Articles | Volume 13, issue 6
https://doi.org/10.5194/tc-13-1753-2019
© Author(s) 2019. 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-13-1753-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Monitoring of snow surface near-infrared bidirectional reflectance factors with added light-absorbing particles
Department of Climate and Space Sciences and Engineering,
Climate & Space Research Building, University of Michigan, 2455 Hayward St., Ann Arbor, MI 48109-2143, USA
Mark Flanner
Department of Climate and Space Sciences and Engineering,
Climate & Space Research Building, University of Michigan, 2455 Hayward St., Ann Arbor, MI 48109-2143, USA
Roger De Roo
Department of Climate and Space Sciences and Engineering,
Climate & Space Research Building, University of Michigan, 2455 Hayward St., Ann Arbor, MI 48109-2143, USA
Alden Adolph
Physics Department, St. Olaf College, 1520 St. Olaf Ave., Northfield, MN 55057, USA
Related authors
Zachary Fair, Mark Flanner, Adam Schneider, and S. McKenzie Skiles
The Cryosphere, 16, 3801–3814, https://doi.org/10.5194/tc-16-3801-2022, https://doi.org/10.5194/tc-16-3801-2022, 2022
Short summary
Short summary
Snow grain size is important to determine the age and structure of snow, but it is difficult to measure. Snow grain size can be found from airborne and spaceborne observations by measuring near-infrared energy reflected from snow. In this study, we use the SNICAR radiative transfer model and a Monte Carlo model to examine how snow grain size measurements change with snow structure and solar zenith angle. We show that improved understanding of these variables improves snow grain size precision.
Adam M. Schneider, Charles S. Zender, and Stephen F. Price
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-247, https://doi.org/10.5194/gmd-2020-247, 2020
Preprint withdrawn
Short summary
Short summary
We enhance the Energy Exascale Earth System Model's land
component (ELM) to better represent multi-year snow (firn) on ice sheets. Our
developments reveal ELM deficiencies regarding firn density, a fundamental
property in glaciology. To improve firn density profiles, we fine tune
ELM's snowpack parameters using statistical modeling. Our findings demonstrate
how ELM can simulate both seasonal snow and firn on ice sheets and advance a
broader effort to better predict sea level rise.
Benjamin Hmiel, Vasilii V. Petrenko, Christo Buizert, Andrew M. Smith, Michael N. Dyonisius, Philip Place, Bin Yang, Quan Hua, Ross Beaudette, Jeffrey P. Severinghaus, Christina Harth, Ray F. Weiss, Lindsey Davidge, Melisa Diaz, Matthew Pacicco, James A. Menking, Michael Kalk, Xavier Faïn, Alden Adolph, Isaac Vimont, and Lee T. Murray
The Cryosphere, 18, 3363–3382, https://doi.org/10.5194/tc-18-3363-2024, https://doi.org/10.5194/tc-18-3363-2024, 2024
Short summary
Short summary
The main aim of this research is to improve understanding of carbon-14 that is produced by cosmic rays in ice sheets. Measurements of carbon-14 in ice cores can provide a range of useful information (age of ice, past atmospheric chemistry, past cosmic ray intensity). Our results show that almost all (>99 %) of carbon-14 that is produced in the upper layer of ice sheets is rapidly lost to the atmosphere. Our results also provide better estimates of carbon-14 production rates in deeper ice.
Cynthia H. Whaley, Kathy S. Law, Jens Liengaard Hjorth, Henrik Skov, Stephen R. Arnold, Joakim Langner, Jakob Boyd Pernov, Garance Bergeron, Ilann Bourgeois, Jesper H. Christensen, Rong-You Chien, Makoto Deushi, Xinyi Dong, Peter Effertz, Gregory Faluvegi, Mark Flanner, Joshua S. Fu, Michael Gauss, Greg Huey, Ulas Im, Rigel Kivi, Louis Marelle, Tatsuo Onishi, Naga Oshima, Irina Petropavlovskikh, Jeff Peischl, David A. Plummer, Luca Pozzoli, Jean-Christophe Raut, Tom Ryerson, Ragnhild Skeie, Sverre Solberg, Manu A. Thomas, Chelsea Thompson, Kostas Tsigaridis, Svetlana Tsyro, Steven T. Turnock, Knut von Salzen, and David W. Tarasick
Atmos. Chem. Phys., 23, 637–661, https://doi.org/10.5194/acp-23-637-2023, https://doi.org/10.5194/acp-23-637-2023, 2023
Short summary
Short summary
This study summarizes recent research on ozone in the Arctic, a sensitive and rapidly warming region. We find that the seasonal cycles of near-surface atmospheric ozone are variable depending on whether they are near the coast, inland, or at high altitude. Several global model simulations were evaluated, and we found that because models lack some of the ozone chemistry that is important for the coastal Arctic locations, they do not accurately simulate ozone there.
Zachary Fair, Mark Flanner, Adam Schneider, and S. McKenzie Skiles
The Cryosphere, 16, 3801–3814, https://doi.org/10.5194/tc-16-3801-2022, https://doi.org/10.5194/tc-16-3801-2022, 2022
Short summary
Short summary
Snow grain size is important to determine the age and structure of snow, but it is difficult to measure. Snow grain size can be found from airborne and spaceborne observations by measuring near-infrared energy reflected from snow. In this study, we use the SNICAR radiative transfer model and a Monte Carlo model to examine how snow grain size measurements change with snow structure and solar zenith angle. We show that improved understanding of these variables improves snow grain size precision.
Cynthia H. Whaley, Rashed Mahmood, Knut von Salzen, Barbara Winter, Sabine Eckhardt, Stephen Arnold, Stephen Beagley, Silvia Becagli, Rong-You Chien, Jesper Christensen, Sujay Manish Damani, Xinyi Dong, Konstantinos Eleftheriadis, Nikolaos Evangeliou, Gregory Faluvegi, Mark Flanner, Joshua S. Fu, Michael Gauss, Fabio Giardi, Wanmin Gong, Jens Liengaard Hjorth, Lin Huang, Ulas Im, Yugo Kanaya, Srinath Krishnan, Zbigniew Klimont, Thomas Kühn, Joakim Langner, Kathy S. Law, Louis Marelle, Andreas Massling, Dirk Olivié, Tatsuo Onishi, Naga Oshima, Yiran Peng, David A. Plummer, Olga Popovicheva, Luca Pozzoli, Jean-Christophe Raut, Maria Sand, Laura N. Saunders, Julia Schmale, Sangeeta Sharma, Ragnhild Bieltvedt Skeie, Henrik Skov, Fumikazu Taketani, Manu A. Thomas, Rita Traversi, Kostas Tsigaridis, Svetlana Tsyro, Steven Turnock, Vito Vitale, Kaley A. Walker, Minqi Wang, Duncan Watson-Parris, and Tahya Weiss-Gibbons
Atmos. Chem. Phys., 22, 5775–5828, https://doi.org/10.5194/acp-22-5775-2022, https://doi.org/10.5194/acp-22-5775-2022, 2022
Short summary
Short summary
Air pollutants, like ozone and soot, play a role in both global warming and air quality. Atmospheric models are often used to provide information to policy makers about current and future conditions under different emissions scenarios. In order to have confidence in those simulations, in this study we compare simulated air pollution from 18 state-of-the-art atmospheric models to measured air pollution in order to assess how well the models perform.
Chloe A. Whicker, Mark G. Flanner, Cheng Dang, Charles S. Zender, Joseph M. Cook, and Alex S. Gardner
The Cryosphere, 16, 1197–1220, https://doi.org/10.5194/tc-16-1197-2022, https://doi.org/10.5194/tc-16-1197-2022, 2022
Short summary
Short summary
Snow and ice surfaces are important to the global climate. Current climate models use measurements to determine the reflectivity of ice. This model uses physical properties to determine the reflectivity of snow, ice, and darkly pigmented impurities that reside within the snow and ice. Therefore, the modeled reflectivity is more accurate for snow/ice columns under varying climate conditions. This model paves the way for improvements in the portrayal of snow and ice within global climate models.
Mark G. Flanner, Julian B. Arnheim, Joseph M. Cook, Cheng Dang, Cenlin He, Xianglei Huang, Deepak Singh, S. McKenzie Skiles, Chloe A. Whicker, and Charles S. Zender
Geosci. Model Dev., 14, 7673–7704, https://doi.org/10.5194/gmd-14-7673-2021, https://doi.org/10.5194/gmd-14-7673-2021, 2021
Short summary
Short summary
We present the technical formulation and evaluation of a publicly available code and web-based model to simulate the spectral albedo of snow. Our model accounts for numerous features of the snow state and ambient conditions, including the the presence of light-absorbing matter like black and brown carbon, mineral dust, volcanic ash, and snow algae. Carbon dioxide snow, found on Mars, is also represented. The model accurately reproduces spectral measurements of clean and contaminated snow.
Zachary Fair, Mark Flanner, Kelly M. Brunt, Helen Amanda Fricker, and Alex Gardner
The Cryosphere, 14, 4253–4263, https://doi.org/10.5194/tc-14-4253-2020, https://doi.org/10.5194/tc-14-4253-2020, 2020
Short summary
Short summary
Ice on glaciers and ice sheets may melt and pond on ice surfaces in summer months. Detection and observation of these meltwater ponds is important for understanding glaciers and ice sheets, and satellite imagery has been used in previous work. However, image-based methods struggle with deep water, so we used data from the Ice, Clouds, and land Elevation Satellite-2 (ICESat-2) and the Airborne Topographic Mapper (ATM) to demonstrate the potential for lidar depth monitoring.
Adam M. Schneider, Charles S. Zender, and Stephen F. Price
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-247, https://doi.org/10.5194/gmd-2020-247, 2020
Preprint withdrawn
Short summary
Short summary
We enhance the Energy Exascale Earth System Model's land
component (ELM) to better represent multi-year snow (firn) on ice sheets. Our
developments reveal ELM deficiencies regarding firn density, a fundamental
property in glaciology. To improve firn density profiles, we fine tune
ELM's snowpack parameters using statistical modeling. Our findings demonstrate
how ELM can simulate both seasonal snow and firn on ice sheets and advance a
broader effort to better predict sea level rise.
Joseph M. Cook, Andrew J. Tedstone, Christopher Williamson, Jenine McCutcheon, Andrew J. Hodson, Archana Dayal, McKenzie Skiles, Stefan Hofer, Robert Bryant, Owen McAree, Andrew McGonigle, Jonathan Ryan, Alexandre M. Anesio, Tristram D. L. Irvine-Fynn, Alun Hubbard, Edward Hanna, Mark Flanner, Sathish Mayanna, Liane G. Benning, Dirk van As, Marian Yallop, James B. McQuaid, Thomas Gribbin, and Martyn Tranter
The Cryosphere, 14, 309–330, https://doi.org/10.5194/tc-14-309-2020, https://doi.org/10.5194/tc-14-309-2020, 2020
Short summary
Short summary
Melting of the Greenland Ice Sheet (GrIS) is a major source of uncertainty for sea level rise projections. Ice-darkening due to the growth of algae has been recognized as a potential accelerator of melting. This paper measures and models the algae-driven ice melting and maps the algae over the ice sheet for the first time. We estimate that as much as 13 % total runoff from the south-western GrIS can be attributed to these algae, showing that they must be included in future mass balance models.
Cheng Dang, Charles S. Zender, and Mark G. Flanner
The Cryosphere, 13, 2325–2343, https://doi.org/10.5194/tc-13-2325-2019, https://doi.org/10.5194/tc-13-2325-2019, 2019
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
Short summary
Short summary
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.
Yang Li and Mark G. Flanner
Atmos. Chem. Phys., 18, 16005–16018, https://doi.org/10.5194/acp-18-16005-2018, https://doi.org/10.5194/acp-18-16005-2018, 2018
Short summary
Short summary
Light-absorbing impurities enhance snowmelt by boosting the absorption of solar energy. It is therefore important for coupled aerosol–climate and ice sheet models to include this effect, and yet most do not. We conduct several thousand simulations and develop a kernel and linear equations relating melt runoff on the Greenland Ice Sheet to the timing and amount of black carbon within precipitation and dry deposition, which can be used to extend the utility of state-of-the-art aerosol models.
Cenlin He, Mark G. Flanner, Fei Chen, Michael Barlage, Kuo-Nan Liou, Shichang Kang, Jing Ming, and Yun Qian
Atmos. Chem. Phys., 18, 11507–11527, https://doi.org/10.5194/acp-18-11507-2018, https://doi.org/10.5194/acp-18-11507-2018, 2018
Short summary
Short summary
Snow albedo plays a key role in the Earth and climate system. It can be affected by impurities and snow properties. This study implements new parameterizations into a widely used snow model to account for effects of snow shape and black carbon–snow mixing state on snow albedo reduction in the Tibetan Plateau. This study points toward an imperative need for extensive measurements and improved model characterization of snow grain shape and aerosol–snow mixing state in Tibet and elsewhere.
Alden C. Adolph, Mary R. Albert, and Dorothy K. Hall
The Cryosphere, 12, 907–920, https://doi.org/10.5194/tc-12-907-2018, https://doi.org/10.5194/tc-12-907-2018, 2018
Short summary
Short summary
In our studies of surface temperature in Greenland, we found that there can be differences between the temperature of the snow surface and the air directly above, depending on wind speed and incoming solar radiation. We also found that temperature measurements of the snow surface from remote sensing instruments may be more accurate than previously thought. Our results are relevant to studies of climate change in the remote sensing community and in studies of the atmospheric boundary layer.
Joseph M. Cook, Andrew J. Hodson, Alex S. Gardner, Mark Flanner, Andrew J. Tedstone, Christopher Williamson, Tristram D. L. Irvine-Fynn, Johan Nilsson, Robert Bryant, and Martyn Tranter
The Cryosphere, 11, 2611–2632, https://doi.org/10.5194/tc-11-2611-2017, https://doi.org/10.5194/tc-11-2611-2017, 2017
Short summary
Short summary
Biological growth darkens snow and ice, causing it to melt faster. This is often referred to as
bioalbedo. Quantifying bioalbedo has not been achieved because of difficulties in isolating the biological contribution from the optical properties of ice and snow, and from inorganic impurities in field studies. In this paper, we provide a physical model that enables bioalbedo to be quantified from first principles and we use it to guide future field studies.
Gunnar Myhre, Wenche Aas, Ribu Cherian, William Collins, Greg Faluvegi, Mark Flanner, Piers Forster, Øivind Hodnebrog, Zbigniew Klimont, Marianne T. Lund, Johannes Mülmenstädt, Cathrine Lund Myhre, Dirk Olivié, Michael Prather, Johannes Quaas, Bjørn H. Samset, Jordan L. Schnell, Michael Schulz, Drew Shindell, Ragnhild B. Skeie, Toshihiko Takemura, and Svetlana Tsyro
Atmos. Chem. Phys., 17, 2709–2720, https://doi.org/10.5194/acp-17-2709-2017, https://doi.org/10.5194/acp-17-2709-2017, 2017
Short summary
Short summary
Over the past decades, the geographical distribution of emissions of substances that alter the atmospheric energy balance has changed due to economic growth and pollution regulations. Here, we show the resulting changes to aerosol and ozone abundances and their radiative forcing using recently updated emission data for the period 1990–2015, as simulated by seven global atmospheric composition models. The global mean radiative forcing is more strongly positive than reported in IPCC AR5.
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
Short summary
Short summary
This manuscript describes the setup of the CMIP6 project Land Surface, Snow and Soil Moisture Model Intercomparison Project (LS3MIP).
D. Singh, M. G. Flanner, and J. Perket
The Cryosphere, 9, 2057–2070, https://doi.org/10.5194/tc-9-2057-2015, https://doi.org/10.5194/tc-9-2057-2015, 2015
Short summary
Short summary
Our work quantifies the effect of snow/ice cover on Earth's top-of-atmosphere solar energy budget. We used higher resolution MODIS data, combined with microwave retrievals of snow presence and radiative kernels produced from 4 different models for Cryosphere Radiative Effect (CrRE) estimation. We have estimated a global land-based CrRE of about -2.6Wm-2 during 2001-2013, with about 59% of the effect originating from Antarctica. We were also be able to resolve contribution from mountain glaciers.
S. Eckhardt, B. Quennehen, D. J. L. Olivié, T. K. Berntsen, R. Cherian, J. H. Christensen, W. Collins, S. Crepinsek, N. Daskalakis, M. Flanner, A. Herber, C. Heyes, Ø. Hodnebrog, L. Huang, M. Kanakidou, Z. Klimont, J. Langner, K. S. Law, M. T. Lund, R. Mahmood, A. Massling, S. Myriokefalitakis, I. E. Nielsen, J. K. Nøjgaard, J. Quaas, P. K. Quinn, J.-C. Raut, S. T. Rumbold, M. Schulz, S. Sharma, R. B. Skeie, H. Skov, T. Uttal, K. von Salzen, and A. Stohl
Atmos. Chem. Phys., 15, 9413–9433, https://doi.org/10.5194/acp-15-9413-2015, https://doi.org/10.5194/acp-15-9413-2015, 2015
Short summary
Short summary
The concentrations of sulfate, black carbon and other aerosols in the Arctic are characterized by high values in late winter and spring (so-called Arctic Haze) and low values in summer. Models have long been struggling to capture this seasonality. In this study, we evaluate sulfate and BC concentrations from different updated models and emissions against a comprehensive pan-Arctic measurement data set. We find that the models improved but still struggle to get the maximum concentrations.
S. J. Doherty, C. M. Bitz, and M. G. Flanner
Atmos. Chem. Phys., 14, 11697–11709, https://doi.org/10.5194/acp-14-11697-2014, https://doi.org/10.5194/acp-14-11697-2014, 2014
Short summary
Short summary
Black carbon in snow lowers its albedo, increasing the absorption of sunlight, leading to positive radiative forcing, climate warming and earlier snow-melt. A series of recent studies have used prescribed rates of black carbon deposition to snow to assess the climate effects of black carbon in snow. Here we show that the use of prescribed deposition fluxes in these model studies leads to high biases in snow BC concentrations, caused by the decoupling of BC and snow deposition to the surface.
C. Zhao, Z. Hu, Y. Qian, L. Ruby Leung, J. Huang, M. Huang, J. Jin, M. G. Flanner, R. Zhang, H. Wang, H. Yan, Z. Lu, and D. G. Streets
Atmos. Chem. Phys., 14, 11475–11491, https://doi.org/10.5194/acp-14-11475-2014, https://doi.org/10.5194/acp-14-11475-2014, 2014
C. Jiao, M. G. Flanner, Y. Balkanski, S. E. Bauer, N. Bellouin, T. K. Berntsen, H. Bian, K. S. Carslaw, M. Chin, N. De Luca, T. Diehl, S. J. Ghan, T. Iversen, A. Kirkevåg, D. Koch, X. Liu, G. W. Mann, J. E. Penner, G. Pitari, M. Schulz, Ø. Seland, R. B. Skeie, S. D. Steenrod, P. Stier, T. Takemura, K. Tsigaridis, T. van Noije, Y. Yun, and K. Zhang
Atmos. Chem. Phys., 14, 2399–2417, https://doi.org/10.5194/acp-14-2399-2014, https://doi.org/10.5194/acp-14-2399-2014, 2014
D. T. Shindell, J.-F. Lamarque, M. Schulz, M. Flanner, C. Jiao, M. Chin, P. J. Young, Y. H. Lee, L. Rotstayn, N. Mahowald, G. Milly, G. Faluvegi, Y. Balkanski, W. J. Collins, A. J. Conley, S. Dalsoren, R. Easter, S. Ghan, L. Horowitz, X. Liu, G. Myhre, T. Nagashima, V. Naik, S. T. Rumbold, R. Skeie, K. Sudo, S. Szopa, T. Takemura, A. Voulgarakis, J.-H. Yoon, and F. Lo
Atmos. Chem. Phys., 13, 2939–2974, https://doi.org/10.5194/acp-13-2939-2013, https://doi.org/10.5194/acp-13-2939-2013, 2013
Y. H. Lee, J.-F. Lamarque, M. G. Flanner, C. Jiao, D. T. Shindell, T. Berntsen, M. M. Bisiaux, J. Cao, W. J. Collins, M. Curran, R. Edwards, G. Faluvegi, S. Ghan, L. W. Horowitz, J. R. McConnell, J. Ming, G. Myhre, T. Nagashima, V. Naik, S. T. Rumbold, R. B. Skeie, K. Sudo, T. Takemura, F. Thevenon, B. Xu, and J.-H. Yoon
Atmos. Chem. Phys., 13, 2607–2634, https://doi.org/10.5194/acp-13-2607-2013, https://doi.org/10.5194/acp-13-2607-2013, 2013
K. M. Sterle, J. R. McConnell, J. Dozier, R. Edwards, and M. G. Flanner
The Cryosphere, 7, 365–374, https://doi.org/10.5194/tc-7-365-2013, https://doi.org/10.5194/tc-7-365-2013, 2013
Related subject area
Discipline: Snow | Subject: Instrumentation
Measuring prairie snow water equivalent with combined UAV-borne gamma spectrometry and lidar
Brief communication: Testing a portable Bullard-type temperature lance confirms highly spatially heterogeneous sediment temperatures under shallow bodies of water in the Arctic
A random forest approach to quality-checking automatic snow-depth sensor measurements
Brief communication: Comparison of in situ ephemeral snow depth measurements over a mixed-use temperate forest landscape
Monitoring snow water equivalent using the phase of RFID signals
Mapping snow depth on Canadian sub-arctic lakes using ground-penetrating radar
Comparison of manual snow water equivalent (SWE) measurements: seeking the reference for a true SWE value in a boreal biome
Brief communication: Application of a muonic cosmic ray snow gauge to monitor the snow water equivalent on alpine glaciers
GNSS signal-based snow water equivalent determination for different snowpack conditions along a steep elevation gradient
Snow water equivalent measurement in the Arctic based on cosmic ray neutron attenuation
Review article: Performance assessment of radiation-based field sensors for monitoring the water equivalent of snow cover (SWE)
Spectral albedo measurements over snow-covered slopes: theory and slope effect corrections
Continuous and autonomous snow water equivalent measurements by a cosmic ray sensor on an alpine glacier
An assessment of sub-snow GPS for quantification of snow water equivalent
Phillip Harder, Warren D. Helgason, and John W. Pomeroy
The Cryosphere, 18, 3277–3295, https://doi.org/10.5194/tc-18-3277-2024, https://doi.org/10.5194/tc-18-3277-2024, 2024
Short summary
Short summary
Remote sensing the amount of water in snow (SWE) at high spatial resolutions is an unresolved challenge. In this work, we tested a drone-mounted passive gamma spectrometer to quantify SWE. We found that the gamma observations could resolve the average and spatial variability of SWE down to 22.5 m resolutions. Further, by combining drone gamma SWE and lidar snow depth we could estimate SWE at sub-metre resolutions which is a new opportunity to improve the measurement of shallow snowpacks.
Frederieke Miesner, William Lambert Cable, Pier Paul Overduin, and Julia Boike
The Cryosphere, 18, 2603–2611, https://doi.org/10.5194/tc-18-2603-2024, https://doi.org/10.5194/tc-18-2603-2024, 2024
Short summary
Short summary
The temperature in the sediment below Arctic lakes determines the stability of the permafrost and microbial activity. However, measurements are scarce because of the remoteness. We present a robust and portable device to fill this gap. Test campaigns have demonstrated its utility in a range of environments during winter and summer. The measured temperatures show a great variability within and across locations. The data can be used to validate models and estimate potential emissions.
Giulia Blandini, Francesco Avanzi, Simone Gabellani, Denise Ponziani, Hervé Stevenin, Sara Ratto, Luca Ferraris, and Alberto Viglione
The Cryosphere, 17, 5317–5333, https://doi.org/10.5194/tc-17-5317-2023, https://doi.org/10.5194/tc-17-5317-2023, 2023
Short summary
Short summary
Automatic snow depth data are a valuable source of information for hydrologists, but they also tend to be noisy. To maximize the value of these measurements for real-world applications, we developed an automatic procedure to differentiate snow cover from grass or bare ground data, as well as to detect random errors. This procedure can enhance snow data quality, thus providing more reliable data for snow models.
Holly Proulx, Jennifer M. Jacobs, Elizabeth A. Burakowski, Eunsang Cho, Adam G. Hunsaker, Franklin B. Sullivan, Michael Palace, and Cameron Wagner
The Cryosphere, 17, 3435–3442, https://doi.org/10.5194/tc-17-3435-2023, https://doi.org/10.5194/tc-17-3435-2023, 2023
Short summary
Short summary
This study compares snow depth measurements from two manual instruments in a field and forest. Snow depths measured using a magnaprobe were typically 1 to 3 cm deeper than those measured using a snow tube. These differences were greater in the forest than in the field.
Mathieu Le Breton, Éric Larose, Laurent Baillet, Yves Lejeune, and Alec van Herwijnen
The Cryosphere, 17, 3137–3156, https://doi.org/10.5194/tc-17-3137-2023, https://doi.org/10.5194/tc-17-3137-2023, 2023
Short summary
Short summary
We monitor the amount of snow on the ground using passive radiofrequency identification (RFID) tags. These small and inexpensive tags are wirelessly read by a stationary reader placed above the snowpack. Variations in the radiofrequency phase delay accurately reflect variations in snow amount, known as snow water equivalent. Additionally, each tag is equipped with a sensor that monitors the snow temperature.
Alicia F. Pouw, Homa Kheyrollah Pour, and Alex MacLean
The Cryosphere, 17, 2367–2385, https://doi.org/10.5194/tc-17-2367-2023, https://doi.org/10.5194/tc-17-2367-2023, 2023
Short summary
Short summary
Collecting spatial lake snow depth data is essential for improving lake ice models. Lake ice growth is directly affected by snow on the lake. However, snow on lake ice is highly influenced by wind redistribution, making it important but challenging to measure accurately in a fast and efficient way. This study utilizes ground-penetrating radar on lakes in Canada's sub-arctic to capture spatial lake snow depth and shows success within 10 % error when compared to manual snow depth measurements.
Maxime Beaudoin-Galaise and Sylvain Jutras
The Cryosphere, 16, 3199–3214, https://doi.org/10.5194/tc-16-3199-2022, https://doi.org/10.5194/tc-16-3199-2022, 2022
Short summary
Short summary
Our study presents an analysis of the uncertainty and measurement error of manual measurement methods of the snow water equivalent (SWE). Snow pit and snow sampler measurements were taken during five consecutive winters. Our results show that, although the snow pit is considered a SWE reference in the literature, it is a method with higher uncertainty and measurement error than large diameter samplers, considered according to our results as the most appropriate reference in a boreal biome.
Rebecca Gugerli, Darin Desilets, and Nadine Salzmann
The Cryosphere, 16, 799–806, https://doi.org/10.5194/tc-16-799-2022, https://doi.org/10.5194/tc-16-799-2022, 2022
Short summary
Short summary
Monitoring the snow water equivalent (SWE) in high mountain regions is highly important and a challenge. We explore the use of muon counts to infer SWE temporally continuously. We deployed muonic cosmic ray snow gauges (µ-CRSG) on a Swiss glacier over the winter 2020/21. Evaluated with manual SWE measurements and SWE estimates inferred from neutron counts, we conclude that the µ-CRSG is a highly promising method for remote high mountain regions with several advantages over other current methods.
Achille Capelli, Franziska Koch, Patrick Henkel, Markus Lamm, Florian Appel, Christoph Marty, and Jürg Schweizer
The Cryosphere, 16, 505–531, https://doi.org/10.5194/tc-16-505-2022, https://doi.org/10.5194/tc-16-505-2022, 2022
Short summary
Short summary
Snow occurrence, snow amount, snow density and liquid water content (LWC) can vary considerably with climatic conditions and elevation. We show that low-cost Global Navigation Satellite System (GNSS) sensors as GPS can be used for reliably measuring the amount of water stored in the snowpack or snow water equivalent (SWE), snow depth and the LWC under a broad range of climatic conditions met at different elevations in the Swiss Alps.
Anton Jitnikovitch, Philip Marsh, Branden Walker, and Darin Desilets
The Cryosphere, 15, 5227–5239, https://doi.org/10.5194/tc-15-5227-2021, https://doi.org/10.5194/tc-15-5227-2021, 2021
Short summary
Short summary
Conventional methods used to measure snow have many limitations which hinder our ability to document annual cycles, test predictive models, or analyze the impact of climate change. A modern snow measurement method using in situ cosmic ray neutron sensors demonstrates the capability of continuously measuring spatially variable snowpacks with considerable accuracy. These sensors can provide important data for testing models, validating remote sensing, and water resource management applications.
Alain Royer, Alexandre Roy, Sylvain Jutras, and Alexandre Langlois
The Cryosphere, 15, 5079–5098, https://doi.org/10.5194/tc-15-5079-2021, https://doi.org/10.5194/tc-15-5079-2021, 2021
Short summary
Short summary
Dense spatially distributed networks of autonomous instruments for continuously measuring the amount of snow on the ground are needed for operational water resource and flood management and the monitoring of northern climate change. Four new-generation non-invasive sensors are compared. A review of their advantages, drawbacks and accuracy is discussed. This performance analysis is intended to help researchers and decision-makers choose the one system that is best suited to their needs.
Ghislain Picard, Marie Dumont, Maxim Lamare, François Tuzet, Fanny Larue, Roberta Pirazzini, and Laurent Arnaud
The Cryosphere, 14, 1497–1517, https://doi.org/10.5194/tc-14-1497-2020, https://doi.org/10.5194/tc-14-1497-2020, 2020
Short summary
Short summary
Surface albedo is an essential variable of snow-covered areas. The measurement of this variable over a tilted terrain with levelled sensors is affected by artefacts that need to be corrected. Here we develop a theory of spectral albedo measurement over slopes from which we derive four correction algorithms. The comparison to in situ measurements taken in the Alps shows the adequacy of the theory, and the application of the algorithms shows systematic improvements.
Rebecca Gugerli, Nadine Salzmann, Matthias Huss, and Darin Desilets
The Cryosphere, 13, 3413–3434, https://doi.org/10.5194/tc-13-3413-2019, https://doi.org/10.5194/tc-13-3413-2019, 2019
Short summary
Short summary
The snow water equivalent (SWE) in high mountain regions is crucial for many applications. Yet its quantification remains difficult. We present autonomous daily SWE observations by a cosmic ray sensor (CRS) deployed on a Swiss glacier for two winter seasons. Combined with snow depth observations, we derive the daily bulk snow density. The validation with manual field observations and its measurement reliability show that the CRS is a promising device for high alpine cryospheric environments.
Ladina Steiner, Michael Meindl, Charles Fierz, and Alain Geiger
The Cryosphere, 12, 3161–3175, https://doi.org/10.5194/tc-12-3161-2018, https://doi.org/10.5194/tc-12-3161-2018, 2018
Short summary
Short summary
The amount of water stored in snow cover is of high importance for flood risks, climate change, and early-warning systems. We evaluate the potential of using GPS to estimate the stored water. We use GPS antennas buried underneath the snowpack and develop a model based on the path elongation of the GPS signals while propagating through the snowpack. The method works well over full seasons, including melt periods. Results correspond within 10 % to the state-of-the-art reference data.
Cited articles
Arnaud, L., Picard, G., Champollion, N., Domine, F., Gallet, J., Lefebvre, E.,
Fily, M., and Barnola, J.: Measurement of vertical profiles of snow specific
surface area with a 1 cm resolution using infrared reflectance: instrument
description and validation, J. Glaciol., 57, 17–29,
https://doi.org/10.3189/002214311795306664,
2011. a, b
Bond, T. C., Doherty, S. J., Fahey, D. W., Forster, P. M., Berntsen, T.,
DeAngelo, B. J., Flanner, M. G., Ghan, S., Kärcher, B., Koch, D., Kinne, S.,
Kondo, Y., Quinn, P. K., Sarofim, M. C., Schultz, M. G., Schulz, M.,
Venkataraman, C., Zhang, H., Zhang, S., Bellouin, N., Guttikunda, S. K.,
Hopke, P. K., Jacobson, M. Z., Kaiser, J. W., Klimont, Z., Lohmann, U.,
Schwarz, J. P., Shindell, D., Storelvmo, T., Warren, S. G., and Zender,
C. S.: Bounding the role of black carbon in the climate system: A
scientific assessment: BLACK CARBON IN THE CLIMATE SYSTEM,
J. Geophys. Res.-Atmos., 118, 5380–5552,
https://doi.org/10.1002/jgrd.50171, 2013. a
Brandt, R. E. and Warren, S. G.: Solar-heating rates and temperature profiles
in Antarctic snow and ice, J. Glaciol., 39, 99–110,
https://doi.org/10.3189/S0022143000015756,
1993. a
Dang, C., Fu, Q., and Warren, S. G.: Effect of Snow Grain Shape on Snow
Albedo, J. Atmos. Sci., 73, 3573–3583,
https://doi.org/10.1175/JAS-D-15-0276.1,
2016. a
Domine, F., Salvatori, R., Legagneux, L., Salzano, R., Fily, M., and Casacchia,
R.: Correlation between the specific surface area and the short wave infrared
(SWIR) reflectance of snow, Cold Reg. Sci. Technol., 46,
60–68, https://doi.org/10.1016/j.coldregions.2006.06.002,
2006. a
Dumont, M., Brissaud, O., Picard, G., Schmitt, B., Gallet, J.-C., and Arnaud, Y.: High-accuracy measurements of snow Bidirectional Reflectance Distribution Function at visible and NIR wavelengths – comparison with modelling results, Atmos. Chem. Phys., 10, 2507–2520, https://doi.org/10.5194/acp-10-2507-2010, 2010. a, b, c
Ebner, P. P., Schneebeli, M., and Steinfeld, A.: Tomography-based monitoring of isothermal snow metamorphism under advective conditions, The Cryosphere, 9, 1363–1371, https://doi.org/10.5194/tc-9-1363-2015, 2015. a, b
Fierz, C., Armstrong, R., Durand, Y., Etchevers, P., Greene, E., McClung, D.,
Nishimura, K., Satyawali, P., and Sokratov, S.: The International
Classification for Seasonal Snow on the Ground, International Hydrological Programme (IHP) of the United Nations Educational, Scientific and Cultural Organization (UNESCO), Paris, France, 2009. a, b, c
Flanner, M. G. and Zender, C. S.: Linking snowpack microphysics and albedo
evolution, J. Geophys. Res., 111, D12208, https://doi.org/10.1029/2005JD006834, 2006. a
Flanner, M. G., Zender, C. S., Randerson, J. T., and Rasch, P. J.: Present-day
climate forcing and response from black carbon in snow, J.
Geophys. Res., 112, D11202, https://doi.org/10.1029/2006JD008003, 2007. a, b, c
Flanner, M. G., Zender, C. S., Hess, P. G., Mahowald, N. M., Painter, T. H., Ramanathan, V., and Rasch, P. J.: Springtime warming and reduced snow cover from carbonaceous particles, Atmos. Chem. Phys., 9, 2481–2497, https://doi.org/10.5194/acp-9-2481-2009, 2009. a
Gallet, J.-C., Domine, F., and Dumont, M.: Measuring the specific surface area of wet snow using 1310 nm reflectance, The Cryosphere, 8, 1139–1148, https://doi.org/10.5194/tc-8-1139-2014, 2014. a, b
Gergely, M., Wolfsperger, F., and Schneebeli, M.: Simulation and Validation
of the InfraSnow: An Instrument to Measure Snow Optically
Equivalent Grain Size, IEEE T. Geosci. Remote, 52, 4236–4247, https://doi.org/10.1109/TGRS.2013.2280502,, 2014. a, b
Grenfell, T. C., Warren, S. G., and Mullen, P. C.: Reflection of solar
radiation by the Antarctic snow surface at ultraviolet, visible, and
near-infrared wavelengths, J. Geophys. Res., 99, 18669,
https://doi.org/10.1029/94JD01484, 1994. a, b
Hadley, O. L. and Kirchstetter, T. W.: Black-carbon reduction of snow albedo,
Nat. Clim. Change, 2, 437–440, https://doi.org/10.1038/nclimate1433,, 2012. a, b
Hagenmuller, P., Matzl, M., Chambon, G., and Schneebeli, M.: Sensitivity of snow density and specific surface area measured by microtomography to different image processing algorithms, The Cryosphere, 10, 1039–1054, https://doi.org/10.5194/tc-10-1039-2016, 2016. a
Hall, A.: The Role of Surface Albedo Feedback in Climate, J.
Climate, 17, 1550–1568,
https://doi.org/10.1175/1520-0442(2004)017<1550:TROSAF>2.0.CO;2,
2004. a
Hudson, S. R., Warren, S. G., Brandt, R. E., Grenfell, T. C., and Six, D.:
Spectral bidirectional reflectance of Antarctic snow: Measurements and
parameterization, J. Geophys. Res., 111, D18106,
https://doi.org/10.1029/2006JD007290, 2006. a
Kaempfer, T. U., Hopkins, M. A., and Perovich, D. K.: A three-dimensional
microstructure-based photon-tracking model of radiative transfer in snow,
J. Geophys. Res., 112, D24113, https://doi.org/10.1029/2006JD008239, 2007. a, b
Kokhanovsky, A. A. and Zege, E. P.: Scattering optics of snow, Appl. Optics, 43, 1589, https://doi.org/10.1364/AO.43.001589,
2004. a, b
Legagneux, L. and Domine, F.: A mean field model of the decrease of the
specific surface area of dry snow during isothermal metamorphism: Model
Of Snow Surface Area Decrease, J. Geophys. Res.-Earth, 110, F04011, https://doi.org/10.1029/2004JF000181, 2005. a
Legagneux, L., Cabanes, A., and Dominé, F.: Measurement of the specific
surface area of 176 snow samples using methane adsorption at 77 K:
Measurement Using Methane Adsorption At 77 K, J.
Geophys. Res.-Atmos., 107, ACH 5-1–ACH 5-15,
https://doi.org/10.1029/2001JD001016, 2002. a, b
Legagneux, L., Taillandier, A.-S., and Domine, F.: Grain growth theories and
the isothermal evolution of the specific surface area of snow, J.
Appl. Phys., 95, 6175–6184, https://doi.org/10.1063/1.1710718, 2004. a
Libois, Q., Picard, G., Arnaud, L., Dumont, M., Lafaysse, M., Morin, S., and Lefebvre, E.: Summertime evolution of snow specific surface area close to the surface on the Antarctic Plateau, The Cryosphere, 9, 2383–2398, https://doi.org/10.5194/tc-9-2383-2015, 2015. a
Lieb-Lappen, R., Golden, E., and Obbard, R.: Metrics for interpreting the
microstructure of sea ice using X-ray micro-computed tomography, Cold Reg.
Sci. Technol., 138, 24–35,
https://doi.org/10.1016/j.coldregions.2017.03.001,
2017. a
Malinka, A. V.: Light scattering in porous materials: Geometrical optics and stereological approach, J. Quant. Spectrosc. Ra., 141, 14–23, https://doi.org/10.1016/j.jqsrt.2014.02.022, 2014. a
Matzl, M. and Schneebeli, M.: Measuring specific surface area of snow by
near-infrared photography, J. Glaciol., 52, 558–564,
https://doi.org/10.3189/172756506781828412,
2006. a
Nicodemus, F., Richmond, J., Hsia, J., Ginsberg, I., and Limperis, T.:
Geometrical considerations and nomenclature for reflectance, U.S. Department of Commerce, National Bureau of Standards, Washington, D.C., USA, 1977. a
Nolin, A. W. and Dozier, J.: A Hyperspectral Method for Remotely Sensing the
Grain Size of Snow, Remote Sens. Environ., 74, 207–216,
https://doi.org/10.1016/S0034-4257(00)00111-5,
2000. a
Painter, T. H., Molotch, N. P., Cassidy, M., Flanner, M., and Steffen, K.:
Contact spectroscopy for determination of stratigraphy of snow optical grain
size, J. Glaciol., 53, 121–127, https://doi.org/10.3189/172756507781833947,
2007. a, b
Picard, G., Arnaud, L., Domine, F., and Fily, M.: Determining snow specific
surface area from near-infrared reflectance measurements: Numerical study
of the influence of grain shape, Cold Reg. Sci. Technol., 56,
10–17, https://doi.org/10.1016/j.coldregions.2008.10.001,
2009. a, b, c
Pinzer, B. R. and Schneebeli, M.: Snow metamorphism under alternating
temperature gradients: Morphology and recrystallization in surface snow,
Geophys. Res. Lett., 36, L23503, https://doi.org/10.1029/2009GL039618, 2009. a, b
Qian, Y., Yasunari, T. J., Doherty, S. J., Flanner, M. G., Lau, W. K. M., Ming,
J., Wang, H., Wang, M., Warren, S. G., and Zhang, R.: Light-absorbing
particles in snow and ice: Measurement and modeling of climatic and
hydrological impact, Adv. Atmos. Sci., 32, 64–91,
https://doi.org/10.1007/s00376-014-0010-0, 2015. a
Qu, X. and Hall, A.: What Controls the Strength of Snow-Albedo
Feedback?, J. Climate, 20, 3971–3981, https://doi.org/10.1175/JCLI4186.1, 2007. a
Ramella-Roman, J. C., Prahl, S. A., and Jacques, S. L.: Three Monte Carlo
programs of polarized light transport into scattering media: part-I, Opt.
Express, 13, 4420, https://doi.org/10.1364/OPEX.13.004420,
2005. a
Schneider, A. and Flanner, M.: Supporting data for the Near-Infrared
Emitting and Reflectance-Monitoring Dome, type: dataset, https://doi.org/10.7302/Z23F4MVC, 2018. a
Skiles, S. M. and Painter, T.: Daily evolution in dust and black carbon
content, snow grain size, and snow albedo during snowmelt, Rocky
Mountains, Colorado, J. Glaciol., 63, 118–132,
https://doi.org/10.1017/jog.2016.125,
2017. a, b
Skiles, S. M., Flanner, M., Cook, J. M., Dumont, M., and Painter, T. H.:
Radiative forcing by light-absorbing particles in snow, Nat. Clim.
Change, 8, 964–971, https://doi.org/10.1038/s41558-018-0296-5, 2018. a
Smith, B. E., Gardner, A., Schneider, A., and Flanner, M.: Modeling biases in
laser-altimetry measurements caused by scattering of green light in snow,
Remote Sens. Environ., 215, 398–410,
https://doi.org/10.1016/j.rse.2018.06.012,
2018. a
van de Hulst, H.: Asymptotic fitting, a method for solving anisotropic transfer
problems in thick layers, J. Comput. Phys., 3, 291–306,
https://doi.org/10.1016/0021-9991(68)90023-5,
1968. a
Vouk, V.: Projected Area of Convex Bodies, Nature, 162, 330–331,
https://doi.org/10.1038/162330a0, 1948. a
Wang, X. and Baker, I.: Evolution of the specific surface area of snow during
high-temperature gradient metamorphism, J. Geophys. Res.-Atmos., 119, 13690–13703, https://doi.org/10.1002/2014JD022131, 2014. a
Warren, S. G. and Wiscombe, W. J.: A Model for the Spectral Albedo of
Snow. II: Snow Containing Atmospheric Aerosols, J.
Atmos. Sci., 37, 2734–2745,
https://doi.org/10.1175/1520-0469(1980)037<2734:AMFTSA>2.0.CO;2,
1980. a
Yang, P., Bi, L., Baum, B. A., Liou, K.-N., Kattawar, G. W., Mishchenko, M. I.,
and Cole, B.: Spectrally Consistent Scattering, Absorption, and
Polarization Properties of Atmospheric Ice Crystals at
Wavelengths from 0.2 to 100 µm, J. Atmos.
Sci., 70, 330–347, https://doi.org/10.1175/JAS-D-12-039.1,
2013. a, b
Short summary
To study the process of snow aging, we engineered a prototype instrument called the Near-Infrared Emitting and Reflectance-Monitoring Dome (NERD). Using the NERD, we observed rapid snow aging in experiments with added light absorbing particles (LAPs). Particulate matter deposited on the snow increased absorption of solar energy and enhanced snow melt. These results indicate the role of LAPs' indirect effect on snow aging through a positive feedback mechanism related to the snow grain size.
To study the process of snow aging, we engineered a prototype instrument called the...