Articles | Volume 15, issue 2
https://doi.org/10.5194/tc-15-793-2021
© Author(s) 2021. 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-15-793-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Feb 2021
Research article |
| 17 Feb 2021
| 17 Feb 2021
Effect of small-scale snow surface roughness on snow albedo and reflectance
Terhikki Manninen
CORRESPONDING AUTHOR
Finnish Meteorological Institute, Helsinki, P.O. Box 503, 00101,
Finland
Kati Anttila
Finnish Meteorological Institute, Helsinki, P.O. Box 503, 00101,
Finland
Emmihenna Jääskeläinen
Finnish Meteorological Institute, Helsinki, P.O. Box 503, 00101,
Finland
Aku Riihelä
Finnish Meteorological Institute, Helsinki, P.O. Box 503, 00101,
Finland
Jouni Peltoniemi
Finnish Geospatial Research Institute, National Land Survey,
Geodeetinrinne 2, 02430 Masala, Finland
Petri Räisänen
Finnish Meteorological Institute, Helsinki, P.O. Box 503, 00101,
Finland
Panu Lahtinen
Finnish Meteorological Institute, Helsinki, P.O. Box 503, 00101,
Finland
Niilo Siljamo
Finnish Meteorological Institute, Helsinki, P.O. Box 503, 00101,
Finland
Laura Thölix
Finnish Meteorological Institute, Helsinki, P.O. Box 503, 00101,
Finland
Outi Meinander
Finnish Meteorological Institute, Helsinki, P.O. Box 503, 00101,
Finland
Anna Kontu
Finnish Meteorological Institute, Helsinki, P.O. Box 503, 00101,
Finland
Hanne Suokanerva
Finnish Meteorological Institute, Helsinki, P.O. Box 503, 00101,
Finland
Roberta Pirazzini
Finnish Meteorological Institute, Helsinki, P.O. Box 503, 00101,
Finland
Juha Suomalainen
Finnish Geospatial Research Institute, National Land Survey,
Geodeetinrinne 2, 02430 Masala, Finland
Teemu Hakala
Finnish Geospatial Research Institute, National Land Survey,
Geodeetinrinne 2, 02430 Masala, Finland
Sanna Kaasalainen
Finnish Geospatial Research Institute, National Land Survey,
Geodeetinrinne 2, 02430 Masala, Finland
Harri Kaartinen
Finnish Geospatial Research Institute, National Land Survey,
Geodeetinrinne 2, 02430 Masala, Finland
Department of Geography and Geology, University of Turku, 20500
Turku, Finland
Antero Kukko
Finnish Geospatial Research Institute, National Land Survey,
Geodeetinrinne 2, 02430 Masala, Finland
Department of Built Environment, Aalto University, 02150 Espoo,
Finland
Olivier Hautecoeur
Météo-France, Toulouse, France
currently at: Exostaff GmbH/EUMETSAT, Darmstadt, Germany
Jean-Louis Roujean
Centre d'Etudes Spatiales de la BIOsphère (CESBIO) – UMR 5126 –
31401 Toulouse, France
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Mariana Batista Campos, Leticia Ferrari Castanheiro, Dipal Shah, Yunsheng Wang, Antero Kukko, and Eetu Puttonen
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A.-M. Raita-Hakola, S. Rahkonen, J. Suomalainen, L. Markelin, R. Oliveira, T. Hakala, N. Koivumäki, E. Honkavaara, and I. Pölönen
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 1771–1778, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1771-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1771-2023, 2023
R. A. Oliveira, R. Näsi, P. Korhonen, A. Mustonen, O. Niemeläinen, N. Koivumäki, T. Hakala, J. Suomalainen, J. Kaivosoja, and E. Honkavaara
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 1861–1866, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1861-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1861-2023, 2023
V. Karjalainen, T. Hakala, A. George, N. Koivumäki, J. Suomalainen, and E. Honkavaara
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 597–603, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-597-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-597-2023, 2023
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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Karl-Göran Karlsson, Martin Stengel, Jan Fokke Meirink, Aku Riihelä, Jörg Trentmann, Tom Akkermans, Diana Stein, Abhay Devasthale, Salomon Eliasson, Erik Johansson, Nina Håkansson, Irina Solodovnik, Nikos Benas, Nicolas Clerbaux, Nathalie Selbach, Marc Schröder, and Rainer Hollmann
Earth Syst. Sci. Data, 15, 4901–4926, https://doi.org/10.5194/essd-15-4901-2023, https://doi.org/10.5194/essd-15-4901-2023, 2023
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This paper presents a global climate data record on cloud parameters, radiation at the surface and at the top of atmosphere, and surface albedo. The temporal coverage is 1979–2020 (42 years) and the data record is also continuously updated until present time. Thus, more than four decades of climate parameters are provided. Based on CLARA-A3, studies on distribution of clouds and radiation parameters can be made and, especially, investigations of climate trends and evaluation of climate models.
L. F. Castanheiro, A. M. G. Tommaselli, T. A. C. Garcia, M. B. Campos, and A. Kukko
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W1-2023, 71–77, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-71-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-71-2023, 2023
T. Faitli, T. Hakala, H. Kaartinen, J. Hyyppä, and A. Kukko
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W1-2023, 145–150, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-145-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-145-2023, 2023
Outi Meinander, Pavla Dagsson-Waldhauserova, Pavel Amosov, Elena Aseyeva, Cliff Atkins, Alexander Baklanov, Clarissa Baldo, Sarah L. Barr, Barbara Barzycka, Liane G. Benning, Bojan Cvetkovic, Polina Enchilik, Denis Frolov, Santiago Gassó, Konrad Kandler, Nikolay Kasimov, Jan Kavan, James King, Tatyana Koroleva, Viktoria Krupskaya, Markku Kulmala, Monika Kusiak, Hanna K. Lappalainen, Michał Laska, Jerome Lasne, Marek Lewandowski, Bartłomiej Luks, James B. McQuaid, Beatrice Moroni, Benjamin Murray, Ottmar Möhler, Adam Nawrot, Slobodan Nickovic, Norman T. O’Neill, Goran Pejanovic, Olga Popovicheva, Keyvan Ranjbar, Manolis Romanias, Olga Samonova, Alberto Sanchez-Marroquin, Kerstin Schepanski, Ivan Semenkov, Anna Sharapova, Elena Shevnina, Zongbo Shi, Mikhail Sofiev, Frédéric Thevenet, Throstur Thorsteinsson, Mikhail Timofeev, Nsikanabasi Silas Umo, Andreas Uppstu, Darya Urupina, György Varga, Tomasz Werner, Olafur Arnalds, and Ana Vukovic Vimic
Atmos. Chem. Phys., 22, 11889–11930, https://doi.org/10.5194/acp-22-11889-2022, https://doi.org/10.5194/acp-22-11889-2022, 2022
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High-latitude dust (HLD) is a short-lived climate forcer, air pollutant, and nutrient source. Our results suggest a northern HLD belt at 50–58° N in Eurasia and 50–55° N in Canada and at >60° N in Eurasia and >58° N in Canada. Our addition to the previously identified global dust belt (GDB) provides crucially needed information on the extent of active HLD sources with both direct and indirect impacts on climate and environment in remote regions, which are often poorly understood and predicted.
Petri Räisänen, Joonas Merikanto, Risto Makkonen, Mikko Savolahti, Alf Kirkevåg, Maria Sand, Øyvind Seland, and Antti-Ilari Partanen
Atmos. Chem. Phys., 22, 11579–11602, https://doi.org/10.5194/acp-22-11579-2022, https://doi.org/10.5194/acp-22-11579-2022, 2022
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A climate model is used to evaluate how the radiative forcing (RF) associated with black carbon (BC) emissions depends on the latitude, longitude, and seasonality of emissions. It is found that both the direct RF (BC absorption of solar radiation in air) and snow RF (BC absorption in snow/ice) depend strongly on the emission region and season. The results suggest that, for a given mass of BC emitted, climatic impacts are likely to be largest for high-latitude emissions due to the large snow RF.
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
Earth Syst. Sci. Data, 14, 3915–3945, https://doi.org/10.5194/essd-14-3915-2022, https://doi.org/10.5194/essd-14-3915-2022, 2022
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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.
J. Suomalainen, R. A. Oliveira, T. Hakala, N. Koivumäki, L. Markelin, R. Näsi, and E. Honkavaara
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2022, 67–72, https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-67-2022, https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-67-2022, 2022
P. Rönnholm, S. Wittke, M. Ingman, P. Putkiranta, H. Kauhanen, H. Kaartinen, and M. T. Vaaja
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 633–639, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-633-2022, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-633-2022, 2022
Jaakko Ahola, Tomi Raatikainen, Muzaffer Ege Alper, Jukka-Pekka Keskinen, Harri Kokkola, Antti Kukkurainen, Antti Lipponen, Jia Liu, Kalle Nordling, Antti-Ilari Partanen, Sami Romakkaniemi, Petri Räisänen, Juha Tonttila, and Hannele Korhonen
Atmos. Chem. Phys., 22, 4523–4537, https://doi.org/10.5194/acp-22-4523-2022, https://doi.org/10.5194/acp-22-4523-2022, 2022
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Clouds are important for the climate, and cloud droplets have a significant role in cloud properties. Cloud droplets form when air rises and cools and water vapour condenses on small particles that can be natural or of anthropogenic origin. Currently, the updraft velocity, meaning how fast the air rises, is poorly represented in global climate models. In our study, we show three methods that will improve the depiction of updraft velocity and which properties are vital to updrafts.
Hanna K. Lappalainen, Tuukka Petäjä, Timo Vihma, Jouni Räisänen, Alexander Baklanov, Sergey Chalov, Igor Esau, Ekaterina Ezhova, Matti Leppäranta, Dmitry Pozdnyakov, Jukka Pumpanen, Meinrat O. Andreae, Mikhail Arshinov, Eija Asmi, Jianhui Bai, Igor Bashmachnikov, Boris Belan, Federico Bianchi, Boris Biskaborn, Michael Boy, Jaana Bäck, Bin Cheng, Natalia Chubarova, Jonathan Duplissy, Egor Dyukarev, Konstantinos Eleftheriadis, Martin Forsius, Martin Heimann, Sirkku Juhola, Vladimir Konovalov, Igor Konovalov, Pavel Konstantinov, Kajar Köster, Elena Lapshina, Anna Lintunen, Alexander Mahura, Risto Makkonen, Svetlana Malkhazova, Ivan Mammarella, Stefano Mammola, Stephany Buenrostro Mazon, Outi Meinander, Eugene Mikhailov, Victoria Miles, Stanislav Myslenkov, Dmitry Orlov, Jean-Daniel Paris, Roberta Pirazzini, Olga Popovicheva, Jouni Pulliainen, Kimmo Rautiainen, Torsten Sachs, Vladimir Shevchenko, Andrey Skorokhod, Andreas Stohl, Elli Suhonen, Erik S. Thomson, Marina Tsidilina, Veli-Pekka Tynkkynen, Petteri Uotila, Aki Virkkula, Nadezhda Voropay, Tobias Wolf, Sayaka Yasunaka, Jiahua Zhang, Yubao Qiu, Aijun Ding, Huadong Guo, Valery Bondur, Nikolay Kasimov, Sergej Zilitinkevich, Veli-Matti Kerminen, and Markku Kulmala
Atmos. Chem. Phys., 22, 4413–4469, https://doi.org/10.5194/acp-22-4413-2022, https://doi.org/10.5194/acp-22-4413-2022, 2022
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We summarize results during the last 5 years in the northern Eurasian region, especially from Russia, and introduce recent observations of the air quality in the urban environments in China. Although the scientific knowledge in these regions has increased, there are still gaps in our understanding of large-scale climate–Earth surface interactions and feedbacks. This arises from limitations in research infrastructures and integrative data analyses, hindering a comprehensive system analysis.
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.
Terhikki Manninen, Emmihenna Jääskeläinen, Niilo Siljamo, Aku Riihelä, and Karl-Göran Karlsson
Atmos. Meas. Tech., 15, 879–893, https://doi.org/10.5194/amt-15-879-2022, https://doi.org/10.5194/amt-15-879-2022, 2022
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A new method for cloud-correcting observations of surface albedo is presented for AVHRR data. Instead of a binary cloud mask, it applies cloud probability values smaller than 20% of the A3 edition of the CLARA (CM SAF cLoud, Albedo and surface Radiation dataset from AVHRR data) record provided by the Satellite Application Facility on Climate Monitoring (CM SAF) project of EUMETSAT. According to simulations, the 90% quantile was 1.1% for the absolute albedo error and 2.2% for the relative error.
Bin Cheng, Yubing Cheng, Timo Vihma, Anna Kontu, Fei Zheng, Juha Lemmetyinen, Yubao Qiu, and Jouni Pulliainen
Earth Syst. Sci. Data, 13, 3967–3978, https://doi.org/10.5194/essd-13-3967-2021, https://doi.org/10.5194/essd-13-3967-2021, 2021
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Climate change strongly impacts the Arctic, with clear signs of higher air temperature and more precipitation. A sustainable observation programme has been carried out in Lake Orajärvi in Sodankylä, Finland. The high-quality air–snow–ice–water temperature profiles have been measured every winter since 2009. The data can be used to investigate the lake ice surface heat balance and the role of snow in lake ice mass balance and parameterization of snow-to-ice transformation in snow/ice models.
Joonas Merikanto, Kalle Nordling, Petri Räisänen, Jouni Räisänen, Declan O'Donnell, Antti-Ilari Partanen, and Hannele Korhonen
Atmos. Chem. Phys., 21, 5865–5881, https://doi.org/10.5194/acp-21-5865-2021, https://doi.org/10.5194/acp-21-5865-2021, 2021
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Human-induced aerosols concentrate around their emission sources, yet their climate effects span far and wide. Here, we use two climate models to robustly identify the mechanisms of how Asian anthropogenic aerosols impact temperatures across the globe. A total removal of Asian anthropogenic aerosols increases the global temperatures by 0.26 ± 0.04 °C in the models, with the strongest warming taking place over the Arctic due to increased atmospheric transport of energy towards the high north.
Johan Ström, Jonas Svensson, Henri Honkanen, Eija Asmi, Nathaniel B. Dkhar, Shresth Tayal, Ved P. Sharma, Rakesh Hooda, Outi Meinander, Matti Leppäranta, Hans-Werner Jacobi, Heikki Lihavainen, and Antti Hyvärinen
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-158, https://doi.org/10.5194/acp-2021-158, 2021
Revised manuscript not accepted
Short summary
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Snow darkening in the Himalaya results from the deposition of different particles. Here we assess the change in the seasonal snow cover duration due to the presence of mineral dust and black carbon particles in the snow of Sunderdhunga valley, Central Himalaya, India. With the use of in situ weather station data, the snow melt-out date is estimated to be shifted ~13 days earlier due to the presence of the particles in the snow.
Richard Essery, Hyungjun Kim, Libo Wang, Paul Bartlett, Aaron Boone, Claire Brutel-Vuilmet, Eleanor Burke, Matthias Cuntz, Bertrand Decharme, Emanuel Dutra, Xing Fang, Yeugeniy Gusev, Stefan Hagemann, Vanessa Haverd, Anna Kontu, Gerhard Krinner, Matthieu Lafaysse, Yves Lejeune, Thomas Marke, Danny Marks, Christoph Marty, Cecile B. Menard, Olga Nasonova, Tomoko Nitta, John Pomeroy, Gerd Schädler, Vladimir Semenov, Tatiana Smirnova, Sean Swenson, Dmitry Turkov, Nander Wever, and Hua Yuan
The Cryosphere, 14, 4687–4698, https://doi.org/10.5194/tc-14-4687-2020, https://doi.org/10.5194/tc-14-4687-2020, 2020
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Climate models are uncertain in predicting how warming changes snow cover. This paper compares 22 snow models with the same meteorological inputs. Predicted trends agree with observations at four snow research sites: winter snow cover does not start later, but snow now melts earlier in spring than in the 1980s at two of the sites. Cold regions where snow can last until late summer are predicted to be particularly sensitive to warming because the snow then melts faster at warmer times of year.
Cited articles
Anttila, K., Manninen, T., Karjalainen, T., Lahtinen, P., Riihelä, A.,
and Siljamo, N.: The temporal and spatial variability in submeter scale
surface roughness of seasonal snow in Sodankylä Finnish Lapland in
2009–2010, J. Geophys. Res.-Atmos., 119, 9236–9252,
https://doi.org/10.1002/2014JD021597, 2014.
Aoki, T., Kuchiki, K., Niwano, M., Kodama, Y., Hosaka, M., and Tanaka, T.:
Physically based snow albedo model for calculating broadband albedos and the
solar heating profile in snowpack for general circulation models,
J. Geophys. Res., 116, D11114, https://doi.org/10.1029/2010JD015507, 2011.
Armstrong, R. L. and Brun, E. (Eds.): Snow and climate, Cambridge University
Press, New York, USA, 2008.
Baldridge, A. M., Hook, S. J., Grove, C. I., and Rivera, G.: The ASTER Spectral
Library Version 2.0, Remote Sens. Environ., 113, 711–715,
https://doi.org/10.1016/j.rse.2008.11.007, 2009.
Beckmann, P. and Spizzicchino, A.: The Scattering of Electromagnetic Waves
from Rough Surfaces, Pergamon Press, Oxford, UK, 497 pp., 1963.
Church, E. L.: Fractal surface finish, Appl. Optics, 27, 1518–1526, 1988.
Cook, J. M., Hodson, A. J., Gardner, A. S., Flanner, M., Tedstone, A. J., Williamson, C., Irvine-Fynn, T. D. L., Nilsson, J., Bryant, R., and Tranter, M.: Quantifying bioalbedo: a new physically based model and discussion of empirical methods for characterising biological influence on ice and snow albedo, The Cryosphere, 11, 2611–2632, https://doi.org/10.5194/tc-11-2611-2017, 2017.
Domine, F., Gallet, J.-C., Bock, J., and Morin, S.: Structure, specific
surface area and thermal conductivity of the snowpack around Barrow, Alaska,
J. Geophys. Res., 117, D00R14, https://doi.org/10.1029/2011JD016647, 2012.
Essery, R.: A factorial snowpack model (FSM 1.0), Geosci. Model Dev., 8, 3867–3876, https://doi.org/10.5194/gmd-8-3867-2015, 2015.
Fierz, C., Armstrong, R. L., Durand, Y., Etchevers, P., Greene, E., McClung,
D. M., Nishimura, K., Satyawali, P. K., and Sokratov, S. A.: The International
Classification for Seasonal Snow on the Ground, IHP-VII Technical Documents
in Hydrology No. 83, IACS Contribution No. 1, UNESCO-IHP,
Paris, 2009.
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.
Flanner, M. G., Shell, K. M., Barlage, M., Perovich, D. K., and Tschudi, M. A.: Radiative forcing and albedo feedback from the northern hemisphere
cryosphere between 1979 and 2008, Nat. Geosci., 4, 151–155,
https://doi.org/10.1038/ngeo1062, 2011.
Fung, A. K.: Microwave Scattering and Emission Models and Their Applications,
Artech House, Boston, USA, 573 pp., 1994.
He, C., Takano, Y., and Liou, K.-N.: Close packing effects on clean and dirty
snow albedo and associated climatic implications, Geophys. Res. Lett., 44, 3719–3727, https://doi.org/10.1002/2017GL072916, 2017.
Jämsä, S., Peltoniemi, J. I., and Lumme, K.: Thermal emission from a
rough surface: ray optics approach, Astron. Astrophys., 271, 319–325, 1993.
Kaasalainen, S., Kaartinen, H., Kukko, A., Anttila, K., and Krooks, A.: Brief communication “Application of mobile laser scanning in snow cover profiling”, The Cryosphere, 5, 135–138, https://doi.org/10.5194/tc-5-135-2011, 2011.
Keller, J. M., Crownover, R. M., and Chen, R. Y.: Characteristics of natural
scenes related to the fractal dimension,
IEEE T. Pattern. Anal., 9, 621–627, 1987.
Knyazikhin, Y., Martonchik, J. V., Myneni, R. B., Diner, D. J., and Running, S. W.: Synergistic algorithm for estimating vegetation canopy leaf area
index and fraction of absorbed photosynthetically active radiation from
MODIS and MISR data, J. Geophys. Res., 103, 32257–32276, 1998.
Kokhanovsky, A. A.: Spectral reflectance of solar light from dirty snow: a simple theoretical model and its validation, The Cryosphere, 7, 1325–1331, https://doi.org/10.5194/tc-7-1325-2013, 2013.
Kokhanovsky, A. A. and Zege, E. P.: Scattering optics of snow,
Appl. Optics, 43, 1589–1602, https://doi.org/10.1364/AO.43.001589, 2004.
Kokhanovsky, A. A., Lamare, M., Di Mauro, B., Picard, G., Arnaud, L., Dumont, M., Tuzet, F., Brockmann, C., and Box, J. E.: On the reflectance spectroscopy of snow, The Cryosphere, 12, 2371–2382, https://doi.org/10.5194/tc-12-2371-2018, 2018.
Komuro, Y. and Suzuki, T.: Relationship between the Concentration of
Impurity and Albedo in Snow Surface, Atmospheric and Climate Sciences, 5,
426–432, https://doi.org/10.4236/acs.2015.54034, 2015.
Kukko, A., Andrei, C.-O., Salminen, V. M., Kaartinen, H., Chen, Y.,
Rönnholm, P., Hyyppä, H., Hyyppä, J., Chen, R., Haggrén, H.,
Kosonen, I., and Čapek, K.: Road Environment Mapping System of the Finnish Geodetic Institute – FGI Roamer, in: ISPRS Workshop on Laser Scanning 2007 and SilviLaser 2007, ISPRS, Espoo, Finland, 12–24 September 2007, 36, 241–247, 2007.
Kukko, A., Anttila, K., Manninen, T., Kaartinen, H., and Kaasalainen, S.:
Snow surface roughness from mobile laser scanning data,
Cold Reg. Sci. Technol., 96, 23–35, 2013.
Larue, F., Picard, G., Arnaud, L., Ollivier, I., Delcourt, C., Lamare, M., Tuzet, F., Revuelto, J., and Dumont, M.: Snow albedo sensitivity to macroscopic surface roughness using a new ray-tracing model, The Cryosphere, 14, 1651–1672, https://doi.org/10.5194/tc-14-1651-2020, 2020.
Leppänen, L., Kontu, A., Vehviläinen, J., Lemmetyinen, J., and
Pulliainen, J.: Comparison of traditional and optical grain-size field
measurements with SNOWPACK simulations in a taiga snowpack,
J. Glaciol., 61, 151–162, https://doi.org/10.3189/2015JoG14J026, 2015.
Leroux, C. and Fily, M.: Modeling the effect of sastrugi on snow
reflectance, J. Geophys. Res., 103, 25779–25788, 1998.
Lhermitte, S., Abermann, J., and Kinnard, C.: Albedo over rough snow and ice surfaces, The Cryosphere, 8, 1069–1086, https://doi.org/10.5194/tc-8-1069-2014, 2014.
Libois, Q., Picard, G., France, J. L., Arnaud, L., Dumont, M., Carmagnola, C. M., and King, M. D.: Influence of grain shape on light penetration in snow, The Cryosphere, 7, 1803–1818, https://doi.org/10.5194/tc-7-1803-2013, 2013.
Libois, Q., Picard, G., Dumont, M., Arnaud, L., Sergent, C., Pougatch, E.,
and Vial, D.: Experimental determination of the absorption enhancement
parameter of snow, J. Glaciol., 60, 714–724,
https://doi.org/10.3189/2014JoG14J015, 2014.
Löwe, H., Egli, S., Bartlett, S., Guala, J. M. M., and Manes, C.: On the
evolution of the snow surface during snowfall, Geophys. Res. Lett., 34, L21507, https://doi.org/10.1029/2007GL031637, 2007.
Lucht, W., Schaaf, C. B., and Strahler, A. H.: An Algorithm for the Retrieval
of Albedo from Space Using Semiempirical BRDF Models,
IEEE T. Geosci. Remote, 38, 977–998, 2000.
Manninen, A. T.: Multiscale surface roughness description for scattering
modelling of bare soil, Physica A, 319, 535–551, 2003.
Manninen, T. and Stenberg, P.: Simulation of the effect of snow covered
forest floor on the total forest albedo,
Agr. Forest Meteorol., 149, 303–319, 2009.
Manninen, T. and Roujean, J.-L.: SNORTEX, Snow Reflectance Transition
Experiment, Finnish Meteorological Institute Reports, Helsinki, Finland, 68, available at: https://helda.helsinki.fi/bitstream/handle/10138/135970/2014nro7.pdf?sequence=1&isAllowed=y (last access: 10 February 2021), 2014.
Manninen, T., Anttila, K., Karjalainen, T., and Lahtinen, P.: Automatic snow
surface roughness estimation using digital photos, J. Glaciol., 58, 993–1007, 2012.
Manninen, T., Lahtinen, P., Anttila, K., and Riihelä, A.: Detection of
snow surface roughness and hoar at Summit, Greenland, using RADARSAT data,
Int. J. Remote Sens., 37, 2860–2880, https://doi.org/10.1080/01431161.2015.1131873, 2016.
Mätzler, C.: Autocorrelation functions of granular media with free
arrangement of spheres, spherical shells or ellipsoids, J. Appl. Phys., 81,
1509–1517, 1997.
Meinander, O., Kazadzis, S., Arola, A., Riihelä, A., Räisänen, P., Kivi, R., Kontu, A., Kouznetsov, R., Sofiev, M., Svensson, J., Suokanerva, H., Aaltonen, V., Manninen, T., Roujean, J.-L., and Hautecoeur, O.: Spectral albedo of seasonal snow during intensive melt period at Sodankylä, beyond the Arctic Circle, Atmos. Chem. Phys., 13, 3793–3810, https://doi.org/10.5194/acp-13-3793-2013, 2013.
Meinander, O., Kontu, A., Virkkula, A., Arola, A., Backman, L., Dagsson-Waldhauserová, P., Järvinen, O., Manninen, T., Svensson, J., de Leeuw, G., and Leppäranta, M.: Brief communication: Light-absorbing impurities can reduce the density of melting snow, The Cryosphere, 8, 991–995, https://doi.org/10.5194/tc-8-991-2014, 2014.
Meinander, O., Kontu, A., Kouznetsov, R., and Sofiev, M.: Snow
samples combined with long-range transport modeling to reveal the origin and
temporal variability of black carbon in seasonal snow in Sodankylä (67∘ N), Front. Earth Sci., 8, 153, https://doi.org/10.3389/feart.2020.00153, 2020.
Mialon, A., Fily, M., and Royer, A.: Seasonal snow cover extent from
microwave remote sensing data: comparison with existing ground and satellite
based measurements, EARSeL eProceeding, 4, 215–225, 2005.
Neshyba, S. P., Grenfell, T. C., and Warren, S. G.: Representation of a
nonspherical ice particle by a collection of independent spheres for
scattering and absorption of radiation: 2. Hexagonal columns and plates, J.
Geophys. Res., 108, 4448, https://doi.org/10.1029/2002JD003302, 2003.
Nousiainen, T. and McFarquhar, G. M.: Light Scattering by Quasi-Spherical
Ice Crystals, J. Atmos. Sci., 61, 2229–2248,
https://doi.org/10.1175/1520-0469(2004)061<2229:LSBQIC>2.0.CO;2, 2004.
Panferov, O., Knyazikhin, Y., Myneni, R. B., Szarzynski, J., Engwald, S.,
Schnitzler, K. G., and Gravenhorst, G.: The role of canopy structure in the
spectral variation of transmission and absorption of solar radiation in
vegetation canopies, IEEE T. Geosci. Remote, 39, 241–253, 2001.
Peltoniemi, J. I., Kaasalainen, S., Näränen, J., Matikainen, L., and
Piironen, J.: Measurement of directional and spectral signatures of light
reflectance by snow, IEEE T. Geosci. Remote, 43, 2294–2304, 2005.
Peltoniemi, J. I., Suomalainen, J., Hakala, T., Puttonen, E.,
Näränen, J., Kaasalainen, S., Torppa, J., and Hirschmugl, M.:
Reflectance of various snow types: measurements, modelling and potential for
snow melt monitoring, in: Light Scattering Reviews 5: Single Light Scattering
and Radiative Transfer, edited by: Kokhanovsky, A. A., Springer Praxis Books, Heidelberg, Berlin, Germany, chap. 9, 393–450, https://doi.org/10.1016/j.jqsrt.2014.04.011, 2010.
Peltoniemi, J. I., Hakala, T., Suomalainen, J., Honkavaara, E., Markelin,
L., Gritsevich, M., Eskelinen, J., Jaanson, P., and Ikonen, E.: A detailed study for the provision of measurement uncertainty and traceability for
goniospectrometers, J. Quant. Spectrosc. Ra., 146, 376–390,
2014.
Peltoniemi, J. I., Gritsevich, M., Hakala, T., Dagsson-Waldhauserová, P., Arnalds, Ó., Anttila, K., Hannula, H.-R., Kivekäs, N., Lihavainen, H., Meinander, O., Svensson, J., Virkkula, A., and de Leeuw, G.: Soot on Snow experiment: bidirectional reflectance factor measurements of contaminated snow, The Cryosphere, 9, 2323–2337, https://doi.org/10.5194/tc-9-2323-2015, 2015.
Picard, G., Libois, Q., and Arnaud, L.: Refinement of the ice absorption spectrum in the visible using radiance profile measurements in Antarctic snow, The Cryosphere, 10, 2655–2672, https://doi.org/10.5194/tc-10-2655-2016, 2016.
Picard, G., Dumont, M., Lamare, M., Tuzet, F., Larue, F., Pirazzini, R., and Arnaud, L.: Spectral albedo measurements over snow-covered slopes: theory and slope effect corrections, The Cryosphere, 14, 1497–1517, https://doi.org/10.5194/tc-14-1497-2020, 2020.
Pirazzini, R., Räisänen, P., Vihma, T., Johansson, M., and Tastula, E.-M.: Measurements and modelling of snow particle size and shortwave infrared albedo over a melting Antarctic ice sheet, The Cryosphere, 9, 2357–2381, https://doi.org/10.5194/tc-9-2357-2015, 2015.
Pomeroy, J. W. and Gray, D. M.: Saltation of Snow, Water Resour. Res.,
26, 1583–1594, 1990.
Rautiainen, M. and Stenberg, P.: Application of photon recollision
probability in coniferous canopy reflectance simulations, Remote Sens.
Environ., 96, 98–107, 2005.
Räisänen, P., Kokhanovsky, A., Guyot, G., Jourdan, O., and Nousiainen, T.: Parameterization of single-scattering properties of snow, The Cryosphere, 9, 1277–1301, https://doi.org/10.5194/tc-9-1277-2015, 2015.
Román, M. O., Schaaf, C. B., Lewis, P., Gao, F., Anderson, G. P., Privette, J. L., Strahler, A. H., Woodcock, C. E., and Barnsley, M.: Assessing the coupling
between surface albedo derived from MODIS and the fraction of diffuse
skylight over spatially-characterized landscapes, Remote Sens. Environ., 114, 738–760, 2010.
Roujean, J.-L., Manninen, T., Sukuvaara, T., Peltoniemi, J., Kaasalainen,
S., Hautecoeur, O., Lahtinen, P., Riihelä, A., Siljamo, N.,
Lötjönen, M., Karjalainen, T., Kontu, A., Suokanerva, H., Aulamo,
O., Lemmetyinen, J., Suomalainen, J., Hakala, T., Kaartinen, H., Thölix,
L., Meinander, O., and Karhu, J.: SNORTEX (Snow Reflectance Transition
Experiment): Remote sensing measurement of snowmelt in northern boreal
forest of Europe, iLEAPS Newsletter, 56–58, 2010.
Russ, J. C.: Fractal surfaces, Plenum Press, New York, USA, 167 pp., 1994.
Schaepman-Strub, G., Schaepman, M. E., Painter, T. H., Dangel, S., and
Martonchik, J. V.: Reflectance quantities in optical remote
sensing–definitions and case studies, Remote Sens. Environ., 103,
27–42, 2006.
Smolander, S. and Stenberg, P.: Simple parameterizations of the radiation
budget of uniform broadleaved and coniferous canopies, Remote Sens. Environ., 94, 355–363, 2005.
Sommer, C. G., Lehning, M., and Fierz, C.: Wind Tunnel Experiments: Influence
of Erosion and Deposition on Wind-Packing of New Snow, Front. Earth Sci.,
6, 4, https://doi.org/10.3389/feart.2018.00004, 2018.
Stenberg, P. and Manninen, T.: The effect of clumping on canopy scattering
and its directional properties: a model simulation using spectral
invariants, Int. J. Remote Sens., 36, 5178–5191.
https://doi.org/10.1080/01431161.2015.1049383, 2015.
Stenberg, P., Mõttus, M., and Rautiainen, M.: Modeling the spectral
signature of forests: Application of remote sensing models to coniferous
canopies, in: Advances in land remote sensing, edited by: Liang, S., Springer
Science+ Business Media B. V., 147–171, 2008.
Stenberg, P., Mõttus, M., and Rautiainen, M.: Photon recollision
probability in modelling the radiation regime of canopies – A review,
Remote Sens. Environ., 183, 98–108, 2016.
Toikka, M.: Field tests with the Snow fork in determining the density and
wetness profiles of a snow pack, in: URSI Symposium on Terrestrial Remote Sensing with Microwaves: Signatures, Techniques and Systems, Igls, Innsbruck, Austria, 1–3 July 1992, available at: https://toikkaoy.com/snowfork_brochure_2018.pdf (last access: 20 January 2020), 1992.
Tsang, L., Kong., J. A., and Shin, R. T.: Theory of Microwave Remote Sensing,
John Wiley & Sons, New York, USA, 613 pp., 1985.
Ulaby, F. T., Moore, R. K., and Fung, A. K.: Microwave Remote Sensing, Active
and Passice, Addison-Wesley, London, UK, 457–106, 1982.
Warren, S. G.: Optical constants of ice from the ultraviolet to the
microwave, Appl. Optics, 23, 1206–1225, https://doi.org/10.1364/AO.23.001206, 1984.
Warren, S. G. and Brandt, R. E.: Optical constants of ice from the
ultraviolet to the microwave: A revised compilation, J. Geophys. Res., 113,
D14220, https://doi.org/10.1029/2007JD009744, 2008.
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, 1980.
Warren, S. G., Brandt, R. E., and O'Rawe Hinton, P.: Effect of surface
roughness on bidirectional reflectance of Antarctic snow, J. Geophys. Res., 103, 25789–25807, 1998.
Zhang, Z., Yang, P., Kattawar, G., Tsay, S., Baum, B., Hu, Y., Heymsfield,
A., and Reichardt, J.: Geometrical-optics solution to light scattering by
droxtal ice crystals, Appl. Optics, 43, 2490–2499, 2004.
Zhuravleva, T. B. and Kokhanovsky, A. A.: Influence of surface roughness on
the reflective properties of snow,
J. Quant. Spectrosc. Ra., 112, 1353–1368, 2011.
Short summary
The primary goal of this paper is to present a model of snow surface albedo (brightness) accounting for small-scale surface roughness effects. It can be combined with any volume scattering model. The results indicate that surface roughness may decrease the albedo by about 1–3 % in midwinter and even more than 10 % during the late melting season. The effect is largest for low solar zenith angle values and lower bulk snow albedo values.
The primary goal of this paper is to present a model of snow surface albedo (brightness)...
