Articles | Volume 11, issue 4
The Cryosphere, 11, 1987–2002, 2017
https://doi.org/10.5194/tc-11-1987-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
The Cryosphere, 11, 1987–2002, 2017
https://doi.org/10.5194/tc-11-1987-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
30 Aug 2017
Research article
| 30 Aug 2017
New methodology to estimate Arctic sea ice concentration from SMOS combining brightness temperature differences in a maximum-likelihood estimator
Carolina Gabarro et al.
Related authors
Jiping Xie, Roshin P. Raj, Laurent Bertino, Justino Martínez, Carolina Gabarró, and Rafael Catany
EGUsphere, https://doi.org/10.5194/egusphere-2022-660, https://doi.org/10.5194/egusphere-2022-660, 2022
Short summary
Short summary
Sea ice melt together with other freshwater sources have effects on the Arctic environment. The sea surface salinity (SSS) plays a key role to represent the water mixing. Recently the satellite SSS from SMOS was developed in Arctic region. In this study, we firstly evaluate the impact of assimilating this satellite data in an Arctic reanalysis system. It shows the SSS errors are reduced by 10–50 % depending on areas, encouraging its use in a long-time reanalysis to monitor the Arctic water cycle.
Verónica González-Gambau, Estrella Olmedo, Antonio Turiel, Cristina González-Haro, Aina García-Espriu, Justino Martínez, Pekka Alenius, Laura Tuomi, Rafael Catany, Manuel Arias, Carolina Gabarró, Nina Hoareau, Marta Umbert, Roberto Sabia, and Diego Fernández
Earth Syst. Sci. Data, 14, 2343–2368, https://doi.org/10.5194/essd-14-2343-2022, https://doi.org/10.5194/essd-14-2343-2022, 2022
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We present the first Soil Moisture and Ocean Salinity Sea Surface Salinity (SSS) dedicated products over the Baltic Sea (ESA Baltic+ Salinity Dynamics). The Baltic+ L3 product covers 9 days in a 0.25° grid. The Baltic+ L4 is derived by merging L3 SSS with sea surface temperature information, giving a daily product in a 0.05° grid. The accuracy of L3 is 0.7–0.8 and 0.4 psu for the L4. Baltic+ products have shown to be useful, covering spatiotemporal data gaps and for validating numerical models.
Justino Martínez, Carolina Gabarró, Antonio Turiel, Verónica González-Gambau, Marta Umbert, Nina Hoareau, Cristina González-Haro, Estrella Olmedo, Manuel Arias, Rafael Catany, Laurent Bertino, Roshin P. Raj, Jiping Xie, Roberto Sabia, and Diego Fernández
Earth Syst. Sci. Data, 14, 307–323, https://doi.org/10.5194/essd-14-307-2022, https://doi.org/10.5194/essd-14-307-2022, 2022
Short summary
Short summary
Measuring salinity from space is challenging since the sensitivity of the brightness temperature to sea surface salinity is low, but the retrieval of SSS in cold waters is even more challenging. In 2019, the ESA launched a specific initiative called Arctic+Salinity to produce an enhanced Arctic SSS product with better quality and resolution than the available products. This paper presents the methodologies used to produce the new enhanced Arctic SMOS SSS product.
Estrella Olmedo, Cristina González-Haro, Nina Hoareau, Marta Umbert, Verónica González-Gambau, Justino Martínez, Carolina Gabarró, and Antonio Turiel
Earth Syst. Sci. Data, 13, 857–888, https://doi.org/10.5194/essd-13-857-2021, https://doi.org/10.5194/essd-13-857-2021, 2021
Short summary
Short summary
After more than 10 years in orbit, the Soil Moisture and Ocean Salinity (SMOS) European mission is still a unique, high-quality instrument for providing soil moisture over land and sea surface salinity (SSS) over the oceans. At the Barcelona
Expert Center (BEC), a new reprocessing of 9 years (2011–2019) of global SMOS SSS maps has been generated. This work presents the algorithms used in the generation of the BEC global SMOS SSS product v2.0, as well as an extensive quality assessment.
Thomas Lavergne, Atle Macdonald Sørensen, Stefan Kern, Rasmus Tonboe, Dirk Notz, Signe Aaboe, Louisa Bell, Gorm Dybkjær, Steinar Eastwood, Carolina Gabarro, Georg Heygster, Mari Anne Killie, Matilde Brandt Kreiner, John Lavelle, Roberto Saldo, Stein Sandven, and Leif Toudal Pedersen
The Cryosphere, 13, 49–78, https://doi.org/10.5194/tc-13-49-2019, https://doi.org/10.5194/tc-13-49-2019, 2019
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The loss of polar sea ice is an iconic indicator of Earth’s climate change. Many satellite-based algorithms and resulting data exist but they differ widely in specific sea-ice conditions. This spread hinders a robust estimate of the future evolution of sea-ice cover.
In this study, we document three new climate data records of sea-ice concentration generated using satellite data available over the last 40 years. We introduce the novel algorithms, the data records, and their uncertainties.
Jiping Xie, Roshin P. Raj, Laurent Bertino, Justino Martínez, Carolina Gabarró, and Rafael Catany
EGUsphere, https://doi.org/10.5194/egusphere-2022-660, https://doi.org/10.5194/egusphere-2022-660, 2022
Short summary
Short summary
Sea ice melt together with other freshwater sources have effects on the Arctic environment. The sea surface salinity (SSS) plays a key role to represent the water mixing. Recently the satellite SSS from SMOS was developed in Arctic region. In this study, we firstly evaluate the impact of assimilating this satellite data in an Arctic reanalysis system. It shows the SSS errors are reduced by 10–50 % depending on areas, encouraging its use in a long-time reanalysis to monitor the Arctic water cycle.
Verónica González-Gambau, Estrella Olmedo, Antonio Turiel, Cristina González-Haro, Aina García-Espriu, Justino Martínez, Pekka Alenius, Laura Tuomi, Rafael Catany, Manuel Arias, Carolina Gabarró, Nina Hoareau, Marta Umbert, Roberto Sabia, and Diego Fernández
Earth Syst. Sci. Data, 14, 2343–2368, https://doi.org/10.5194/essd-14-2343-2022, https://doi.org/10.5194/essd-14-2343-2022, 2022
Short summary
Short summary
We present the first Soil Moisture and Ocean Salinity Sea Surface Salinity (SSS) dedicated products over the Baltic Sea (ESA Baltic+ Salinity Dynamics). The Baltic+ L3 product covers 9 days in a 0.25° grid. The Baltic+ L4 is derived by merging L3 SSS with sea surface temperature information, giving a daily product in a 0.05° grid. The accuracy of L3 is 0.7–0.8 and 0.4 psu for the L4. Baltic+ products have shown to be useful, covering spatiotemporal data gaps and for validating numerical models.
Justino Martínez, Carolina Gabarró, Antonio Turiel, Verónica González-Gambau, Marta Umbert, Nina Hoareau, Cristina González-Haro, Estrella Olmedo, Manuel Arias, Rafael Catany, Laurent Bertino, Roshin P. Raj, Jiping Xie, Roberto Sabia, and Diego Fernández
Earth Syst. Sci. Data, 14, 307–323, https://doi.org/10.5194/essd-14-307-2022, https://doi.org/10.5194/essd-14-307-2022, 2022
Short summary
Short summary
Measuring salinity from space is challenging since the sensitivity of the brightness temperature to sea surface salinity is low, but the retrieval of SSS in cold waters is even more challenging. In 2019, the ESA launched a specific initiative called Arctic+Salinity to produce an enhanced Arctic SSS product with better quality and resolution than the available products. This paper presents the methodologies used to produce the new enhanced Arctic SMOS SSS product.
Estrella Olmedo, Verónica González-Gambau, Antonio Turiel, Cristina González-Haro, Aina García-Espriu, Marilaure Gregoire, Aida Álvera-Azcárate, Luminita Buga, and Marie-Hélène Rio
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-364, https://doi.org/10.5194/essd-2021-364, 2021
Revised manuscript not accepted
Short summary
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We present the first dedicated satellite salinity product in the Black Sea. We use the measurements provided by the European Soil Moisture and Ocean Salinity mission. We introduce enhanced algorithms for dealing with the contamination produced by the Radio Frequency Interferences that strongly affect this basin. We also provide a complete quality assessment of the new product and give an estimated accuracy of it.
Estrella Olmedo, Cristina González-Haro, Nina Hoareau, Marta Umbert, Verónica González-Gambau, Justino Martínez, Carolina Gabarró, and Antonio Turiel
Earth Syst. Sci. Data, 13, 857–888, https://doi.org/10.5194/essd-13-857-2021, https://doi.org/10.5194/essd-13-857-2021, 2021
Short summary
Short summary
After more than 10 years in orbit, the Soil Moisture and Ocean Salinity (SMOS) European mission is still a unique, high-quality instrument for providing soil moisture over land and sea surface salinity (SSS) over the oceans. At the Barcelona
Expert Center (BEC), a new reprocessing of 9 years (2011–2019) of global SMOS SSS maps has been generated. This work presents the algorithms used in the generation of the BEC global SMOS SSS product v2.0, as well as an extensive quality assessment.
Roshin P. Raj, Sourav Chatterjee, Laurent Bertino, Antonio Turiel, and Marcos Portabella
Ocean Sci., 15, 1729–1744, https://doi.org/10.5194/os-15-1729-2019, https://doi.org/10.5194/os-15-1729-2019, 2019
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In this study we investigated the variability of the Arctic Front (AF), an important biologically productive region in the Norwegian Sea, using a suite of satellite data, atmospheric reanalysis and a regional coupled ocean–sea ice data assimilation system. We show evidence of the two-way interaction between the atmosphere and the ocean at the AF. The North Atlantic Oscillation is found to influence the strength of the AF and may have a profound influence on the region's biological productivity.
Thomas Lavergne, Atle Macdonald Sørensen, Stefan Kern, Rasmus Tonboe, Dirk Notz, Signe Aaboe, Louisa Bell, Gorm Dybkjær, Steinar Eastwood, Carolina Gabarro, Georg Heygster, Mari Anne Killie, Matilde Brandt Kreiner, John Lavelle, Roberto Saldo, Stein Sandven, and Leif Toudal Pedersen
The Cryosphere, 13, 49–78, https://doi.org/10.5194/tc-13-49-2019, https://doi.org/10.5194/tc-13-49-2019, 2019
Short summary
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The loss of polar sea ice is an iconic indicator of Earth’s climate change. Many satellite-based algorithms and resulting data exist but they differ widely in specific sea-ice conditions. This spread hinders a robust estimate of the future evolution of sea-ice cover.
In this study, we document three new climate data records of sea-ice concentration generated using satellite data available over the last 40 years. We introduce the novel algorithms, the data records, and their uncertainties.
Ana M. Mancho, Emilio Hernández-García, Cristóbal López, Antonio Turiel, Stephen Wiggins, and Vicente Pérez-Muñuzuri
Nonlin. Processes Geophys., 25, 125–127, https://doi.org/10.5194/npg-25-125-2018, https://doi.org/10.5194/npg-25-125-2018, 2018
Jordi Isern-Fontanet, Joaquim Ballabrera-Poy, Antonio Turiel, and Emilio García-Ladona
Nonlin. Processes Geophys., 24, 613–643, https://doi.org/10.5194/npg-24-613-2017, https://doi.org/10.5194/npg-24-613-2017, 2017
Short summary
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Ocean currents play a key role in Earth’s climate – they are of major importance for navigation and human activities at sea and impact almost all processes that take place in the ocean. Nevertheless, their observation and forecasting are still difficult. Here, we review the main techniques used to derive surface currents from satellite measurements and the existing approaches to assimilate this information into ocean models.
A. Turiel, J. Isern-Fontanet, and M. Umbert
Nonlin. Processes Geophys., 21, 291–301, https://doi.org/10.5194/npg-21-291-2014, https://doi.org/10.5194/npg-21-291-2014, 2014
W. Lin, M. Portabella, A. Stoffelen, and A. Verhoef
Atmos. Meas. Tech., 6, 1053–1060, https://doi.org/10.5194/amt-6-1053-2013, https://doi.org/10.5194/amt-6-1053-2013, 2013
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Surge dynamics of Shisper Glacier revealed by time-series correlation of optical satellite images and their utility to substantiate a generalized sliding law
Offset of MODIS land surface temperatures from in situ air temperatures in the upper Kaskawulsh Glacier region (St. Elias Mountains) indicates near-surface temperature inversions
Contribution of ground ice melting to the expansion of Selin Co (lake) on the Tibetan Plateau
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Empirical correction of systematic orthorectification error in Sentinel-2 velocity fields for Greenlandic outlet glaciers
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The Cryosphere, 16, 3123–3148, https://doi.org/10.5194/tc-16-3123-2022, https://doi.org/10.5194/tc-16-3123-2022, 2022
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Understanding sliding at the bed of glaciers is essential to understand the future of sea-level rise and glacier-related hazards. Yet there is currently no universal law to describe this mechanism. We propose a universal glacier sliding law and a method to qualitatively constrain it. We use satellite remote sensing to create velocity maps over 6 years at Shisper Glacier, Pakistan, including its recent surge, and show that the observations corroborate the generalized theory.
Ingalise Kindstedt, Kristin M. Schild, Dominic Winski, Karl Kreutz, Luke Copland, Seth Campbell, and Erin McConnell
The Cryosphere, 16, 3051–3070, https://doi.org/10.5194/tc-16-3051-2022, https://doi.org/10.5194/tc-16-3051-2022, 2022
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We show that neither the large spatial footprint of the MODIS sensor nor poorly constrained snow emissivity values explain the observed cold offset in MODIS land surface temperatures (LSTs) in the St. Elias. Instead, the offset is most prominent under conditions associated with near-surface temperature inversions. This work represents an advance in the application of MODIS LSTs to glaciated alpine regions, where we often depend solely on remote sensing products for temperature information.
Lingxiao Wang, Lin Zhao, Huayun Zhou, Shibo Liu, Erji Du, Defu Zou, Guangyue Liu, Yao Xiao, Guojie Hu, Chong Wang, Zhe Sun, Zhibin Li, Yongping Qiao, Tonghua Wu, Chengye Li, and Xubing Li
The Cryosphere, 16, 2745–2767, https://doi.org/10.5194/tc-16-2745-2022, https://doi.org/10.5194/tc-16-2745-2022, 2022
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Selin Co has exhibited the greatest increase in water storage among all the lakes on the Tibetan Plateau in the past decades. This study presents the first attempt to quantify the water contribution of ground ice melting to the expansion of Selin Co by evaluating the ground surface deformation since terrain surface settlement provides a
windowto detect the subsurface ground ice melting. Results reveal that ground ice meltwater contributed ~ 12 % of the lake volume increase during 2017–2020.
Aldo Bertone, Chloé Barboux, Xavier Bodin, Tobias Bolch, Francesco Brardinoni, Rafael Caduff, Hanne H. Christiansen, Margaret M. Darrow, Reynald Delaloye, Bernd Etzelmüller, Ole Humlum, Christophe Lambiel, Karianne S. Lilleøren, Volkmar Mair, Gabriel Pellegrinon, Line Rouyet, Lucas Ruiz, and Tazio Strozzi
The Cryosphere, 16, 2769–2792, https://doi.org/10.5194/tc-16-2769-2022, https://doi.org/10.5194/tc-16-2769-2022, 2022
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We present the guidelines developed by the IPA Action Group and within the ESA Permafrost CCI project to include InSAR-based kinematic information in rock glacier inventories. Nine operators applied these guidelines to 11 regions worldwide; more than 3600 rock glaciers are classified according to their kinematics. We test and demonstrate the feasibility of applying common rules to produce homogeneous kinematic inventories at global scale, useful for hydrological and climate change purposes.
Thomas R. Chudley, Ian M. Howat, Bidhyananda Yadav, and Myoung-Jong Noh
The Cryosphere, 16, 2629–2642, https://doi.org/10.5194/tc-16-2629-2022, https://doi.org/10.5194/tc-16-2629-2022, 2022
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Sentinel-2 images are subject to distortion due to orthorectification error, which makes it difficult to extract reliable glacier velocity fields from images from different orbits. Here, we use a complete record of velocity fields at four Greenlandic outlet glaciers to empirically estimate the systematic error, allowing us to correct cross-track glacier velocity fields to a comparable accuracy to other medium-resolution satellite datasets.
Frank Paul, Livia Piermattei, Désirée Treichler, Lin Gilbert, Luc Girod, Andreas Kääb, Ludivine Libert, Thomas Nagler, Tazio Strozzi, and Jan Wuite
The Cryosphere, 16, 2505–2526, https://doi.org/10.5194/tc-16-2505-2022, https://doi.org/10.5194/tc-16-2505-2022, 2022
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Glacier surges are widespread in the Karakoram and have been intensely studied using satellite data and DEMs. We use time series of such datasets to study three glacier surges in the same region of the Karakoram. We found strongly contrasting advance rates and flow velocities, maximum velocities of 30 m d−1, and a change in the surge mechanism during a surge. A sensor comparison revealed good agreement, but steep terrain and the two smaller glaciers caused limitations for some of them.
Bas Altena, Andreas Kääb, and Bert Wouters
The Cryosphere, 16, 2285–2300, https://doi.org/10.5194/tc-16-2285-2022, https://doi.org/10.5194/tc-16-2285-2022, 2022
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Repeat overflights of satellites are used to estimate surface displacements. However, such products lack a simple error description for individual measurements, but variation in precision occurs, since the calculation is based on the similarity of texture. Fortunately, variation in precision manifests itself in the correlation peak, which is used for the displacement calculation. This spread is used to make a connection to measurement precision, which can be of great use for model inversion.
Weiran Li, Cornelis Slobbe, and Stef Lhermitte
The Cryosphere, 16, 2225–2243, https://doi.org/10.5194/tc-16-2225-2022, https://doi.org/10.5194/tc-16-2225-2022, 2022
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This study proposes a new method for correcting the slope-induced errors in satellite radar altimetry. The slope-induced errors can significantly affect the height estimations of ice sheets if left uncorrected. This study applies the method to radar altimetry data (CryoSat-2) and compares the performance with two existing methods. The performance is assessed by comparison with independent height measurements from ICESat-2. The assessment shows that the method performs promisingly.
Joëlle Voglimacci-Stephanopoli, Anna Wendleder, Hugues Lantuit, Alexandre Langlois, Samuel Stettner, Andreas Schmitt, Jean-Pierre Dedieu, Achim Roth, and Alain Royer
The Cryosphere, 16, 2163–2181, https://doi.org/10.5194/tc-16-2163-2022, https://doi.org/10.5194/tc-16-2163-2022, 2022
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Changes in the state of the snowpack in the context of observed global warming must be considered to improve our understanding of the processes within the cryosphere. This study aims to characterize an arctic snowpack using the TerraSAR-X satellite. Using a high-spatial-resolution vegetation classification, we were able to quantify the variability in snow depth, as well as the topographic soil wetness index, which provided a better understanding of the electromagnetic wave–ground interaction.
Alexis Anne Denton and Mary-Louise Timmermans
The Cryosphere, 16, 1563–1578, https://doi.org/10.5194/tc-16-1563-2022, https://doi.org/10.5194/tc-16-1563-2022, 2022
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Arctic sea ice has a distribution of ice sizes that provides insight into the physics of the ice. We examine this distribution from satellite imagery from 1999 to 2014 in the Canada Basin. We find that it appears as a power law whose power becomes less negative with increasing ice concentrations and has a seasonality tied to that of ice concentration. Results suggest ice concentration be considered in models of this distribution and are important for understanding sea ice in a warming Arctic.
Jayson Eppler, Bernhard Rabus, and Peter Morse
The Cryosphere, 16, 1497–1521, https://doi.org/10.5194/tc-16-1497-2022, https://doi.org/10.5194/tc-16-1497-2022, 2022
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We introduce a new method for mapping changes in the snow water equivalent (SWE) of dry snow based on differences between time-repeated synthetic aperture radar (SAR) images. It correlates phase differences with variations in the topographic slope which allows the method to work without any "reference" targets within the imaged area and without having to numerically unwrap the spatial phase maps. This overcomes the key challenges faced in using SAR interferometry for SWE change mapping.
Elisabeth D. Hafner, Patrick Barton, Rodrigo Caye Daudt, Jan Dirk Wegner, Konrad Schindler, and Yves Bühler
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-80, https://doi.org/10.5194/tc-2022-80, 2022
Revised manuscript accepted for TC
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Knowing where avalanches occur is very important information for several disciplines, for example avalanche warning, hazard zonation or risk management. Satellite imagery can provide such data systematically over large regions. In our work we propose a machine learning model to automize the time- consuming manual mapping. Additionally, we investigate expert agreement for manual avalanche mapping, showing that our network is equally good as the experts in identifying avalanches.
Stephen E. L. Howell, Mike Brady, and Alexander S. Komarov
The Cryosphere, 16, 1125–1139, https://doi.org/10.5194/tc-16-1125-2022, https://doi.org/10.5194/tc-16-1125-2022, 2022
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We describe, apply, and validate the Environment and Climate Change Canada automated sea ice tracking system (ECCC-ASITS) that routinely generates large-scale sea ice motion (SIM) over the pan-Arctic domain using synthetic aperture radar (SAR) images. The ECCC-ASITS was applied to the incoming image streams of Sentinel-1AB and the RADARSAT Constellation Mission from March 2020 to October 2021 using a total of 135 471 SAR images and generated new SIM datasets (i.e., 7 d 25 km and 3 d 6.25 km).
Wayne de Jager and Marcello Vichi
The Cryosphere, 16, 925–940, https://doi.org/10.5194/tc-16-925-2022, https://doi.org/10.5194/tc-16-925-2022, 2022
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Ice motion can be used to better understand how weather and climate change affect the ice. Antarctic sea ice extent has shown large variability over the observed period, and dynamical features may also have changed. Our method allows for the quantification of rotational motion caused by wind and how this may have changed with time. Cyclonic motion dominates the Atlantic sector, particularly from 2015 onwards, while anticyclonic motion has remained comparatively small and unchanged.
Benjamin Aubrey Robson, Shelley MacDonell, Álvaro Ayala, Tobias Bolch, Pål Ringkjøb Nielsen, and Sebastián Vivero
The Cryosphere, 16, 647–665, https://doi.org/10.5194/tc-16-647-2022, https://doi.org/10.5194/tc-16-647-2022, 2022
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This work uses satellite and aerial data to study glaciers and rock glacier changes in La Laguna catchment within the semi-arid Andes of Chile, where ice melt is an important factor in river flow. The results show the rate of ice loss of Tapado Glacier has been increasing since the 1950s, which possibly relates to a dryer, warmer climate over the previous decades. Several rock glaciers show high surface velocities and elevation changes between 2012 and 2020, indicating they may be ice-rich.
Sebastian Buchelt, Kirstine Skov, Kerstin Krøier Rasmussen, and Tobias Ullmann
The Cryosphere, 16, 625–646, https://doi.org/10.5194/tc-16-625-2022, https://doi.org/10.5194/tc-16-625-2022, 2022
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In this paper, we present a threshold and a derivative approach using Sentinel-1 synthetic aperture radar time series to capture the small-scale heterogeneity of snow cover (SC) and snowmelt. Thereby, we can identify start of runoff and end of SC as well as perennial snow and SC extent during melt with high spatiotemporal resolution. Hence, our approach could support monitoring of distribution patterns and hydrological cascading effects of SC from the catchment scale to pan-Arctic observations.
Stefan Kern, Thomas Lavergne, Leif Toudal Pedersen, Rasmus Tage Tonboe, Louisa Bell, Maybritt Meyer, and Luise Zeigermann
The Cryosphere, 16, 349–378, https://doi.org/10.5194/tc-16-349-2022, https://doi.org/10.5194/tc-16-349-2022, 2022
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High-resolution clear-sky optical satellite imagery has rarely been used to evaluate satellite passive microwave sea-ice concentration products beyond case-study level. By comparing 10 such products with sea-ice concentration estimated from > 350 such optical images in both hemispheres, we expand results of earlier evaluation studies for these products. Results stress the need to look beyond precision and accuracy and to discuss the evaluation data’s quality and filters applied in the products.
Wenkai Guo, Polona Itkin, Johannes Lohse, Malin Johansson, and Anthony Paul Doulgeris
The Cryosphere, 16, 237–257, https://doi.org/10.5194/tc-16-237-2022, https://doi.org/10.5194/tc-16-237-2022, 2022
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This study uses radar satellite data categorized into different sea ice types to detect ice deformation, which is significant for climate science and ship navigation. For this, we examine radar signal differences of sea ice between two similar satellite sensors and show an optimal way to apply categorization methods across sensors, so more data can be used for this purpose. This study provides a basis for future reliable and constant detection of ice deformation remotely through satellite data.
Hans Lievens, Isis Brangers, Hans-Peter Marshall, Tobias Jonas, Marc Olefs, and Gabriëlle De Lannoy
The Cryosphere, 16, 159–177, https://doi.org/10.5194/tc-16-159-2022, https://doi.org/10.5194/tc-16-159-2022, 2022
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Snow depth observations at high spatial resolution from the Sentinel-1 satellite mission are presented over the European Alps. The novel observations can improve our knowledge of seasonal snow mass in areas with complex topography, where satellite-based estimates are currently lacking, and benefit a number of applications including water resource management, flood forecasting, and numerical weather prediction.
Julie Z. Miller, Riley Culberg, David G. Long, Christopher A. Shuman, Dustin M. Schroeder, and Mary J. Brodzik
The Cryosphere, 16, 103–125, https://doi.org/10.5194/tc-16-103-2022, https://doi.org/10.5194/tc-16-103-2022, 2022
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We use L-band brightness temperature imagery from NASA's Soil Moisture Active Passive (SMAP) satellite to map the extent of perennial firn aquifer and ice slab areas within the Greenland Ice Sheet. As Greenland's climate continues to warm and seasonal surface melting increases in extent, intensity, and duration, quantifying the possible rapid expansion of perennial firn aquifers and ice slab areas has significant implications for understanding the stability of the Greenland Ice Sheet.
Christian Sommer, Thorsten Seehaus, Andrey Glazovsky, and Matthias H. Braun
The Cryosphere, 16, 35–42, https://doi.org/10.5194/tc-16-35-2022, https://doi.org/10.5194/tc-16-35-2022, 2022
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Arctic glaciers have been subject to extensive warming due to global climate change, yet their contribution to sea level rise has been relatively small in the past. In this study we provide mass changes of most glaciers of the Russian High Arctic (Franz Josef Land, Severnaya Zemlya, Novaya Zemlya). We use TanDEM-X satellite measurements to derive glacier surface elevation changes. Our results show an increase in glacier mass loss and a sea level rise contribution of 0.06 mm/a (2010–2017).
Julien Meloche, Alexandre Langlois, Nick Rutter, Alain Royer, Josh King, Branden Walker, Philip Marsh, and Evan J. Wilcox
The Cryosphere, 16, 87–101, https://doi.org/10.5194/tc-16-87-2022, https://doi.org/10.5194/tc-16-87-2022, 2022
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To estimate snow water equivalent from space, model predictions of the satellite measurement (brightness temperature in our case) have to be used. These models allow us to estimate snow properties from the brightness temperature by inverting the model. To improve SWE estimate, we proposed incorporating the variability of snow in these model as it has not been taken into account yet. A new parameter (coefficient of variation) is proposed because it improved simulation of brightness temperature.
Christopher Donahue, S. McKenzie Skiles, and Kevin Hammonds
The Cryosphere, 16, 43–59, https://doi.org/10.5194/tc-16-43-2022, https://doi.org/10.5194/tc-16-43-2022, 2022
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The amount of water within a snowpack is important information for predicting snowmelt and wet-snow avalanches. From within a controlled laboratory, the optimal method for measuring liquid water content (LWC) at the snow surface or along a snow pit profile using near-infrared imagery was determined. As snow samples melted, multiple models to represent wet-snow reflectance were assessed against a more established LWC instrument. The best model represents snow as separate spheres of ice and water.
Philipp Bernhard, Simon Zwieback, Nora Bergner, and Irena Hajnsek
The Cryosphere, 16, 1–15, https://doi.org/10.5194/tc-16-1-2022, https://doi.org/10.5194/tc-16-1-2022, 2022
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We present an investigation of retrogressive thaw slumps in 10 study sites across the Arctic. These slumps have major impacts on hydrology and ecosystems and can also reinforce climate change by the mobilization of carbon. Using time series of digital elevation models, we found that thaw slump change rates follow a specific type of distribution that is known from landslides in more temperate landscapes and that the 2D area change is strongly related to the 3D volumetric change.
Jan Bouke Pronk, Tobias Bolch, Owen King, Bert Wouters, and Douglas I. Benn
The Cryosphere, 15, 5577–5599, https://doi.org/10.5194/tc-15-5577-2021, https://doi.org/10.5194/tc-15-5577-2021, 2021
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About 10 % of Himalayan glaciers flow directly into lakes. This study finds, using satellite imagery, that such glaciers show higher flow velocities than glaciers without ice–lake contact. In particular near the glacier tongue the impact of a lake on the glacier flow can be dramatic. The development of current and new meltwater bodies will influence the flow of an increasing number of Himalayan glaciers in the future, a scenario not currently considered in regional ice loss projections.
Florent Garnier, Sara Fleury, Gilles Garric, Jérôme Bouffard, Michel Tsamados, Antoine Laforge, Marion Bocquet, Renée Mie Fredensborg Hansen, and Frédérique Remy
The Cryosphere, 15, 5483–5512, https://doi.org/10.5194/tc-15-5483-2021, https://doi.org/10.5194/tc-15-5483-2021, 2021
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Snow depth data are essential to monitor the impacts of climate change on sea ice volume variations and their impacts on the climate system. For that purpose, we present and assess the altimetric snow depth product, computed in both hemispheres from CryoSat-2 and SARAL satellite data. The use of these data instead of the common climatology reduces the sea ice thickness by about 30 cm over the 2013–2019 period. These data are also crucial to argue for the launch of the CRISTAL satellite mission.
Armin Dachauer, Richard Hann, and Andrew J. Hodson
The Cryosphere, 15, 5513–5528, https://doi.org/10.5194/tc-15-5513-2021, https://doi.org/10.5194/tc-15-5513-2021, 2021
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This study investigated the aerodynamic roughness length (z0) – an important parameter to determine the surface roughness – of crevassed tidewater glaciers on Svalbard using drone data. The results point out that the range of z0 values across a crevassed glacier is large but in general significantly higher compared to non-crevassed glacier surfaces. The UAV approach proved to be an ideal tool to provide distributed z0 estimates of crevassed glaciers which can be used to model turbulent fluxes.
Lanqing Huang, Georg Fischer, and Irena Hajnsek
The Cryosphere, 15, 5323–5344, https://doi.org/10.5194/tc-15-5323-2021, https://doi.org/10.5194/tc-15-5323-2021, 2021
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This study shows an elevation difference between the radar interferometric measurements and the optical measurements from a coordinated campaign over the snow-covered deformed sea ice in the western Weddell Sea, Antarctica. The objective is to correct the penetration bias of microwaves and to generate a precise sea ice topographic map, including the snow depth on top. Excellent performance for sea ice topographic retrieval is achieved with the proposed model and the developed retrieval scheme.
Rajashree Tri Datta and Bert Wouters
The Cryosphere, 15, 5115–5132, https://doi.org/10.5194/tc-15-5115-2021, https://doi.org/10.5194/tc-15-5115-2021, 2021
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The ICESat-2 laser altimeter can detect the surface and bottom of a supraglacial lake. We introduce the Watta algorithm, automatically calculating lake surface, corrected bottom, and (sub-)surface ice at high resolution adapting to signal strength. ICESat-2 depths constrain full lake depths of 46 lakes over Jakobshavn glacier using multiple sources of imagery, including very high-resolution Planet imagery, used for the first time to extract supraglacial lake depths empirically using ICESat-2.
Melanie Marochov, Chris R. Stokes, and Patrice E. Carbonneau
The Cryosphere, 15, 5041–5059, https://doi.org/10.5194/tc-15-5041-2021, https://doi.org/10.5194/tc-15-5041-2021, 2021
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Research into the use of deep learning for pixel-level classification of landscapes containing marine-terminating glaciers is lacking. We adapt a novel and transferable deep learning workflow to classify satellite imagery containing marine-terminating outlet glaciers in Greenland. Our workflow achieves high accuracy and mimics human visual performance, potentially providing a useful tool to monitor glacier change and further understand the impacts of climate change in complex glacial settings.
Zacharie Barrou Dumont, Simon Gascoin, Olivier Hagolle, Michaël Ablain, Rémi Jugier, Germain Salgues, Florence Marti, Aurore Dupuis, Marie Dumont, and Samuel Morin
The Cryosphere, 15, 4975–4980, https://doi.org/10.5194/tc-15-4975-2021, https://doi.org/10.5194/tc-15-4975-2021, 2021
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Since 2020, the Copernicus High Resolution Snow & Ice Monitoring Service has distributed snow cover maps at 20 m resolution over Europe in near-real time. These products are derived from the Sentinel-2 Earth observation mission, with a revisit time of 5 d or less (cloud-permitting). Here we show the good accuracy of the snow detection over a wide range of regions in Europe, except in dense forest regions where the snow cover is hidden by the trees.
Paul Willem Leclercq, Andreas Kääb, and Bas Altena
The Cryosphere, 15, 4901–4907, https://doi.org/10.5194/tc-15-4901-2021, https://doi.org/10.5194/tc-15-4901-2021, 2021
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In this study we present a novel method to detect glacier surge activity. Surges are relevant as they disturb the link between glacier change and climate, and studying surges can also increase understanding of glacier flow. We use variations in Sentinel-1 radar backscatter strength, calculated with the use of Google Earth Engine, to detect surge activity. In our case study for the year 2018–2019 we find 69 cases of surging glaciers globally. Many of these were not previously known to be surging.
Isolde A. Glissenaar, Jack C. Landy, Alek A. Petty, Nathan T. Kurtz, and Julienne C. Stroeve
The Cryosphere, 15, 4909–4927, https://doi.org/10.5194/tc-15-4909-2021, https://doi.org/10.5194/tc-15-4909-2021, 2021
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Scientists can estimate sea ice thickness using satellites that measure surface height. To determine the sea ice thickness, we also need to know the snow depth and density. This paper shows that the chosen snow depth product has a considerable impact on the findings of sea ice thickness state and trends in Baffin Bay, showing mean thinning with some snow depth products and mean thickening with others. This shows that it is important to better understand and monitor snow depth on sea ice.
Sophie Goliber, Taryn Black, Ginny Catania, James M. Lea, Helene Olsen, Daniel Cheng, Suzanne Bevan, Anders Bjørk, Charlie Bunce, Stephen Brough, J. Rachel Carr, Tom Cowton, Alex Gardner, Dominik Fahrner, Emily Hill, Ian Joughin, Niels Korsgaard, Adrian Luckman, Twila Moon, Tavi Murray, Andrew Sole, Michael Wood, and Enze Zhang
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-311, https://doi.org/10.5194/tc-2021-311, 2021
Revised manuscript accepted for TC
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Terminus traces have been used to understand how Greenland's glaciers have changed over time, however, manual tracing is time-intensive and lack of coordination leads to duplication of efforts. We have compiled a dataset of over 39,000 terminus traces for 278 glaciers for scientific and machine learning applications. We also provide an overview of an updated version of The Google Earth Engine Digitization Tool (GEEDiT), which has been developed specifically for the Greenland Ice Sheet.
George Brencher, Alexander L. Handwerger, and Jeffrey S. Munroe
The Cryosphere, 15, 4823–4844, https://doi.org/10.5194/tc-15-4823-2021, https://doi.org/10.5194/tc-15-4823-2021, 2021
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We use satellite InSAR to inventory and monitor rock glaciers, frozen bodies of ice and rock debris that are an important water resource in the Uinta Mountains, Utah, USA. Our inventory contains 205 rock glaciers, which occur within a narrow elevation band and deform at 1.94 cm yr-1 on average. Uinta rock glacier movement changes seasonally and appears to be driven by spring snowmelt. The role of rock glaciers as a perennial water resource is threatened by ice loss due to climate change.
YoungHyun Koo, Hongjie Xie, Stephen F. Ackley, Alberto M. Mestas-Nuñez, Grant J. Macdonald, and Chang-Uk Hyun
The Cryosphere, 15, 4727–4744, https://doi.org/10.5194/tc-15-4727-2021, https://doi.org/10.5194/tc-15-4727-2021, 2021
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This study demonstrates for the first time the potential of Google Earth Engine (GEE) cloud-computing platform and Sentinel-1 synthetic aperture radar (SAR) images for semi-automated tracking of area changes and movements of iceberg B43. Our novel GEE-based iceberg tracking can be used to construct a large iceberg database for a better understanding of the behavior of icebergs and their interactions with surrounding environments.
Leung Tsang, Michael Durand, Chris Derksen, Ana P. Barros, Do-Hyuk Kang, Hans Lievens, Hans-Peter Marshall, Jiyue Zhu, Joel Johnson, Joshua King, Juha Lemmetyinen, Melody Sandells, Nick Rutter, Paul Siqueira, Anne Nolin, Batu Osmanoglu, Carrie Vuyovich, Edward J. Kim, Drew Taylor, Ioanna Merkouriadi, Ludovic Brucker, Mahdi Navari, Marie Dumont, Richard Kelly, Rhae Sung Kim, Tien-Hao Liao, and Xiaolan Xu
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-295, https://doi.org/10.5194/tc-2021-295, 2021
Revised manuscript accepted for TC
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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 will serve to inform the broad snow research, monitoring, and applications communities on the progress made in recent decades to move towards a new satellite mission capable of addressing the needs of the geoscience researchers and users.
Adina E. Racoviteanu, Lindsey Nicholson, and Neil F. Glasser
The Cryosphere, 15, 4557–4588, https://doi.org/10.5194/tc-15-4557-2021, https://doi.org/10.5194/tc-15-4557-2021, 2021
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Supraglacial debris cover comprises ponds, exposed ice cliffs, debris material and vegetation. Understanding these features is important for glacier hydrology and related hazards. We use linear spectral unmixing of satellite data to assess the composition of map supraglacial debris across the Himalaya range in 2015. One of the highlights of this study is the automated mapping of supraglacial ponds, which complements and expands the existing supraglacial debris and lake databases.
Marek Muchow, Amelie U. Schmitt, and Lars Kaleschke
The Cryosphere, 15, 4527–4537, https://doi.org/10.5194/tc-15-4527-2021, https://doi.org/10.5194/tc-15-4527-2021, 2021
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Linear-like openings in sea ice, also called leads, occur with widths from meters to kilometers. We use satellite images from Sentinel-2 with a resolution of 10 m to identify leads and measure their widths. With that we investigate the frequency of lead widths using two different statistical methods, since other studies have shown a dependency of heat exchange on the lead width. We are the first to address the sea-ice lead-width distribution in the Weddell Sea, Antarctica.
Corey Scher, Nicholas C. Steiner, and Kyle C. McDonald
The Cryosphere, 15, 4465–4482, https://doi.org/10.5194/tc-15-4465-2021, https://doi.org/10.5194/tc-15-4465-2021, 2021
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Time series synthetic aperture radar enables detection of seasonal reach-scale glacier surface melting across continents, a key component of surface energy balance for mountain glaciers. We observe melting across all areas of the Hindu Kush Himalaya (HKH) cryosphere. Surface melting for the HKH lasts for close to 5 months per year on average and for just below 2 months at elevations exceeding 7000 m a.s.l. Further, there are indications that melting is more than superficial at high elevations.
Lander Van Tricht, Philippe Huybrechts, Jonas Van Breedam, Alexander Vanhulle, Kristof Van Oost, and Harry Zekollari
The Cryosphere, 15, 4445–4464, https://doi.org/10.5194/tc-15-4445-2021, https://doi.org/10.5194/tc-15-4445-2021, 2021
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We conducted innovative research on the use of drones to determine the surface mass balance (SMB) of two glaciers. Considering appropriate spatial scales, we succeeded in determining the SMB in the ablation area with large accuracy. Consequently, we are convinced that our method and the use of drones to monitor the mass balance of a glacier’s ablation area can be an add-on to stake measurements in order to obtain a broader picture of the heterogeneity of the SMB of glaciers.
Yuting Dong, Ji Zhao, Dana Floricioiu, Lukas Krieger, Thomas Fritz, and Michael Eineder
The Cryosphere, 15, 4421–4443, https://doi.org/10.5194/tc-15-4421-2021, https://doi.org/10.5194/tc-15-4421-2021, 2021
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We generated a consistent, gapless and high-resolution (12 m) topography product of the Antarctic Peninsula by combining the complementary advantages of the two most recent high-resolution digital elevation model (DEM) products: the TanDEM-X DEM and the Reference Elevation Model of Antarctica. The generated DEM maintains the characteristics of the TanDEM-X DEM, has a better quality due to the correction of the residual height errors in the non-edited TanDEM-X DEM and will be freely available.
Helmut Rott, Stefan Scheiblauer, Jan Wuite, Lukas Krieger, Dana Floricioiu, Paola Rizzoli, Ludivine Libert, and Thomas Nagler
The Cryosphere, 15, 4399–4419, https://doi.org/10.5194/tc-15-4399-2021, https://doi.org/10.5194/tc-15-4399-2021, 2021
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We studied relations between interferometric synthetic aperture radar (InSAR) signals and snow–firn properties and tested procedures for correcting the penetration bias of InSAR digital elevation models at Union Glacier, Antarctica. The work is based on SAR data of the TanDEM-X mission, topographic data from optical sensors and field measurements. We provide new insights on radar signal interactions with polar snow and show the performance of penetration bias retrievals using InSAR coherence.
Xiaodan Wu, Kathrin Naegeli, Valentina Premier, Carlo Marin, Dujuan Ma, Jingping Wang, and Stefan Wunderle
The Cryosphere, 15, 4261–4279, https://doi.org/10.5194/tc-15-4261-2021, https://doi.org/10.5194/tc-15-4261-2021, 2021
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We performed a comprehensive accuracy assessment of an Advanced Very High Resolution Radiometer global area coverage snow-cover extent time series dataset for the Hindu Kush Himalayan (HKH) region. The sensor-to-sensor consistency, the accuracy related to snow depth, elevations, land-cover types, slope, and aspects, and topographical variability were also explored. Our analysis shows an overall accuracy of 94 % in comparison with in situ station data, which is the same with MOD10A1 V006.
Sergey Samsonov, Kristy Tiampo, and Ryan Cassotto
The Cryosphere, 15, 4221–4239, https://doi.org/10.5194/tc-15-4221-2021, https://doi.org/10.5194/tc-15-4221-2021, 2021
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The direction and intensity of glacier surface flow adjust in response to a warming climate, causing sea level rise, seasonal flooding and droughts, and changing landscapes and habitats. We developed a technique that measures the evolution of surface flow for a glaciated region in three dimensions with high temporal and spatial resolution and used it to map the temporal evolution of glaciers in southeastern Alaska (Agassiz, Seward, Malaspina, Klutlan, Walsh, and Kluane) during 2016–2021.
Gemma M. Brett, Daniel Price, Wolfgang Rack, and Patricia J. Langhorne
The Cryosphere, 15, 4099–4115, https://doi.org/10.5194/tc-15-4099-2021, https://doi.org/10.5194/tc-15-4099-2021, 2021
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Ice shelf meltwater in the surface ocean affects sea ice formation, causing it to be thicker and, in particular conditions, to have a loose mass of platelet ice crystals called a sub‐ice platelet layer beneath. This causes the sea ice freeboard to stand higher above sea level. In this study, we demonstrate for the first time that the signature of ice shelf meltwater in the surface ocean manifesting as higher sea ice freeboard in McMurdo Sound is detectable from space using satellite technology.
Thomas Krumpen, Luisa von Albedyll, Helge F. Goessling, Stefan Hendricks, Bennet Juhls, Gunnar Spreen, Sascha Willmes, H. Jakob Belter, Klaus Dethloff, Christian Haas, Lars Kaleschke, Christian Katlein, Xiangshan Tian-Kunze, Robert Ricker, Philip Rostosky, Janna Rückert, Suman Singha, and Julia Sokolova
The Cryosphere, 15, 3897–3920, https://doi.org/10.5194/tc-15-3897-2021, https://doi.org/10.5194/tc-15-3897-2021, 2021
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We use satellite data records collected along the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) drift to categorize ice conditions that shaped and characterized the floe and surroundings during the expedition. A comparison with previous years is made whenever possible. The aim of this analysis is to provide a basis and reference for subsequent research in the six main research areas of atmosphere, ocean, sea ice, biogeochemistry, remote sensing and ecology.
Anne Braakmann-Folgmann, Andrew Shepherd, and Andy Ridout
The Cryosphere, 15, 3861–3876, https://doi.org/10.5194/tc-15-3861-2021, https://doi.org/10.5194/tc-15-3861-2021, 2021
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We investigate the disintegration of the B30 iceberg using satellite remote sensing and find that the iceberg lost 378 km3 of ice in 6.5 years, corresponding to 80 % of its initial volume. About two thirds are due to fragmentation at the sides, and one third is due to melting at the iceberg’s base. The release of fresh water and nutrients impacts ocean circulation, sea ice formation, and biological production. We show that adding a snow layer is important when deriving iceberg thickness.
Joschka Geissler, Christoph Mayer, Juilson Jubanski, Ulrich Münzer, and Florian Siegert
The Cryosphere, 15, 3699–3717, https://doi.org/10.5194/tc-15-3699-2021, https://doi.org/10.5194/tc-15-3699-2021, 2021
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The study demonstrates the potential of photogrammetry for analyzing glacier retreat with high spatial resolution. Twenty-three glaciers within the Ötztal Alps are analyzed. We compare photogrammetric and glaciologic mass balances of the Vernagtferner by using the ELA for our density assumption and an UAV survey for a temporal correction of the geodetic mass balances. The results reveal regions of anomalous mass balance and allow estimates of the imbalance between mass balances and ice dynamics.
Thomas Lavergne, Montserrat Piñol Solé, Emily Down, and Craig Donlon
The Cryosphere, 15, 3681–3698, https://doi.org/10.5194/tc-15-3681-2021, https://doi.org/10.5194/tc-15-3681-2021, 2021
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Pushed by winds and ocean currents, polar sea ice is on the move. We use passive microwave satellites to observe this motion. The images from their orbits are often put together into daily images before motion is measured. In our study, we measure motion from the individual orbits directly and not from the daily images. We obtain many more motion vectors, and they are more accurate. This can be used for current and future satellites, e.g. the Copernicus Imaging Microwave Radiometer (CIMR).
Cited articles
AMAP: Arctic Climate Issues 2011: Changes in Arctic Snow, Water, Ice and Permafrost, SWIPA 2011 Overview Report, Arctic Monitoring and Assessment Programme (AMAP), Oslo, xi + 97 pp., 2012.
Becker, F. and Choudhury, B. J.: Relative Sensitivity of Normalized Difference Vegetation Index (NDVI) and Microwave Polarization Difference Index (MPDI) for Vegetation and Desertification Monitoring, Remote Sens. Environ., 24, 297–311, https://doi.org/10.1016/0034-4257(88)90031-4, 1988.
Brodzik, M. J. and Knowles, K. W.: EASE-Grid: A Versatile Set of Equal-Area Projections and Grids, in: Discrete Global Grids, edited by: Goodchild, M., National Center for Geographic Information & Analysis, Santa Barbara, California, USA, 2002.
Burke, W., Schmugge, T., and Paris, J.: Comparison of 2.8- and 21-cm Microwave Radiometer Observations Over Soils With Emission Model Calculations, J. Geophys. Res., 84, 287–294, https://doi.org/10.1029/JC084iC01p00287, 1979.
Camps, A., Vall-llossera, M., Duffo, N., Torres, F., and Corbella, I.: Performance of Sea Surface Salinity and Soil Moisture Retrieval Algorithms with Different Ancillary Data Sets in 2D L-band Aperture Synthesis Interferometic Radiometers, IEEE T. Geosci. Remote, 43, 1189–1200, https://doi.org/10.1109/TGRS.2004.842096, 2005.
Cavalieri, D., Gloersen, P., and Campbell, W.: Determination of sea ice parameters with the NIMBUS 7 SMMR, J. Geophys. Res., 89, 5355–5369, https://doi.org/10.1029/JD089iD04p05355, 1984.
Cohen, J., Screen, J. A., Furtado, J. C., Barlow, M., Whittleston, D., Coumou, D., Francis, J., Dethloff, K., Entekhabi, D., Overland, J., and Jones, J.: Recent Arctic amplification and extreme mid-latitude weather, Nat. Geosci., 7, 627–637, https://doi.org/10.1038/NGEO2234, 2014.
Comiso, J. C.: Characteristics of arctic winter sea ice from satellite multispectral microwave observations, J. Geophys. Res., 91, 975–994, https://doi.org/10.1029/JC091iC01p00975, 1986.
Comiso, J. C.: Large Decadal Decline of the Arctic Multiyear Ice Cover, J. Climate, 25, 1176–1193, https://doi.org/10.1175/JCLI-D-11-00113.1, 2012.
Comiso, J. C., Cavalieri, D. J., Parkinson, C. L., and Gloersen, P.: Passive microwave algorithms for sea ice concentration: A comparison of two techniques, Remote Sens. Environ., 60, 357–384, https://doi.org/10.1016/S0034-4257(96)00220-9, 1997.
Corbella, I., Torres, F., Duffo, N., Gonzalez-Gambau, V., Pablos, M., Duran, I., and Martin-Neira, M.: MIRAS Calibration and Performance: Results From the SMOS In-Orbit Commissioning Phase, IEEE T. Geosci. Remote, 49, 3147–3155, https://doi.org/10.1109/TGRS.2010.2102769, 2011.
Cox, G. F. N. and Weeks, W. F.: Equations for Determining the Gas and Brine Volumes in Sea-Ice Samples, J. Glaciol., 29, 306–316, https://doi.org/10.1017/S0022143000008364, 1983.
Deimos: SMOS L1 Processor Algorithm Theoretical Baseline Definition, SO-DS-DME-L1PP-0011, Tech. rep., Deimos Engenharia, Portugal, 2010.
Fetterer, F. and Fowler, C.: National Ice Center Arctic Sea Ice Charts and Climatologies in Gridded Format, Version 1, National Snow and Ice Data Center, Boulder, Colorado, USA, https://doi.org/10.7265/N5X34VDB, 2009.
Font, J., Boutin, J., Reul, N., Spurgeon, P., Ballabrera-Poy, J., Chuprin, A., Gabarró, C., Gourrion, J., Guimbard, S., Hénocq, C., Lavender, S., Martin, N., Martínez, J., McCulloch, M., Meirold-Mautner, I., Mugerin, C., Petitcolin, F., Portabella, M., Sabia, R., Talone, M., Tenerelli, J., Turiel, A., Vergely, J., Waldteufel, P., Yin, X., Zine, S., and Delwart, S.: SMOS first data analysis for sea surface salinity determination, Int. J. Remote Sens., 34, 3654–3670, https://doi.org/10.1080/01431161.2012.716541, 2013.
Gabarro, C.: The dynamical estimation of summer sea ice tie-points using low frequency passive microwave channels, Associated Visiting Scientist 16/03, OSISAF/EUMETSAT, 2017.
Heygster, G., Huntemann, M., Ivanova, N., Saldo, R., and Pedersen, L. T.: Response of passive microwave sea ice concentration algorithms to thin ice, in: 2014 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Quebec City, QC, Canada, 13–18 July 2014, IEEE, 3618–3621, https://doi.org/10.1109/IGARSS.2014.6947266, 2014.
Holland, M. M. and Bitz, C. M.: Polar amplification of climate change in coupled models, Clim. Dynam., 21, 221–232, https://doi.org/10.1007/s00382-003-0332-6, 2003.
Huntemann, M., Heygster, G., Kaleschke, L., Krumpen, T., Mäkynen, M., and Drusch, M.: Empirical sea ice thickness retrieval during the freeze-up period from SMOS high incident angle observations, The Cryosphere, 8, 439–451, https://doi.org/10.5194/tc-8-439-2014, 2014.
IPCC: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp., 2013.
Ivanova, N., Pedersen, L. T., Tonboe, R. T., Kern, S., Heygster, G., Lavergne, T., Sørensen, A., Saldo, R., Dybkjær, G., Brucker, L., and Shokr, M.: Inter-comparison and evaluation of sea ice algorithms: towards further identification of challenges and optimal approach using passive microwave observations, The Cryosphere, 9, 1797–1817, https://doi.org/10.5194/tc-9-1797-2015, 2015.
Kaleschke, L., Lupkes, C., Vihma, T., Haarpaintner, J., Bochert, A., Hartmann, J., and Heygster, G.: SSM/I Sea ice remote sensing for mesoscale ocean–atmosphere interaction analysis, Can. J. Remote Sens., 27, 526–537, https://doi.org/10.1080/07038992.2001.10854892, 2001.
Kaleschke, L., Maaß, N., Haas, C., Hendricks, S., Heygster, G., and Tonboe, R. T.: A sea-ice thickness retrieval model for 1.4 GHz radiometry and application to airborne measurements over low salinity sea-ice, The Cryosphere, 4, 583–592, https://doi.org/10.5194/tc-4-583-2010, 2010.
Kaleschke, L., Tian-Kunze, X., Maaß, N., Mäkynen, M., and Drusch, M.: Sea ice thickness retrieval from SMOS brightness temperatures during the Arctic freeze-up period, Geophys. Res. Lett., 39, L05501, https://doi.org/10.1029/2012GL050916, 2012.
Kaleschke, L., Tian-Kunze, X., Maaß, N., Heygster, G., Huntemann, M., Wang, H., Hendricks, S., Krumpen, T., Tonboe, R., Mäkynen, M., and Haas, C.: SMOS Sea Ice Retrieval Study (SMOSIce), Final Report, Tech. rep., ESA ESTEC Contract No.: 4000101476/10/NL/CT, available at: https://icdc.cen.uni-hamburg.de/fileadmin/user_upload/icdc_Dokumente/SMOS_SIT/SMOSICE_FinalReport_2013.pdf (last access: 21 August 2017), 2013.
Kern, S., Rösel, A., Pedersen, L. T., Ivanova, N., Saldo, R., and Tonboe, R. T.: The impact of melt ponds on summertime microwave brightness temperatures and sea-ice concentrations, The Cryosphere, 10, 2217–2239, https://doi.org/10.5194/tc-10-2217-2016, 2016.
Kerr, Y. H., Waldteufel, P., Wigneron, J.-P., Delwart, S., Cabot, F., Boutin, J., Escorihuela, M. J., Font, J., Reul, N., Gruhier, C., Juglea, S. E., Drinkwater, M. R., Hahne, A., Martin-Neira, M., and Mecklenburg, S.: The SMOS mission: New tool for monitoring key elements of the global water cycle, Proc. IGARSS, 98, 666–687, 2010.
Khoshelham, K.: Role of Tie Points in Integrated Sensor Orientation for Photogrammetric Map Compilation, Photogramm. Eng. Rem. S., 75, 305–311, https://doi.org/10.14358/PERS.75.3.305, 2009.
Klein, L. and Swift, C.: An Improved Model for the Dielectric Constant of Sea Water at Microwave Frequencies, IEEE T. Antenn. Propag., AP-25, 104–111, https://doi.org/10.1109/JOE.1977.1145319, 1977.
Leppäranta, M. and Manninen, T.: The brine and gas contents of sea-ice with attention to low salinities and high temperatures, Finnish Institute of Marine Research, Helsinki, Finland, Internal Report, 2, 15 pp., 1998.
Maaß, N.: Remote Sensing of Sea Ice thickness Using SMOS data, PhD thesis, Hamburg University, Hamburg, Germany, https://doi.org/10.17617/2.1737721, 2013.
Maaß, N., Kaleschke, L., Tian-Kunze, X., and T., R.: Snow thickness retrieval from L-band brightness temperatures: a model comparison, Ann. Glaciol., 56, 9–17, 2015.
Markus, T. and Cavalieri, D.: An enhancement of the NASA Team sea ice algorithm, IEEE T. Geosci. Remote, 38, 1387–1398, https://doi.org/10.1109/36.843033, 2000.
Martin-Neira, M., Ribó, S., and Martin-Polegre, A. J.: Polarimetric mode of MIRAS, IEEE T. Geosci. Remote, 40, 1755–1768, https://doi.org/10.1109/TGRS.2002.802489, 2002.
Matzler, C.: Microwave permittivity of dry snow, IEEE T. Geosci. Remote, 34, 573–581, https://doi.org/10.1109/36.485133, 1996.
Mecklenburg, S., Wright, N., Bouzina, C., and Delwart, S.: Getting down to business – SMOS operations and products, ESA Bulletin, 137, 25–30, 2009.
Mills, P. and Heygster, G.: Retrieving Ice Concentration From SMOS, IEEE Geosci. Remote S., 8, 283–287, https://doi.org/10.1109/LGRS.2010.2064157, 2011a.
Mills, P. and Heygster, G.: Sea Ice Emissivity Modeling at L-Band and Application to 2007 Pol-Ice Campaign Field Data, IEEE T. Geosci. Remote, 49, 612–627, https://doi.org/10.1109/TGRS.2010.2060729, 2011b.
Myung, J.: Tutorial on maximum likelihood estimation, J. Math. Psychol., 47, 90–100, https://doi.org/10.1016/S0022-2496(02)00028-7, 2003.
Owe, M., Jeu, R., and Walker, J.: A Methodology for Surface Soil Moisture and Vegetation Optical Depth Retrieval Using the Microwave Polarization Difference Index, IEEE T. Geosci. Remote, 39, 1643–1654, https://doi.org/10.1109/36.942542, 2001.
Ramseier, R.: Sea ice validation, in: DMSP Special Sensor Microwave/Imager Calibration/Validation, edited by: Hollinger, J. P., Naval Research Laboratory, Washington, DC, USA, 1991.
Schwank, M., Mätzler, C., Wiesmann, A., Wegmüller, U., Pulliainen, J., Lemmetyinen, J., Rautiainen, K., Derksen, C., Toose, P., and Drusch, M.: Snow Density and Ground Permittivity Retrieved from L-Band Radiometry: A Synthetic Analysis, IEEE J. Sel. Top. Appl., 8, 3833–3845, https://doi.org/10.1109/JSTARS.2015.2422998, 2015.
SEARCH: Research, Synthesis, and Knowledge Transfer in a Changing Arctic: The Study of Environmental Arctic Change (SEARCH), Arctic Research Consortium of the United States, Technical report, 2013.
Shokr, M. and Dabboor, M.: Interannual Variability of Young Ice in the Arctic Estimated Between 2002 and 2009, IEEE T. Geosci. Remote, 51, 3354–3370, https://doi.org/10.1109/TGRS.2012.2225432, 2013.
Shokr, M. and Sinha, N.: Sea Ice: Physics and Remote Sensing, AGU and John Wiley & Sons, 600 pp., 2015.
Shokr, M., Lambe, A., and Agnew, T.: A new algorithm (ECICE) to estimate ice concentration from remote sensing observations: an application to 85-GHz passive microwave data, IEEE T. Geosci. Remote, 46, 4104–4121, https://doi.org/10.1109/TGRS.2008.2000624, 2008.
Smith, D.: Extraction of winter total sea-ice concentration in the Greenland and Barents Seas from SSM/I data, Int. J. Remote Sens., 17, 2625–2646, https://doi.org/10.1080/01431169608949096, 1996.
Stroeve, J., Serreze, M., Holland, M., Kay, J., Malanik, J., and Barrett, A.: The Arctic's rapidly shrinking sea ice cover: a research synthesis, Climatic Change, 110, 1005–1027, https://doi.org/10.1007/s10584-011-0101-1, 2012.
Talone, M., Portabella, M., Martínez, J., and González-Gambau, V.: About the Optimal Grid for SMOS Level 1C and Level 2 Products, IEEE Geosci. Remote S., 12, 1630–1634, https://doi.org/10.1109/LGRS.2015.2416920, 2015.
Thomas, D. N. and Dieckmann, G. S. (Eds.): Sea Ice: An Introduction o its physics, Chemistry, Biology and geology, Blackwell, 2003.
Tiuri, M., Sihvola, A., Nyfors, E., and Hallikaiken, M.: The complex dielectric constant of snow at microwave frequencies, IEEE J. Oceanic Eng., 9, 377–382, https://doi.org/10.1109/JOE.1984.1145645, 1984.
Tonboe, R. T., Eastwood, S., Lavergne, T., Sørensen, A. M., Rathmann, N., Dybkjær, G., Pedersen, L. T., Høyer, J. L., and Kern, S.: The EUMETSAT sea ice concentration climate data record, The Cryosphere, 10, 2275–2290, https://doi.org/10.5194/tc-10-2275-2016, 2016.
Ulaby, F. T. and Long, D. G.: Microwave Radar and Radiometric Remote Sensing, University of Michigan Press, Ann Arbor, MI, USA, 2014.
Ulaby, F. T., Moore, R. K., and Fung, A. K.: Microwave Remote Sensing: Active and Passive, Addison-Wesley Publishing Company, Advanced Book Program/World Science Division, Boston, USA, 1986.
Vant, M., Ramseier, R., and Makios, V.: The complex-dielectric constant of sea ice at frequencies in the range 0.1–40 GHz, J. Appl. Phys., 49, 1264–1280, https://doi.org/10.1063/1.325018, 1978.
Vihma, T.: Effects of Arctic Sea Ice Decline on Weather and Climate: A Review, Surv. Geophys., 35, 1175–1214, https://doi.org/10.1007/s10712-014-9284-0, 2014.
Wilheit, T. T.: A review of applications of microwave radiometry to oceanography, Bound.-Lay. Meteorol., 13, 277–293, https://doi.org/10.1007/BF00913878, 1978.
Zine, S., Boutin, J., Font, J., Reul, N., Waldteufel, P., Gabarro, C., Tenerelli, J., Petitcolin, F., Vergely, J., Talone, M., and Delwart, S.: Overview of the SMOS Sea Surface Salinity Prototype Processor, IEEE T. Geosci. Remote, 46, 621–645, https://doi.org/10.1109/TGRS.2008.915543, 2008.
Short summary
We present a new method to estimate sea ice concentration in the Arctic Ocean using different brightness temperature observations from the Soil Moisture Ocean Salinity (SMOS) satellite. The method employs a maximum-likelihood estimator. Observations at L-band frequencies such as those from SMOS (i.e. 1.4 GHz) are advantageous to remote sensing of sea ice because the atmosphere is virtually transparent at that frequency and little affected by physical temperature changes.
We present a new method to estimate sea ice concentration in the Arctic Ocean using different...
