Articles | Volume 13, issue 8
https://doi.org/10.5194/essd-13-3967-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/essd-13-3967-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
13 Aug 2021
Data description paper |
| 13 Aug 2021
| 13 Aug 2021
Inter-annual variation in lake ice composition in the European Arctic: observations based on high-resolution thermistor strings
Finnish Meteorological Institute (FMI), Helsinki, Finland
Yubing Cheng
CORRESPONDING AUTHOR
Institute of Atmospheric Physics, Chinese Academy of Sciences,
Beijing, China
University of Chinese Academy of Sciences, Beijing, China
Finnish Meteorological Institute (FMI), Helsinki, Finland
Timo Vihma
Finnish Meteorological Institute (FMI), Helsinki, Finland
Anna Kontu
Finnish Meteorological Institute (FMI), Helsinki, Finland
Fei Zheng
Institute of Atmospheric Physics, Chinese Academy of Sciences,
Beijing, China
Juha Lemmetyinen
Finnish Meteorological Institute (FMI), Helsinki, Finland
FMI-AIR, Joint Research Center for Arctic Observations,
Sodankylä, Finland
Yubao Qiu
Aerospace information Research Institute (AIR), Chinese Academy of
Sciences, Beijing, China
FMI-AIR, Joint Research Center for Arctic Observations,
Sodankylä, Finland
Jouni Pulliainen
Finnish Meteorological Institute (FMI), Helsinki, Finland
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Short summary
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Manuscript not accepted for further review
Short summary
Short summary
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Atmos. Chem. Phys., 23, 345–353, https://doi.org/10.5194/acp-23-345-2023, https://doi.org/10.5194/acp-23-345-2023, 2023
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Previous studies have noted a significant relationship between the Subtropical Indian Ocean Dipole and the South Atlantic Ocean Dipole indices, but little is known about the stability of their relationship. We found a significant positive correlation between the two indices prior to the year 2000 but an insignificant correlation afterwards.
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Atmos. Chem. Phys., 22, 12207–12220, https://doi.org/10.5194/acp-22-12207-2022, https://doi.org/10.5194/acp-22-12207-2022, 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
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.
Elena Shevnina, Miguel Potes, Timo Vihma, Tuomas Naakka, Pankaj Ramji Dhote, and Praveen Kumar Thakur
The Cryosphere, 16, 3101–3121, https://doi.org/10.5194/tc-16-3101-2022, https://doi.org/10.5194/tc-16-3101-2022, 2022
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The evaporation over an ice-free glacial lake was measured in January 2018, and the uncertainties inherent to five indirect methods were quantified. Results show that in summer up to 5 mm of water evaporated daily from the surface of the lake located in Antarctica. The indirect methods underestimated the evaporation over the lake's surface by up to 72 %. The results are important for estimating the evaporation over polar regions where a growing number of glacial lakes have recently been evident.
Janosch Michaelis, Amelie U. Schmitt, Christof Lüpkes, Jörg Hartmann, Gerit Birnbaum, and Timo Vihma
Earth Syst. Sci. Data, 14, 1621–1637, https://doi.org/10.5194/essd-14-1621-2022, https://doi.org/10.5194/essd-14-1621-2022, 2022
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A major goal of the Springtime Atmospheric Boundary Layer Experiment (STABLE) aircraft campaign was to observe atmospheric conditions during marine cold-air outbreaks (MCAOs) originating from the sea-ice-covered Arctic ocean. Quality-controlled measurements of several meteorological variables collected during 15 vertical aircraft profiles and by 22 dropsondes are presented. The comprehensive data set may be used for validating model results to improve the understanding of future trends in MCAOs.
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.
Yu Liang, Haibo Bi, Haijun Huang, Ruibo Lei, Xi Liang, Bin Cheng, and Yunhe Wang
The Cryosphere, 16, 1107–1123, https://doi.org/10.5194/tc-16-1107-2022, https://doi.org/10.5194/tc-16-1107-2022, 2022
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A record minimum July sea ice extent, since 1979, was observed in 2020. Our results reveal that an anomalously high advection of energy and water vapor prevailed during spring (April to June) 2020 over regions with noticeable sea ice retreat. The large-scale atmospheric circulation and cyclones act in concert to trigger the exceptionally warm and moist flow. The convergence of the transport changed the atmospheric characteristics and the surface energy budget, thus causing a severe sea ice melt.
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.
Ruibo Lei, Mario Hoppmann, Bin Cheng, Guangyu Zuo, Dawei Gui, Qiongqiong Cai, H. Jakob Belter, and Wangxiao Yang
The Cryosphere, 15, 1321–1341, https://doi.org/10.5194/tc-15-1321-2021, https://doi.org/10.5194/tc-15-1321-2021, 2021
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Quantification of ice deformation is useful for understanding of the role of ice dynamics in climate change. Using data of 32 buoys, we characterized spatiotemporal variations in ice kinematics and deformation in the Pacific sector of Arctic Ocean for autumn–winter 2018/19. Sea ice in the south and west has stronger mobility than in the east and north, which weakens from autumn to winter. An enhanced Arctic dipole and weakened Beaufort Gyre in winter lead to an obvious turning of ice drifting.
Terhikki Manninen, Kati Anttila, Emmihenna Jääskeläinen, Aku Riihelä, Jouni Peltoniemi, Petri Räisänen, Panu Lahtinen, Niilo Siljamo, Laura Thölix, Outi Meinander, Anna Kontu, Hanne Suokanerva, Roberta Pirazzini, Juha Suomalainen, Teemu Hakala, Sanna Kaasalainen, Harri Kaartinen, Antero Kukko, Olivier Hautecoeur, and Jean-Louis Roujean
The Cryosphere, 15, 793–820, https://doi.org/10.5194/tc-15-793-2021, https://doi.org/10.5194/tc-15-793-2021, 2021
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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.
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.
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.
Henna-Reetta Hannula, Kirsikka Heinilä, Kristin Böttcher, Olli-Pekka Mattila, Miia Salminen, and Jouni Pulliainen
Earth Syst. Sci. Data, 12, 719–740, https://doi.org/10.5194/essd-12-719-2020, https://doi.org/10.5194/essd-12-719-2020, 2020
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We publish and describe a surface spectral reflectance data record of seasonal snow (dry, wet, shadowed), forest ground (lichen, moss) and forest canopy (spruce and pine, branches) constituting the main elements of the boreal landscape and collected at four scales. The data record describes the characteristics and variability of the satellite scene reflectance contributors in boreal landscape, thus enabling the development of improved optical satellite snow mapping methods for forested areas.
Silvan Leinss, Henning Löwe, Martin Proksch, and Anna Kontu
The Cryosphere, 14, 51–75, https://doi.org/10.5194/tc-14-51-2020, https://doi.org/10.5194/tc-14-51-2020, 2020
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The anisotropy of the snow microstructure, given by horizontally aligned ice crystals and vertically interlinked crystal chains, is a key quantity to understand mechanical, dielectric, and thermodynamical properties of snow. We present a model which describes the temporal evolution of the anisotropy. The model is driven by snow temperature, temperature gradient, and the strain rate. The model is calibrated by polarimetric radar data (CPD) and validated by computer tomographic 3-D snow images.
Cécile B. Ménard, Richard Essery, Alan Barr, Paul Bartlett, Jeff Derry, Marie Dumont, Charles Fierz, Hyungjun Kim, Anna Kontu, Yves Lejeune, Danny Marks, Masashi Niwano, Mark Raleigh, Libo Wang, and Nander Wever
Earth Syst. Sci. Data, 11, 865–880, https://doi.org/10.5194/essd-11-865-2019, https://doi.org/10.5194/essd-11-865-2019, 2019
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This paper describes long-term meteorological and evaluation datasets from 10 reference sites for use in snow modelling. We demonstrate how data sharing is crucial to the identification of errors and how the publication of these datasets contributes to good practice, consistency, and reproducibility in geosciences. The ease of use, availability, and quality of the datasets will help model developers quantify and reduce model uncertainties and errors.
Wenfeng Huang, Bin Cheng, Jinrong Zhang, Zheng Zhang, Timo Vihma, Zhijun Li, and Fujun Niu
Hydrol. Earth Syst. Sci., 23, 2173–2186, https://doi.org/10.5194/hess-23-2173-2019, https://doi.org/10.5194/hess-23-2173-2019, 2019
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Up to now, little has been known on ice thermodynamics and lake–atmosphere interaction over the Tibetan Plateau during ice-covered seasons due to a lack of field data. Here, model experiments on ice thermodynamics were conducted in a shallow lake using HIGHTSI. Water–ice heat flux was a major source of uncertainty for lake ice thickness. Heat and mass budgets were estimated within the vertical air–ice–water system. Strong ice sublimation occurred and was responsible for water loss during winter.
Lejiang Yu, Shiyuan Zhong, and Timo Vihma
The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-38, https://doi.org/10.5194/tc-2019-38, 2019
Manuscript not accepted for further review
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Arctic sea ice cover has been decreasing in recent decades. The reason for the decrease remains unclear. In this study, we examine the contributions of the North Pacific SST anomalies to the decrease. There are global warming and Pacific Decadal Oscillation (PDO) modesof the North Pacific SST variability in boreal summer and autumn. The global warming mode explains 44.9% and 50.1% of the Arctic sea ice loss in boreal summer and autumn, respectively. There are 22.0% and 22.2% for PDO mode.
Timo Vihma, Petteri Uotila, Stein Sandven, Dmitry Pozdnyakov, Alexander Makshtas, Alexander Pelyasov, Roberta Pirazzini, Finn Danielsen, Sergey Chalov, Hanna K. Lappalainen, Vladimir Ivanov, Ivan Frolov, Anna Albin, Bin Cheng, Sergey Dobrolyubov, Viktor Arkhipkin, Stanislav Myslenkov, Tuukka Petäjä, and Markku Kulmala
Atmos. Chem. Phys., 19, 1941–1970, https://doi.org/10.5194/acp-19-1941-2019, https://doi.org/10.5194/acp-19-1941-2019, 2019
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The Arctic marine climate system, ecosystems, and socio-economic systems are changing rapidly. This calls for the establishment of a marine Arctic component of the Pan-Eurasian Experiment (MA-PEEX), for which we present a plan. The program will promote international collaboration; sustainable marine meteorological, sea ice, and oceanographic observations; advanced data management; and multidisciplinary research on the marine Arctic and its interaction with the Eurasian continent.
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.
Elena Shevnina, Karoliina Pilli-Sihvola, Riina Haavisto, Timo Vihma, and Andrey Silaev
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-473, https://doi.org/10.5194/hess-2018-473, 2018
Manuscript not accepted for further review
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Projections of a potential hydropower production were evaluated in terms of probability of water resources available in the future. The future projections of annual river runoff were evaluated on average, as well as on low and high exceedance probabilities under several climate change scenarios. The main idea of the modelling method used is to simulate statistical estimators of annual river runoff (mean, variation and skewness) instead of runoff time series.
Peng Lu, Matti Leppäranta, Bin Cheng, Zhijun Li, Larysa Istomina, and Georg Heygster
The Cryosphere, 12, 1331–1345, https://doi.org/10.5194/tc-12-1331-2018, https://doi.org/10.5194/tc-12-1331-2018, 2018
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It is the first time that the color of melt ponds on Arctic sea ice was quantitatively and thoroughly investigated. We answer the question of why the color of melt ponds can change and what the physical and optical reasons are that lead to such changes. More importantly, melt-pond color was provided as potential data in determining ice thickness, especially under the summer conditions when other methods such as remote sensing are unavailable.
Jonas Svensson, Johan Ström, Niku Kivekäs, Nathaniel B. Dkhar, Shresth Tayal, Ved P. Sharma, Arttu Jutila, John Backman, Aki Virkkula, Meri Ruppel, Antti Hyvärinen, Anna Kontu, Henna-Reetta Hannula, Matti Leppäranta, Rakesh K. Hooda, Atte Korhola, Eija Asmi, and Heikki Lihavainen
Atmos. Meas. Tech., 11, 1403–1416, https://doi.org/10.5194/amt-11-1403-2018, https://doi.org/10.5194/amt-11-1403-2018, 2018
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Receding glaciers in the Himalayas are of concern. Here we present measurements of light-absorbing impurities, known to contribute to the ongoing glacier decrease, in snow from Indian Himalayas and compare them to snow samples from the Finnish Arctic. The soot particles in the snow are shown to have lower light absorbing efficiency, possibly affecting their radiative forcing potential in the snow. Further, dust influences the snow in the Himalayas to a much greater extent than in Finland.
Elena Shevnina, Ekaterina Kourzeneva, Viktor Kovalenko, and Timo Vihma
Hydrol. Earth Syst. Sci., 21, 2559–2578, https://doi.org/10.5194/hess-21-2559-2017, https://doi.org/10.5194/hess-21-2559-2017, 2017
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This paper presents the probabilistic approach to evaluate design floods in a changing climate, adapted in this case to the northern territories. For the Russian Arctic, the regions are delineated, where it is suggested to correct engineering hydrological calculations to account for climate change. An example of the calculation of a maximal discharge of 1 % exceedance probability for the Nadym River at Nadym is provided.
Melody Sandells, Richard Essery, Nick Rutter, Leanne Wake, Leena Leppänen, and Juha Lemmetyinen
The Cryosphere, 11, 229–246, https://doi.org/10.5194/tc-11-229-2017, https://doi.org/10.5194/tc-11-229-2017, 2017
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This study looks at a wide range of options for simulating sensor signals for satellite monitoring of water stored as snow, though an ensemble of 1323 coupled snow evolution and microwave scattering models. The greatest improvements will be made with better computer simulations of how the snow microstructure changes, followed by how the microstructure scatters radiation at microwave frequencies. Snow compaction should also be considered in systems to monitor snow mass from space.
Craig D. Smith, Anna Kontu, Richard Laffin, and John W. Pomeroy
The Cryosphere, 11, 101–116, https://doi.org/10.5194/tc-11-101-2017, https://doi.org/10.5194/tc-11-101-2017, 2017
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One of the objectives of the WMO Solid Precipitation Intercomparison Experiment (SPICE) was to assess the performance of automated instruments that measure snow water equivalent and make recommendations on the best measurement practices and data interpretation. This study assesses the Campbell Scientific CS725 and the Sommer SSG100 for measuring SWE. Different measurement principals of the instruments as well as site characteristics influence the way that the SWE data should be interpreted.
Hanna K. Lappalainen, Veli-Matti Kerminen, Tuukka Petäjä, Theo Kurten, Aleksander Baklanov, Anatoly Shvidenko, Jaana Bäck, Timo Vihma, Pavel Alekseychik, Meinrat O. Andreae, Stephen R. Arnold, Mikhail Arshinov, Eija Asmi, Boris Belan, Leonid Bobylev, Sergey Chalov, Yafang Cheng, Natalia Chubarova, Gerrit de Leeuw, Aijun Ding, Sergey Dobrolyubov, Sergei Dubtsov, Egor Dyukarev, Nikolai Elansky, Kostas Eleftheriadis, Igor Esau, Nikolay Filatov, Mikhail Flint, Congbin Fu, Olga Glezer, Aleksander Gliko, Martin Heimann, Albert A. M. Holtslag, Urmas Hõrrak, Juha Janhunen, Sirkku Juhola, Leena Järvi, Heikki Järvinen, Anna Kanukhina, Pavel Konstantinov, Vladimir Kotlyakov, Antti-Jussi Kieloaho, Alexander S. Komarov, Joni Kujansuu, Ilmo Kukkonen, Ella-Maria Duplissy, Ari Laaksonen, Tuomas Laurila, Heikki Lihavainen, Alexander Lisitzin, Alexsander Mahura, Alexander Makshtas, Evgeny Mareev, Stephany Mazon, Dmitry Matishov, Vladimir Melnikov, Eugene Mikhailov, Dmitri Moisseev, Robert Nigmatulin, Steffen M. Noe, Anne Ojala, Mari Pihlatie, Olga Popovicheva, Jukka Pumpanen, Tatjana Regerand, Irina Repina, Aleksei Shcherbinin, Vladimir Shevchenko, Mikko Sipilä, Andrey Skorokhod, Dominick V. Spracklen, Hang Su, Dmitry A. Subetto, Junying Sun, Arkady Y. Terzhevik, Yuri Timofeyev, Yuliya Troitskaya, Veli-Pekka Tynkkynen, Viacheslav I. Kharuk, Nina Zaytseva, Jiahua Zhang, Yrjö Viisanen, Timo Vesala, Pertti Hari, Hans Christen Hansson, Gennady G. Matvienko, Nikolai S. Kasimov, Huadong Guo, Valery Bondur, Sergej Zilitinkevich, and Markku Kulmala
Atmos. Chem. Phys., 16, 14421–14461, https://doi.org/10.5194/acp-16-14421-2016, https://doi.org/10.5194/acp-16-14421-2016, 2016
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After kick off in 2012, the Pan-Eurasian Experiment (PEEX) program has expanded fast and today the multi-disciplinary research community covers ca. 80 institutes and a network of ca. 500 scientists from Europe, Russia, and China. Here we introduce scientific topics relevant in this context. This is one of the first multi-disciplinary overviews crossing scientific boundaries, from atmospheric sciences to socio-economics and social sciences.
Juha Lemmetyinen, Anna Kontu, Jouni Pulliainen, Juho Vehviläinen, Kimmo Rautiainen, Andreas Wiesmann, Christian Mätzler, Charles Werner, Helmut Rott, Thomas Nagler, Martin Schneebeli, Martin Proksch, Dirk Schüttemeyer, Michael Kern, and Malcolm W. J. Davidson
Geosci. Instrum. Method. Data Syst., 5, 403–415, https://doi.org/10.5194/gi-5-403-2016, https://doi.org/10.5194/gi-5-403-2016, 2016
Silvan Leinss, Henning Löwe, Martin Proksch, Juha Lemmetyinen, Andreas Wiesmann, and Irena Hajnsek
The Cryosphere, 10, 1771–1797, https://doi.org/10.5194/tc-10-1771-2016, https://doi.org/10.5194/tc-10-1771-2016, 2016
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Four years of anisotropy measurements of seasonal snow are presented in the paper. The anisotropy was measured every 4 h with a ground-based polarimetric radar. An electromagnetic model has been developed to measured the anisotropy with radar instruments from ground and from space. The anisotropic permittivity was derived with Maxwell–Garnett-type mixing formulas which are shown to be equivalent to series expansions of the permittivity tensor based on spatial correlation function of snow.
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.
Kaisa Lakkala, Hanne Suokanerva, Juha Matti Karhu, Antti Aarva, Antti Poikonen, Tomi Karppinen, Markku Ahponen, Henna-Reetta Hannula, Anna Kontu, and Esko Kyrö
Geosci. Instrum. Method. Data Syst., 5, 315–320, https://doi.org/10.5194/gi-5-315-2016, https://doi.org/10.5194/gi-5-315-2016, 2016
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This paper describes the laboratory facilities at the Finnish Meteorological Institute – Arctic Research Centre (FMI-ARC). They comprise an optical laboratory, a facility for biological studies, and an office. The facilities are ideal for responding to the needs of international multidisciplinary research, giving the possibility to calibrate and characterize the research instruments as well as handle and store samples.
Richard Essery, Anna Kontu, Juha Lemmetyinen, Marie Dumont, and Cécile B. Ménard
Geosci. Instrum. Method. Data Syst., 5, 219–227, https://doi.org/10.5194/gi-5-219-2016, https://doi.org/10.5194/gi-5-219-2016, 2016
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Physically based models that predict the properties of snow on the ground are used in many applications, but meteorological input data required by these models are hard to obtain in cold regions. Monitoring at the Sodankyla research station allows construction of model input and evaluation datasets covering several years for the first time in the Arctic. The data are used to show that a sophisticated snow model developed for warmer and wetter sites can perform well in very different conditions.
Leena Leppänen, Anna Kontu, Henna-Reetta Hannula, Heidi Sjöblom, and Jouni Pulliainen
Geosci. Instrum. Method. Data Syst., 5, 163–179, https://doi.org/10.5194/gi-5-163-2016, https://doi.org/10.5194/gi-5-163-2016, 2016
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The manual snow survey program of Finnish Meteorological Institute consists of numerous observations of natural seasonal snowpack in Sodankylä, in northern Finland. Systematic snow measurements began in 1911 with snow depth and snow water equivalent. In 2006 the manual snow survey program expanded to cover snow macro- and microstructure from snow pits. Extensive time series of manual snow measurements are important for the monitoring of temporal and spatial changes in seasonal snowpack.
Jaakko Ikonen, Juho Vehviläinen, Kimmo Rautiainen, Tuomo Smolander, Juha Lemmetyinen, Simone Bircher, and Jouni Pulliainen
Geosci. Instrum. Method. Data Syst., 5, 95–108, https://doi.org/10.5194/gi-5-95-2016, https://doi.org/10.5194/gi-5-95-2016, 2016
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A comprehensive, distributed network of in situ measurement stations gathering information on soil moisture has been set up in recent years at the Finnish Meteorological Institute's (FMI) Sodankylä Arctic research station. The network is used as a tool to evaluate the validity of satellite retrievals of soil properties. We present the soil moisture observation network and the results of comparisons of top layer soil moisture between 2012 and 2014 against ESA CCI product soil moisture retrievals.
William Maslanka, Leena Leppänen, Anna Kontu, Mel Sandells, Juha Lemmetyinen, Martin Schneebeli, Martin Proksch, Margret Matzl, Henna-Reetta Hannula, and Robert Gurney
Geosci. Instrum. Method. Data Syst., 5, 85–94, https://doi.org/10.5194/gi-5-85-2016, https://doi.org/10.5194/gi-5-85-2016, 2016
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The paper presents the initial findings of the Arctic Snow Microstructure Experiment in Sodankylä, Finland. The experiment observed the microwave emission of extracted snow slabs on absorbing and reflecting bases. Snow parameters were recorded to simulate the emission upon those bases using two different emission models. The smallest simulation errors were associated with the absorbing base at vertical polarization. The observations will be used for the development of snow emission modelling.
P. Hari, T. Petäjä, J. Bäck, V.-M. Kerminen, H. K. Lappalainen, T. Vihma, T. Laurila, Y. Viisanen, T. Vesala, and M. Kulmala
Atmos. Chem. Phys., 16, 1017–1028, https://doi.org/10.5194/acp-16-1017-2016, https://doi.org/10.5194/acp-16-1017-2016, 2016
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This manuscript introduces a conceptual design of a global, hierarchical observation network which provides tools and increased understanding to tackle the inter-connected environmental and societal challenges that we will face in the coming decades. Each ecosystem type on the globe has its own characteristic features that need to be taken into consideration. The hierarchical network is able to tackle problems related to large spatial scales, heterogeneity of ecosystems and their complexity.
O. Meinander, A. Aarva, A. Poikonen, A. Kontu, H. Suokanerva, E. Asmi, K. Neitola, E. Rodriguez, R. Sanchez, M. Mei, G. de Leeuw, and E. Kyrö
Geosci. Instrum. Method. Data Syst. Discuss., https://doi.org/10.5194/gi-2015-31, https://doi.org/10.5194/gi-2015-31, 2016
Revised manuscript not accepted
R. Pirazzini, P. Räisänen, T. Vihma, M. Johansson, and E.-M. Tastula
The Cryosphere, 9, 2357–2381, https://doi.org/10.5194/tc-9-2357-2015, https://doi.org/10.5194/tc-9-2357-2015, 2015
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We illustrate a method to measure the size distribution of a snow particle metric from macro photos of snow particles. This snow particle metric corresponds well to the optically equivalent effective radius. Our results evidence the impact of grain shape on albedo, indicate that more than just one particle metric distribution is needed to characterize the snow scattering properties at all optical wavelengths, and suggest an impact of surface roughness on the shortwave infrared albedo.
M. Proksch, C. Mätzler, A. Wiesmann, J. Lemmetyinen, M. Schwank, H. Löwe, and M. Schneebeli
Geosci. Model Dev., 8, 2611–2626, https://doi.org/10.5194/gmd-8-2611-2015, https://doi.org/10.5194/gmd-8-2611-2015, 2015
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The measurement of snow properties on global scale relies on microwave remote sensing data. The interpretation of the data is however challenging. Here we introduce MEMLS3&a, an extension of the snow emission model MEMLS, to include a backscatter model for active microwave remote sensing. In MEMLS3&a, snow input parameters can be derived by objective measurement methods, which avoids fitting the scattering efficiency of snow. The model is validated with combined active and passive measurements.
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.
J. Svensson, A. Virkkula, O. Meinander, N. Kivekäs, H.-R. Hannula, O. Järvinen, J. I. Peltoniemi, M. Gritsevich, A. Heikkilä, A. Kontu, A.-P. Hyvärinen, K. Neitola, D. Brus, P. Dagsson-Waldhauserova, K. Anttila, T. Hakala, H. Kaartinen, M. Vehkamäki, G. de Leeuw, and H. Lihavainen
The Cryosphere Discuss., https://doi.org/10.5194/tcd-9-1227-2015, https://doi.org/10.5194/tcd-9-1227-2015, 2015
Revised manuscript not accepted
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Soot's (including black carbon and organics) negative effect on a natural snow pack is experimentally addressed in this paper through a series of experiments. Soot concentrations in the snow in the range of 200-200 000 ppb verify the negative effects on the albedo, the physical snow characteristics, as well as increasing the melt rate of the snow pack. Our experimental data generally agrees when compared with the Snow, Ice and Aerosol Radiation model.
F. Zheng and J. Zhu
Ocean Sci., 11, 187–194, https://doi.org/10.5194/os-11-187-2015, https://doi.org/10.5194/os-11-187-2015, 2015
R. Döscher, T. Vihma, and E. Maksimovich
Atmos. Chem. Phys., 14, 13571–13600, https://doi.org/10.5194/acp-14-13571-2014, https://doi.org/10.5194/acp-14-13571-2014, 2014
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The article reviews progress in understanding of the Arctic sea ice decline. Processes are revisited from an atmospheric, ocean and sea ice perspective. There is strong evidence for decisive atmospheric drivers of sea ice change. Large-scale ocean influences on the Arctic Ocean hydrology and circulation are highly evident. Ocean heat fluxes are clearly impacting the ice margins. Little indication exists for a direct decisive influence of the warming ocean on the central Arctic sea ice cover.
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.
A. Tetzlaff, C. Lüpkes, G. Birnbaum, J. Hartmann, T. Nygård, and T. Vihma
The Cryosphere, 8, 1757–1762, https://doi.org/10.5194/tc-8-1757-2014, https://doi.org/10.5194/tc-8-1757-2014, 2014
T. Vihma, R. Pirazzini, I. Fer, I. A. Renfrew, J. Sedlar, M. Tjernström, C. Lüpkes, T. Nygård, D. Notz, J. Weiss, D. Marsan, B. Cheng, G. Birnbaum, S. Gerland, D. Chechin, and J. C. Gascard
Atmos. Chem. Phys., 14, 9403–9450, https://doi.org/10.5194/acp-14-9403-2014, https://doi.org/10.5194/acp-14-9403-2014, 2014
I. Välisuo, T. Vihma, and J. C. King
The Cryosphere, 8, 1519–1538, https://doi.org/10.5194/tc-8-1519-2014, https://doi.org/10.5194/tc-8-1519-2014, 2014
O. Meinander, A. Kontu, A. Virkkula, A. Arola, L. Backman, P. Dagsson-Waldhauserová, O. Järvinen, T. Manninen, J. Svensson, G. de Leeuw, and M. Leppäranta
The Cryosphere, 8, 991–995, https://doi.org/10.5194/tc-8-991-2014, https://doi.org/10.5194/tc-8-991-2014, 2014
T. Nygård, T. Valkonen, and T. Vihma
Atmos. Chem. Phys., 14, 1959–1971, https://doi.org/10.5194/acp-14-1959-2014, https://doi.org/10.5194/acp-14-1959-2014, 2014
C. E. Chung, H. Cha, T. Vihma, P. Räisänen, and D. Decremer
Atmos. Chem. Phys., 13, 11209–11219, https://doi.org/10.5194/acp-13-11209-2013, https://doi.org/10.5194/acp-13-11209-2013, 2013
L. Jakobson, T. Vihma, E. Jakobson, T. Palo, A. Männik, and J. Jaagus
Atmos. Chem. Phys., 13, 11089–11099, https://doi.org/10.5194/acp-13-11089-2013, https://doi.org/10.5194/acp-13-11089-2013, 2013
A. Tetzlaff, L. Kaleschke, C. Lüpkes, F. Ament, and T. Vihma
The Cryosphere, 7, 153–166, https://doi.org/10.5194/tc-7-153-2013, https://doi.org/10.5194/tc-7-153-2013, 2013
Related subject area
Snow and Sea Ice
Mapping of sea ice concentration using the NASA NIMBUS 5 Electrically Scanning Microwave Radiometer data from 1972–1977
A climate data record of year-round global sea-ice drift from the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF)
Snow accumulation and ablation measurements in a midlatitude mountain coniferous forest (Col de Porte, France, 1325 m altitude): the Snow Under Forest (SnoUF) field campaign data set
A new sea ice concentration product in the polar regions derived from the FengYun-3 MWRI sensors
NH-SWE: Northern Hemisphere Snow Water Equivalent dataset based on in situ snow depth time series
IT-SNOW: a snow reanalysis for Italy blending modeling, in situ data, and satellite observations (2010–2021)
HMRFS–TP: long-term daily gap-free snow cover products over the Tibetan Plateau from 2002 to 2021 based on hidden Markov random field model
Large ensemble of downscaled historical daily snowfall from an earth system model to 5.5 km resolution over Dronning Maud Land, Antarctica
The S2M meteorological and snow cover reanalysis over the French mountainous areas: description and evaluation (1958–2021)
A new Greenland digital elevation model derived from ICESat-2 during 2018–2019
Reconstruction of a daily gridded snow water equivalent product for the land region above 45° N based on a ridge regression machine learning approach
Snow depth product over Antarctic sea ice from 2002 to 2020 using multisource passive microwave radiometers
The NIEER AVHRR snow cover extent product over China – a long-term daily snow record for regional climate research
Canadian historical Snow Water Equivalent dataset (CanSWE, 1928–2020)
Meteorological, snow and soil data (2013–2019) from a herb tundra permafrost site at Bylot Island, Canadian high Arctic, for driving and testing snow and land surface models
Daily Terra–Aqua MODIS cloud-free snow and Randolph Glacier Inventory 6.0 combined product (M*D10A1GL06) for high-mountain Asia between 2002 and 2019
High-resolution mapping of circum-Antarctic landfast sea ice distribution, 2000–2018
Laboratory, field, mast-borne and airborne spectral reflectance measurements of boreal landscape during spring
An improved Terra–Aqua MODIS snow cover and Randolph Glacier Inventory 6.0 combined product (MOYDGL06*) for high-mountain Asia between 2002 and 2018
Integrated hydrometeorological, snow and frozen-ground observations in the alpine region of the Heihe River Basin, China
Meteorological and evaluation datasets for snow modelling at 10 reference sites: description of in situ and bias-corrected reanalysis data
Hydrometeorological data from Marmot Creek Research Basin, Canadian Rockies
Wiebke Margitta Kolbe, Rasmus T. Tonboe, and Julienne Stroeve
Earth Syst. Sci. Data, 16, 1247–1264, https://doi.org/10.5194/essd-16-1247-2024, https://doi.org/10.5194/essd-16-1247-2024, 2024
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Current satellite-based sea-ice climate data records (CDRs) usually begin in October 1978 with the first multichannel microwave radiometer data. Here, we present a sea ice dataset based on the single-channel Electrical Scanning Microwave Radiometer (ESMR) that operated from 1972-1977 onboard NASA’s Nimbus 5 satellite. The data were processed using modern methods and include uncertainty estimations in order to provide an important, easy-to-use reference period of good quality for current CDRs.
Thomas Lavergne and Emily Down
Earth Syst. Sci. Data, 15, 5807–5834, https://doi.org/10.5194/essd-15-5807-2023, https://doi.org/10.5194/essd-15-5807-2023, 2023
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Sea ice in the Arctic and Antarctic can move several tens of kilometers per day due to wind and ocean currents. By analysing thousands of satellite images, we measured how sea ice has been moving every single day from 1991 through to 2020. We compare our data to how buoys attached to the ice moved and find good agreement. Other scientists will now use our data to better understand if climate change has modified the way sea ice moves and in what way.
Jean Emmanuel Sicart, Victor Ramseyer, Ghislain Picard, Laurent Arnaud, Catherine Coulaud, Guilhem Freche, Damien Soubeyrand, Yves Lejeune, Marie Dumont, Isabelle Gouttevin, Erwan Le Gac, Frédéric Berger, Jean-Matthieu Monnet, Laurent Borgniet, Éric Mermin, Nick Rutter, Clare Webster, and Richard Essery
Earth Syst. Sci. Data, 15, 5121–5133, https://doi.org/10.5194/essd-15-5121-2023, https://doi.org/10.5194/essd-15-5121-2023, 2023
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Forests strongly modify the accumulation, metamorphism and melting of snow in midlatitude and high-latitude regions. Two field campaigns during the winters 2016–17 and 2017–18 were conducted in a coniferous forest in the French Alps to study interactions between snow and vegetation. This paper presents the field site, instrumentation and collection methods. The observations include forest characteristics, meteorology, snow cover and snow interception by the canopy during precipitation events.
Ying Chen, Ruibo Lei, Xi Zhao, Shengli Wu, Yue Liu, Pei Fan, Qing Ji, Peng Zhang, and Xiaoping Pang
Earth Syst. Sci. Data, 15, 3223–3242, https://doi.org/10.5194/essd-15-3223-2023, https://doi.org/10.5194/essd-15-3223-2023, 2023
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The sea ice concentration product derived from the Microwave Radiation Image sensors on board the FengYun-3 satellites can reasonably and independently identify the seasonal and long-term changes of sea ice, as well as extreme cases of annual maximum and minimum sea ice extent in polar regions. It is comparable with other sea ice concentration products and applied to the studies of climate and marine environment.
Adrià Fontrodona-Bach, Bettina Schaefli, Ross Woods, Adriaan J. Teuling, and Joshua R. Larsen
Earth Syst. Sci. Data, 15, 2577–2599, https://doi.org/10.5194/essd-15-2577-2023, https://doi.org/10.5194/essd-15-2577-2023, 2023
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We provide a dataset of snow water equivalent, the depth of liquid water that results from melting a given depth of snow. The dataset contains 11 071 sites over the Northern Hemisphere, spans the period 1950–2022, and is based on daily observations of snow depth on the ground and a model. The dataset fills a lack of accessible historical ground snow data, and it can be used for a variety of applications such as the impact of climate change on global and regional snow and water resources.
Francesco Avanzi, Simone Gabellani, Fabio Delogu, Francesco Silvestro, Flavio Pignone, Giulia Bruno, Luca Pulvirenti, Giuseppe Squicciarino, Elisabetta Fiori, Lauro Rossi, Silvia Puca, Alexander Toniazzo, Pietro Giordano, Marco Falzacappa, Sara Ratto, Hervè Stevenin, Antonio Cardillo, Matteo Fioletti, Orietta Cazzuli, Edoardo Cremonese, Umberto Morra di Cella, and Luca Ferraris
Earth Syst. Sci. Data, 15, 639–660, https://doi.org/10.5194/essd-15-639-2023, https://doi.org/10.5194/essd-15-639-2023, 2023
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Snow cover has profound implications for worldwide water supply and security, but knowledge of its amount and distribution across the landscape is still elusive. We present IT-SNOW, a reanalysis comprising daily maps of snow amount and distribution across Italy for 11 snow seasons from September 2010 to August 2021. The reanalysis was validated using satellite images and snow measurements and will provide highly needed data to manage snow water resources in a warming climate.
Yan Huang, Jiahui Xu, Jingyi Xu, Yelei Zhao, Bailang Yu, Hongxing Liu, Shujie Wang, Wanjia Xu, Jianping Wu, and Zhaojun Zheng
Earth Syst. Sci. Data, 14, 4445–4462, https://doi.org/10.5194/essd-14-4445-2022, https://doi.org/10.5194/essd-14-4445-2022, 2022
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Reliable snow cover information is important for understating climate change and hydrological cycling. We generate long-term daily gap-free snow products over the Tibetan Plateau (TP) at 500 m resolution from 2002 to 2021 based on the hidden Markov random field model. The accuracy is 91.36 %, and is especially improved during snow transitional period and over complex terrains. This dataset has great potential to study climate change and to facilitate water resource management in the TP.
Nicolas Ghilain, Stéphane Vannitsem, Quentin Dalaiden, Hugues Goosse, Lesley De Cruz, and Wenguang Wei
Earth Syst. Sci. Data, 14, 1901–1916, https://doi.org/10.5194/essd-14-1901-2022, https://doi.org/10.5194/essd-14-1901-2022, 2022
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Modeling the climate at high resolution is crucial to represent the snowfall accumulation over the complex orography of the Antarctic coast. While ice cores provide a view constrained spatially but over centuries, climate models can give insight into its spatial distribution, either at high resolution over a short period or vice versa. We downscaled snowfall accumulation from climate model historical simulations (1850–present day) over Dronning Maud Land at 5.5 km using a statistical method.
Matthieu Vernay, Matthieu Lafaysse, Diego Monteiro, Pascal Hagenmuller, Rafife Nheili, Raphaëlle Samacoïts, Deborah Verfaillie, and Samuel Morin
Earth Syst. Sci. Data, 14, 1707–1733, https://doi.org/10.5194/essd-14-1707-2022, https://doi.org/10.5194/essd-14-1707-2022, 2022
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This paper introduces the latest version of the freely available S2M dataset which provides estimates of both meteorological and snow cover variables, as well as various avalanche hazard diagnostics at different elevations, slopes and aspects for the three main French high-elevation mountainous regions. A complete description of the system and the dataset is provided, as well as an overview of the possible uses of this dataset and an objective assessment of its limitations.
Yubin Fan, Chang-Qing Ke, and Xiaoyi Shen
Earth Syst. Sci. Data, 14, 781–794, https://doi.org/10.5194/essd-14-781-2022, https://doi.org/10.5194/essd-14-781-2022, 2022
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A new digital elevation model of Greenland was provided based on the ICESat-2 observations acquired from November 2018 to November 2019. A model fit method was applied within the grid cells at different spatial resolutions to estimate the surface elevations with a modal resolution of 500 m. We estimated the uncertainty with a median difference of −0.48 m for all of Greenland, which can benefit studies of elevation change and mass balance in Greenland.
Donghang Shao, Hongyi Li, Jian Wang, Xiaohua Hao, Tao Che, and Wenzheng Ji
Earth Syst. Sci. Data, 14, 795–809, https://doi.org/10.5194/essd-14-795-2022, https://doi.org/10.5194/essd-14-795-2022, 2022
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The temporal series and spatial distribution discontinuity of the existing snow water equivalent (SWE) products in the pan-Arctic region severely restricts the use of SWE data in cryosphere change and climate change studies. Using a ridge regression machine learning algorithm, this study developed a set of spatiotemporally seamless and high-precision SWE products. This product could contribute to the study of cryosphere change and climate change at large spatial scales.
Xiaoyi Shen, Chang-Qing Ke, and Haili Li
Earth Syst. Sci. Data, 14, 619–636, https://doi.org/10.5194/essd-14-619-2022, https://doi.org/10.5194/essd-14-619-2022, 2022
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Snow over Antarctic sea ice controls energy budgets and thus has essential effects on the climate. Here, we estimated snow depth using microwave radiometers and a newly constructed, robust method by incorporating lower frequencies, which have been available from AMSR-E and AMSR-2. Comparing the new retrieval with in situ and shipborne snow depth measurements showed that this method outperformed the previously available method.
Xiaohua Hao, Guanghui Huang, Tao Che, Wenzheng Ji, Xingliang Sun, Qin Zhao, Hongyu Zhao, Jian Wang, Hongyi Li, and Qian Yang
Earth Syst. Sci. Data, 13, 4711–4726, https://doi.org/10.5194/essd-13-4711-2021, https://doi.org/10.5194/essd-13-4711-2021, 2021
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Long-term snow cover data are not only of importance for climate research. Currently China still lacks a high-quality snow cover extent (SCE) product for climate research. This study develops a multi-level decision tree algorithm for cloud and snow discrimination and gap-filled technique based on AVHRR surface reflectance data. We generate a daily 5 km SCE product across China from 1981 to 2019. It has high accuracy and will serve as baseline data for climate and other applications.
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.
Florent Domine, Georg Lackner, Denis Sarrazin, Mathilde Poirier, and Maria Belke-Brea
Earth Syst. Sci. Data, 13, 4331–4348, https://doi.org/10.5194/essd-13-4331-2021, https://doi.org/10.5194/essd-13-4331-2021, 2021
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Current sophisticated snow physics models were mostly designed for alpine conditions and cannot adequately simulate the physical properties of Arctic snowpacks. New snow models will require Arctic data sets for forcing and validation. We provide an extensive driving and testing data set from a high Arctic herb tundra site in Canada. Unique validating data include continuous time series of snow and soil thermal conductivity and temperature profiles. Field observations in spring are provided.
Sher Muhammad and Amrit Thapa
Earth Syst. Sci. Data, 13, 767–776, https://doi.org/10.5194/essd-13-767-2021, https://doi.org/10.5194/essd-13-767-2021, 2021
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Snow is a dominant water resource in high-mountain Asia and crucial for mountain communities and downstream populations. The present MODIS snow products are significantly uncertain and not useful for observation and simulation of climate, hydrology, and other water-related studies. This study reduces uncertainty in the daily MODIS snow data and generates a MODIS Terra–Aqua combined product reducing uncertainties due to cloud cover, data gaps, and other errors caused by sensor limitations.
Alexander D. Fraser, Robert A. Massom, Kay I. Ohshima, Sascha Willmes, Peter J. Kappes, Jessica Cartwright, and Richard Porter-Smith
Earth Syst. Sci. Data, 12, 2987–2999, https://doi.org/10.5194/essd-12-2987-2020, https://doi.org/10.5194/essd-12-2987-2020, 2020
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Landfast ice, or
fast ice, is a form of sea ice which is mechanically fastened to stationary parts of the coast. Long-term and accurate knowledge of its extent around Antarctica is critical for understanding a number of important Antarctic coastal processes, yet no accurate, large-scale, long-term dataset of its extent has been available. We address this data gap with this new dataset compiled from satellite imagery, containing high-resolution maps of Antarctic fast ice from 2000 to 2018.
Henna-Reetta Hannula, Kirsikka Heinilä, Kristin Böttcher, Olli-Pekka Mattila, Miia Salminen, and Jouni Pulliainen
Earth Syst. Sci. Data, 12, 719–740, https://doi.org/10.5194/essd-12-719-2020, https://doi.org/10.5194/essd-12-719-2020, 2020
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We publish and describe a surface spectral reflectance data record of seasonal snow (dry, wet, shadowed), forest ground (lichen, moss) and forest canopy (spruce and pine, branches) constituting the main elements of the boreal landscape and collected at four scales. The data record describes the characteristics and variability of the satellite scene reflectance contributors in boreal landscape, thus enabling the development of improved optical satellite snow mapping methods for forested areas.
Sher Muhammad and Amrit Thapa
Earth Syst. Sci. Data, 12, 345–356, https://doi.org/10.5194/essd-12-345-2020, https://doi.org/10.5194/essd-12-345-2020, 2020
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Snow is the major water resource in high-mountain Asia; therefore, it is crucial to continuously monitor it. Currently, remote sensing, mainly MODIS, is used for snow monitoring. However, the available MODIS snow product is not useful for various applications without postprocessing and improvement. This study reduces uncertainty in the MODIS snow data. We found approximately 50% underestimation and overestimation of snow cover by MODIS Terra–Aqua products, which were improved in this study.
Tao Che, Xin Li, Shaomin Liu, Hongyi Li, Ziwei Xu, Junlei Tan, Yang Zhang, Zhiguo Ren, Lin Xiao, Jie Deng, Rui Jin, Mingguo Ma, Jian Wang, and Xiaofan Yang
Earth Syst. Sci. Data, 11, 1483–1499, https://doi.org/10.5194/essd-11-1483-2019, https://doi.org/10.5194/essd-11-1483-2019, 2019
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The paper presents a suite of datasets consisting of long-term hydrometeorological, snow cover and frozen ground data for investigating watershed science and functions from an integrated, distributed and multiscale observation network in the upper reaches of the Heihe River Basin in China. These data are expected to serve as a testing platform to provide accurate forcing data and validate and evaluate remote-sensing products and hydrological models in cold regions for a broader community.
Cécile B. Ménard, Richard Essery, Alan Barr, Paul Bartlett, Jeff Derry, Marie Dumont, Charles Fierz, Hyungjun Kim, Anna Kontu, Yves Lejeune, Danny Marks, Masashi Niwano, Mark Raleigh, Libo Wang, and Nander Wever
Earth Syst. Sci. Data, 11, 865–880, https://doi.org/10.5194/essd-11-865-2019, https://doi.org/10.5194/essd-11-865-2019, 2019
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This paper describes long-term meteorological and evaluation datasets from 10 reference sites for use in snow modelling. We demonstrate how data sharing is crucial to the identification of errors and how the publication of these datasets contributes to good practice, consistency, and reproducibility in geosciences. The ease of use, availability, and quality of the datasets will help model developers quantify and reduce model uncertainties and errors.
Xing Fang, John W. Pomeroy, Chris M. DeBeer, Phillip Harder, and Evan Siemens
Earth Syst. Sci. Data, 11, 455–471, https://doi.org/10.5194/essd-11-455-2019, https://doi.org/10.5194/essd-11-455-2019, 2019
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Meteorological, snow survey, streamflow, and groundwater data are presented from Marmot Creek Research Basin, a small alpine-montane forest headwater catchment in the Alberta Rockies. It was heavily instrumented, experimented upon, and operated by several federal government agencies between 1962 and 1986 and was re-established starting in 2004 by the University of Saskatchewan Centre for Hydrology. These long-term legacy data serve to advance our knowledge of hydrology of the Canadian Rockies.
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Short summary
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.
Climate change strongly impacts the Arctic, with clear signs of higher air temperature and more...
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