Articles | Volume 14, issue 5
https://doi.org/10.5194/tc-14-1579-2020
© Author(s) 2020. 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-14-1579-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 May 2020
Research article |
| 15 May 2020
| 15 May 2020
Evaluation of long-term Northern Hemisphere snow water equivalent products
Colleen Mortimer
CORRESPONDING AUTHOR
Climate Research Division, Environment and Climate Change Canada,
Toronto, Canada
Lawrence Mudryk
Climate Research Division, Environment and Climate Change Canada,
Toronto, Canada
Chris Derksen
Climate Research Division, Environment and Climate Change Canada,
Toronto, Canada
Kari Luojus
Finnish Meteorological Institute, Helsinki, Finland
Ross Brown
Climate Research Division, Environment and Climate Change Canada,
Toronto, Canada
Richard Kelly
Department of Geography and Environmental Management, University of
Waterloo, Waterloo, Canada
Marco Tedesco
Lamont Doherty Earth Observatory, Columbia University, Palisades NY, NASA Goddard Institute for Space Studies, New York, USA
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Water equivalent of snow cover (SWE) is a key variable for water management, hydrological forecasting and climate monitoring. A new Canadian SWE dataset (CanSWE) is presented in this paper. It compiles data collected by multiple agencies and companies at more than 2500 different locations across Canada over the period 1928–2020. Snow depth and derived bulk snow density are also included when available.
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We developed a technique to improve the outputs of a model that calculates the gain and loss of Greenland and consequently its contribution to sea level rise. Our technique generates “sharper” images of the maps generated by the model to better understand and quantify where losses occur. This has implications for improving models, understanding what drives the contributions of Greenland to sea level rise, and more.
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We examine Arctic snow cover trends through the lens of climate assessments. We determine the sensitivity of change in snow cover extent to year-over-year increases in time series length, reference period, the use of a statistical methodology to improve inter-dataset agreement, version changes in snow products, and snow product ensemble size. By identifying the sensitivity to the range of choices available to investigators, we increase confidence in reported Arctic snow extent changes.
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We use repeated satellite measurements of the height of the Greenland ice sheet to learn about how three computational models of snowfall, melt, and snow compaction represent actual changes in the ice sheet. We find that the models do a good job of estimating how the parts of the ice sheet near the coast have changed but that two of the models have trouble representing surface melt for the highest part of the ice sheet. This work provides suggestions for how to better model snowmelt.
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The Cryosphere, 16, 3531–3573, https://doi.org/10.5194/tc-16-3531-2022, https://doi.org/10.5194/tc-16-3531-2022, 2022
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Snow water equivalent (SWE) is of fundamental importance to water, energy, and geochemical cycles but is poorly observed globally. Synthetic aperture radar (SAR) measurements at X- and Ku-band can address this gap. This review serves to inform the broad snow research, monitoring, and application communities about the progress made in recent decades to move towards a new satellite mission capable of addressing the needs of the geoscience researchers and users.
Juha Lemmetyinen, Juval Cohen, Anna Kontu, Juho Vehviläinen, Henna-Reetta Hannula, Ioanna Merkouriadi, Stefan Scheiblauer, Helmut Rott, Thomas Nagler, Elisabeth Ripper, Kelly Elder, Hans-Peter Marshall, Reinhard Fromm, Marc Adams, Chris Derksen, Joshua King, Adriano Meta, Alex Coccia, Nick Rutter, Melody Sandells, Giovanni Macelloni, Emanuele Santi, Marion Leduc-Leballeur, Richard Essery, Cecile Menard, and Michael Kern
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The manuscript describes airborne, dual-polarised X and Ku band synthetic aperture radar (SAR) data collected over several campaigns over snow-covered terrain in Finland, Austria and Canada. Colocated snow and meteorological observations are also presented. The data are meant for science users interested in investigating X/Ku band radar signatures from natural environments in winter conditions.
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.
Vincent Vionnet, Colleen Mortimer, Mike Brady, Louise Arnal, and Ross Brown
Earth Syst. Sci. Data, 13, 4603–4619, https://doi.org/10.5194/essd-13-4603-2021, https://doi.org/10.5194/essd-13-4603-2021, 2021
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Water equivalent of snow cover (SWE) is a key variable for water management, hydrological forecasting and climate monitoring. A new Canadian SWE dataset (CanSWE) is presented in this paper. It compiles data collected by multiple agencies and companies at more than 2500 different locations across Canada over the period 1928–2020. Snow depth and derived bulk snow density are also included when available.
Pinja Venäläinen, Kari Luojus, Juha Lemmetyinen, Jouni Pulliainen, Mikko Moisander, and Matias Takala
The Cryosphere, 15, 2969–2981, https://doi.org/10.5194/tc-15-2969-2021, https://doi.org/10.5194/tc-15-2969-2021, 2021
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Information about snow water equivalent (SWE) is needed in many applications, including climate model evaluation and forecasting fresh water availability. Space-borne radiometer observations combined with ground snow depth measurements can be used to make global estimates of SWE. In this study, we investigate the possibility of using sparse snow density measurement in satellite-based SWE retrieval and show that using the snow density information in post-processing improves SWE estimations.
Paolo Colosio, Marco Tedesco, Roberto Ranzi, and Xavier Fettweis
The Cryosphere, 15, 2623–2646, https://doi.org/10.5194/tc-15-2623-2021, https://doi.org/10.5194/tc-15-2623-2021, 2021
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We use a new satellite dataset to study the spatiotemporal evolution of surface melting over Greenland at an enhanced resolution of 3.125 km. Using meteorological data and the MAR model, we observe that a dynamic algorithm can best detect surface melting. We found that the melting season is elongating, the melt extent is increasing and that high-resolution data better describe the spatiotemporal evolution of the melting season, which is crucial to improve estimates of sea level rise.
Matthew G. Cooper, Laurence C. Smith, Asa K. Rennermalm, Marco Tedesco, Rohi Muthyala, Sasha Z. Leidman, Samiah E. Moustafa, and Jessica V. Fayne
The Cryosphere, 15, 1931–1953, https://doi.org/10.5194/tc-15-1931-2021, https://doi.org/10.5194/tc-15-1931-2021, 2021
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We measured sunlight transmitted into glacier ice to improve models of glacier ice melt and satellite measurements of glacier ice surfaces. We found that very small concentrations of impurities inside the ice increase absorption of sunlight, but the amount was small enough to enable an estimate of ice absorptivity. We confirmed earlier results that the absorption minimum is near 390 nm. We also found that a layer of highly reflective granular "white ice" near the surface reduces transmittance.
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.
Joshua King, Stephen Howell, Mike Brady, Peter Toose, Chris Derksen, Christian Haas, and Justin Beckers
The Cryosphere, 14, 4323–4339, https://doi.org/10.5194/tc-14-4323-2020, https://doi.org/10.5194/tc-14-4323-2020, 2020
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Physical measurements of snow on sea ice are sparse, making it difficulty to evaluate satellite estimates or model representations. Here, we introduce new measurements of snow properties on sea ice to better understand variability at distances less than 200 m. Our work shows that similarities in the snow structure are found at longer distances on younger ice than older ice.
Xavier Fettweis, Stefan Hofer, Uta Krebs-Kanzow, Charles Amory, Teruo Aoki, Constantijn J. Berends, Andreas Born, Jason E. Box, Alison Delhasse, Koji Fujita, Paul Gierz, Heiko Goelzer, Edward Hanna, Akihiro Hashimoto, Philippe Huybrechts, Marie-Luise Kapsch, Michalea D. King, Christoph Kittel, Charlotte Lang, Peter L. Langen, Jan T. M. Lenaerts, Glen E. Liston, Gerrit Lohmann, Sebastian H. Mernild, Uwe Mikolajewicz, Kameswarrao Modali, Ruth H. Mottram, Masashi Niwano, Brice Noël, Jonathan C. Ryan, Amy Smith, Jan Streffing, Marco Tedesco, Willem Jan van de Berg, Michiel van den Broeke, Roderik S. W. van de Wal, Leo van Kampenhout, David Wilton, Bert Wouters, Florian Ziemen, and Tobias Zolles
The Cryosphere, 14, 3935–3958, https://doi.org/10.5194/tc-14-3935-2020, https://doi.org/10.5194/tc-14-3935-2020, 2020
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We evaluated simulated Greenland Ice Sheet surface mass balance from 5 kinds of models. While the most complex (but expensive to compute) models remain the best, the faster/simpler models also compare reliably with observations and have biases of the same order as the regional models. Discrepancies in the trend over 2000–2012, however, suggest that large uncertainties remain in the modelled future SMB changes as they are highly impacted by the meltwater runoff biases over the current climate.
Paul Donchenko, Joshua King, and Richard Kelly
The Cryosphere Discuss., https://doi.org/10.5194/tc-2020-283, https://doi.org/10.5194/tc-2020-283, 2020
Publication in TC not foreseen
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Estimating Arctic sea ice surface elevation from the CryoSat-2 instrument may not fully compensate for the incomplete penetration of radar through the snow cover and overestimate the ice thickness. This study investigates the accuracy of the ice surface measurement and how it is affected by the properties snow and ice properties. It was found that deep or salty snow, and rough ice can make the surface appear higher, but including these properties in the calculation may improve the estimate.
Shujie Wang, Marco Tedesco, Patrick Alexander, Min Xu, and Xavier Fettweis
The Cryosphere, 14, 2687–2713, https://doi.org/10.5194/tc-14-2687-2020, https://doi.org/10.5194/tc-14-2687-2020, 2020
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Glacial algal blooms play a significant role in darkening the Greenland Ice Sheet during summertime. The dark pigments generated by glacial algae could substantially reduce the bare ice albedo and thereby enhance surface melt. We used satellite data to map the spatial distribution of glacial algae and characterized the seasonal growth pattern and interannual trends of glacial algae in southwestern Greenland. Our study is important for bridging microbial activities with ice sheet mass balance.
Lawrence Mudryk, María Santolaria-Otín, Gerhard Krinner, Martin Ménégoz, Chris Derksen, Claire Brutel-Vuilmet, Mike Brady, and Richard Essery
The Cryosphere, 14, 2495–2514, https://doi.org/10.5194/tc-14-2495-2020, https://doi.org/10.5194/tc-14-2495-2020, 2020
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We analyze how well updated state-of-the-art climate models reproduce observed historical snow cover extent and snow mass and how they project that these quantities will change up to the year 2100. Overall the updated models better represent historical snow extent than previous models, and they simulate stronger historical trends in snow extent and snow mass. They project that spring snow extent will decrease by 8 % for each degree Celsius that the global surface air temperature increases.
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.
Marco Tedesco and Xavier Fettweis
The Cryosphere, 14, 1209–1223, https://doi.org/10.5194/tc-14-1209-2020, https://doi.org/10.5194/tc-14-1209-2020, 2020
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Unprecedented atmospheric conditions occurring in the summer of 2019 over Greenland promoted new record or close-to-record values of mass loss. Summer of 2019 was characterized by an exceptional persistence of anticyclonic conditions that enhanced melting.
Marco Tedesco, Steven McAlpine, and Jeremy R. Porter
Nat. Hazards Earth Syst. Sci., 20, 907–920, https://doi.org/10.5194/nhess-20-907-2020, https://doi.org/10.5194/nhess-20-907-2020, 2020
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Quantifying the exposure of house property to extreme weather events is crucial to study their impact on economy. Here, we show that value of property exposed to Hurricane Florence in September 2018 was USD 52 billion vs. USD 10 billion that would have occurred at the beginning of the 19th century due to urban expansion that increased after 1950s and the increasing number of houses built near water, showing the importance of accounting for the distribution of new buildings in risk and exposure.
Nick Rutter, Melody J. Sandells, Chris Derksen, Joshua King, Peter Toose, Leanne Wake, Tom Watts, Richard Essery, Alexandre Roy, Alain Royer, Philip Marsh, Chris Larsen, and Matthew Sturm
The Cryosphere, 13, 3045–3059, https://doi.org/10.5194/tc-13-3045-2019, https://doi.org/10.5194/tc-13-3045-2019, 2019
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Impact of natural variability in Arctic tundra snow microstructural characteristics on the capacity to estimate snow water equivalent (SWE) from Ku-band radar was assessed. Median values of metrics quantifying snow microstructure adequately characterise differences between snowpack layers. Optimal estimates of SWE required microstructural values slightly less than the measured median but tolerated natural variability for accurate estimation of SWE in shallow snowpacks.
Marilena Oltmanns, Fiammetta Straneo, and Marco Tedesco
The Cryosphere, 13, 815–825, https://doi.org/10.5194/tc-13-815-2019, https://doi.org/10.5194/tc-13-815-2019, 2019
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By combining reanalysis, weather station and satellite data, we show that increases in surface melt over Greenland are initiated by large-scale precipitation events year-round. Estimates from a regional climate model suggest that the initiated melting more than doubled between 1988 and 2012, amounting to ~28 % of the overall melt and revealing that, despite the involved mass gain, precipitation events are contributing to the ice sheet's decline.
Gerhard Krinner, Chris Derksen, Richard Essery, Mark Flanner, Stefan Hagemann, Martyn Clark, Alex Hall, Helmut Rott, Claire Brutel-Vuilmet, Hyungjun Kim, Cécile B. Ménard, Lawrence Mudryk, Chad Thackeray, Libo Wang, Gabriele Arduini, Gianpaolo Balsamo, Paul Bartlett, Julia Boike, Aaron Boone, Frédérique Chéruy, Jeanne Colin, Matthias Cuntz, Yongjiu Dai, Bertrand Decharme, Jeff Derry, Agnès Ducharne, Emanuel Dutra, Xing Fang, Charles Fierz, Josephine Ghattas, Yeugeniy Gusev, Vanessa Haverd, Anna Kontu, Matthieu Lafaysse, Rachel Law, Dave Lawrence, Weiping Li, Thomas Marke, Danny Marks, Martin Ménégoz, Olga Nasonova, Tomoko Nitta, Masashi Niwano, John Pomeroy, Mark S. Raleigh, Gerd Schaedler, Vladimir Semenov, Tanya G. Smirnova, Tobias Stacke, Ulrich Strasser, Sean Svenson, Dmitry Turkov, Tao Wang, Nander Wever, Hua Yuan, Wenyan Zhou, and Dan Zhu
Geosci. Model Dev., 11, 5027–5049, https://doi.org/10.5194/gmd-11-5027-2018, https://doi.org/10.5194/gmd-11-5027-2018, 2018
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This paper provides an overview of a coordinated international experiment to determine the strengths and weaknesses in how climate models treat snow. The models will be assessed at point locations using high-quality reference measurements and globally using satellite-derived datasets. How well climate models simulate snow-related processes is important because changing snow cover is an important part of the global climate system and provides an important freshwater resource for human use.
Kang Yang, Laurence C. Smith, Leif Karlstrom, Matthew G. Cooper, Marco Tedesco, Dirk van As, Xiao Cheng, Zhuoqi Chen, and Manchun Li
The Cryosphere, 12, 3791–3811, https://doi.org/10.5194/tc-12-3791-2018, https://doi.org/10.5194/tc-12-3791-2018, 2018
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A high-resolution spatially lumped hydrologic surface routing model is proposed to simulate meltwater transport over bare ice surfaces. In an ice-covered catchment, meltwater is routed by slow interfluve flow (~10−3–10−4 m s−1) followed by fast open-channel flow (~10−1 m s−1). Seasonal evolution of supraglacial stream-river networks substantially alters the magnitude and timing of moulin discharge with implications for subglacial hydrology and ice dynamics.
Rajashree Tri Datta, Marco Tedesco, Cecile Agosta, Xavier Fettweis, Peter Kuipers Munneke, and Michiel R. van den Broeke
The Cryosphere, 12, 2901–2922, https://doi.org/10.5194/tc-12-2901-2018, https://doi.org/10.5194/tc-12-2901-2018, 2018
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Surface melting on the East Antarctic Peninsula (East AP) has been linked to ice shelf collapse, including the Larsen A (1995) and Larsen B (2002) ice shelves. Regional climate models (RCMs) are a valuable tool to understand how wind patterns and general warming can impact the stability of ice shelves through surface melt. Here, we evaluate one such RCM (Modèle Atmosphérique Régionale) over the East AP, including the remaining Larsen C ice shelf, by comparing it to satellite and ground data.
Achim Heilig, Olaf Eisen, Michael MacFerrin, Marco Tedesco, and Xavier Fettweis
The Cryosphere, 12, 1851–1866, https://doi.org/10.5194/tc-12-1851-2018, https://doi.org/10.5194/tc-12-1851-2018, 2018
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This paper presents data on temporal changes in snow and firn, which were not available before. We present data on water infiltration in the percolation zone of the Greenland Ice Sheet that improve our understanding of liquid water retention in snow and firn and mass transfer. We compare those findings with model simulations. It appears that simulated accumulation in terms of SWE is fairly accurate, while modeling of the individual parameters density and liquid water content is incorrect.
Paul J. Kushner, Lawrence R. Mudryk, William Merryfield, Jaison T. Ambadan, Aaron Berg, Adéline Bichet, Ross Brown, Chris Derksen, Stephen J. Déry, Arlan Dirkson, Greg Flato, Christopher G. Fletcher, John C. Fyfe, Nathan Gillett, Christian Haas, Stephen Howell, Frédéric Laliberté, Kelly McCusker, Michael Sigmond, Reinel Sospedra-Alfonso, Neil F. Tandon, Chad Thackeray, Bruno Tremblay, and Francis W. Zwiers
The Cryosphere, 12, 1137–1156, https://doi.org/10.5194/tc-12-1137-2018, https://doi.org/10.5194/tc-12-1137-2018, 2018
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Here, the Canadian research network CanSISE uses state-of-the-art observations of snow and sea ice to assess how Canada's climate model and climate prediction systems capture variability in snow, sea ice, and related climate parameters. We find that the system performs well, accounting for observational uncertainty (especially for snow), model uncertainty, and chaotic climate variability. Even for variables like sea ice, where improvement is needed, useful prediction tools can be developed.
Lawrence R. Mudryk, Chris Derksen, Stephen Howell, Fred Laliberté, Chad Thackeray, Reinel Sospedra-Alfonso, Vincent Vionnet, Paul J. Kushner, and Ross Brown
The Cryosphere, 12, 1157–1176, https://doi.org/10.5194/tc-12-1157-2018, https://doi.org/10.5194/tc-12-1157-2018, 2018
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This paper presents changes in both snow and sea ice that have occurred over Canada during the recent past and shows climate model estimates for future changes expected to occur by the year 2050. The historical changes of snow and sea ice are generally coherent and consistent with the regional history of temperature and precipitation changes. It is expected that snow and sea ice will continue to decrease in the future, declining by an additional 15–30 % from present day values by the year 2050.
Julienne C. Stroeve, John R. Mioduszewski, Asa Rennermalm, Linette N. Boisvert, Marco Tedesco, and David Robinson
The Cryosphere, 11, 2363–2381, https://doi.org/10.5194/tc-11-2363-2017, https://doi.org/10.5194/tc-11-2363-2017, 2017
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As the sea ice has declined strongly in recent years there has been a corresponding increase in Greenland melting. While both are likely a result of changes in atmospheric circulation patterns that favor summer melt, this study evaluates whether or not sea ice reductions around the Greenland ice sheet are having an influence on Greenland summer melt through enhanced sensible and latent heat transport from open water areas onto the ice sheet.
Kimberly A. Casey, Chris M. Polashenski, Justin Chen, and Marco Tedesco
The Cryosphere, 11, 1781–1795, https://doi.org/10.5194/tc-11-1781-2017, https://doi.org/10.5194/tc-11-1781-2017, 2017
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We analyzed Greenland Ice Sheet (GrIS) average summer surface reflectance and albedo (2001–2016). MODIS Collection 6 data show a decreased magnitude of change over time due to sensor calibration corrections. Spectral band maps provide insight into GrIS surface processes likely occurring. Correctly measuring albedo and surface reflectance changes over time is crucial to monitoring atmosphere–ice interactions and ice mass balance. The results are applicable to many long-term MODIS studies.
Peter Toose, Alexandre Roy, Frederick Solheim, Chris Derksen, Tom Watts, Alain Royer, and Anne Walker
Geosci. Instrum. Method. Data Syst., 6, 39–51, https://doi.org/10.5194/gi-6-39-2017, https://doi.org/10.5194/gi-6-39-2017, 2017
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Radio-frequency interference (RFI) can significantly contaminate the measured radiometric signal of current spaceborne L-band passive microwave radiometers used for monitoring essential climate variables. A 385-channel hyperspectral L-band radiometer system was designed with the means to quantify the strength and type of RFI. The compact design makes it ideal for mounting on both surface and airborne platforms to be used for calibrating and validating measurement from spaceborne sensors.
Libo Wang, Peter Toose, Ross Brown, and Chris Derksen
The Cryosphere, 10, 2589–2602, https://doi.org/10.5194/tc-10-2589-2016, https://doi.org/10.5194/tc-10-2589-2016, 2016
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The conventional wisdom is that Arctic warming will result in an increase in the frequency of winter melt events. However, results in this study show little evidence of trends in winter melt frequency over 1988–2013 period. The frequency of winter melt events is strongly influenced by the selection of the start and end dates of winter period, and a fixed-window method for analyzing winter melt events is observed to generate false increasing trends from a shift in the timing of snow cover season.
Tom Watts, Nick Rutter, Peter Toose, Chris Derksen, Melody Sandells, and John Woodward
The Cryosphere, 10, 2069–2074, https://doi.org/10.5194/tc-10-2069-2016, https://doi.org/10.5194/tc-10-2069-2016, 2016
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Ice layers in snowpacks introduce uncertainty in satellite-derived estimates of snow water equivalent, have ecological impacts on plants and animals, and change the thermal and vapour transport properties of the snowpack. Here we present a new field method for measuring the density of ice layers. The method was used in the Arctic and mid-latitudes; the mean measured ice layer density was significantly higher than values typically used in the literature.
Bart van den Hurk, Hyungjun Kim, Gerhard Krinner, Sonia I. Seneviratne, Chris Derksen, Taikan Oki, Hervé Douville, Jeanne Colin, Agnès Ducharne, Frederique Cheruy, Nicholas Viovy, Michael J. Puma, Yoshihide Wada, Weiping Li, Binghao Jia, Andrea Alessandri, Dave M. Lawrence, Graham P. Weedon, Richard Ellis, Stefan Hagemann, Jiafu Mao, Mark G. Flanner, Matteo Zampieri, Stefano Materia, Rachel M. Law, and Justin Sheffield
Geosci. Model Dev., 9, 2809–2832, https://doi.org/10.5194/gmd-9-2809-2016, https://doi.org/10.5194/gmd-9-2809-2016, 2016
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This manuscript describes the setup of the CMIP6 project Land Surface, Snow and Soil Moisture Model Intercomparison Project (LS3MIP).
Lora S. Koenig, Alvaro Ivanoff, Patrick M. Alexander, Joseph A. MacGregor, Xavier Fettweis, Ben Panzer, John D. Paden, Richard R. Forster, Indrani Das, Joesph R. McConnell, Marco Tedesco, Carl Leuschen, and Prasad Gogineni
The Cryosphere, 10, 1739–1752, https://doi.org/10.5194/tc-10-1739-2016, https://doi.org/10.5194/tc-10-1739-2016, 2016
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Contemporary climate warming over the Arctic is accelerating mass loss from the Greenland Ice Sheet through increasing surface melt, emphasizing the need to closely monitor surface mass balance in order to improve sea-level rise predictions. Here, we quantify the net annual accumulation over the Greenland Ice Sheet, which comprises the largest component of surface mass balance, at a higher spatial resolution than currently available using high-resolution, airborne-radar data.
Henna-Reetta Hannula, Juha Lemmetyinen, Anna Kontu, Chris Derksen, and Jouni Pulliainen
Geosci. Instrum. Method. Data Syst., 5, 347–363, https://doi.org/10.5194/gi-5-347-2016, https://doi.org/10.5194/gi-5-347-2016, 2016
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The paper described an extensive in situ data set of bulk snow depth, snow water equivalent, and snow density collected as a support of SnowSAR-2 airborne campaign in northern Finland. The spatial and temporal variability of these snow properties was analyzed in different land cover types. The success of the chosen measurement protocol to provide an accurate reference for the simultaneous SAR data products was analyzed in the context of spatial scale, sample size, and uncertainty.
Stephen E. L. Howell, Frédéric Laliberté, Ron Kwok, Chris Derksen, and Joshua King
The Cryosphere, 10, 1463–1475, https://doi.org/10.5194/tc-10-1463-2016, https://doi.org/10.5194/tc-10-1463-2016, 2016
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The Canadian Ice Service record of observed landfast ice and snow thickness represents one of the longest in the Arctic that spans over 5 decades. We analyze this record to report on long-term trends and variability of ice and snow thickness within the Canadian Arctic Archipelago (CAA). Results indicate a thinning of ice at several sites in the CAA. State-of-the-art climate models still have difficultly capturing observed ice thickness values in the CAA and should be used with caution.
Patrick M. Alexander, Marco Tedesco, Nicole-Jeanne Schlegel, Scott B. Luthcke, Xavier Fettweis, and Eric Larour
The Cryosphere, 10, 1259–1277, https://doi.org/10.5194/tc-10-1259-2016, https://doi.org/10.5194/tc-10-1259-2016, 2016
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We compared satellite-derived estimates of spatial and seasonal variations in Greenland Ice Sheet mass with a set of model simulations, revealing an agreement between models and satellite estimates for the ice-sheet-wide seasonal fluctuations in mass, but disagreement at finer spatial scales. The model simulations underestimate low-elevation mass loss. Improving the ability of models to capture variations and trends in Greenland Ice Sheet mass is important for estimating future sea level rise.
Marco Tedesco, Sarah Doherty, Xavier Fettweis, Patrick Alexander, Jeyavinoth Jeyaratnam, and Julienne Stroeve
The Cryosphere, 10, 477–496, https://doi.org/10.5194/tc-10-477-2016, https://doi.org/10.5194/tc-10-477-2016, 2016
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Summer surface albedo over Greenland decreased at a rate of 0.02 per decade between 1996 and 2012. The decrease is due to snow grain growth, the expansion of bare ice areas, and trends in light-absorbing impurities on snow and ice surfaces. Neither aerosol models nor in situ observations indicate increasing trends in impurities in the atmosphere over Greenland. Albedo projections through to the end of the century under different warming scenarios consistently point to continued darkening.
M. Navari, S. A. Margulis, S. M. Bateni, M. Tedesco, P. Alexander, and X. Fettweis
The Cryosphere, 10, 103–120, https://doi.org/10.5194/tc-10-103-2016, https://doi.org/10.5194/tc-10-103-2016, 2016
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An ensemble batch smoother was used to assess the feasibility of generating a reanalysis estimate of the Greenland ice sheet (GrIS) surface mass fluxes (SMF) via integrating measured ice surface temperatures with a regional climate model estimate. The results showed that assimilation of IST were able to overcome uncertainties in meteorological forcings that drive the GrIS surface processes. We showed that the proposed methodology is able to generate posterior reanalysis estimates of the SMF.
E. Malnes, A. Buanes, T. Nagler, G. Bippus, D. Gustafsson, C. Schiller, S. Metsämäki, J. Pulliainen, K. Luojus, H. E. Larsen, R. Solberg, A. Diamandi, and A. Wiesmann
The Cryosphere, 9, 1191–1202, https://doi.org/10.5194/tc-9-1191-2015, https://doi.org/10.5194/tc-9-1191-2015, 2015
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The paper provides detailed information on the outcome of a user survey carried out in the EU FP7 project CryoLand. The project focuses on monitoring of seasonal snow, glaciers and lake/river ice. The user survey showed that a European operational snow and land ice service is required and that there exists products that can meet the specific needs. The majority of the users were mainly interested in the snow services, but also the lake/river ice products and the glacier products were desired.
P. Räisänen, A. Luomaranta, H. Järvinen, M. Takala, K. Jylhä, O. N. Bulygina, K. Luojus, A. Riihelä, A. Laaksonen, J. Koskinen, and J. Pulliainen
Geosci. Model Dev., 7, 3037–3057, https://doi.org/10.5194/gmd-7-3037-2014, https://doi.org/10.5194/gmd-7-3037-2014, 2014
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Snowmelt influences greatly the climatic conditions in spring. This study evaluates the timing of springtime end of snowmelt in the ECHAM5 model. A key finding is that, in much of northern Eurasia, snow disappears too early in ECHAM5, in spite of a slight cold bias in spring. This points to the need for a more comprehensive treatment of the surface energy budget. In particular, the surface temperature for the snow-covered and snow-free parts of a climate model grid cell should be separated.
C. J. Legleiter, M. Tedesco, L. C. Smith, A. E. Behar, and B. T. Overstreet
The Cryosphere, 8, 215–228, https://doi.org/10.5194/tc-8-215-2014, https://doi.org/10.5194/tc-8-215-2014, 2014
K. A. Luus, Y. Gel, J. C. Lin, R. E. J. Kelly, and C. R. Duguay
Biogeosciences, 10, 7575–7597, https://doi.org/10.5194/bg-10-7575-2013, https://doi.org/10.5194/bg-10-7575-2013, 2013
S. E. L. Howell, T. Wohlleben, A. Komarov, L. Pizzolato, and C. Derksen
The Cryosphere, 7, 1753–1768, https://doi.org/10.5194/tc-7-1753-2013, https://doi.org/10.5194/tc-7-1753-2013, 2013
M. Tedesco, X. Fettweis, T. Mote, J. Wahr, P. Alexander, J. E. Box, and B. Wouters
The Cryosphere, 7, 615–630, https://doi.org/10.5194/tc-7-615-2013, https://doi.org/10.5194/tc-7-615-2013, 2013
Related subject area
Discipline: Snow | Subject: Seasonal Snow
Snow water equivalent retrieval over Idaho – Part 2: Using L-band UAVSAR repeat-pass interferometry
Spatiotemporal snow water storage uncertainty in the midlatitude American Cordillera
Evaluation of snow cover properties in ERA5 and ERA5-Land with several satellite-based datasets in the Northern Hemisphere in spring 1982–2018
Snow depth in high-resolution regional climate model simulations over southern Germany – suitable for extremes and impact-related research?
Multi-decadal analysis of past winter temperature, precipitation and snow cover data in the European Alps from reanalyses, climate models and observational datasets
Spatially continuous snow depth mapping by aeroplane photogrammetry for annual peak of winter from 2017 to 2021 in open areas
Change in the potential snowfall phenology: past, present, and future in the Chinese Tianshan mountainous region, Central Asia
The benefits of homogenising snow depth series – Impacts on decadal trends and extremes for Switzerland
Assessing the seasonal evolution of snow depth spatial variability and scaling in complex mountain terrain
Impact of measured and simulated tundra snowpack properties on heat transfer
Homogeneity assessment of Swiss snow depth series: comparison of break detection capabilities of (semi-)automatic homogenization methods
Propagating information from snow observations with CrocO ensemble data assimilation system: a 10-years case study over a snow depth observation network
Evaluation of Northern Hemisphere snow water equivalent in CMIP6 models during 1982–2014
Multilayer observation and estimation of the snowpack cold content in a humid boreal coniferous forest of eastern Canada
Spatiotemporal distribution of seasonal snow water equivalent in High Mountain Asia from an 18-year Landsat–MODIS era snow reanalysis dataset
Local-scale variability of seasonal mean and extreme values of in situ snow depth and snowfall measurements
Observed snow depth trends in the European Alps: 1971 to 2019
Snow Ensemble Uncertainty Project (SEUP): quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling
Quantification of the radiative impact of light-absorbing particles during two contrasted snow seasons at Col du Lautaret (2058 m a.s.l., French Alps)
Snow depth estimation and historical data reconstruction over China based on a random forest machine learning approach
Towards a webcam-based snow cover monitoring network: methodology and evaluation
Simulated single-layer forest canopies delay Northern Hemisphere snowmelt
Converting snow depth to snow water equivalent using climatological variables
Avalanches and micrometeorology driving mass and energy balance of the lowest perennial ice field of the Alps: a case study
The optical characteristics and sources of chromophoric dissolved organic matter (CDOM) in seasonal snow of northwestern China
Brief Communication: Early season snowpack loss and implications for oversnow vehicle recreation travel planning
Multi-component ensembles of future meteorological and natural snow conditions for 1500 m altitude in the Chartreuse mountain range, Northern French Alps
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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We used changes in radar echo travel time from multiple airborne flights to estimate changes in snow depths across Idaho for two winters. We compared our radar-derived retrievals to snow pits, weather stations, and a 100 m resolution numerical snow model. We had a strong Pearson correlation and root mean squared error of 10 cm relative to in situ measurements. Our retrievals also correlated well with our model, especially in regions of dry snow and low tree coverage.
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.
Benjamin Poschlod and Anne Sophie Daloz
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-137, https://doi.org/10.5194/tc-2023-137, 2023
Revised manuscript accepted for TC
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Information about snow depth, snow accumulation, and snow melt are important within climate research but also for many different sectors, such as tourism, mobility, civil engineering, and ecology. Climate models often feature a spatial resolution, which is too coarse to investigate snow depth. Here, we analyse high-resolution simulations and identify added value compared to a state-of-the-art product. Still, daily extremes are represented with limitations and need to be carefully evaluated.
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.
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
Andreadis, K. and Lettenmaier, D.: Implications of representing snowpack
stratigraphy for the assimilation of passive microwave satellite
observations, J. Hydrometeorol., 13, 1493–1506,
https://doi.org/10.1175/JHM-D-11-056.1, 2012.
Balsamo, G., Albergel, C., Beljaars, A., Boussetta, S., Brun, E., Cloke, H., Dee, D., Dutra, E., Muñoz-Sabater, J., Pappenberger, F., de Rosnay, P., Stockdale, T., and Vitart, F.: ERA-Interim/Land: a global land surface reanalysis data set, Hydrol. Earth Syst. Sci., 19, 389–407, https://doi.org/10.5194/hess-19-389-2015, 2015.
Barnett, T. P., Adam, J. C., and Lettenmaier, D. P.: Potential impacts
of a warming climate on water availability in snow-dominated regions,
Nature, 438, 303–309, https://doi.org/10.1038/nature04141, 2005.
Brodzik, M. J., Billingsley, B., Haran, T., Raup, B., and Savoie, M. H.:
EASE-Grid 2.0: Incremental but significant improvements for Earth-gridded
data sets, ISPRS Int. J. Geo-Info., 1, 32–45,
https://doi.org/10.3390/ijgi1010032, 2012.
Brown, R. and Braaten, R.: Spatial and temporal variability of Canadian
monthly snow depths, 1946–1995, Atmos.-Ocean, 36, 37–54,
https://doi.org/10.1080/07055900.1998.9649605, 1998.
Brown, R. and Derksen, C.: Is Eurasian October snow cover extent
increasing?, Environ. Res. Lett., 8, 024006,
https://doi.org/10.1088/1748-9326/8/2/024006, 2013.
Brown, R. and Mote, P.: The response of Northern Hemisphere snow cover to a
changing climate, J. Climate, 22, 2124–2145,
https://doi.org/10.1175/2008JCLI2665.1, 2009.
Brown, R., Brasnett, B., and Robinson, D.: Gridded North American monthly
snow depth and snow water equivalent for GCM evaluation, Atmos.-Ocean, 41,
1–14, https://doi.org/10.3137/ao.410101, 2003.
Brown, R., Derksen, C., and Wang, L.: A multi-dataset analysis of
variability and change in Arctic spring snow cover extent, 1967–2008, J.
Geophys. Res., 115, D16111, https://doi.org/10.1029/2010JD013975, 2010.
Brown, R., Tapsoba, D., and Derksen, C.: Evaluation of snow water equivalent
datasets over the Saint-Maurice river basin region of southern Québec,
Hydrol. Process., 32, 2748–2764, https://doi.org/10.1002/hyp.13221, 2018.
Brown, R. D., Fang, B., and Mudryk, L.: Update of Canadian historical snow
survey data and analysis of snow water equivalent trends, 1967–2016,
Atmos.-Ocean, 57, 1–8, https://doi.org/10.1080/07055900.2019.1598843, 2019.
Broxton, P. D., Dawson, N., and Zeng, X.: Linking snowfall and snow
accumulation to generate spatial maps of SWE and snow depth, Earth Space
Sci., 3, 246–256, https://doi.org/10.1002/2016EA000174, 2016.
Brun, E., Vionnet, V., Boone, A., Decharme, B., Peings, Y., Vallette, R.,
Karbou, F., and Morin, S.: Simulation of northern Eurasian local snow depth,
mass, and density using a detailed snowpack model and meteorological
reanalyses, J. Hydrometeorol., 14, 203–219,
https://doi.org/10.1175/JHM-D-12-012.1, 2013.
Bulygina, O., Groisman, P. Ya., Razuvaev, V., and Korshunova, N.: Changes in
snow cover characteristics over Northern Eurasia since 1966, Environ. Res.
Lett., 6, 045204, https://doi.org/10.1088/1748-9326/6/4/045204, 2011.
Chang, A., Foster, J., and Hall, D.: Satellite sensor estimates of northern
hemisphere snow volume, Int. J. Remote Sens., 11, 167–171,
https://doi.org/10.1080/01431169008955009, 1990.
Clark, M. P., Hendrix, J., Slater, A. G., Kavetski, D., Anderson, B.,
Cullen, N. J., Kerr, T., Hreinsson, E. O., and Woods, R. A.: Representing
spatial variability of snow water equivalent in hydrologic and land-surface
models: a review, Water Resour. Res., 47, W07539,
https://doi.org/10.1029/2011WR010745, 2011.
Clifford, D.: Global estimates of snow water equivalent from passive
microwave instruments: history, challenges and future developments, Int. J.
Remote Sens., 31, 3707–3726, https://doi.org/10.1080/01431161.2010.483482,
2010.
Copernicus Climate Change Service (C3S): ERA5: Fifth generation of ECMWF
atmospheric reanalyses of the global climate, Copernicus Climate Change
Service Climate Data Store (CDS), 2019-02-19,
available at: https://cds.climate.copernicus.eu/cdsapp#!/home (last access: 20 January 2020), 2017.
Deeb, E., Forster, R., and Kane, D.: Monitoring snowpack evolution using
interferometric synthetic aperture radar on the North Slope of Alaska, USA,
Int. J. Remote Sens., 32, 3985–4003,
https://doi.org/10.1080/01431161003801351, 2011.
Derksen, C. and Nagler, T.: ESA CCI+ Snow ECV: User Requirements Document,
version 1.0, January 2019.
Derksen, C., Brown, R., and Walker, A.: Merging conventional (1915–92) and
passive microwave (1978–2002) estimates of snow extent and water equivalent
over central North America, J. Hydrometeorol., 5, 850–861,
https://doi.org/10.1175/1525-7541(2004)005<0850:MCAPME>2.0.CO;2, 2004.
Derksen, C., Lemmetyinen, J., Toose, P., Silis, A., Pulliainen, J., and
Sturm, M.: Physical properties of Arctic versus subarctic snow: Implications
for high latitude passive microwave snow water equivalent retrievals, J.
Geophys. Res.-Atmos., 119, 7254–7270,
https://doi.org/10.1002/2013JD021264, 2014.
Durand, M. and Liu, D.: The need for prior information in characterizing
snow water equivalent from microwave brightness temperatures, Remote Sens.
Environ., 126, 248–257, https://doi.org/10.1016/j.rse.2011.10.015, 2012.
Durand, M., Kim, E., Margulis, S., and Molotch, N.: A first-order
characterization of errors from neglecting stratigraphy in forward and
inverse passive microwave modeling of snow, IEEE Geosci. Remote S. Lett., 8,
730–734, https://doi.org/10.1109/LGRS.2011.2105243, 2011.
Dyer, J. and Mote, T.: Spatial variability and trends in observed snow
depth over North America, Geophys. Res. Lett., 33, L16503,
https://doi.org/10.1029/2006GL027258, 2006.
Forman, B. A. and Reichle, R. H.: Using a support vector machine and a land
surface model to estimate large-scale passive microwave brightness
temperatures over snow-covered land in North America, IEEE J.
Select. Top. Appl. Earth Observ. Remote Sens., 8,
4431–4441, https://doi.org/10.1109/JSTARS.2014.2325780, 2014.
Foster, J. L., Sun, C., Walker, J. P., Kelly, R., Chang, A., Dong, J., and
Powell, H.: Quantifying the uncertainty in passive microwave snow water
equivalent observations, Remote Sens. Environ., 94, 187–203,
https://doi.org/10.1016/j.rse.2004.09.012, 2005.
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.,
da Silva, A. M., Gu, W., Kim, G., 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.
GMAO (Global Modeling and Assimilation Office): tavg1_2d_lnd_Nx: MERRA 2D IAU Diagnostic, Land Only
States and Diagnostics, Time Average 1-hourly V5.2.0, Greenbelt, MD, USA,
Goddard Earth Sciences Data and Information Services Center (GES DISC),
https://doi.org/10.5067/YL8Z7MICQZF9, 2008.
GMAO (Global Modeling and Assimilation Office): 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),
https://doi.org/10.5067/RKPHT8KC1Y1T, 2015.
Goodison, B. E., Ferguson, H. L., and McKay, G. A.: Measurement and data
analysis, in: Handbook of Snow, edited by: Gray, D. M. and Male, D. H.,
191–274, Reprint, Caldwell, NJ, USA, The Blackburn Press, 1981.
Haberkorn, A. (Ed.): European Snow Booklet, 363 pp.,
https://doi.org/10.16904/envidat.59, 2019.
Hall, D.: Influence of depth hoar on microwave emission from snow in
northern Alaska, Cold Reg. Sci. Technol., 13, 225–231,
https://doi.org/10.1016/0165-232X(87)90003-6, 1987.
Hall, D., Sturm, M., Benson, C., Chang, A., Foster, J., Garbeil, H., and
Chacho, E.: Passive microwave remote and in situ measurements of Arctic and
Subarctic snow covers in Alaska, Remote Sens. Environ., 38, 161–172,
https://doi.org/10.1016/0034-4257(91)90086-L, 1991.
Henn, B., Newman, A., Livneh, B., Daly, C., and Lundquist, J.: An assessment
of differences in gridded precipitation datasets in complex terrain, J.
Hydrol., 556, 1205–1219, https://doi.org/10.1016/j.jhydrol.2017.03.008,
2018.
Hersbach, H., Bell, W., Berrisford, P., Horányi, A., Muñoz-Sabater,
J., Nicolas, J., Radu, R., Schepers, D., Simmons, A., Soci, C., and Dee, D.:
Global reanalysis: goodbye ERA-Interim, hello ERA5, ECMWF Newsletter, 159,
17–24, https://doi.org/10.21957/vf291hehd7, 2019.
Kelly, R. E. J.: The AMSR-E Snow Depth Algorithm: Description and Initial
Results, J. Remote Sens. Soc. JPN, 29, 307–317,
https://doi.org/10.11440/rssj.29.307, 2009.
Kelly, R. E., Change, A. T., Tsang, L., and Foster, J. L.: A prototype
AMSR-E global snow area and snow depth algorithm, IEEE Trans. Geoci. Remote
S., 41, 230–242, https://doi.org/10.1109/TGRS.2003.809118, 2003.
Krenke, A.: Edited by National Snow and Ice Data Center, Former Soviet Union
Hydrological Snow Surveys, 1966–1996, Version 1. Boulder, Colorado USA,
NSIDC: National Snow and Ice Data Center, https://10.7265/N58C9T60, 1998
(updated 2004).
Krinner, G., Derksen, C., Essery, R., Flanner, M., Hagemann, S., Clark, M., Hall, A., Rott, H., Brutel-Vuilmet, C., Kim, H., Ménard, C. B., Mudryk, L., Thackeray, C., Wang, L., Arduini, G., Balsamo, G., Bartlett, P., Boike, J., Boone, A., Chéruy, F., Colin, J., Cuntz, M., Dai, Y., Decharme, B., Derry, J., Ducharne, A., Dutra, E., Fang, X., Fierz, C., Ghattas, J., Gusev, Y., Haverd, V., Kontu, A., Lafaysse, M., Law, R., Lawrence, D., Li, W., Marke, T., Marks, D., Ménégoz, M., Nasonova, O., Nitta, T., Niwano, M., Pomeroy, J., Raleigh, M. S., Schaedler, G., Semenov, V., Smirnova, T. G., Stacke, T., Strasser, U., Svenson, S., Turkov, D., Wang, T., Wever, N., Yuan, H., Zhou, W., and Zhu, D.: ESM-SnowMIP: assessing snow models and quantifying snow-related climate feedbacks, Geosci. Model Dev., 11, 5027–5049, https://doi.org/10.5194/gmd-11-5027-2018, 2018.
Larue, F., Royer, A., De Sève, D., Langlois, A., Roy, A., and Brucker,
L.: Validation of GlobSnow-2 snow water equivalent over Eastern Canada,
Remote Sens. Environ., 194, 264–277,
https://doi.org/10.1016/j.rse.2017.03.027, 2017.
Lemmetyinen, J., Kontu, A., Kärnä, J.-P., Vehviläinen, J.,
Takala, M., and Pulliainen, J.: Correcting for the influence of frozen lakes
in satellite microwave radiometer observations through application of a
microwave emission model, Remote Sens. Environ., 115, 3695–3706,
https://doi.org/10.1016/j.rse.2011.09.008, 2011.
Li, D., Durand, M., and Margulis, S.: Potential for hydrologic
characterization of deep mountain snowpack via passive microwave remote
sensing in the Kern River basin, Sierra Nevada, USA, Remote Sens. Environ.,
125, 34–48, https://doi.org/10.1016/j.rse.2012.06.027, 2012.
Liston, G. and Hiemstra, C.: The changing cryosphere: pan-Arctic snow
trends (1979–2009), J. Climate, 24, 5691–5712,
https://doi.org/10.1175/JCLI-D-11-00081.1, 2011.
Livens, H., Demuzere, M., Marshall, H. P., Reichle, R. H., Brucker, L., Brangers, I., de Rosanry, P., Dumont, M., Girotto, M., Immerzeel, W. W., Jonas, T., Kim, E. J., Marty, C., Saloranta, T., Schöber, J., and De Lannoy, G. J. M.: Snow depth variability in the Northern Hemisphere mountains observed from space, Nat. Commun., 10, 1–2, https://doi.org/10.1038/s41467-019-12566-y, 2019.
Long, D. and Brodzik, M.-J.: Optimum image formation for spaceborne
microwave radiometer products, IEEE Geosci. Remote Sens., 54, 2763–2779,
https://doi.org/10.1109/TGRS.2015.2505677, 2016.
Lundquist, J. D., Hughes, M., Henn, B., Gutmann, E. D., Livneh, B., Dozier,
J., and Neiman, P.: High-elevation precipitation patterns: using snow
measurements to assess daily gridded datasets across the Sierra Nevada,
California, J. Hydrometeorol., 16, 1773–1792,
https://doi.org/10.1175/JHM-D-15-0019.1, 2015.
Lundquist, J., Hughes, M., Gutmann, E., and Kapnick, S.: Our skill in modeling mountain rain and snow is bypassing the skill of our observational networks, BAMS, December 2019, 2473–2490, https://doi.org/10.1175/BAMS-D-19-0001.1, 2019.
Markus, T., Powell, D., and Wang, J.: Sensitivity of passive microwave snow
depth retrievals to weather effects and snow evolution, IEEE Geosci. Remote
S., 44, 68–77, https://doi.org/10.1109/TGRS.2005.860208, 2006.
Meromy, L., Molotch, N. P., Link, T. E., Fassnacht, S. R., and Rice, R.:
Subgrid variability of snow water equivalent at operational snow stations in
the western USA, Hydrol. Process., 27, 2383–2400,
https://doi.org/10.1002/hyp.9355, 2012.
Mudryk, L., Derksen, C., Kushner, P., 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.
Mudryk, L., Kushner, P., Derksen, C., and Thackeray, C.: Snow cover response
to temperature in observational and climate model ensembles, Geophys. Res.
Lett., 44, 919–926, https://doi.org/10.1002/2016GL071789, 2017.
Mudryk, L. R., Derksen, C., Howell, S., Laliberté, F., Thackeray, C., Sospedra-Alfonso, R., Vionnet, V., Kushner, P. J., and Brown, R.: Canadian snow and sea ice: historical trends and projections, The Cryosphere, 12, 1157–1176, https://doi.org/10.5194/tc-12-1157-2018, 2018a.
Mudryk, L., Brown, R., Derksen, C., Luojus, K., Decharme, B., and Helfrich,
S.: Terrestrial Snow Cover [in Arctic Report Card],
available at: https://www.arctic.noaa.gov/Report-Card, last access: 28 November 2018b.
Mudryk, L., Brown, R., Derksen, C., Luojus, K., and Dechame, B.: Terrestrial
Snow Cover, in: “State of the Climate 2018”, Am. Meteorol. Soc., 100,
S181–S185, https://doi.org/10.1175/2019BAMSStateoftheClimate.1, 2019.
Neumann, N., Smith, C., Derksen, C., and Goodison, B.: Characterizing local
scale snow cover using point measurements during the winter season,
Atmos.-Ocean, 44, 257–269, https://doi.org/10.3137/ao.440304, 2006.
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., Berisford, D., Boardman, J., Bormann, K., Deems, J., Gehrke,
F., Hedrick, A., Joyce, M., Laidlaw, R., Marks, D., Mattmann, C., McGurk,
B., Ramirez, P., Richardson, M., Skiles, S. M., Seidel, F., and Winstral,
A.: The Airborne Snow Observatory: Fusion of scanning lidar, imaging
spectrometer, and physically-based modeling for mapping snow water
equivalent and snow albedo, Remote Sens. Environ., 184, 139–152,
https://doi.org/10.1016/j.rse.2016.06.018, 2016.
Pulliainen, J.: Mapping of snow water equivalent and snow depth in boreal
and sub-arctic zones by assimilating space-borne microwave radiometer data
and ground-based observations, Remote Sens. Environ., 101, 257–269,
https://doi.org/10.1016/j.rse.2006.01.002, 2006.
Rawlins, M. A., Fahnestock, M., Frolking, S., and Vörösmarty, C. J.: On
the evaluation of snow water equivalent estimates over the terrestrial
Arctic drainage basin, Hydrol. Process., 21, 1616–1623,
https://doi.org/10.1002/hyp.6724, 2007.
Rienecker, M. M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeister, J.,
Liu, E., Bosilovich, M. G., Schubert, S. D., Takacs, L., Kim, G., Bloom, S.,
Chen, J., Collins, D., Conaty, A., da Silva, A., Gu, W., Joiner, J., Koster,
R. D., Lucchesi, R., Molod, A., Owens, T., Pawson, S., Pegion, P., Redder,
C. R., Reichle, R., Robertson, F. R., Ruddick, A. G., Sienkiewicz, M., and
Woollen, J.: MERRA: NASA's Modern-Era Retrospective Analysis for Research
and Applications, J. Climate, 24, 3624–3648,
https://doi.org/10.1175/JCLI-D-11-00015.1, 2011.
Robertson, F. R., Bosilovich, M. G., Chen, J., and Miller, T. L.: The effect
of satellite observing system changes on MERRA water and energy fluxes, J.
Climate, 24, 5197–5217, https://doi.org/10.1175/2011JCLI4227.1, 2011.
Rodell, M., Houser, P. R., Jambor, U. E. A., 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.
Rott, H., Yueh, S. H., Cline, D. W., Duguay, C., Essery, R., Haas, C.,
Hélière, F., Kern, M. G., Malnes, E., Nagler, T., Pulliainen, J.,
Rebhan, H., and Thompson, A.: Cold regions hydrology high-resolution
observatory for Snow and Cold Land Processes, Proc. IEEE, 98, 752–765,
https://doi.org/10.1109/JPROC.2009.2038947, 2010.
Sospedra-Alfonso, R., Mudryk, L., Merryfield, W., and Derksen, C.:
Representation of snow in the Canadian seasonal to interannual prediction
system. Part I: Initialization, J. Hydrometerol., 17, 1467–1488,
https://doi.org/10.1175/JHM-D-14-0223.1, 2016.
Sturm, M., Holmgren, J., and Liston, G.: A seasonal snow cover
classification system for local to global applications, J. Climate, 8,
1261–1283, https://doi.org/10.1175/1520-0442(1995)008<1261:ASSCCS>2.0.CO;2, 1995.
Sturm, M., Holmgren, J., and Liston, G.: Global Seasonal Snow Classification
System. Version 1.0. UCAR/NCAR – Earth Observing Laboratory,
https://doi.org/10.5065/D69G5JX5, 2009.
Sturm, M., Taras, B., Liston, G., Derksen, C., Jonas, T., and Lea, J.:
Estimating snow water equivalent using snow depth data and climate classes,
J. Hydrometeorol., 11, 1380–1394, https://doi.org/10.1175/2010JHM1202.1,
2010.
Takala, M., Luojus, K., Pulliainen, J., Derksen, C., Lemmetyinen, J.,
Kärnä, J.-P., and Koskinen, J.: Estimating northern hemisphere snow
water equivalent for climate research through assimilation of space-borne
radiometer data and ground-based measurements, Remote Sens. Environ., 115,
3517–3529, https://doi.org/10.1016/j.rse.2011.08.014, 2011.
Takala, M., Ikonen, J., Luojus, K., Lemmetyinen, J., Metsämäki, S.,
Cohen, J., Arslan, A. N., and Pulliainen, J.: New snow water equivalent
processing system with improved resolution over Europe and its applications
in hydrology, IEEE J. Select. Top. Appl. Remote Sens., 10,
428–436, https://doi.org/10.1109/JSTARS.2016.2586179, 2017.
Tedesco, M. and Jeyaratnam, J.: A new operational snow retrieval algorithm
applied to historical AMSR-E brightness temperatures, Remote Sens., 8,
1–25, https://doi.org/10.3390/rs8121037, 2016.
Tedesco, M. and Narvekar, P.: Assessment of the NASA AMSR-E SWE product,
IEEE J. Select. Top. Appl. Earth Observ. Remote
Sens., 3, 141–159, https://doi.org/10.1109/JSTARS.2010.2040462, 2010.
Tedesco, M., Kelly, R., Foster, J. L., and Change, A. T.: AMSR-E/Aqua Daily
L3 Global Snow Water Equivalent EASE-Grids, Version 2. Boulder, Colorado
USA, NASA Snow and Ice Data Center Distributed Active Archive Center,
https://doi.org/10.5067/AMSR-E/AE_DYSNO.002, 2004.
Vuyovich, C. M., Jacobs, J. M., and Daly, S. F.: Comparison of passive
microwave and modeled estimates of total watershed SWE in the continental
United States, Water Resour. Res., 50, 9088–9102,
https://doi.org/10.1002/2013WR014734, 2014.
Wrzesien, M. L., Durand, M. T., Pavelsky, T. M., Kapnick, S. B., Zhang, Y.,
Guo, J., and Shum, C. K..: A new estimate of North American mountain snow
accumulation from regional climate model simulations, Geophys. Res. Lett.,
45, 1423–1432, https://doi.org/10.1002/2017GL076664, 2018.
Wrzesien, M. L., Pavelsky, T. M., Durand, M. T., Dozier, J., and Lundquist,
J. D.: Characterizing biases in mountain snow accumulation from global data
sets, Water Res. Res., 55, 9873–9891, https://doi.org/10.1029/2019WR025350,
2019.
Short summary
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.
Existing stand-alone passive microwave SWE products have markedly different climatological SWE...
