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The Cryosphere
An interactive open-access journal of the European Geosciences Union
Journal topic
- About
- Editorial board
- Articles
- Special issues
- Highlight articles
- Manuscript tracking
- Subscribe to alerts
- Peer review
- For authors
- For reviewers
- EGU publications
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IF 4.713
4.927CiteScore
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index 71h5-index 53
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Abstracted/indexed
TC | Articles | Volume 13, issue 1
The Cryosphere, 13, 49–78, 2019
https://doi.org/10.5194/tc-13-49-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-13-49-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
The Cryosphere, 13, 49–78, 2019
https://doi.org/10.5194/tc-13-49-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-13-49-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article 09 Jan 2019
Research article | 09 Jan 2019
Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records
Thomas Lavergne et al.
Related authors
Stefan Kern, Thomas Lavergne, Dirk Notz, Leif Toudal Pedersen, and Rasmus Tage Tonboe
The Cryosphere Discuss., https://doi.org/10.5194/tc-2020-35, https://doi.org/10.5194/tc-2020-35, 2020
Revised manuscript accepted for TC
Short summary
Short summary
In winter, satellite passive microwave imagery is excellent for weather- / daylight-independent monitoring of Arctic sea ice but in summer it is challenged by melting snow and melt ponds on sea ice. We compare products of 10 algorithms used for such monitoring with independent satellite and ship-based ice-cover data. All products disagree with these data with large regional and inter-product differences. We hypothesize inadequate treatment of melt conditions in the algorithms as the main reason.
Stefan Kern, Thomas Lavergne, Dirk Notz, Leif Toudal Pedersen, Rasmus Tage Tonboe, Roberto Saldo, and Atle MacDonald Sørensen
The Cryosphere, 13, 3261–3307, https://doi.org/10.5194/tc-13-3261-2019, https://doi.org/10.5194/tc-13-3261-2019, 2019
Short summary
Short summary
A systematic evaluation of 10 global satellite data products of the polar sea-ice area is performed. Inter-product differences in evaluation results call for careful consideration of data product limitations when performing sea-ice area trend analyses and for further mitigation of the effects of sensor changes. We open a discussion about evaluation strategies for such data products near-0 % and near-100 % sea-ice concentration, e.g. with the aim to improve high-concentration evaluation accuracy.
Anton Andreevich Korosov, Pierre Rampal, Leif Toudal Pedersen, Roberto Saldo, Yufang Ye, Georg Heygster, Thomas Lavergne, Signe Aaboe, and Fanny Girard-Ardhuin
The Cryosphere, 12, 2073–2085, https://doi.org/10.5194/tc-12-2073-2018, https://doi.org/10.5194/tc-12-2073-2018, 2018
Short summary
Short summary
A new algorithm for estimating sea ice age in the Arctic is presented. The algorithm accounts for motion, deformation, melting and freezing of sea ice and uses daily sea ice drift and sea ice concentration products. The major advantage of the new algorithm is the ability to generate individual ice age fractions in each pixel or, in other words, to provide a frequency distribution of the ice age. Multi-year ice concentration can be computed as a sum of all ice fractions older than 1 year.
Hoyeon Shi, Byung-Ju Sohn, Gorm Dybkjær, Rasmus Tage Tonboe, and Sang-Moo Lee
The Cryosphere Discuss., https://doi.org/10.5194/tc-2020-27, https://doi.org/10.5194/tc-2020-27, 2020
Revised manuscript under review for TC
Short summary
Short summary
To estimate sea ice thickness from satellite freeboard measurements, snow depth information has been required, however, the snow depth estimate has been considered largely uncertain. We propose a new method to estimate ice thickness and snow depth simultaneously from sea ice freeboards by imposing a thermodynamic constraint. Obtained ice thickness and snow depth were consistent with airborne measurements, suggesting that uncertainty of ice thickness caused by uncertain snow depth can be reduced.
Stefan Kern, Thomas Lavergne, Dirk Notz, Leif Toudal Pedersen, and Rasmus Tage Tonboe
The Cryosphere Discuss., https://doi.org/10.5194/tc-2020-35, https://doi.org/10.5194/tc-2020-35, 2020
Revised manuscript accepted for TC
Short summary
Short summary
In winter, satellite passive microwave imagery is excellent for weather- / daylight-independent monitoring of Arctic sea ice but in summer it is challenged by melting snow and melt ponds on sea ice. We compare products of 10 algorithms used for such monitoring with independent satellite and ship-based ice-cover data. All products disagree with these data with large regional and inter-product differences. We hypothesize inadequate treatment of melt conditions in the algorithms as the main reason.
Clara Burgard, Dirk Notz, Leif T. Pedersen, and Rasmus T. Tonboe
The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-317, https://doi.org/10.5194/tc-2019-317, 2020
Revised manuscript accepted for TC
Short summary
Short summary
The high disagreement between observations of Arctic sea ice makes it difficult to evaluate climate models with observations. We investigate the possibility of translating the model state into what a satellite could observe, especially applied to sea ice. We find that we do not need complex information about the vertical distribution of temperature and salinity inside the ice, but rather can assume simplified distributions, to reasonably translate the simulated sea ice into satellite language.
Clara Burgard, Dirk Notz, Leif T. Pedersen, and Rasmus T. Tonboe
The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-318, https://doi.org/10.5194/tc-2019-318, 2020
Revised manuscript accepted for TC
Short summary
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The high disagreement between observations of Arctic sea ice inhibits the evaluation of climate models with observations. We develop a tool which translates the simulated Arctic Ocean state into what a satellite could observe from space in the form of brightness temperatures, a measure for the radiation emitted by the surface. We find that the simulated brightness temperatures compare well with the observed brightness temperatures. This tool brings a new perspective for climate model evaluation.
Larysa Istomina, Henrik Marks, Marcus Huntemann, Georg Heygster, and Gunnar Spreen
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2019-413, https://doi.org/10.5194/amt-2019-413, 2020
Preprint under review for AMT
Stefan Kern, Thomas Lavergne, Dirk Notz, Leif Toudal Pedersen, Rasmus Tage Tonboe, Roberto Saldo, and Atle MacDonald Sørensen
The Cryosphere, 13, 3261–3307, https://doi.org/10.5194/tc-13-3261-2019, https://doi.org/10.5194/tc-13-3261-2019, 2019
Short summary
Short summary
A systematic evaluation of 10 global satellite data products of the polar sea-ice area is performed. Inter-product differences in evaluation results call for careful consideration of data product limitations when performing sea-ice area trend analyses and for further mitigation of the effects of sensor changes. We open a discussion about evaluation strategies for such data products near-0 % and near-100 % sea-ice concentration, e.g. with the aim to improve high-concentration evaluation accuracy.
Yufang Ye, Mohammed Shokr, Signe Aaboe, Wiebke Aldenhoff, Leif E. B. Eriksson, Georg Heygster, Christian Melsheimer, and Fanny Girard-Ardhuin
The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-200, https://doi.org/10.5194/tc-2019-200, 2019
Revised manuscript not accepted
Short summary
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Sea ice has been monitored with microwave satellite observations since the late 1970s. However, the question remains as to which sea ice type concentration (SITC) method is most appropriate for ice type distribution and hence climate monitoring. This paper presents key results of inter-comparison and evaluation for eight SITC methods. The SITC methods were inter-compared with sea ice age and sea ice type products. Their performances were evaluated quantitatively and qualitatively.
Arantxa M. Triana-Gómez, Georg Heygster, Christian Melsheimer, and Gunnar Spreen
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2019-253, https://doi.org/10.5194/amt-2019-253, 2019
Revised manuscript accepted for AMT
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In the Arctic, in-situ measurements are sparse and standard remote sensing retrieval methods have problems. We present advances in a retrieval algorithm for vertically integrated water vapour tuned for polar regions. In addition to the initial sensor used (AMSU-B), we can now also use data from the successor instrument (MHS). Additionally, certain artifacts are now filtered out. Comparison with radiosondes shows overall good performance of the updated algorithm.
Pia Nielsen-Englyst, Jacob L. Høyer, Kristine S. Madsen, Rasmus T. Tonboe, and Gorm Dybkjær
The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-126, https://doi.org/10.5194/tc-2019-126, 2019
Revised manuscript not accepted
Short summary
Short summary
The Arctic region is responding heavily to climate change, and yet, the air temperature of Arctic, ice covered areas is heavily under-sampled when it comes to in situ measurements. This paper presents a method for estimating daily mean 2 meter air temperatures (T2m) in the Arctic from satellite observations of skin temperature, providing spatially detailed observations of the Arctic. The satellite derived T2m product covers clear sky snow and ice surfaces in the Arctic for the period 2000–2009.
Lise Kilic, Rasmus Tage Tonboe, Catherine Prigent, and Georg Heygster
The Cryosphere, 13, 1283–1296, https://doi.org/10.5194/tc-13-1283-2019, https://doi.org/10.5194/tc-13-1283-2019, 2019
Short summary
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In this study, we develop and present simple algorithms to derive the snow depth, the snow–ice interface temperature, and the effective temperature of Arctic sea ice. This is achieved using satellite observations collocated with buoy measurements. The errors of the retrieved parameters are estimated and compared with independent data. These parameters are useful for sea ice concentration mapping, understanding sea ice properties and variability, and for atmospheric sounding applications.
Pia Nielsen-Englyst, Jacob L. Høyer, Kristine S. Madsen, Rasmus Tonboe, Gorm Dybkjær, and Emy Alerskans
The Cryosphere, 13, 1005–1024, https://doi.org/10.5194/tc-13-1005-2019, https://doi.org/10.5194/tc-13-1005-2019, 2019
Short summary
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The paper facilitates the construction of a satellite-derived 2 m air temperature (T2m) product for Arctic snow/ice areas. The relationship between skin temperature (Tskin) and T2m is analysed using weather stations. The main factors influencing the relationship are seasonal variations, wind speed and clouds. A clear-sky bias is estimated to assess the effect of cloud-limited satellite observations. The results are valuable when validating satellite Tskin or estimating T2m from satellite Tskin.
Cătălin Paţilea, Georg Heygster, Marcus Huntemann, and Gunnar Spreen
The Cryosphere, 13, 675–691, https://doi.org/10.5194/tc-13-675-2019, https://doi.org/10.5194/tc-13-675-2019, 2019
Short summary
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Sea ice thickness is important for representing atmosphere–ocean interactions in climate models. A validated satellite sea ice thickness measurement algorithm is transferred to a new sensor. The results offer a better temporal and spatial coverage of satellite measurements in the polar regions. Here we describe the calibration procedure between the two sensors, taking into account their technical differences. In addition a new filter for interference from artificial radio sources is implemented.
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
Short summary
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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.
Erlend M. Knudsen, Bernd Heinold, Sandro Dahlke, Heiko Bozem, Susanne Crewell, Irina V. Gorodetskaya, Georg Heygster, Daniel Kunkel, Marion Maturilli, Mario Mech, Carolina Viceto, Annette Rinke, Holger Schmithüsen, André Ehrlich, Andreas Macke, Christof Lüpkes, and Manfred Wendisch
Atmos. Chem. Phys., 18, 17995–18022, https://doi.org/10.5194/acp-18-17995-2018, https://doi.org/10.5194/acp-18-17995-2018, 2018
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The paper describes the synoptic development during the ACLOUD/PASCAL airborne and ship-based field campaign near Svalbard in spring 2017. This development is presented using near-surface and upperair meteorological observations, satellite, and model data. We first present time series of these data, from which we identify and characterize three key periods. Finally, we put our observations in historical and regional contexts and compare our findings to other Arctic field campaigns.
Stephan Paul, Stefan Hendricks, Robert Ricker, Stefan Kern, and Eero Rinne
The Cryosphere, 12, 2437–2460, https://doi.org/10.5194/tc-12-2437-2018, https://doi.org/10.5194/tc-12-2437-2018, 2018
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During ESA's second phase of the Sea Ice Climate Change Initiative (SICCI-2), we developed a novel approach to creating a consistent freeboard data set from Envisat and CryoSat-2. We used consistent procedures that are directly related to the sensors' waveform-echo parameters, instead of applying corrections as a post-processing step. This data set is to our knowledge the first of its kind providing consistent freeboard for the Arctic as well as the Antarctic.
Anton Andreevich Korosov, Pierre Rampal, Leif Toudal Pedersen, Roberto Saldo, Yufang Ye, Georg Heygster, Thomas Lavergne, Signe Aaboe, and Fanny Girard-Ardhuin
The Cryosphere, 12, 2073–2085, https://doi.org/10.5194/tc-12-2073-2018, https://doi.org/10.5194/tc-12-2073-2018, 2018
Short summary
Short summary
A new algorithm for estimating sea ice age in the Arctic is presented. The algorithm accounts for motion, deformation, melting and freezing of sea ice and uses daily sea ice drift and sea ice concentration products. The major advantage of the new algorithm is the ability to generate individual ice age fractions in each pixel or, in other words, to provide a frequency distribution of the ice age. Multi-year ice concentration can be computed as a sum of all ice fractions older than 1 year.
Aleksey Malinka, Eleonora Zege, Larysa Istomina, Georg Heygster, Gunnar Spreen, Donald Perovich, and Chris Polashenski
The Cryosphere, 12, 1921–1937, https://doi.org/10.5194/tc-12-1921-2018, https://doi.org/10.5194/tc-12-1921-2018, 2018
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Melt ponds occupy a large part of the Arctic sea ice in summer and strongly affect the radiative budget of the atmosphere–ice–ocean system. The melt pond reflectance is modeled in the framework of the radiative transfer theory and validated with field observations. It improves understanding of melting sea ice and enables better parameterization of the surface in Arctic atmospheric remote sensing (clouds, aerosols, trace gases) and re-evaluating Arctic climatic feedbacks at a new accuracy level.
Elena V. Shalina and Stein Sandven
The Cryosphere, 12, 1867–1886, https://doi.org/10.5194/tc-12-1867-2018, https://doi.org/10.5194/tc-12-1867-2018, 2018
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In this paper we analyze snow data from Soviet airborne expeditions, Sever, which operated in late winter 1959-1986, in the Arctic and made snow measurements on the ice around plane landing sites. The snow measurements were made on the multiyear ice in the central Arctic and on the first-year ice in the Eurasian seas in the areas for which snow characteristics are poorly described in the literature. The main goal of this study is to produce an improved data set of snow depth on the sea ice.
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.
Raul Cristian Scarlat, Christian Melsheimer, and Georg Heygster
Atmos. Meas. Tech., 11, 2067–2084, https://doi.org/10.5194/amt-11-2067-2018, https://doi.org/10.5194/amt-11-2067-2018, 2018
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An obstacle in achieving reliable satellite measurements of atmospheric water vapour in the Arctic is the presence of sea ice. Here we have built on a previous method that achieves consistent atmospheric measurements over sea-ice-covered regions. The main focus was to adapt the method for better coverage in shallow-ice-covered and ice-free areas by accounting for the signal from the open-ocean surface. This approach extends the coverage from the central Arctic to the entire Arctic region.
Igor A. Dmitrenko, Sergey A. Kirillov, Bert Rudels, David G. Babb, Leif Toudal Pedersen, Søren Rysgaard, Yngve Kristoffersen, and David G. Barber
Ocean Sci., 13, 1045–1060, https://doi.org/10.5194/os-13-1045-2017, https://doi.org/10.5194/os-13-1045-2017, 2017
Tim Carlsen, Gerit Birnbaum, André Ehrlich, Johannes Freitag, Georg Heygster, Larysa Istomina, Sepp Kipfstuhl, Anaïs Orsi, Michael Schäfer, and Manfred Wendisch
The Cryosphere, 11, 2727–2741, https://doi.org/10.5194/tc-11-2727-2017, https://doi.org/10.5194/tc-11-2727-2017, 2017
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The optical size of snow grains (ropt) affects the reflectivity of snow surfaces and thus the local surface energy budget in particular in polar regions. The temporal evolution of ropt retrieved from ground-based, airborne, and spaceborne remote sensing could reproduce optical in situ measurements for a 2-month period in central Antarctica (2013/14). The presented validation study provided a unique testbed for retrievals of ropt under Antarctic conditions where in situ data are scarce.
Sergei Kirillov, Igor Dmitrenko, Søren Rysgaard, David Babb, Leif Toudal Pedersen, Jens Ehn, Jørgen Bendtsen, and David Barber
Ocean Sci., 13, 947–959, https://doi.org/10.5194/os-13-947-2017, https://doi.org/10.5194/os-13-947-2017, 2017
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This paper reports the analysis of 3-week oceanographic data obtained in the front of Flade Isblink Glacier in northeast Greenland. The major focus of research is considering the changes of water dynamics and the altering of temperature and salinity vertical distribution occurring during the storm event. We discuss the mechanisms that are responsible for the formation of two-layer circulation cell and release of cold and relatively fresh sub-glacial waters into the ocean.
Carolina Gabarro, Antonio Turiel, Pedro Elosegui, Joaquim A. Pla-Resina, and Marcos Portabella
The Cryosphere, 11, 1987–2002, https://doi.org/10.5194/tc-11-1987-2017, https://doi.org/10.5194/tc-11-1987-2017, 2017
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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.
Stefan Muckenhuber and Stein Sandven
The Cryosphere, 11, 1835–1850, https://doi.org/10.5194/tc-11-1835-2017, https://doi.org/10.5194/tc-11-1835-2017, 2017
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Sea ice drift has a strong impact on sea ice distribution on different temporal and spatial scales. An open-source sea ice drift algorithm for Sentinel-1 satellite imagery is introduced based on the combination of feature tracking and pattern matching. The algorithm is designed to utilise the respective advantages of the two approaches and allows drift calculation at user-defined locations.
Christopher J. Merchant, Frank Paul, Thomas Popp, Michael Ablain, Sophie Bontemps, Pierre Defourny, Rainer Hollmann, Thomas Lavergne, Alexandra Laeng, Gerrit de Leeuw, Jonathan Mittaz, Caroline Poulsen, Adam C. Povey, Max Reuter, Shubha Sathyendranath, Stein Sandven, Viktoria F. Sofieva, and Wolfgang Wagner
Earth Syst. Sci. Data, 9, 511–527, https://doi.org/10.5194/essd-9-511-2017, https://doi.org/10.5194/essd-9-511-2017, 2017
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Climate data records (CDRs) contain data describing Earth's climate and should address uncertainty in the data to communicate what is known about climate variability or change and what range of doubt exists. This paper discusses good practice for including uncertainty information in CDRs for the essential climate variables (ECVs) derived from satellite data. Recommendations emerge from the shared experience of diverse ECV projects within the European Space Agency Climate Change Initiative.
Aleksey Malinka, Eleonora Zege, Georg Heygster, and Larysa Istomina
The Cryosphere, 10, 2541–2557, https://doi.org/10.5194/tc-10-2541-2016, https://doi.org/10.5194/tc-10-2541-2016, 2016
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The number of melt ponds on Arctic summer sea ice and its reflectance are required for better climate modeling and weather prediction. In order to derive these quantities from optical satellite observations, simple analytical formulas for the bidirectional reflectance factor and albedo at direct and diffuse incidence are derived from basic assumptions and verified with in situ measurements made during the expedition ARK-XXVII/3 of research vessel Polarstern in 2012.
Rasmus T. Tonboe, Steinar Eastwood, Thomas Lavergne, Atle M. Sørensen, Nicholas Rathmann, Gorm Dybkjær, Leif Toudal Pedersen, Jacob L. Høyer, and Stefan Kern
The Cryosphere, 10, 2275–2290, https://doi.org/10.5194/tc-10-2275-2016, https://doi.org/10.5194/tc-10-2275-2016, 2016
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The EUMETSAT sea ice climate record (ESICR) is based on the Nimbus 7 SMMR (1978–1987), the SSM/I (1987–2009), and the SSMIS (2003–today) microwave radiometer data. It uses a combination of two sea ice concentration algorithms with dynamical tie points, explicit atmospheric correction using numerical weather prediction data for error reduction and it comes with spatially and temporally varying uncertainty estimates describing the residual uncertainties.
Stefan Kern, Anja Rösel, Leif Toudal Pedersen, Natalia Ivanova, Roberto Saldo, and Rasmus Tage Tonboe
The Cryosphere, 10, 2217–2239, https://doi.org/10.5194/tc-10-2217-2016, https://doi.org/10.5194/tc-10-2217-2016, 2016
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Sea ice, frozen seawater floating on polar oceans, is covered by meltwater puddles, so-called melt ponds, during summer. Methods used to compute Arctic sea-ice concentration (SIC) from microwave satellite data are influenced by melt ponds. We apply eight such methods to one microwave dataset and compare SIC with visible data. We conclude all methods fail to distinguish melt ponds from leads between ice floes; SIC biases are negative (positive) for ponded (non-ponded) sea ice and can exceed 20 %.
Dirk Notz, Alexandra Jahn, Marika Holland, Elizabeth Hunke, François Massonnet, Julienne Stroeve, Bruno Tremblay, and Martin Vancoppenolle
Geosci. Model Dev., 9, 3427–3446, https://doi.org/10.5194/gmd-9-3427-2016, https://doi.org/10.5194/gmd-9-3427-2016, 2016
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The large-scale evolution of sea ice is both an indicator and a driver of climate changes. Hence, a realistic simulation of sea ice is key for a realistic simulation of the climate system of our planet. To assess and to improve the realism of sea-ice simulations, we present here a new protocol for climate-model output that allows for an in-depth analysis of the simulated evolution of sea ice.
Sebastian Bathiany, Bregje van der Bolt, Mark S. Williamson, Timothy M. Lenton, Marten Scheffer, Egbert H. van Nes, and Dirk Notz
The Cryosphere, 10, 1631–1645, https://doi.org/10.5194/tc-10-1631-2016, https://doi.org/10.5194/tc-10-1631-2016, 2016
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We examine if a potential "tipping point" in Arctic sea ice, causing abrupt and irreversible sea-ice loss, could be foreseen with statistical early warning signals. We assess this idea by using several models of different complexity. We find robust and consistent trends in variability that are not specific to the existence of a tipping point. While this makes an early warning impossible, it allows to estimate sea-ice variability from only short observational records or reconstructions.
N. Ivanova, L. T. Pedersen, R. T. Tonboe, S. Kern, G. Heygster, T. Lavergne, A. Sørensen, R. Saldo, G. Dybkjær, L. Brucker, and M. Shokr
The Cryosphere, 9, 1797–1817, https://doi.org/10.5194/tc-9-1797-2015, https://doi.org/10.5194/tc-9-1797-2015, 2015
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Thirty sea ice algorithms are inter-compared and evaluated systematically over low and high sea ice concentrations, as well as in the presence of thin ice and melt ponds. A hybrid approach is suggested to retrieve sea ice concentration globally for climate monitoring purposes. This approach consists of a combination of two algorithms plus the implementation of a dynamic tie point and atmospheric correction of input brightness temperatures.
L. Istomina, G. Heygster, M. Huntemann, P. Schwarz, G. Birnbaum, R. Scharien, C. Polashenski, D. Perovich, E. Zege, A. Malinka, A. Prikhach, and I. Katsev
The Cryosphere, 9, 1551–1566, https://doi.org/10.5194/tc-9-1551-2015, https://doi.org/10.5194/tc-9-1551-2015, 2015
L. Istomina, G. Heygster, M. Huntemann, H. Marks, C. Melsheimer, E. Zege, A. Malinka, A. Prikhach, and I. Katsev
The Cryosphere, 9, 1567–1578, https://doi.org/10.5194/tc-9-1567-2015, https://doi.org/10.5194/tc-9-1567-2015, 2015
S. Kern, K. Khvorostovsky, H. Skourup, E. Rinne, Z. S. Parsakhoo, V. Djepa, P. Wadhams, and S. Sandven
The Cryosphere, 9, 37–52, https://doi.org/10.5194/tc-9-37-2015, https://doi.org/10.5194/tc-9-37-2015, 2015
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Snow depth and ice density are equally important parameters for sea ice thickness retrieval from radar altimetry of Arctic sea ice. Development of a new snow depth data set is mandatory as the Warren snow depth climatology does not represent the actual snow depth distribution. An optimal choice of ice density can be realized by including ice type and degree of deformation. Retrieval and validation enhancement requires more contemporary ice freeboard, thickness, and density and snow depth data.
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
S. Rysgaard, F. Wang, R. J. Galley, R. Grimm, D. Notz, M. Lemes, N.-X. Geilfus, A. Chaulk, A. A. Hare, O. Crabeck, B. G. T. Else, K. Campbell, L. L. Sørensen, J. Sievers, and T. Papakyriakou
The Cryosphere, 8, 1469–1478, https://doi.org/10.5194/tc-8-1469-2014, https://doi.org/10.5194/tc-8-1469-2014, 2014
D. Zanchettin, O. Bothe, C. Timmreck, J. Bader, A. Beitsch, H.-F. Graf, D. Notz, and J. H. Jungclaus
Earth Syst. Dynam., 5, 223–242, https://doi.org/10.5194/esd-5-223-2014, https://doi.org/10.5194/esd-5-223-2014, 2014
M. Huntemann, G. Heygster, L. Kaleschke, T. Krumpen, M. Mäkynen, and M. Drusch
The Cryosphere, 8, 439–451, https://doi.org/10.5194/tc-8-439-2014, https://doi.org/10.5194/tc-8-439-2014, 2014
D. Notz
The Cryosphere, 8, 229–243, https://doi.org/10.5194/tc-8-229-2014, https://doi.org/10.5194/tc-8-229-2014, 2014
M. Zygmuntowska, K. Khvorostovsky, V. Helm, and S. Sandven
The Cryosphere, 7, 1315–1324, https://doi.org/10.5194/tc-7-1315-2013, https://doi.org/10.5194/tc-7-1315-2013, 2013
M. Vancoppenolle, D. Notz, F. Vivier, J. Tison, B. Delille, G. Carnat, J. Zhou, F. Jardon, P. Griewank, A. Lourenço, and T. Haskell
The Cryosphere Discuss., https://doi.org/10.5194/tcd-7-3209-2013, https://doi.org/10.5194/tcd-7-3209-2013, 2013
Revised manuscript not accepted
S. Tietsche, D. Notz, J. H. Jungclaus, and J. Marotzke
Ocean Sci., 9, 19–36, https://doi.org/10.5194/os-9-19-2013, https://doi.org/10.5194/os-9-19-2013, 2013
Related subject area
Discipline: Sea ice | Subject: Remote Sensing
Opportunistic evaluation of modelled sea ice drift using passively drifting telemetry collars in Hudson Bay, Canada
Combining TerraSAR-X and time-lapse photography for seasonal sea ice monitoring: the case of Deception Bay, Nunavik
Satellite observations of unprecedented phytoplankton blooms in the Maud Rise polynya, Southern Ocean
Satellite Passive Microwave Sea-Ice Concentration Data Set Intercomparison for Arctic Summer Conditions
Effects of decimetre-scale surface roughness on L-band brightness temperature of sea ice
Brief communication: Conventional assumptions involving the speed of radar waves in snow introduce systematic underestimates to sea ice thickness and seasonal growth rate estimates
Broadband albedo of Arctic sea ice from MERIS optical data
Satellite passive microwave sea-ice concentration data set intercomparison: closed ice and ship-based observations
Estimating the sea ice floe size distribution using satellite altimetry: theory, climatology, and model comparison
The 2018 North Greenland polynya observed by a newly introduced merged optical and passive microwave sea-ice concentration dataset
Estimation of turbulent heat flux over leads using satellite thermal images
Snow-driven uncertainty in CryoSat-2-derived Antarctic sea ice thickness – insights from McMurdo Sound
Instantaneous sea ice drift speed from TanDEM-X interferometry
Estimating the snow depth, the snow–ice interface temperature, and the effective temperature of Arctic sea ice using Advanced Microwave Scanning Radiometer 2 and ice mass balance buoy data
Assessment of contemporary satellite sea ice thickness products for Arctic sea ice
Baffin Bay sea ice inflow and outflow: 1978–1979 to 2016–2017
Combined SMAP–SMOS thin sea ice thickness retrieval
Leads and ridges in Arctic sea ice from RGPS data and a new tracking algorithm
Mapping pan-Arctic landfast sea ice stability using Sentinel-1 interferometry
Satellite-derived sea ice export and its impact on Arctic ice mass balance
A scatterometer record of sea ice extents and backscatter: 1992–2016
Estimation of Arctic land-fast ice cover based on dual-polarized Sentinel-1 SAR imagery
Empirical parametrization of Envisat freeboard retrieval of Arctic and Antarctic sea ice based on CryoSat-2: progress in the ESA Climate Change Initiative
A new tracking algorithm for sea ice age distribution estimation
Warm winter, thin ice?
Arctic lead detection using a waveform mixture algorithm from CryoSat-2 data
Open-source algorithm for detecting sea ice surface features in high-resolution optical imagery
Ron R. Togunov, Natasha J. Klappstein, Nicholas J. Lunn, Andrew E. Derocher, and Marie Auger-Méthé
The Cryosphere, 14, 1937–1950, https://doi.org/10.5194/tc-14-1937-2020, https://doi.org/10.5194/tc-14-1937-2020, 2020
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Sea ice drift affects important geophysical and biological processes in the Arctic. Using the motion of dropped polar bear GPS collars, our study evaluated the accuracy of a popular satellite-based ice drift model in Hudson Bay. We observed that velocity was underestimated, particularly at higher speeds. Direction was unbiased, but it was less precise at lower speeds. These biases should be accounted for in climate and ecological research relying on accurate/absolute drift velocities.
Sophie Dufour-Beauséjour, Anna Wendleder, Yves Gauthier, Monique Bernier, Jimmy Poulin, Véronique Gilbert, Juupi Tuniq, Amélie Rouleau, and Achim Roth
The Cryosphere, 14, 1595–1609, https://doi.org/10.5194/tc-14-1595-2020, https://doi.org/10.5194/tc-14-1595-2020, 2020
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Inuit have reported greater variability in seasonal sea ice conditions. For Deception Bay (Nunavik), an area prized for seal and caribou hunting, an increase in snow precipitation and a shorter snow cover period is expected in the near future. In this context, and considering ice-breaking transport in the fjord by mining companies, we combined satellite images and time-lapse photography to monitor sea ice in the area between 2015 and 2018.
Babula Jena and Anilkumar N. Pillai
The Cryosphere, 14, 1385–1398, https://doi.org/10.5194/tc-14-1385-2020, https://doi.org/10.5194/tc-14-1385-2020, 2020
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Records of multiple ocean color satellite data indicated unprecedented phytoplankton blooms on the Maud Rise with a backdrop of anomalous upper ocean warming and sea ice loss in 2017. The bloom appearance may indicate it as a potential sink of atmospheric CO2 through biological pumping, and it can be a major source of carbon and energy for the regional food web. The reoccurrence of the bloom is important considering the high-nutrient low-chlorophyll conditions of the Southern Ocean.
Stefan Kern, Thomas Lavergne, Dirk Notz, Leif Toudal Pedersen, and Rasmus Tage Tonboe
The Cryosphere Discuss., https://doi.org/10.5194/tc-2020-35, https://doi.org/10.5194/tc-2020-35, 2020
Revised manuscript accepted for TC
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In winter, satellite passive microwave imagery is excellent for weather- / daylight-independent monitoring of Arctic sea ice but in summer it is challenged by melting snow and melt ponds on sea ice. We compare products of 10 algorithms used for such monitoring with independent satellite and ship-based ice-cover data. All products disagree with these data with large regional and inter-product differences. We hypothesize inadequate treatment of melt conditions in the algorithms as the main reason.
Maciej Miernecki, Lars Kaleschke, Nina Maaß, Stefan Hendricks, and Sten Schmidl Søbjærg
The Cryosphere, 14, 461–476, https://doi.org/10.5194/tc-14-461-2020, https://doi.org/10.5194/tc-14-461-2020, 2020
Robbie D. C. Mallett, Isobel R. Lawrence, Julienne C. Stroeve, Jack C. Landy, and Michel Tsamados
The Cryosphere, 14, 251–260, https://doi.org/10.5194/tc-14-251-2020, https://doi.org/10.5194/tc-14-251-2020, 2020
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Soils store large carbon and are important for global warming. We do not know what factors are important for soil carbon storage in the alpine Andes and how they work. We studied how rainfall affects soil carbon storage related to soil structure. We found soil structure is not important, but soil carbon storage and stability controlled by rainfall are dependent on rocks under the soils. The results indicate that we should pay attention to the rocks when studying soil carbon storage in the Andes.
Christine Pohl, Larysa Istomina, Steffen Tietsche, Evelyn Jäkel, Johannes Stapf, Gunnar Spreen, and Georg Heygster
The Cryosphere, 14, 165–182, https://doi.org/10.5194/tc-14-165-2020, https://doi.org/10.5194/tc-14-165-2020, 2020
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A spectral to broadband conversion is developed empirically that can be used in combination with the Melt Pond Detector algorithm to derive broadband albedo (300–3000 nm) of Arctic sea ice from MERIS data. It is validated and shows better performance compared to existing conversion methods. A comparison of MERIS broadband albedo with respective values from ERA5 reanalysis suggests a revision of the albedo values used in ERA5. MERIS albedo might be useful for improving albedo representation.
Stefan Kern, Thomas Lavergne, Dirk Notz, Leif Toudal Pedersen, Rasmus Tage Tonboe, Roberto Saldo, and Atle MacDonald Sørensen
The Cryosphere, 13, 3261–3307, https://doi.org/10.5194/tc-13-3261-2019, https://doi.org/10.5194/tc-13-3261-2019, 2019
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A systematic evaluation of 10 global satellite data products of the polar sea-ice area is performed. Inter-product differences in evaluation results call for careful consideration of data product limitations when performing sea-ice area trend analyses and for further mitigation of the effects of sensor changes. We open a discussion about evaluation strategies for such data products near-0 % and near-100 % sea-ice concentration, e.g. with the aim to improve high-concentration evaluation accuracy.
Christopher Horvat, Lettie A. Roach, Rachel Tilling, Cecilia M. Bitz, Baylor Fox-Kemper, Colin Guider, Kaitlin Hill, Andy Ridout, and Andrew Shepherd
The Cryosphere, 13, 2869–2885, https://doi.org/10.5194/tc-13-2869-2019, https://doi.org/10.5194/tc-13-2869-2019, 2019
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Changes in the floe size distribution (FSD) are important for sea ice evolution but to date largely unobserved and unknown. Climate models, forecast centres, ship captains, and logistic specialists cannot currently obtain statistical information about sea ice floe size on demand. We develop a new method to observe the FSD at global scales and high temporal and spatial resolution. With refinement, this method can provide crucial information for polar ship routing and real-time forecasting.
Valentin Ludwig, Gunnar Spreen, Christian Haas, Larysa Istomina, Frank Kauker, and Dmitrii Murashkin
The Cryosphere, 13, 2051–2073, https://doi.org/10.5194/tc-13-2051-2019, https://doi.org/10.5194/tc-13-2051-2019, 2019
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Sea-ice concentration, the fraction of an area covered by sea ice, can be observed from satellites with different methods. We combine two methods to obtain a product which is better than either of the input measurements alone. The benefit of our product is demonstrated by observing the formation of an open water area which can now be observed with more detail. Additionally, we find that the open water area formed because the sea ice drifted in the opposite direction and faster than usual.
Meng Qu, Xiaoping Pang, Xi Zhao, Jinlun Zhang, Qing Ji, and Pei Fan
The Cryosphere, 13, 1565–1582, https://doi.org/10.5194/tc-13-1565-2019, https://doi.org/10.5194/tc-13-1565-2019, 2019
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Can we ignore the contribution of small ice leads when estimating turbulent heat flux? Combining bulk formulae and a fetch-limited model with surface temperature from MODIS and Landsat-8 Thermal Infrared Sensor (TIRS) images, we found small leads account for 25 % of the turbulent heat flux, due to its large total area. Estimated turbulent heat flux is larger from TIRS than that from MODIS with a coarser resolution and larger using a fetch-limited model than that using bulk formulae.
Daniel Price, Iman Soltanzadeh, Wolfgang Rack, and Ethan Dale
The Cryosphere, 13, 1409–1422, https://doi.org/10.5194/tc-13-1409-2019, https://doi.org/10.5194/tc-13-1409-2019, 2019
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Snow depth on Antarctic sea ice is poorly mapped. We examine the usefulness of various snow products to provide snow depth information over Antarctic fast ice in McMurdo Sound, with a focus on a novel approach using a high-resolution numerical snow accumulation model. We find the model performs better than existing snow products from reanalysis products. However, when combining this information with satellite data to retrieve sea ice thickness, large uncertainties in thickness remain.
Dyre Oliver Dammann, Leif E. B. Eriksson, Joshua M. Jones, Andrew R. Mahoney, Roland Romeiser, Franz J. Meyer, Hajo Eicken, and Yasushi Fukamachi
The Cryosphere, 13, 1395–1408, https://doi.org/10.5194/tc-13-1395-2019, https://doi.org/10.5194/tc-13-1395-2019, 2019
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We evaluate single-pass synthetic aperture radar interferometry (InSAR) as a tool to assess sea ice drift and deformation. Initial validation shows that TanDEM-X phase-derived drift speed corresponds well with ground-based radar-derived motion. We further show that InSAR enables the identification of potentially important short-lived dynamic processes otherwise difficult to observe, with possible implication for engineering and sea ice modeling.
Lise Kilic, Rasmus Tage Tonboe, Catherine Prigent, and Georg Heygster
The Cryosphere, 13, 1283–1296, https://doi.org/10.5194/tc-13-1283-2019, https://doi.org/10.5194/tc-13-1283-2019, 2019
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In this study, we develop and present simple algorithms to derive the snow depth, the snow–ice interface temperature, and the effective temperature of Arctic sea ice. This is achieved using satellite observations collocated with buoy measurements. The errors of the retrieved parameters are estimated and compared with independent data. These parameters are useful for sea ice concentration mapping, understanding sea ice properties and variability, and for atmospheric sounding applications.
Heidi Sallila, Sinéad Louise Farrell, Joshua McCurry, and Eero Rinne
The Cryosphere, 13, 1187–1213, https://doi.org/10.5194/tc-13-1187-2019, https://doi.org/10.5194/tc-13-1187-2019, 2019
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We assess 8 years of sea ice thickness observations derived from measurements of CryoSat-2 (CS2), AVHRR and SMOS satellites, collating key details of primary interest to users. We find a number of differences among data products but find that CS2 measurements are reliable for sea ice thickness, particularly between ~ 0.5 and 4 m. Regional comparisons reveal noticeable differences in ice thickness between products, particularly in the marginal seas in areas of considerable ship traffic.
Haibo Bi, Zehua Zhang, Yunhe Wang, Xiuli Xu, Yu Liang, Jue Huang, Yilin Liu, and Min Fu
The Cryosphere, 13, 1025–1042, https://doi.org/10.5194/tc-13-1025-2019, https://doi.org/10.5194/tc-13-1025-2019, 2019
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Baffin Bay serves as a huge reservoir of sea ice which provides solid freshwater sources for the seas downstream. Based on satellite observations, significant increasing trends are found for the annual sea ice area flux through the three gates. These trends are chiefly related to the increasing ice motion which is associated with thinner ice owing to the warmer climate (i.e., higher surface air temperature and shortened freezing period) and increased air and water drag coefficients.
Cătălin Paţilea, Georg Heygster, Marcus Huntemann, and Gunnar Spreen
The Cryosphere, 13, 675–691, https://doi.org/10.5194/tc-13-675-2019, https://doi.org/10.5194/tc-13-675-2019, 2019
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Sea ice thickness is important for representing atmosphere–ocean interactions in climate models. A validated satellite sea ice thickness measurement algorithm is transferred to a new sensor. The results offer a better temporal and spatial coverage of satellite measurements in the polar regions. Here we describe the calibration procedure between the two sensors, taking into account their technical differences. In addition a new filter for interference from artificial radio sources is implemented.
Nils Hutter, Lorenzo Zampieri, and Martin Losch
The Cryosphere, 13, 627–645, https://doi.org/10.5194/tc-13-627-2019, https://doi.org/10.5194/tc-13-627-2019, 2019
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Arctic sea ice is an aggregate of ice floes with various sizes. The different sizes result from constant deformation of the ice pack. If a floe breaks, open ocean is exposed in a lead. Collision of floes forms pressure ridges. Here, we present algorithms that detect and track these deformation features in satellite observations and model output. The tracked features are used to provide a comprehensive description of localized deformation of sea ice and help to understand its material properties.
Dyre O. Dammann, Leif E. B. Eriksson, Andrew R. Mahoney, Hajo Eicken, and Franz J. Meyer
The Cryosphere, 13, 557–577, https://doi.org/10.5194/tc-13-557-2019, https://doi.org/10.5194/tc-13-557-2019, 2019
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We present an approach for mapping bottomfast sea ice and landfast sea ice stability using Synthetic Aperture Radar Interferometry. This is the first comprehensive assessment of Arctic bottomfast sea ice extent with implications for subsea permafrost and marine habitats. Our pan-Arctic analysis also provides a new understanding of sea ice dynamics in five marginal seas of the Arctic Ocean relevant for strategic planning and tactical decision-making for different uses of coastal ice.
Robert Ricker, Fanny Girard-Ardhuin, Thomas Krumpen, and Camille Lique
The Cryosphere, 12, 3017–3032, https://doi.org/10.5194/tc-12-3017-2018, https://doi.org/10.5194/tc-12-3017-2018, 2018
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We present ice volume flux estimates through the Fram Strait using CryoSat-2 ice thickness data. This study presents a detailed analysis of temporal and spatial variability of ice volume export through the Fram Strait and shows the impact of ice volume export on Arctic ice mass balance.
Maria Belmonte Rivas, Ines Otosaka, Ad Stoffelen, and Anton Verhoef
The Cryosphere, 12, 2941–2953, https://doi.org/10.5194/tc-12-2941-2018, https://doi.org/10.5194/tc-12-2941-2018, 2018
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We provide a novel record of scatterometer sea ice extents and backscatter that complements the passive microwave products nicely, particularly for the correction of summer melt errors. The sea ice backscatter maps help differentiate between seasonal and perennial Arctic ice classes, and between second-year and older multiyear ice, revealing the emergence of SY ice as the dominant perennial ice type after the record loss in 2007 and attesting to its use as a proxy for ice thickness.
Juha Karvonen
The Cryosphere, 12, 2595–2607, https://doi.org/10.5194/tc-12-2595-2018, https://doi.org/10.5194/tc-12-2595-2018, 2018
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We have developed an algorithm for detecting LFI over a test area in the Kara and Barents seas using daily Sentinel-1 dual-polarized (HH/HV) SAR mosaics. Both SAR channels have been used jointly for reliably estimating the LFI area. We have generated daily LFI area estimates for a period ranging from Oct 2015 to Aug 2017. The data were also evaluated against Russian AARI ice charts, and the correspondence was rather good. According to this study the algorithm is suitable for operational use.
Stephan Paul, Stefan Hendricks, Robert Ricker, Stefan Kern, and Eero Rinne
The Cryosphere, 12, 2437–2460, https://doi.org/10.5194/tc-12-2437-2018, https://doi.org/10.5194/tc-12-2437-2018, 2018
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During ESA's second phase of the Sea Ice Climate Change Initiative (SICCI-2), we developed a novel approach to creating a consistent freeboard data set from Envisat and CryoSat-2. We used consistent procedures that are directly related to the sensors' waveform-echo parameters, instead of applying corrections as a post-processing step. This data set is to our knowledge the first of its kind providing consistent freeboard for the Arctic as well as the Antarctic.
Anton Andreevich Korosov, Pierre Rampal, Leif Toudal Pedersen, Roberto Saldo, Yufang Ye, Georg Heygster, Thomas Lavergne, Signe Aaboe, and Fanny Girard-Ardhuin
The Cryosphere, 12, 2073–2085, https://doi.org/10.5194/tc-12-2073-2018, https://doi.org/10.5194/tc-12-2073-2018, 2018
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A new algorithm for estimating sea ice age in the Arctic is presented. The algorithm accounts for motion, deformation, melting and freezing of sea ice and uses daily sea ice drift and sea ice concentration products. The major advantage of the new algorithm is the ability to generate individual ice age fractions in each pixel or, in other words, to provide a frequency distribution of the ice age. Multi-year ice concentration can be computed as a sum of all ice fractions older than 1 year.
Julienne C. Stroeve, David Schroder, Michel Tsamados, and Daniel Feltham
The Cryosphere, 12, 1791–1809, https://doi.org/10.5194/tc-12-1791-2018, https://doi.org/10.5194/tc-12-1791-2018, 2018
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This paper looks at the impact of the warm winter and anomalously low number of total freezing degree days during winter 2016/2017 on thermodynamic ice growth and overall thickness anomalies. The approach relies on evaluation of satellite data (CryoSat-2) and model output. While there is a negative feedback between rapid ice growth for thin ice, with thermodynamic ice growth increasing over time, since 2012 that relationship is changing, in part because the freeze-up is happening later.
Sanggyun Lee, Hyun-cheol Kim, and Jungho Im
The Cryosphere, 12, 1665–1679, https://doi.org/10.5194/tc-12-1665-2018, https://doi.org/10.5194/tc-12-1665-2018, 2018
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Arctic sea ice leads play a major role in exchanging heat and momentum between the Arctic atmosphere and ocean. In this study, we propose a novel lead
detection approach based on waveform mixture analysis. The performance of the proposed approach in detecting leads was promising when compared to the
existing methods. The robustness of the proposed approach also lies in the fact that it does not require the rescaling of parameters, as it directly uses L1B waveform data.
Nicholas C. Wright and Chris M. Polashenski
The Cryosphere, 12, 1307–1329, https://doi.org/10.5194/tc-12-1307-2018, https://doi.org/10.5194/tc-12-1307-2018, 2018
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Satellites, planes, and drones capture thousands of images of the Arctic sea ice cover each year. However, few methods exist to reliably and automatically process these images for scientifically usable information. In this paper, we take the next step towards a community standard for analyzing these images by presenting an open-source platform able to accurately classify sea ice imagery into several important surface types.
Cited articles
Andersen, S., Tonboe, R., Kern, S., and Schyberg, H.: Improved retrieval of
sea ice total concentration from spaceborne passive microwave observations
using Numerical Weather Prediction model fields: An intercomparison of nine
algorithms, Remote Sens. Environ., 104, 374–392, 2006.
Andersen, S., Toudal Pedersen, L., Heygster, G., Tonboe, R., and Kaleschke,
L.: Intercomparison of passive microwave sea ice concentration retrievals
over the high concentration Arctic sea ice, J. Geophys. Res., 112, C08004,
https://doi.org/10.1029/2006JC003543, 2007.
Ashcroft, P. and Wentz, F. J.: AMSR-E/Aqua L2A Global Swath
Spatially-Resampled Brightness Temperatures, Version 3 [2002–2010], NASA
National Snow and Ice Data Center Distributed Active Archive Center, Boulder,
Colorado, USA, https://doi.org/10.5067/AMSR-E/AE_L2A.003, 2013.
Bellprat, O., Massonnet, F., Siegert, S., Prodhomme, C., Macias-Gómez,
D., Guemas, V., and Doblas-Reyes, F.: Uncertainty propagation in
observational references to climate model scales, Remote Sens. Environ., 203, 101–108, https://doi.org/10.1016/j.rse.2017.06.034, 2017.
Brodzik, M. J., Billingsley, B., Haran, T., Raup, B., and Savoie, M. H.:
EASE-Grid 2.0: Incremental but Significant Improvements for Earth-Gridded
Data Sets, ISPRS Int. Geo.-Inf., 1, 32–45, https://doi.org/10.3390/ijgi1010032, 2012.
Brodzik, M. J., Billingsley, B., Haran, T., Raup, B., and Savoie, M. H.:
Correction: Brodzik, M. J. et al.: EASE-Grid 2.0: Incremental but Significant
Improvements for Earth-Gridded Data Sets, ISPRS Int. Geo.-Inf., 1, 32–45,
2012, ISPRS Int. Geo.-Inf., 3, 1154–1156, https://doi.org/10.3390/ijgi3031154, 2014.
Brooks, C. E. P.: The Problem of Warm Polar Climates, Q. J. Roy. Meteor.
Soc., 51, 83–91, 1925.
Cavalieri, D. J., Gloersen, P., and Campbell, W. J.: Determination of Sea Ice
Parameters With the NIMBUS 7 SMMR, J. Geophys. Res., 89, 5355–5369, 1984.
Cavalieri, D. J., Crawford, J., Drinkwater, M., Emery, W. J., Eppler, D. T.,
Farmer, L. D., Goodberlet, M., Jentz, R., Milman, A., Morris, C., Onstott,
R., Schweiger, A., Shuchman, R., Steffen, K., Swift, C. T., Wackerman, C.,
and Weaver, R. L.: NASA sea ice validation program for the DMSP SSM/I: final
report, NASA Technical Memorandum 104559, National Aeronautics and Space
Administration, Washington, D.C., 126 pp., 1992.
Cavalieri, D. J.: A microwave technique for mapping thin sea ice, J. Geophys.
Res., 99, 12561–12572, 1994.
Cavalieri, D. J., St. Germain, K. M., and Swift, C. T.: Reduction of weather
effects in the calculation of sea ice concentration with the DMSP SSM/I, J.
Glaciol., 41, 455–464, 1995.
Cavalieri, D. J., Parkinson, C. L., Gloersen, P., Comiso, J. C., and Zwally,
H. J.: Deriving long-term time series of sea ice cover from satellite
passive-microwave multisensor data sets, J. Geophys. Res., 104, 15803–15814,
https://doi.org/10.1029/1999JC900081, 1999.
Colton, M. C. and Poe, G. A.: Intersensor calibration of DMSP SSM/Is: F-8 to
F-14, 1987–1997, IEEE T. Geosci. Remote, 37, 418–439, 1999.
Comiso, J. C.: Characteristics of arctic winter sea ice from satellite
multispectral microwave observations, J. Geophys. Res., 91, 975–994, 1986.
Comiso, J. C. and Nishio, F.: Trends in the sea ice cover using enhanced and
compatible AMSR-E, SSM/I, and SMMR data, J. Geophys. Res., 113, C02S07, https://doi.org/10.1029/2007JC004257, 2008.
Comiso, J. C., Gersten, R. A., Stock, L. V., Turner, J., Perez, G. J., and
Cho, K.: Positive Trend in the Antarctic Sea Ice Cover and Associated Changes
in Surface Temperature, J. Climate, 30, 2251–2267,
https://doi.org/10.1175/JCLI-D-16-0408.1, 2017a.
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., Meier, W. N., and Gersten, R.: Variability and trends in the
Arctic Sea ice cover: Results from different techniques, J. Geophys.
Res.-Oceans, 122, 6883–6900, 2017b.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P.,
Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P.,
Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N.,
Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S.
B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler,
M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J.,
Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and
Vitart, F.: The ERA-Interim reanalysis: configuration and performance of the
data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, 2011.
Donlon, C. J., Martin, M., Stark, J. D., Roberts-Jones, J., Fiedler, E., and
Wimmer, W.: The Operational Sea Surface Temperature and Sea Ice analysis
(OSTIA), Remote Sens. Environ., 116, 140–158, https://doi.org/10.1016/j.rse.2010.10.017,
2012.
Fennig, K., Schröder, M., and Hollmann, R.: Fundamental Climate Data
Record of Microwave Imager Radiances, Edition 3, Satellite Application
Facility on Climate Monitoring, https://doi.org/10.5676/EUM_SAF_CM/FCDR_MWI/V003,
2017.
GCOS-IP: GCOS Implementation Plan 2016, GCOS-200, available at:
https://library.wmo.int/opac/doc_num.php?explnum_id=3417 (last access: 1 November 2018), 2016.
Ghaffari, P., Pedersen, L., Eastwood, S., and Lavergne, T.: Sea Ice in the
Caspian Sea, Technical Report OSI_AS11_P04, available at: https://osisaf.met.no (last access: 1 November 2018), 2011.
Gregory, J. M., Stott, P. A., Cresswell, D. J., Rayner, N. A., Gordon, C.,
and Sexton, D. M. H.: Recent and Future Changes in Arctic Sea Ice Simulated
by the HadCM3 AOGCM, Geophys. Res. Lett. 29, 2175, https://doi.org/10.1029/2001GL014575,
2002.
Herrington, T. and Zickfeld, K.: Path independence of climate and carbon
cycle response over a broad range of cumulative carbon emissions, Earth Syst.
Dynam., 5, 409–422, https://doi.org/10.5194/esd-5-409-2014, 2014.
Hersbach, H. and Dee, D.: ERA5 reanalysis is in production, ECMWF Newsletter,
Number 147 – Spring 2016, available at:
https://www.ecmwf.int/en/newsletter/147/news/era5-reanalysis-production
(last access: 1 November 2018), 2016.
Hobbs, W., Massom, R., Stammerjohn, S., Reid, P., Williams, G., and Meier,
W.: A review of recent changes in Southern Ocean sea ice, their drivers and
forcings, Global Planet. Change, 143, 228–250, https://doi.org/10.1016/j.gloplacha.2016.06.008,
2016.
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.
Johannessen, O. M.: Decreasing Arctic Sea Ice Mirrors Increasing CO2
on Decadal Time Scale, Atmospheric and Oceanic Science Letters, 1, 51–56,
https://doi.org/10.1080/16742834.2008.11446766, 2008.
Kaminski, T. and Mathieu, P.-P.: Reviews and syntheses: Flying the satellite
into your model: on the role of observation operators in constraining models
of the Earth system and the carbon cycle, Biogeosciences, 14, 2343–2357,
https://doi.org/10.5194/bg-14-2343-2017, 2017.
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.
Korosov, A. A., Rampal, P., Pedersen, L. T., Saldo, R., Ye, Y., Heygster, G.,
Lavergne, T., Aaboe, S., and Girard-Ardhuin, F.: A new tracking algorithm for
sea ice age distribution estimation, The Cryosphere, 12, 2073–2085,
https://doi.org/10.5194/tc-12-2073-2018, 2018.
Kwok, R.: Sea ice concentration estimates from satellite passive microwave
radiometry and openings from SAR ice motion, Geophys. Res. Lett., 29, 1311,
https://doi.org/10.1029/2002GL014787, 2002.
Lavergne, T., Eastwood, S., Teffah, Z., Schyberg, H., and Breivik, L.-A.: Sea
ice motion from low-resolution satellite sensors: An alternative method and
its validation in the Arctic, J. Geophys. Res., 115, C10032,
https://doi.org/10.1029/2009JC005958, 2010.
Lavergne, T.: A step back is a move forward, figshare, 16 October 2017,
https://doi.org/10.6084/m9.figshare.5501536.v1, 2017.
Long, D. G. and Daum, D. L.: Spatial resolution enhancement of SSM/I data,
IEEE T. Geosci. Remote, 36, 407–417, 1998.
Long, D. G. and Brodzik, M. J.: Optimum Image Formation for Spaceborne
Microwave Radiometer Products, IEEE T. Geosci. Remote, 54, 2763–2779,
https://doi.org/10.1109/TGRS.2015.2505677, 2016.
Lu, J., Heygster, G., and Spreen, G.: Atmospheric Correction of Sea Ice
Concentration Retrieval for 89 GHz AMSR-E Observations, IEEE J-STARS., 11,
1442–1457, https://doi.org/10.1109/JSTARS.2018.2805193, 2018.
Maass, N. and Kaleschke, L. Improving passive microwave sea ice concentration
algorithms for coastal areas: applications to the Baltic Sea, Tellus A, 62,
393–410, https://doi.org/10.1111/j.1600-0870.2010.00452.x, 2010.
Mahlstein, I. and Knutti, R.: September Arctic sea ice predicted to disappear
near 2 ∘C global warming above present, J. Geophys. Res., 117,
D06104, https://doi.org/10.1029/2011JD016709, 2012.
Maykut, G. A.: Energy Exchange over Young Sea Ice in the Central Arctic, J.
Geophys. Res.-Oceans, 83, 3646–3658, https://doi.org/10.1029/JC083iC07p03646, 1978.
Meier, W., Fetterer, F., Savoie, M., Mallory, S., Duerr, R., and Stroeve, J.:
NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration,
Version 3, NSIDC: National Snow and Ice Data Center, Boulder, Colorado, USA,
https://doi.org/10.7265/N59P2ZTG, 2017.
Meier, W. N. and Ivanoff, A.: Intercalibration of AMSR2 NASA Team 2 Algorithm
Sea Ice Concentrations With AMSR-E Slow Rotation Data, in: IEEE J.-Stars, 10, 1–11, https://doi.org/10.1109/JSTARS.2017.2719624, 2017.
Merchant, C. J., Paul, F., Popp, T., Ablain, M., Bontemps, S., Defourny, P.,
Hollmann, R., Lavergne, T., Laeng, A., de Leeuw, G., Mittaz, J., Poulsen, C.,
Povey, A. C., Reuter, M., Sathyendranath, S., Sandven, S., Sofieva, V. F.,
and Wagner, W.: Uncertainty information in climate data records from Earth
observation, Earth Syst. Sci. Data, 9, 511–527,
https://doi.org/10.5194/essd-9-511-2017, 2017.
Niederdrenk, A.-L. and Notz, D.: Arctic Sea Ice in a 1.5 ∘C Warmer
World, Geophys. Res. Lett., 45, 1963–1971, https://doi.org/10.1002/2017GL076159, 2018.
Njoku, E. G., Rague, B., and Fleming, K.: The Nimbus-7 SMMR Pathfinder
Brightness Temperature Data Set, Jet Propulsion Laboratory Publication, Pasadena, USA, 98-4, 1998.
Notz, D. and Marotzke, J.: Observations reveal external driver for Arctic
sea-ice retreat, Geophys. Res. Lett., 39, L08502, https://doi.org/10.1029/2012GL051094,
2012.
Notz, D. and Stroeve, J.: Observed Arctic Sea-Ice Loss Directly Follows
Anthropogenic CO2 Emission, Science, 354, 747–750,
https://doi.org/10.1126/science.aag2345, 2016.
Pedersen, L. T., Saldo, R., Ivanova, N., Kern, S., Heygster, G., Tonboe, R.,
Huntemann, M., Ozsoy, B., Ardhuin, F., and Kaleschke, L.: Reference dataset
for sea ice concentration, https://doi.org/10.6084/m9.figshare.6626549.v3, 2018.
Poe, G. A., Uliana, E. A., Gardiner, B. A., von Rentzell, T. E., and Kunkee,
D. B.: Geolocation Error Analysis of the Special Sensor Microwave
Imager/Sounder, in: IEEE T. Geosci. Remote, 46, 913–922,
https://doi.org/10.1109/TGRS.2008.917981, 2008.
Sigmond, M., Fyfe, J. C., and Swart, N. C.: Ice-Free Arctic Projections under
the Paris Agreement, Nat. Clim. Change, 8, 404–408, https://doi.org/10.1038/s41558-018-0124-y,
2018.
Screen, J. A. and Williamson, D: Ice-Free Arctic at 1.5 ∘C?, Nat.
Clim. Change., 7, 230–31, https://doi.org/10.1038/nclimate3248, 2017.
Smith, D. M. and Barrett, E. C.: Satellite mapping and monitoring of sea ice,
CB/RAE/9/2/4/2034/113/ARE, RSU, University of Bristol, Bristol, UK, 1994.
Smith, D. M.: Extraction of winter total sea ice concentration in the
Greenland and Barents Seas from SSM/I data, Int. J. Remote Sens., 17,
2625–2646, 1996.
Spreen, G., Kaleschke, L., and Heygster, G.: Sea ice remote sensing using
AMSR-E 89-GHz channels, J. Geophys. Res., 113, C02S03,
https://doi.org/10.1029/2005JC003384, 2008.
Strong, C. and Golden, K. M.: Filling the Polar Data Gap in Sea Ice
Concentration Fields Using Partial Differential Equations, Remote Sens., 8,
442, https://doi.org/10.3390/rs8060442, 2016.
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.
Toudal Pedersen, L., Dybkjær, G., Eastwood, S., Killie, M. A., Lavelle, J.,
Lavergne, T., Pfeiffer, H., Sørensen, A., and Tonboe, R.: EUMETSAT SAF on
Ocean and Sea Ice, Global Sea Ice Concentration Climate Data Record v2.0 –
Multimission, OSI SAF, https://doi.org/10.15770/EUM_SAF_OSI_0008, 2017a.
Toudal Pedersen, L., Dybkjær, G., Eastwood, S., Heygster, G., Ivanova,
N., Kern, S., Lavergne, T., Saldo, R., Sandven, S., Sørensen, A., and Tonboe,
R.: ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Sea Ice
Concentration Climate Data Record from the AMSR-E and AMSR-2 instruments at
25 km grid spacing, version 2.1, Centre for Environmental Data Analysis,
https://doi.org/10.5285/f17f146a31b14dfd960cde0874236ee5, 5 October 2017b.
Toudal Pedersen, L., Dybkjær, G., Eastwood, S., Heygster, G., Ivanova,
N., Kern, S., Lavergne, T., Saldo, R., Sandven, S., Sørensen, A., and
Tonboe, R.: ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Sea Ice
Concentration Climate Data Record from the AMSR-E and AMSR-2 instruments at
50 km grid spacing, version 2.1, Centre for Environmental Data Analysis,
https://doi.org/10.5285/5f75fcb0c58740d99b07953797bc041e, 5 October 2017c.
Wentz, F. J.: A model function for ocean microwave brightness temperatures,
J. Geophys. Res., 88, 1892–1908, https://doi.org/10.1029/JC088iC03p01892, 1983.
Wentz, F. J.: A well-calibrated ocean algorithm for SSM/I, J. Geophys. Res.,
102, 8703–8718, https://doi.org/10.1029/96JC01751, 1997.
Wentz, F. J. and Meissner, T.: AMSR ocean algorithm version 2, RSS Tech,
Proposal 121599A-1, Remote Sensing Systems, Santa Rosa, California, 2000.
Yang, Q., Losch, M., Losa, S. N., Jung, T., Nerger, L., and Lavergne, T.:
Brief communication: The challenge and benefit of using sea ice concentration
satellite data products with uncertainty estimates in summer sea ice data
assimilation, The Cryosphere, 10, 761–774,
https://doi.org/10.5194/tc-10-761-2016, 2016.
Yang, W., John, V. O., Zhao, X., Lu, H., and Knapp, K. R.: Satellite Climate
Data Records: Development, Applications, and Societal Benefits, Remote Sens.,
8, 331, https://doi.org/10.3390/rs8040331, 2016.
Zickfeld, K., Arora, V. K., and Gillett, N. P.: Is the climate response to
CO2 emissions path dependent?, Geophys. Res. Lett., 39, L05703,
https://doi.org/10.1029/2011GL050205, 2012.
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Short summary
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.
The loss of polar sea ice is an iconic indicator of Earth’s climate change. Many...
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