org/10.5194/acp-21-6035-2021, 2021
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Atmos. Chem. Phys., 21, 269–288, https://doi.org/10.5194/acp-21-269-2021, https://doi.org/10.5194/acp-21-269-2021, 2021
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Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-252, https://doi.org/10.5194/amt-2020-252, 2020
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The hydroxyl radical (OH) is responsible for the degradation of trace gases and plays key roles in major environmental issues such as photochemical pollution. However, the measurement of atmospheric OH radical is a huge challenge due to its high reactivity. Our study provides systematic optimization of a chemical ionization mass spectrometer (CIMS) for OH measurement as a reference for other CIMS users. The ambient result demonstrates the capability of the CIMS for ambient OH measurement.
Dandan Zhao, Guangjing Liu, Jinyuan Xin, Jiannong Quan, Yuesi Wang, Xin Wang, Lindong Dai, Wenkang Gao, Guiqian Tang, Bo Hu, Yongxiang Ma, Xiaoyan Wu, Lili Wang, Zirui Liu, and Fangkun Wu
Atmos. Chem. Phys., 20, 4575–4592, https://doi.org/10.5194/acp-20-4575-2020, https://doi.org/10.5194/acp-20-4575-2020, 2020
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Under strong atmospheric oxidization capacity, haze pollution in the summer in Beijing was the result of the synergistic effect of the physicochemical process in the atmospheric boundary layer (ABL). With the premise of an extremely stable ABL structure, the formation of secondary aerosols dominated by nitrate was quite intense, driving the outbreak of haze pollution.
Xin Wang, Xueying Zhang, and Wenjing Di
Atmos. Meas. Tech., 13, 39–52, https://doi.org/10.5194/amt-13-39-2020, https://doi.org/10.5194/amt-13-39-2020, 2020
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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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Dust storms are thought to be a worldwide societal issue, and numerical modeling is an effective way to help us to predict dust events. Here we present the first comprehensive evaluation of dust emission modules in four commonly used air quality models for northeastern China. The results showed that most of these models were able to capture this dust event and indicated the dust source maps should be carefully selected or replaced with a new one that is constructed with local data.
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Atmos. Chem. Phys., 19, 9949–9968, https://doi.org/10.5194/acp-19-9949-2019, https://doi.org/10.5194/acp-19-9949-2019, 2019
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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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Yue Zhou, Hui Wen, Jun Liu, Wei Pu, Qingcai Chen, and Xin Wang
The Cryosphere, 13, 157–175, https://doi.org/10.5194/tc-13-157-2019, https://doi.org/10.5194/tc-13-157-2019, 2019
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We first investigated the optical characteristics and potential sources of chromophoric dissolved organic matter (CDOM) in seasonal snow over northwestern China. The abundance of CDOM showed regional variation. At some sites strongly influenced by local soil, the absorption of CDOM cannot be neglected compared to black carbon. We found two humic-like and one protein-like fluorophores in snow. The major sources of snow CDOM were soil, biomass burning, and anthropogenic pollution.
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The Cryosphere, 12, 3177–3186, https://doi.org/10.5194/tc-12-3177-2018, https://doi.org/10.5194/tc-12-3177-2018, 2018
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Cryoconites from glaciers on the Tibetan Plateau and surrounding area were studied for iron oxides. We found that goethite is the predominant iron oxide form. Using the abundance, speciation and optical properties of iron oxides, the total light absorption was quantitatively attributed to goethite, hematite, black carbon and organic matter. Such findings are essential to understand the relative significance of anthropogenic and natural impacts.
Xin Wang, Hui Wen, Jinsen Shi, Jianrong Bi, Zhongwei Huang, Beidou Zhang, Tian Zhou, Kaiqi Fu, Quanliang Chen, and Jinyuan Xin
Atmos. Chem. Phys., 18, 2119–2138, https://doi.org/10.5194/acp-18-2119-2018, https://doi.org/10.5194/acp-18-2119-2018, 2018
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A ground-based mobile laboratory was deployed near the dust source regions over northwestern China.
We not only captured natural dust but also characterized the properties of anthropogenic soil dust produced by agricultural cultivations.
The results indicate that large differences were found between the optical and microphysical properties of anthropogenic and natural dust.
Jianrong Bi, Jianping Huang, Jinsen Shi, Zhiyuan Hu, Tian Zhou, Guolong Zhang, Zhongwei Huang, Xin Wang, and Hongchun Jin
Atmos. Chem. Phys., 17, 7775–7792, https://doi.org/10.5194/acp-17-7775-2017, https://doi.org/10.5194/acp-17-7775-2017, 2017
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We conducted a field campaign on exploring dust aerosol in Dunhuang farmland nearby Gobi deserts. The anthropogenic dust produced by agricultural cultivations exerted a significant superimposed effect on elevated dust loadings. Strong south wind in daytime scavenged the pollution and weak northeast wind at night favorably accumulated air pollutants near the surface. The local emissions remarkably modified the absorptive and optical characteristics of mineral dust in desert source region.
Ling Qi, Qinbin Li, Cenlin He, Xin Wang, and Jianping Huang
Atmos. Chem. Phys., 17, 7459–7479, https://doi.org/10.5194/acp-17-7459-2017, https://doi.org/10.5194/acp-17-7459-2017, 2017
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Black carbon (BC) is the second only to CO2 in heating the planet, but the simulation of BC is associated with large uncertainties. BC burden is largely underestimated over land and overestimated over ocean. Our study finds that a missing process in current Wegener–Bergeron–Findeisen models largely explains the discrepancy in BC simulation over land. We call for more observations of BC in mixed-phase clouds to understand this process and improve the simulation of global BC.
Wei Pu, Xin Wang, Hailun Wei, Yue Zhou, Jinsen Shi, Zhiyuan Hu, Hongchun Jin, and Quanliang Chen
The Cryosphere, 11, 1213–1233, https://doi.org/10.5194/tc-11-1213-2017, https://doi.org/10.5194/tc-11-1213-2017, 2017
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We conducted a large field campaign to collect snow samples in Xinjiang. We measured insoluble light-absorbing particles with estimated black carbon concentrations of 10–150 ngg-1. We found a probable shift in emission sources with the progression of winter and dominated contributions of BC and OC to light absorption. A PMF model indicated an optimal three-factor/source solution that included industrial pollution, biomass burning, and soil dust.
Xin Wang, Wei Pu, Yong Ren, Xuelei Zhang, Xueying Zhang, Jinsen Shi, Hongchun Jin, Mingkai Dai, and Quanliang Chen
Atmos. Chem. Phys., 17, 2279–2296, https://doi.org/10.5194/acp-17-2279-2017, https://doi.org/10.5194/acp-17-2279-2017, 2017
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A 2014 snow survey was performed across northeastern China to analyze light absorption of ILAPs in seasonal snow, and modeling studies were conducted to compare snow albedo reduction due to assumptions of internal–external mixing of BC in snow and different snow grain shapes. The results show that the simulated snow albedos from both SAMDS and SNICAR agree well with the observed values at low ILAP mixing ratios, but they tend to be higher than surface observations at high ILAP mixing ratios.
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.
Xuezhe Xu, Weixiong Zhao, Qilei Zhang, Shuo Wang, Bo Fang, Weidong Chen, Dean S. Venables, Xinfeng Wang, Wei Pu, Xin Wang, Xiaoming Gao, and Weijun Zhang
Atmos. Chem. Phys., 16, 6421–6439, https://doi.org/10.5194/acp-16-6421-2016, https://doi.org/10.5194/acp-16-6421-2016, 2016
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We report on the field measurement of the optical properties and chemical composition of PM1.0 particles in a suburban environment in Beijing during the winter coal heating season. Organic mass was the largest contributor to the total extinction of PM1.0, while EC, owing to its high absorption efficiency, contributed appreciably to PM1.0 extinction and should be a key target to air quality controls. Non-BC absorption from secondary organic aerosol also contributes to particle absorption.
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Discipline: Snow | Subject: Seasonal Snow
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The Cryosphere, 18, 1959–1981, https://doi.org/10.5194/tc-18-1959-2024, https://doi.org/10.5194/tc-18-1959-2024, 2024
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Zachary Hoppinen, Shadi Oveisgharan, Hans-Peter Marshall, Ross Mower, Kelly Elder, and Carrie Vuyovich
The Cryosphere, 18, 575–592, https://doi.org/10.5194/tc-18-575-2024, https://doi.org/10.5194/tc-18-575-2024, 2024
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Aleksandra Elias Chereque, Paul J. Kushner, Lawrence Mudryk, Chris Derksen, and Colleen Mortimer
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We look at three commonly used snow depth datasets that come from a complex combination of snow modeling and historical measurements. When compared with each other, these datasets have differences that arise for various reasons. We show that a simple snow model can be used to examine consistency and highlight issues with the complex datasets. This method indicates that one of the complex datasets should be excluded from further studies.
Yiwen Fang, Yufei Liu, Dongyue Li, Haorui Sun, and Steven A. Margulis
The Cryosphere, 17, 5175–5195, https://doi.org/10.5194/tc-17-5175-2023, https://doi.org/10.5194/tc-17-5175-2023, 2023
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Using newly developed snow reanalysis datasets as references, snow water storage is at high uncertainty among commonly used global products in the Andes and low-resolution products in the western United States, where snow is the key element of water resources. In addition to precipitation, elevation differences and model mechanism variances drive snow uncertainty. This work provides insights for research applying these products and generating future products in areas with limited in situ data.
Kerttu Kouki, Kari Luojus, and Aku Riihelä
The Cryosphere, 17, 5007–5026, https://doi.org/10.5194/tc-17-5007-2023, https://doi.org/10.5194/tc-17-5007-2023, 2023
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We evaluated snow cover properties in state-of-the-art reanalyses (ERA5 and ERA5-Land) with satellite-based datasets. Both ERA5 and ERA5-Land overestimate snow mass, whereas albedo estimates are more consistent between the datasets. Snow cover extent (SCE) is accurately described in ERA5-Land, while ERA5 shows larger SCE than the satellite-based datasets. The trends in snow mass, SCE, and albedo are mostly negative in 1982–2018, and the negative trends become more apparent when spring advances.
Diego Monteiro and Samuel Morin
The Cryosphere, 17, 3617–3660, https://doi.org/10.5194/tc-17-3617-2023, https://doi.org/10.5194/tc-17-3617-2023, 2023
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Beyond directly using in situ observations, often sparsely available in mountain regions, climate model simulations and so-called reanalyses are increasingly used for climate change impact studies. Here we evaluate such datasets in the European Alps from 1950 to 2020, with a focus on snow cover information and its main drivers: air temperature and precipitation. In terms of variability and trends, we identify several limitations and provide recommendations for future use of these datasets.
Leon J. Bührle, Mauro Marty, Lucie A. Eberhard, Andreas Stoffel, Elisabeth D. Hafner, and Yves Bühler
The Cryosphere, 17, 3383–3408, https://doi.org/10.5194/tc-17-3383-2023, https://doi.org/10.5194/tc-17-3383-2023, 2023
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Information on the snow depth distribution is crucial for numerous applications in high-mountain regions. However, only specific measurements can accurately map the present variability of snow depths within complex terrain. In this study, we show the reliable processing of images from aeroplane to large (> 100 km2) detailed and accurate snow depth maps around Davos (CH). We use these maps to describe the existing snow depth distribution, other special features and potential applications.
Xuemei Li, Xinyu Liu, Kaixin Zhao, Xu Zhang, and Lanhai Li
The Cryosphere, 17, 2437–2453, https://doi.org/10.5194/tc-17-2437-2023, https://doi.org/10.5194/tc-17-2437-2023, 2023
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Quantifying change in the potential snowfall phenology (PSP) is an important area of research for understanding regional climate change past, present, and future. However, few studies have focused on the PSP and its change in alpine mountainous regions. We proposed three innovative indicators to characterize the PSP and its spatial–temporal variation. Our study provides a novel approach to understanding PSP in alpine mountainous regions and can be easily extended to other snow-dominated regions.
Moritz Buchmann, Gernot Resch, Michael Begert, Stefan Brönnimann, Barbara Chimani, Wolfgang Schöner, and Christoph Marty
The Cryosphere, 17, 653–671, https://doi.org/10.5194/tc-17-653-2023, https://doi.org/10.5194/tc-17-653-2023, 2023
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Our current knowledge of spatial and temporal snow depth trends is based almost exclusively on time series of non-homogenised observational data. However, like other long-term series from observations, they are susceptible to inhomogeneities that can affect the trends and even change the sign. To assess the relevance of homogenisation for daily snow depths, we investigated its impact on trends and changes in extreme values of snow indices between 1961 and 2021 in the Swiss observation network.
Zachary S. Miller, Erich H. Peitzsch, Eric A. Sproles, Karl W. Birkeland, and Ross T. Palomaki
The Cryosphere, 16, 4907–4930, https://doi.org/10.5194/tc-16-4907-2022, https://doi.org/10.5194/tc-16-4907-2022, 2022
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Snow depth varies across steep, complex mountain landscapes due to interactions between dynamic natural processes. Our study of a winter time series of high-resolution snow depth maps found that spatial resolutions greater than 0.5 m do not capture the complete patterns of snow depth spatial variability at a couloir study site in the Bridger Range of Montana, USA. The results of this research have the potential to reduce uncertainty associated with snowpack and snow water resource analysis.
Victoria R. Dutch, Nick Rutter, Leanne Wake, Melody Sandells, Chris Derksen, Branden Walker, Gabriel Hould Gosselin, Oliver Sonnentag, Richard Essery, Richard Kelly, Phillip Marsh, Joshua King, and Julia Boike
The Cryosphere, 16, 4201–4222, https://doi.org/10.5194/tc-16-4201-2022, https://doi.org/10.5194/tc-16-4201-2022, 2022
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Measurements of the properties of the snow and soil were compared to simulations of the Community Land Model to see how well the model represents snow insulation. Simulations underestimated snow thermal conductivity and wintertime soil temperatures. We test two approaches to reduce the transfer of heat through the snowpack and bring simulated soil temperatures closer to measurements, with an alternative parameterisation of snow thermal conductivity being more appropriate.
Moritz Buchmann, John Coll, Johannes Aschauer, Michael Begert, Stefan Brönnimann, Barbara Chimani, Gernot Resch, Wolfgang Schöner, and Christoph Marty
The Cryosphere, 16, 2147–2161, https://doi.org/10.5194/tc-16-2147-2022, https://doi.org/10.5194/tc-16-2147-2022, 2022
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Knowledge about inhomogeneities in a data set is important for any subsequent climatological analysis. We ran three well-established homogenization methods and compared the identified break points. By only treating breaks as valid when detected by at least two out of three methods, we enhanced the robustness of our results. We found 45 breaks within 42 of 184 investigated series; of these 70 % could be explained by events recorded in the station history.
Bertrand Cluzet, Matthieu Lafaysse, César Deschamps-Berger, Matthieu Vernay, and Marie Dumont
The Cryosphere, 16, 1281–1298, https://doi.org/10.5194/tc-16-1281-2022, https://doi.org/10.5194/tc-16-1281-2022, 2022
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The mountainous snow cover is highly variable at all temporal and spatial scales. Snow cover models suffer from large errors, while snowpack observations are sparse. Data assimilation combines them into a better estimate of the snow cover. A major challenge is to propagate information from observed into unobserved areas. This paper presents a spatialized version of the particle filter, in which information from in situ snow depth observations is successfully used to constrain nearby simulations.
Kerttu Kouki, Petri Räisänen, Kari Luojus, Anna Luomaranta, and Aku Riihelä
The Cryosphere, 16, 1007–1030, https://doi.org/10.5194/tc-16-1007-2022, https://doi.org/10.5194/tc-16-1007-2022, 2022
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We analyze state-of-the-art climate models’ ability to describe snow mass and whether biases in modeled temperature or precipitation can explain the discrepancies in snow mass. In winter, biases in precipitation are the main factor affecting snow mass, while in spring, biases in temperature becomes more important, which is an expected result. However, temperature or precipitation cannot explain all snow mass discrepancies. Other factors, such as models’ structural errors, are also significant.
Achut Parajuli, Daniel F. Nadeau, François Anctil, and Marco Alves
The Cryosphere, 15, 5371–5386, https://doi.org/10.5194/tc-15-5371-2021, https://doi.org/10.5194/tc-15-5371-2021, 2021
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Cold content is the energy required to attain an isothermal (0 °C) state and resulting in the snow surface melt. This study focuses on determining the multi-layer cold content (30 min time steps) relying on field measurements, snow temperature profile, and empirical formulation in four distinct forest sites of Montmorency Forest, eastern Canada. We present novel research where the effect of forest structure, local topography, and meteorological conditions on cold content variability is explored.
Yufei Liu, Yiwen Fang, and Steven A. Margulis
The Cryosphere, 15, 5261–5280, https://doi.org/10.5194/tc-15-5261-2021, https://doi.org/10.5194/tc-15-5261-2021, 2021
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We examined the spatiotemporal distribution of stored water in the seasonal snowpack over High Mountain Asia, based on a new snow reanalysis dataset. The dataset was derived utilizing satellite-observed snow information, which spans across 18 water years, at a high spatial (~ 500 m) and temporal (daily) resolution. Snow mass and snow storage distribution over space and time are analyzed in this paper, which brings new insights into understanding the snowpack variability over this region.
Moritz Buchmann, Michael Begert, Stefan Brönnimann, and Christoph Marty
The Cryosphere, 15, 4625–4636, https://doi.org/10.5194/tc-15-4625-2021, https://doi.org/10.5194/tc-15-4625-2021, 2021
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We investigated the impacts of local-scale variations by analysing snow climate indicators derived from parallel snow measurements. We found the largest relative inter-pair differences for all indicators in spring and the smallest in winter. The findings serve as an important basis for our understanding of uncertainties of commonly used snow indicators and provide, in combination with break-detection methods, the groundwork in view of any homogenization efforts regarding snow time series.
Michael Matiu, Alice Crespi, Giacomo Bertoldi, Carlo Maria Carmagnola, Christoph Marty, Samuel Morin, Wolfgang Schöner, Daniele Cat Berro, Gabriele Chiogna, Ludovica De Gregorio, Sven Kotlarski, Bruno Majone, Gernot Resch, Silvia Terzago, Mauro Valt, Walter Beozzo, Paola Cianfarra, Isabelle Gouttevin, Giorgia Marcolini, Claudia Notarnicola, Marcello Petitta, Simon C. Scherrer, Ulrich Strasser, Michael Winkler, Marc Zebisch, Andrea Cicogna, Roberto Cremonini, Andrea Debernardi, Mattia Faletto, Mauro Gaddo, Lorenzo Giovannini, Luca Mercalli, Jean-Michel Soubeyroux, Andrea Sušnik, Alberto Trenti, Stefano Urbani, and Viktor Weilguni
The Cryosphere, 15, 1343–1382, https://doi.org/10.5194/tc-15-1343-2021, https://doi.org/10.5194/tc-15-1343-2021, 2021
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The first Alpine-wide assessment of station snow depth has been enabled by a collaborative effort of the research community which involves more than 30 partners, 6 countries, and more than 2000 stations. It shows how snow in the European Alps matches the climatic zones and gives a robust estimate of observed changes: stronger decreases in the snow season at low elevations and in spring at all elevations, however, with considerable regional differences.
Rhae Sung Kim, Sujay Kumar, Carrie Vuyovich, Paul Houser, Jessica Lundquist, Lawrence Mudryk, Michael Durand, Ana Barros, Edward J. Kim, Barton A. Forman, Ethan D. Gutmann, Melissa L. Wrzesien, Camille Garnaud, Melody Sandells, Hans-Peter Marshall, Nicoleta Cristea, Justin M. Pflug, Jeremy Johnston, Yueqian Cao, David Mocko, and Shugong Wang
The Cryosphere, 15, 771–791, https://doi.org/10.5194/tc-15-771-2021, https://doi.org/10.5194/tc-15-771-2021, 2021
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High SWE uncertainty is observed in mountainous and forested regions, highlighting the need for high-resolution snow observations in these regions. Substantial uncertainty in snow water storage in Tundra regions and the dominance of water storage in these regions points to the need for high-accuracy snow estimation. Finally, snow measurements during the melt season are most needed at high latitudes, whereas observations at near peak snow accumulations are most beneficial over the midlatitudes.
François Tuzet, Marie Dumont, Ghislain Picard, Maxim Lamare, Didier Voisin, Pierre Nabat, Mathieu Lafaysse, Fanny Larue, Jesus Revuelto, and Laurent Arnaud
The Cryosphere, 14, 4553–4579, https://doi.org/10.5194/tc-14-4553-2020, https://doi.org/10.5194/tc-14-4553-2020, 2020
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This study presents a field dataset collected over 30 d from two snow seasons at a Col du Lautaret site (French Alps). The dataset compares different measurements or estimates of light-absorbing particle (LAP) concentrations in snow, highlighting a gap in the current understanding of the measurement of these quantities. An ensemble snowpack model is then evaluated for this dataset estimating that LAPs shorten each snow season by around 10 d despite contrasting meteorological conditions.
Jianwei Yang, Lingmei Jiang, Kari Luojus, Jinmei Pan, Juha Lemmetyinen, Matias Takala, and Shengli Wu
The Cryosphere, 14, 1763–1778, https://doi.org/10.5194/tc-14-1763-2020, https://doi.org/10.5194/tc-14-1763-2020, 2020
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There are many challenges for accurate snow depth estimation using passive microwave data. Machine learning (ML) techniques are deemed to be powerful tools for establishing nonlinear relations between independent variables and a given target variable. In this study, we investigate the potential capability of the random forest (RF) model on snow depth estimation at temporal and spatial scales. The result indicates that the fitted RF algorithms perform better on temporal than spatial scales.
Colleen Mortimer, Lawrence Mudryk, Chris Derksen, Kari Luojus, Ross Brown, Richard Kelly, and Marco Tedesco
The Cryosphere, 14, 1579–1594, https://doi.org/10.5194/tc-14-1579-2020, https://doi.org/10.5194/tc-14-1579-2020, 2020
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Existing stand-alone passive microwave SWE products have markedly different climatological SWE patterns compared to reanalysis-based datasets. The AMSR-E SWE has low spatial and temporal correlations with the four reanalysis-based products evaluated and GlobSnow and perform poorly in comparisons with snow transect data from Finland, Russia, and Canada. There is better agreement with in situ data when multiple SWE products, excluding the stand-alone passive microwave SWE products, are combined.
Céline Portenier, Fabia Hüsler, Stefan Härer, and Stefan Wunderle
The Cryosphere, 14, 1409–1423, https://doi.org/10.5194/tc-14-1409-2020, https://doi.org/10.5194/tc-14-1409-2020, 2020
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We present a method to derive snow cover maps from freely available webcam images in the Swiss Alps. With marginal manual user input, we can transform a webcam image into a georeferenced map and therewith perform snow cover analyses with a high spatiotemporal resolution over a large area. Our evaluation has shown that webcams could not only serve as a reference for improved validation of satellite-based approaches, but also complement satellite-based snow cover retrieval.
Markus Todt, Nick Rutter, Christopher G. Fletcher, and Leanne M. Wake
The Cryosphere, 13, 3077–3091, https://doi.org/10.5194/tc-13-3077-2019, https://doi.org/10.5194/tc-13-3077-2019, 2019
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Vegetation is often represented by a single layer in global land models. Studies have found deficient simulation of thermal radiation beneath forest canopies when represented by single-layer vegetation. This study corrects thermal radiation in forests for a global land model using single-layer vegetation in order to assess the effect of deficient thermal radiation on snow cover and snowmelt. Results indicate that single-layer vegetation causes snow in forests to be too cold and melt too late.
David F. Hill, Elizabeth A. Burakowski, Ryan L. Crumley, Julia Keon, J. Michelle Hu, Anthony A. Arendt, Katreen Wikstrom Jones, and Gabriel J. Wolken
The Cryosphere, 13, 1767–1784, https://doi.org/10.5194/tc-13-1767-2019, https://doi.org/10.5194/tc-13-1767-2019, 2019
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We present a new statistical model for converting snow depths to water equivalent. The only variables required are snow depth, day of year, and location. We use the location to look up climatological parameters such as mean winter precipitation and mean temperature difference (difference between hottest month and coldest month). The model is simple by design so that it can be applied to depth measurements anywhere, anytime. The model is shown to perform better than other widely used approaches.
Rebecca Mott, Andreas Wolf, Maximilian Kehl, Harald Kunstmann, Michael Warscher, and Thomas Grünewald
The Cryosphere, 13, 1247–1265, https://doi.org/10.5194/tc-13-1247-2019, https://doi.org/10.5194/tc-13-1247-2019, 2019
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The mass balance of very small glaciers is often governed by anomalous snow accumulation, winter precipitation being multiplied by snow redistribution processes, or by suppressed snow ablation driven by micrometeorological effects lowering net radiation and turbulent heat exchange. In this study we discuss the relative contribution of snow accumulation (avalanches) versus micrometeorology (katabatic flow) on the mass balance of the lowest perennial ice field of the Alps, the Ice Chapel.
Yue Zhou, Hui Wen, Jun Liu, Wei Pu, Qingcai Chen, and Xin Wang
The Cryosphere, 13, 157–175, https://doi.org/10.5194/tc-13-157-2019, https://doi.org/10.5194/tc-13-157-2019, 2019
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We first investigated the optical characteristics and potential sources of chromophoric dissolved organic matter (CDOM) in seasonal snow over northwestern China. The abundance of CDOM showed regional variation. At some sites strongly influenced by local soil, the absorption of CDOM cannot be neglected compared to black carbon. We found two humic-like and one protein-like fluorophores in snow. The major sources of snow CDOM were soil, biomass burning, and anthropogenic pollution.
Benjamin J. Hatchett and Hilary G. Eisen
The Cryosphere, 13, 21–28, https://doi.org/10.5194/tc-13-21-2019, https://doi.org/10.5194/tc-13-21-2019, 2019
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We examine the timing of early season snowpack relevant to oversnow vehicle (OSV) recreation over the past 3 decades in the Lake Tahoe region (USA). Data from two independent data sources suggest that the timing of achieving sufficient snowpack has shifted later by 2 weeks. Increasing rainfall and more dry days play a role in the later onset. Adaptation strategies are provided for winter travel management planning to address negative impacts of loss of early season snowpack for OSV usage.
Deborah Verfaillie, Matthieu Lafaysse, Michel Déqué, Nicolas Eckert, Yves Lejeune, and Samuel Morin
The Cryosphere, 12, 1249–1271, https://doi.org/10.5194/tc-12-1249-2018, https://doi.org/10.5194/tc-12-1249-2018, 2018
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This article addresses local changes of seasonal snow and its meteorological drivers, at 1500 m altitude in the Chartreuse mountain range in the Northern French Alps, for the period 1960–2100. We use an ensemble of adjusted RCM outputs consistent with IPCC AR5 GCM outputs (RCPs 2.6, 4.5 and 8.5) and the snowpack model Crocus. Beyond scenario-based approach, global temperature levels on the order of 1.5 °C and 2 °C above preindustrial levels correspond to 25 and 32% reduction of mean snow depth.
Cited articles
Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P.-P., Janowiak, J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind, J., Arkin, P., and Nelkin, E.: The Version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present), J. Hydrometeorol., 4, 1147–1167, https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2, 2003.
Andreadis, K. M. and Lettenmaier, D. P.: Assimilating remotely sensed snow observations into a macroscale hydrology model, Adv. Water Resour., 29, 872–886, https://doi.org/10.1016/j.advwatres.2005.08.004, 2006.
Bair, E.: edwardbair/SPIRES, GitHub [code], https://github.com/edwardbair/SPIRES, last access: 4 February 2023.
Bair, E. H., Stillinger, T., and Dozier, J.: Snow Property Inversion from Remote Sensing (SPIReS): a generalized multispectral unmixing approach with examples from MODIS and Landsat 8 OLI, IEEE T. Geosci. Remote, 59, 7270–7284, https://doi.org/10.1109/TGRS.2020.3040328, 2021.
Beaudoing, H. and Rodell, M.: NASA/GSFC/HSL: GLDAS Noah Land Surface Model L4 3 hourly 0.25 × 0.25 degree V2.1, Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/E7TYRXPJKWOQ, 2020a.
Beaudoing, H. and Rodell, M.: NASA/GSFC/HSL GLDAS Noah Land Surface Model L4 3 hourly 1.0 × 1.0 degree V2.1, Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/IIG8FHR17DA9, 2020b.
Beaudoing, H. and Rodell, M.: NASA/GSFC/HSL GLDAS VIC Land Surface Model L4 3 hourly 1.0 × 1.0 degree V2.1, Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/ZOG6BCSE26HV, 2020c.
Bian, Q., Xu, Z., Zhao, L., Zhang, Y.-F., Zheng, H., Shi, C., Zhang, S., Xie, C., and Yang, Z.-L.: Evaluation and intercomparison of multiple snow water equivalent products over the Tibetan Plateau, J. Hydrometeorol., 20, 2043–2055, https://doi.org/10.1175/JHM-D-19-0011.1, 2019.
Brown, R. D. and Mote, P. W.: The response of Northern Hemisphere snow cover to a changing climate, J. Climate, 22, 2124–2145, https://doi.org/10.1175/2008JCLI2665.1, 2009.
Cao, B., Gruber, S., Zheng, D., and Li, X.: The ERA5-Land soil temperature bias in permafrost regions, The Cryosphere, 14, 2581–2595, https://doi.org/10.5194/tc-14-2581-2020, 2020.
CMA Meteorological Data Centre: CMA Meteorological Data Centre [data set], https://doi.org/10.12065/2.C.GLB.2019.5.v1, 1979.
Danielson, J. J. and Gesch, D. B.: Global multi-resolution terrain elevation data 2010 (GMTED2010), USGS, report, https://doi.org/10.3133/ofr20111073, 2011.
Deng, H., Pepin, N. C., and Chen, Y.: Changes of snowfall under warming in the Tibetan Plateau, J. Geophys. Res. Atmos., 122, 7323–7341, https://doi.org/10.1002/2017JD026524, 2017.
de Rosnay, P., Balsamo, G., Albergel, C., Muñoz-Sabater, J., and Isaksen, L.: Initialisation of land surface variables for numerical weather prediction, Surv. Geophys., 35, 607–621, https://doi.org/10.1007/s10712-012-9207-x, 2014.
Déry, S. J. and Yau, M. K.: Large-scale mass balance effects of blowing snow and surface sublimation, J. Geophys. Res. Atmos., 107, ACL 8-1–ACL 8-17, https://doi.org/10.1029/2001JD001251, 2002.
Ding, B., Yang, K., Qin, J., Wang, L., Chen, Y., and He, X.: The dependence of precipitation types on surface elevation and meteorological conditions and its parameterization, J. Hydrol., 513, 154–163, https://doi.org/10.1016/j.jhydrol.2014.03.038, 2014.
Dozier, J., Painter, T. H., Rittger, K., and Frew, J. E.: Time–space continuity of daily maps of fractional snow cover and albedo from MODIS, Adv. Water Resour., 31, 1515–1526, https://doi.org/10.1016/j.advwatres.2008.08.011, 2008.
Dutra, E., Kotlarski, S., Viterbo, P., Balsamo, G., Miranda, P. M. A., Schär, C., Bissolli, P., and Jonas, T.: Snow cover sensitivity to horizontal resolution, parameterizations, and atmospheric forcing in a land surface model, J. Geophys. Res.-Atmos., 116, D21109, https://doi.org/10.1029/2011JD016061, 2011.
ECMWF: IFS Documentation CY45R1 – Part IV : Physical processes, in: IFS Documentation CY45R1, ECMWF, https://doi.org/10.21957/4whwo8jw0, 2018.
Ek, M. B., Mitchell, K. E., Lin, Y., Rogers, E., Grunmann, P., Koren, V., Gayno, G., and Tarpley, J. D.: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model, J. Geophys. Res.-Atmos., 108, 2002JD003296, https://doi.org/10.1029/2002JD003296, 2003.
Fujiwara, M., Wright, J. S., Manney, G. L., Gray, L. J., Anstey, J., Birner, T., Davis, S., Gerber, E. P., Harvey, V. L., Hegglin, M. I., Homeyer, C. R., Knox, J. A., Krüger, K., Lambert, A., Long, C. S., Martineau, P., Molod, A., Monge-Sanz, B. M., Santee, M. L., Tegtmeier, S., Chabrillat, S., Tan, D. G. H., Jackson, D. R., Polavarapu, S., Compo, G. P., Dragani, R., Ebisuzaki, W., Harada, Y., Kobayashi, C., McCarty, W., Onogi, K., Pawson, S., Simmons, A., Wargan, K., Whitaker, J. S., and Zou, C.-Z.: Introduction to the SPARC Reanalysis Intercomparison Project (S-RIP) and overview of the reanalysis systems, Atmos. Chem. Phys., 17, 1417–1452, https://doi.org/10.5194/acp-17-1417-2017, 2017.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., Silva, A. M. da, Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2), J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
Global Modeling and Assimilation Office (GMAO): MERRA-2 tavg1_2d_lnd_Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Land Surface Diagnostics V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/RKPHT8KC1Y1T, 2015a.
Global Modeling and Assimilation Office (GMAO): MERRA-2 statD_2d_slv_Nx: 2d,Daily,Aggregated Statistics,Single-Level,Assimilation,Single-Level Diagnostics V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/9SC1VNTWGWV3, 2015b.
Hall, D. K., Riggs, G. A., Salomonson, V. V., DiGirolamo, N. E., and Bayr, K. J.: MODIS snow-cover products, Remote Sens. Environ., 83, 181–194, https://doi.org/10.1016/S0034-4257(02)00095-0, 2002.
Helfrich, S. R., McNamara, D., Ramsay, B. H., Baldwin, T., and Kasheta, T.: Enhancements to, and forthcoming developments in the Interactive Multisensor Snow and Ice Mapping System (IMS), Hydrol. Process., 21, 1576–1586, https://doi.org/10.1002/hyp.6720, 2007.
Hernández-Henríquez, M. A., Déry, S. J., and Derksen, C.: Polar amplification and elevation-dependence in trends of Northern Hemisphere snow cover extent, 1971–2014, Environ. Res. Lett., 10, 044010, https://doi.org/10.1088/1748-9326/10/4/044010, 2015.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023.
Huang, J., Zhou, X., Wu, G., Xu, X., Zhao, Q., Liu, Y., Duan, A., Xie, Y., Ma, Y., Zhao, P., Yang, S., Yang, K., Yang, H., Bian, J., Fu, Y., Ge, J., Liu, Y., Wu, Q., Yu, H., Wang, B., Bao, Q., and Qie, K.: Global climate impacts of land-surface and atmospheric processes over the Tibetan Plateau, Rev. Geophys., 61, e2022RG000771, https://doi.org/10.1029/2022RG000771, 2023.
Huffman, G. J., Adler, R. F., Morrissey, M. M., Bolvin, D. T., Curtis, S., Joyce, R., McGavock, B., and Susskind, J.: Global precipitation at one-degree daily resolution from multisatellite observations, J. Hydrometeorol., 2, 36–50, https://doi.org/10.1175/1525-7541(2001)002<0036:GPAODD>2.0.CO;2, 2001.
Immerzeel, W. W., Van Beek, L. P. H., and Bierkens, M. F. P.: Climate change will affect the Asian water towers, Science, 328, 1382–1385, https://doi.org/10.1126/science.1183188, 2010.
Japan Meteorological Agency/Japan: JRA-55: Japanese 55-year Reanalysis, Daily 3-Hourly and 6-Hourly Data, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], https://doi.org/10.5065/D6HH6H41, 2013, updated monthly.
Jiang, Y., Chen, F., Gao, Y., He, C., Barlage, M., and Huang, W.: Assessment of uncertainty sources in snow cover simulation in the Tibetan Plateau, J. Geophys. Res.-Atmos., 125, e2020JD032674, https://doi.org/10.1029/2020JD032674, 2020.
Jiang, Y., Yang, K., Qi, Y., Zhou, X., He, J., Lu, H., Li, X., Chen, Y., Li, X., Zhou, B., Mamtimin, A., Shao, C., Ma, X., Tian, J., and Zhou, J.: TPHiPr: a long-term (1979–2020) high-accuracy precipitation dataset (1/30°, daily) for the Third Pole region based on high-resolution atmospheric modeling and dense observations, Earth Syst. Sci. Data, 15, 621–638, https://doi.org/10.5194/essd-15-621-2023, 2023.
Kendall, M. G.: Rank Correlation Methods, J. Inst. Actuar., 75, 140–141, https://doi.org/10.1017/S0020268100013019, 1975.
Kitoh, A. and Arakawa, O.: Reduction in the east–west contrast in water budget over the Tibetan Plateau under a future climate, Hydrol. Res. Lett., 10, 113–118, https://doi.org/10.3178/hrl.10.113, 2016.
Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi, K., Kamahori, H., Kobayashi, C., Endo, H., Miyaoka, K., and Takahashi, K.: The JRA-55 Reanalysis: General Specifications and Basic Characteristics, J. Meteorolog. Soc. Jpn., 93, 5–48, https://doi.org/10.2151/jmsj.2015-001, 2015.
Koster, R. D., Suarez, M. J., Ducharne, A., Stieglitz, M., and Kumar, P.: A catchment-based approach to modeling land surface processes in a general circulation model: 1. Model structure, J. Geophys. Res.-Atmos., 105, 24809–24822, https://doi.org/10.1029/2000JD900327, 2000.
Lehner, B., Verdin, K., and Jarvis, A.: New Global Hydrography Derived From Spaceborne Elevation Data, EoS Transactions, 89, 93–94, https://doi.org/10.1029/2008EO100001, 2008.
Lei, Y., Pan, J., Xiong, C., Jiang, L., and Shi, J.: Snow depth and snow cover over the Tibetan Plateau observed from space in against ERA5: matters of scale, Clim. Dynam., 60, 1523–1541, https://doi.org/10.1007/s00382-022-06376-0, 2023.
Li, B., Beaudoing, H., and Rodell, M.: NASA/GSFC/HSL GLDAS Catchment Land Surface Model L4 3 hourly 1.0 × 1.0 degree V2.1, Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/VCO8OCV72XO0, 2020.
Li, Q., Yang, T., and Li, L.: Evaluation of snow depth and snow cover represented by multiple datasets over the Tianshan Mountains: Remote sensing, reanalysis, and simulation, Int. J. Climatol., 42, 4223–4239, https://doi.org/10.1002/joc.7459, 2022.
Liang, X., Jiang, L., Pan, Y., Shi, C., Liu, Z., and Zhou, Z.: A 10-Yr global land surface reanalysis interim dataset (CRA-Interim/Land): Implementation and preliminary evaluation, J. Meteorolog. Res., 34, 101–116, https://doi.org/10.1007/s13351-020-9083-0, 2020.
Lin, H. and Wu, Z.: Contribution of the autumn Tibetan Plateau snow cover to seasonal prediction of North American winter temperature, J. Climate, 24, 2801–2813, https://doi.org/10.1175/2010JCLI3889.1, 2011.
Liu, Y., Fang, Y., and Margulis, S. A.: Spatiotemporal distribution of seasonal snow water equivalent in High Mountain Asia from an 18-year Landsat–MODIS era snow reanalysis dataset, The Cryosphere, 15, 5261–5280, https://doi.org/10.5194/tc-15-5261-2021, 2021a.
Liu, Y., Fang, Y., and Margulis, S. A.: High Mountain Asia UCLA Daily Snow Reanalysis, Version 1, Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/HNAUGJQXSCVU, 2021b.
Liu, Y., Fang, Y., Li, D., and Margulis, S. A.: How well do global snow products characterize snow storage in High Mountain Asia?, Geophys. Res. Lett., 49, e2022GL100082, https://doi.org/10.1029/2022GL100082, 2022.
Liu, Z., Jiang, L., Shi, C., Zhang, T., Zhou, Z., Liao, J., Yao, S., Liu, J., Wang, M., Wang, H., Liang, X., Zhang, Z., Yao, Y., Zhu, T., Chen, Z., Xu, W., Cao, L., Jiang, H., and Hu, K.: CRA-40/Atmosphere – the first-generation Chinese atmospheric reanalysis (1979–2018): System description and performance evaluation, J. Meteorol. Res., 37, 1–19, https://doi.org/10.1007/s13351-023-2086-x, 2023.
Luo, J., Chen, H., and Zhou, B.: Comparison of Snowfall Variations over China Identified from Different Snowfall/Rainfall Discrimination Methods, J. Meteorol. Res., 34, 1114–1128, https://doi.org/10.1007/s13351-020-0004-z, 2020.
Lyu, M., Wen, M., and Wu, Z.: Possible contribution of the inter-annual Tibetan Plateau snow cover variation to the Madden-Julian oscillation convection variability, Int. J. Climatol., 38, 3787–3800, https://doi.org/10.1002/joc.5533, 2018.
Ma, Y., Ma, W., Zhong, L., Hu, Z., Li, M., Zhu, Z., Han, C., Wang, B., and Liu, X.: Monitoring and Modeling the Tibetan Plateau's climate system and its impact on East Asia, Sci. Rep., 7, 44574, https://doi.org/10.1038/srep44574, 2017.
Mann, H. B.: Nonparametric tests against trend, Econometrica, 13, 245–259, https://doi.org/10.2307/1907187, 1945.
Meng, J., Yang, R., Wei, H., Ek, M., Gayno, G., Xie, P., and Mitchell, K.: The land surface analysis in the NCEP climate forecast system reanalysis, J. Hydrometeorol., 13, 1621–1630, https://doi.org/10.1175/JHM-D-11-090.1, 2012.
Mortimer, C., Mudryk, L., Derksen, C., Luojus, K., Brown, R., Kelly, R., and Tedesco, M.: Evaluation of long-term Northern Hemisphere snow water equivalent products, The Cryosphere, 14, 1579–1594, https://doi.org/10.5194/tc-14-1579-2020, 2020.
Mudryk, L. R., Derksen, C., Kushner, P. J., and Brown, R.: Characterization of Northern Hemisphere snow water equivalent datasets, 1981–2010, J. Climate, 28, 8037–8051, https://doi.org/10.1175/JCLI-D-15-0229.1, 2015.
Muñoz Sabater, J.: ERA5-Land hourly data from 1950 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.e2161bac, 2019.
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021.
Onogi, K., Tsutsui, J., Koide, H., Sakamoto, M., Kobayashi, S., Hatsushika, H., Matsumoto, T., Yamazaki, N., Kamahori, H., Takahashi, K., Kadokura, S., Wada, K., Kato, K., Oyama, R., Ose, T., Mannoji, N., and Taira, R.: The JRA-25 reanalysis, J. Meteorolog. Soc. Jpn., 85, 369–432, https://doi.org/10.2151/jmsj.85.369, 2007.
Orsolini, Y., Wegmann, M., Dutra, E., Liu, B., Balsamo, G., Yang, K., de Rosnay, P., Zhu, C., Wang, W., Senan, R., and Arduini, G.: Evaluation of snow depth and snow cover over the Tibetan Plateau in global reanalyses using in situ and satellite remote sensing observations, The Cryosphere, 13, 2221–2239, https://doi.org/10.5194/tc-13-2221-2019, 2019.
Painter, T. H., Rittger, K., McKenzie, C., Slaughter, P., Davis, R. E., and Dozier, J.: Retrieval of subpixel snow covered area, grain size, and albedo from MODIS, Remote Sens. Environ., 113, 868–879, https://doi.org/10.1016/j.rse.2009.01.001, 2009.
Pu, W., Cui, J., Wu, D., Shi, T., Chen, Y., Xing, Y., Zhou, Y., and Wang, X.: Unprecedented snow darkening and melting in New Zealand due to 2019–2020 Australian wildfires, Fundam. Res., 1, 224–231, https://doi.org/10.1016/j.fmre.2021.04.001, 2021.
Qiu, J.: China: The third pole, Nature, 454, 393–396, https://doi.org/10.1038/454393a, 2008.
Reichle, R. H., Draper, C. S., Liu, Q., Girotto, M., Mahanama, S. P. P., Koster, R. D., and Lannoy, G. J. M. D.: Assessment of MERRA-2 Land Surface Hydrology Estimates, J. Climate, 30, 2937–2960, https://doi.org/10.1175/JCLI-D-16-0720.1, 2017.
Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C.-J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin, J. K., Walker, J. P., Lohmann, D., and Toll, D.: The global land data assimilation system, B. Am. Meteorol. Soc., 85, 381–394, https://doi.org/10.1175/BAMS-85-3-381, 2004.
Saha, S., Moorthi, S., Pan, H.-L., Wu, X., Wang, J., Nadiga, S., Tripp, P., Kistler, R., Woollen, J., Behringer, D., Liu, H., Stokes, D., Grumbine, R., Gayno, G., Wang, J., Hou, Y.-T., Chuang, H., Juang, H.-M. H., Sela, J., Iredell, M., Treadon, R., Kleist, D., Van Delst, P., Keyser, D., Derber, J., Ek, M., Meng, J., Wei, H., Yang, R., Lord, S., Van Den Dool, H., Kumar, A., Wang, W., Long, C., Chelliah, M., Xue, Y., Huang, B., Schemm, J.-K., Ebisuzaki, W., Lin, R., Xie, P., Chen, M., Zhou, S., Higgins, W., Zou, C.-Z., Liu, Q., Chen, Y., Han, Y., Cucurull, L., Reynolds, R. W., Rutledge, G., and Goldberg, M.: The NCEP climate forecast system reanalysis, B. Am. Meteorol. Soc., 91, 1015–1058, https://doi.org/10.1175/2010BAMS3001.1, 2010a.
Saha, S., Moorthi, S., Pan, H., Wu, X., Wang, J., Nadiga, S., Tripp, P., Kistler, R., Woollen, J., Behringer, D., Liu, H., Stokes, D., Grumbine, R., Gayno, G., Wang, J., Hou, Y., Chuang, H., Juang, H. H., Sela, J., Iredell, M., Treadon, R., Kleist, D., Van Delst, P., Keyser, D., Derber, J., Ek, M., Meng, J., Wei, H., Yang, R., Lord, S., van den Dool, H., Kumar, A., Wang, W., Long, C., Chelliah, M., Xue, Y., Huang, B., Schemm, J., Ebisuzaki, W., Lin, R., Xie, P., Chen, M., Zhou, S., Higgins, W., Zou, C., Liu, Q., Chen, Y., Han, Y., Cucurull, L., Reynolds, R. W., Rutledge, G., and Goldberg, M.: NCEP Climate Forecast System Reanalysis (CFSR) 6-hourly Products, January 1979 to December 2010, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], https://doi.org/10.5065/D69K487J, 2010b.
Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer, D., Hou, Y., Chuang, H., Iredell, M., Ek, M., Meng, J., Yang, R., Mendez, M. P., van den Dool, H., Zhang, Q., Wang, W., Chen, M., and Becker, E.: NCEP Climate Forecast System Version 2 (CFSv2) 6-hourly Products, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], https://doi.org/10.5065/D61C1TXF, 2011.
Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer, D., Hou, Y.-T., Chuang, H., Iredell, M., Ek, M., Meng, J., Yang, R., Mendez, M. P., Van Den Dool, H., Zhang, Q., Wang, W., Chen, M., and Becker, E.: The NCEP climate forecast system version 2, J. Climate, 27, 2185–2208, https://doi.org/10.1175/JCLI-D-12-00823.1, 2014.
Sato, N., Sellers, P. J., Randall, D. A., Schneider, E. K., Shukla, J., Kinter, J. L., Hou, Y.-T., and Albertazzi, E.: Effects of implementing the Simple Biosphere Model in a general circulation model, J. Atmos. Sci., 46, 2757–2782, https://doi.org/10.1175/1520-0469(1989)046<2757:EOITSB>2.0.CO;2, 1989.
Sellers, P. J., Mintz, Y., Sud, Y. C., and Dalcher, A.: A Simple Biosphere Model (SIB) for use within general circulation models, J. Atmos. Sci., 43, 505–531, https://doi.org/10.1175/1520-0469(1986)043<0505:ASBMFU>2.0.CO;2, 1986.
Serquet, G., Marty, C., Dulex, J.-P., and Rebetez, M.: Seasonal trends and temperature dependence of the snowfall/precipitation-day ratio in Switzerland, Geophys. Res. Lett., 38, L07703, https://doi.org/10.1029/2011GL046976, 2011.
Sheffield, J., Goteti, G., and Wood, E. F.: Development of a 50-Year High-Resolution Global Dataset of Meteorological Forcings for Land Surface Modeling, J. Climate, 19, 3088–3111, https://doi.org/10.1175/JCLI3790.1, 2006.
Stillinger, T., Rittger, K., Raleigh, M. S., Michell, A., Davis, R. E., and Bair, E. H.: Landsat, MODIS, and VIIRS snow cover mapping algorithm performance as validated by airborne lidar datasets, The Cryosphere, 17, 567–590, https://doi.org/10.5194/tc-17-567-2023, 2023.
Sun, C., Walker, J. P., and Houser, P. R.: A methodology for snow data assimilation in a land surface model, J. Geophys. Res.-Atmos., 109, 2003JD003765, https://doi.org/10.1029/2003JD003765, 2004.
Sun, S. and Xue, Y.: Implementing a new snow scheme in Simplified Simple Biosphere Model, Adv. Atmos. Sci., 18, 335–354, https://doi.org/10.1007/BF02919314, 2001.
Sun, S., Shi, C., Liang, X., Zhang, S., Gu, J., Han, S., Jiang, H., Xu, B., Yu, Q., Liang, Y., and Deng, S.: The Evaluation of Snow Depth Simulated by Different Land Surface Models in China Based on Station Observations, Sustainability, 15, 11284, https://doi.org/10.3390/su151411284, 2023.
Thackeray, C. W., Fletcher, C. G., Mudryk, L. R., and Derksen, C.: Quantifying the Uncertainty in Historical and Future Simulations of Northern Hemisphere Spring Snow Cover, J. Climate, 29, 8647–8663, https://doi.org/10.1175/JCLI-D-16-0341.1, 2016.
Vorkauf, M., Marty, C., Kahmen, A., and Hiltbrunner, E.: Past and future snowmelt trends in the Swiss Alps: the role of temperature and snowpack, Clim. Change, 165, 44, https://doi.org/10.1007/s10584-021-03027-x, 2021.
Wang, A. and Zeng, X.: Evaluation of multireanalysis products with in situ observations over the Tibetan Plateau, J. Geophys. Res.-Atmos., 117, D05102, https://doi.org/10.1029/2011JD016553, 2012.
Wegmann, M., Orsolini, Y., Dutra, E., Bulygina, O., Sterin, A., and Brönnimann, S.: Eurasian snow depth in long-term climate reanalyses, The Cryosphere, 11, 923–935, https://doi.org/10.5194/tc-11-923-2017, 2017.
Xie, P. and Arkin, P. A.: Global precipitation: A 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model Outputs, B. Am. Meteorol. Soc., 78, 2539–2558, https://doi.org/10.1175/1520-0477(1997)078<2539:GPAYMA>2.0.CO;2, 1997.
Xie, P., Chen, M., Yang, S., Yatagai, A., Hayasaka, T., Fukushima, Y., and Liu, C.: A gauge-based analysis of daily precipitation over East Asia, J. Hydrometeorol., 8, 607–626, https://doi.org/10.1175/JHM583.1, 2007.
Xu, W., Ma, L., Ma, M., Zhang, H., and Yuan, W.: Spatial–temporal variability of snow cover and depth in the Qinghai–Tibetan Plateau, J. Climate, 30, 1521–1533, https://doi.org/10.1175/JCLI-D-15-0732.1, 2017.
Xu, X., Lu, C., Shi, X., and Gao, S.: World water tower: An atmospheric perspective, Geophys. Res. Lett., 35, L20815, https://doi.org/10.1029/2008GL035867, 2008.
Xue, Y., Sun, S., Kahan, D. S., and Jiao, Y.: Impact of parameterizations in snow physics and interface processes on the simulation of snow cover and runoff at several cold region sites, J. Geophys. Res.-Atmos., 108, 2002JD003174, https://doi.org/10.1029/2002JD003174, 2003.
Yang, D., Ding, M., Dou, T., Han, W., Liu, W., Zhang, J., Du, Z., and Xiao, C.: On the Differences in Precipitation Type Between the Arctic, Antarctica and Tibetan Plateau, Front. Earth Sci., 9, 607487, https://doi.org/10.3389/feart.2021.607487, 2021.
Yang, K., Jiang, Y., Tang, W., He, J., Shao, C., Zhou, X., Lu, H., Chen, Y., Li, X., and Shi, J.: A high-resolution near-surface meteorological forcing dataset for the Third Pole region (TPMFD, 1979–2022), National Tibetan Plateau/Third Pole Environment Data Center [data set], https://doi.org/10.11888/Atmos.tpdc.300398, 2023.
Yang, M., Wang, X., Pang, G., Wan, G., and Liu, Z.: The Tibetan Plateau cryosphere: Observations and model simulations for current status and recent changes, Earth Sci. Rev., 190, 353–369, https://doi.org/10.1016/j.earscirev.2018.12.018, 2019.
Yao, T., Thompson, L., Yang, W., Yu, W., Gao, Y., Guo, X., Yang, X., Duan, K., Zhao, H., Xu, B., Pu, J., Lu, A., Xiang, Y., Kattel, D. B., and Joswiak, D.: Different glacier status with atmospheric circulations in Tibetan Plateau and surroundings, Nat. Clim. Change, 2, 663–667, https://doi.org/10.1038/nclimate1580, 2012.
Yao, T., Xue, Y., Chen, D., Chen, F., Thompson, L., Cui, P., Koike, T., Lau, W. K.-M., Lettenmaier, D., Mosbrugger, V., Zhang, R., Xu, B., Dozier, J., Gillespie, T., Gu, Y., Kang, S., Piao, S., Sugimoto, S., Ueno, K., Wang, L., Wang, W., Zhang, F., Sheng, Y., Guo, W., Ailikun, Yang, X., Ma, Y., Shen, S. S. P., Su, Z., Chen, F., Liang, S., Liu, Y., Singh, V. P., Yang, K., Yang, D., Zhao, X., Qian, Y., Zhang, Y., and Li, Q.: Recent Third Pole's Rapid Warming Accompanies Cryospheric Melt and Water Cycle Intensification and Interactions between Monsoon and Environment: Multidisciplinary Approach with Observations, Modeling, and Analysis, B. Am. Meteorol. Soc., 100, 423–444, https://doi.org/10.1175/BAMS-D-17-0057.1, 2019.
You, Q., Wu, T., Shen, L., Pepin, N., Zhang, L., Jiang, Z., Wu, Z., Kang, S., and AghaKouchak, A.: Review of snow cover variation over the Tibetan Plateau and its influence on the broad climate system, Earth Sci. Rev., 201, 103043, https://doi.org/10.1016/j.earscirev.2019.103043, 2020a.
You, Q., Wu, F., Wang, H., Jiang, Z., Pepin, N., and Kang, S.: Projected changes in snow water equivalent over the Tibetan Plateau under global warming of 1.5 ° and 2 °C, J. Climate, 33, 5141–5154, https://doi.org/10.1175/JCLI-D-19-0719.1, 2020b.
Yu, L., Zhang, S., Bu, K., Yang, J., Yan, F., and Chang, L.: A review on snow data sets, Scientia Geographica Sinica, 33, 878–883, 2013.
Zhang, F., Zhang, H., Hagen, S. C., Ye, M., Wang, D., Gui, D., Zeng, C., Tian, L., and Liu, J.: Snow cover and runoff modelling in a high mountain catchment with scarce data: effects of temperature and precipitation parameters, Hydrol. Process., 29, 52–65, https://doi.org/10.1002/hyp.10125, 2015.
Zhang, H., Zhang, F., Che, T., Yan, W., and Ye, M.: Investigating the ability of multiple reanalysis datasets to simulate snow depth variability over mainland China from 1981 to 2018, J. Climate, 34, 9957–9972, https://doi.org/10.1175/JCLI-D-20-0804.1, 2021.
Zhang, H., Immerzeel, W. W., Zhang, F., De Kok, R. J., Chen, D., and Yan, W.: Snow cover persistence reverses the altitudinal patterns of warming above and below 5000 m on the Tibetan Plateau, Sci. Total Environ., 803, 149889, https://doi.org/10.1016/j.scitotenv.2021.149889, 2022.
Zhang, T.: Influence of the seasonal snow cover on the ground thermal regime: An overview, Rev. Geophys., 43, RG4002, https://doi.org/10.1029/2004RG000157, 2005.
Zhou, X., Yang, K., Ouyang, L., Wang, Y., Jiang, Y., Li, X., Chen, D., and Prein, A.: Added value of kilometer-scale modeling over the third pole region: a CORDEX-CPTP pilot study, Clim. Dynam., 57, 1673–1687, https://doi.org/10.1007/s00382-021-05653-8, 2021.
Zhu, X., Wu, T., Li, R., Wang, S., Hu, G., Wang, W., Qin, Y., and Yang, S.: Characteristics of the ratios of snow, rain and sleet to precipitation on the Qinghai-Tibet Plateau during 1961–2014, Quat. Int., 444, 137–150, https://doi.org/10.1016/j.quaint.2016.07.030, 2017.
Short summary
The snow cover over the Tibetan Plateau (TP) plays a role in climate and hydrological systems, yet there are uncertainties in snow cover fraction (SCF) estimations within reanalysis datasets. This study utilized the Snow Property Inversion from Remote Sensing (SPIReS) SCF data to assess the accuracy of eight widely used reanalysis SCF datasets over the TP. Factors contributing to uncertainties were analyzed, and a combined averaging method was employed to provide optimized SCF simulations.
The snow cover over the Tibetan Plateau (TP) plays a role in climate and hydrological systems,...
