Articles | Volume 13, issue 1
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 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 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
Research and Development Department, Norwegian Meteorological Institute, Oslo, Norway
Atle Macdonald Sørensen
Research and Development Department, Norwegian Meteorological Institute, Oslo, Norway
Stefan Kern
Integrated Climate Data Center, CEN, University of Hamburg, Hamburg, Germany
Rasmus Tonboe
Danish Meteorological Institute, Copenhagen, Denmark
Dirk Notz
Max-Planck Institut für Meteorologie, Hamburg, Germany
Signe Aaboe
Research and Development Department, Norwegian Meteorological Institute, Oslo, Norway
Louisa Bell
Max-Planck Institut für Meteorologie, Hamburg, Germany
Gorm Dybkjær
Danish Meteorological Institute, Copenhagen, Denmark
Steinar Eastwood
Research and Development Department, Norwegian Meteorological Institute, Oslo, Norway
Carolina Gabarro
Barcelona Expert Center, ICM-CSIC, Barcelona, Spain
Georg Heygster
Institute of Environmental Physics, University of Bremen, Bremen, Germany
Mari Anne Killie
Research and Development Department, Norwegian Meteorological Institute, Oslo, Norway
Matilde Brandt Kreiner
Danish Meteorological Institute, Copenhagen, Denmark
John Lavelle
Danish Meteorological Institute, Copenhagen, Denmark
Roberto Saldo
Danish Technical University-Space, Copenhagen, Denmark
Stein Sandven
Nansen Environmental and Remote Sensing Center, Bergen, Norway
Leif Toudal Pedersen
Danish Technical University-Space, Copenhagen, Denmark
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Julienne Stroeve, Vishnu Nandan, Rosemary Willatt, Rasmus Tonboe, Stefan Hendricks, Robert Ricker, James Mead, Robbie Mallett, Marcus Huntemann, Polona Itkin, Martin Schneebeli, Daniela Krampe, Gunnar Spreen, Jeremy Wilkinson, Ilkka Matero, Mario Hoppmann, and Michel Tsamados
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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 sea ice thickness and snow depth simultaneously from freeboards by imposing a thermodynamic constraint. Obtained ice thicknesses and snow depths 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 Tonboe
The Cryosphere, 14, 2469–2493, https://doi.org/10.5194/tc-14-2469-2020, https://doi.org/10.5194/tc-14-2469-2020, 2020
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Arctic sea-ice concentration (SIC) estimates based on satellite passive microwave observations are highly inaccurate during summer melt. We compare 10 different SIC products with independent satellite data of true SIC and melt pond fraction (MPF). All products disagree with the true SIC. Regional and inter-product differences can be large and depend on the MPF. An inadequate treatment of melting snow and melt ponds in the products’ algorithms appears to be the main explanation for our findings.
Clara Burgard, Dirk Notz, Leif T. Pedersen, and Rasmus T. Tonboe
The Cryosphere, 14, 2369–2386, https://doi.org/10.5194/tc-14-2369-2020, https://doi.org/10.5194/tc-14-2369-2020, 2020
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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. We find that we do not need complex information about the vertical distribution of temperature and salinity inside the ice but instead are able to 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, 14, 2387–2407, https://doi.org/10.5194/tc-14-2387-2020, https://doi.org/10.5194/tc-14-2387-2020, 2020
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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 that 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.
Arantxa M. Triana-Gómez, Georg Heygster, Christian Melsheimer, Gunnar Spreen, Monia Negusini, and Boyan H. Petkov
Atmos. Meas. Tech., 13, 3697–3715, https://doi.org/10.5194/amt-13-3697-2020, https://doi.org/10.5194/amt-13-3697-2020, 2020
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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 artefacts are now filtered out. Comparison with radiosondes shows the overall good performance of the updated algorithm.
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.
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
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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.
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
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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
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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
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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
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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
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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
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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.
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
Wind redistribution of snow impacts the Ka- and Ku-band radar signatures of Arctic sea ice
First observations of sea ice flexural–gravity waves with ground-based radar interferometry in Utqiaġvik, Alaska
Feasibility of retrieving Arctic sea ice thickness from the Chinese HY-2B Ku-band radar altimeter
Sea ice classification of TerraSAR-X ScanSAR images for the MOSAiC expedition incorporating per-class incidence angle dependency of image texture
Aerial observations of sea ice breakup by ship waves
Monitoring Arctic thin ice: a comparison between CryoSat-2 SAR altimetry data and MODIS thermal-infrared imagery
The effects of surface roughness on the calculated, spectral, conical–conical reflectance factor as an alternative to the bidirectional reflectance distribution function of bare sea ice
Inter-comparison and evaluation of Arctic sea ice type products
A simple model for daily basin-wide thermodynamic sea ice thickness growth retrieval
Ice ridge density signatures in high-resolution SAR images
Rain on snow (ROS) understudied in sea ice remote sensing: a multi-sensor analysis of ROS during MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate)
Quantifying the effects of background concentrations of crude oil pollution on sea ice albedo
Characterizing the sea-ice floe size distribution in the Canada Basin from high-resolution optical satellite imagery
Generating large-scale sea ice motion from Sentinel-1 and the RADARSAT Constellation Mission using the Environment and Climate Change Canada automated sea ice tracking system
Rotational drift in Antarctic sea ice: pronounced cyclonic features and differences between data products
Satellite passive microwave sea-ice concentration data set intercomparison using Landsat data
Cross-platform classification of level and deformed sea ice considering per-class incident angle dependency of backscatter intensity
Advances in altimetric snow depth estimates using bi-frequency SARAL and CryoSat-2 Ka–Ku measurements
Antarctic snow-covered sea ice topography derivation from TanDEM-X using polarimetric SAR interferometry
Impacts of snow data and processing methods on the interpretation of long-term changes in Baffin Bay early spring sea ice thickness
A lead-width distribution for Antarctic sea ice: a case study for the Weddell Sea with high-resolution Sentinel-2 images
Satellite altimetry detection of ice-shelf-influenced fast ice
MOSAiC drift expedition from October 2019 to July 2020: sea ice conditions from space and comparison with previous years
Towards a swath-to-swath sea-ice drift product for the Copernicus Imaging Microwave Radiometer mission
Spaceborne infrared imagery for early detection of Weddell Polynya opening
Estimating instantaneous sea-ice dynamics from space using the bi-static radar measurements of Earth Explorer 10 candidate Harmony
Estimating subpixel turbulent heat flux over leads from MODIS thermal infrared imagery with deep learning
An improved sea ice detection algorithm using MODIS: application as a new European sea ice extent indicator
Faster decline and higher variability in the sea ice thickness of the marginal Arctic seas when accounting for dynamic snow cover
Estimation of degree of sea ice ridging in the Bay of Bothnia based on geolocated photon heights from ICESat-2
Linking sea ice deformation to ice thickness redistribution using high-resolution satellite and airborne observations
Simulated Ka- and Ku-band radar altimeter height and freeboard estimation on snow-covered Arctic sea ice
Improved machine-learning-based open-water–sea-ice–cloud discrimination over wintertime Antarctic sea ice using MODIS thermal-infrared imagery
Spring melt pond fraction in the Canadian Arctic Archipelago predicted from RADARSAT-2
Simultaneous estimation of wintertime sea ice thickness and snow depth from space-borne freeboard measurements
Observations of sea ice melt from Operation IceBridge imagery
Estimating statistical errors in retrievals of ice velocity and deformation parameters from satellite images and buoy arrays
Brief Communication: Mesoscale and submesoscale dynamics in the marginal ice zone from sequential synthetic aperture radar observations
Classification of sea ice types in Sentinel-1 synthetic aperture radar images
A linear model to derive melt pond depth on Arctic sea ice from hyperspectral data
Satellite passive microwave sea-ice concentration data set inter-comparison for Arctic summer conditions
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
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
Vishnu Nandan, Rosemary Willatt, Robbie Mallett, Julienne Stroeve, Torsten Geldsetzer, Randall Scharien, Rasmus Tonboe, John Yackel, Jack Landy, David Clemens-Sewall, Arttu Jutila, David N. Wagner, Daniela Krampe, Marcus Huntemann, Mallik Mahmud, David Jensen, Thomas Newman, Stefan Hendricks, Gunnar Spreen, Amy Macfarlane, Martin Schneebeli, James Mead, Robert Ricker, Michael Gallagher, Claude Duguay, Ian Raphael, Chris Polashenski, Michel Tsamados, Ilkka Matero, and Mario Hoppmann
The Cryosphere, 17, 2211–2229, https://doi.org/10.5194/tc-17-2211-2023, https://doi.org/10.5194/tc-17-2211-2023, 2023
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We show that wind redistributes snow on Arctic sea ice, and Ka- and Ku-band radar measurements detect both newly deposited snow and buried snow layers that can affect the accuracy of snow depth estimates on sea ice. Radar, laser, meteorological, and snow data were collected during the MOSAiC expedition. With frequent occurrence of storms in the Arctic, our results show that
wind-redistributed snow needs to be accounted for to improve snow depth estimates on sea ice from satellite radars.
Dyre Oliver Dammann, Mark A. Johnson, Andrew R. Mahoney, and Emily R. Fedders
The Cryosphere, 17, 1609–1622, https://doi.org/10.5194/tc-17-1609-2023, https://doi.org/10.5194/tc-17-1609-2023, 2023
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We investigate the GAMMA Portable Radar Interferometer (GPRI) as a tool for evaluating flexural–gravity waves in sea ice in near real time. With a GPRI mounted on grounded ice near Utqiaġvik, Alaska, we identify 20–50 s infragravity waves in landfast ice with ~1 mm amplitude during 23–24 April 2021. Observed wave speed and periods compare well with modeled wave propagation and on-ice accelerometers, confirming the ability to track propagation and properties of waves over hundreds of meters.
Zhaoqing Dong, Lijian Shi, Mingsen Lin, Yongjun Jia, Tao Zeng, and Suhui Wu
The Cryosphere, 17, 1389–1410, https://doi.org/10.5194/tc-17-1389-2023, https://doi.org/10.5194/tc-17-1389-2023, 2023
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We try to explore the application of SGDR data in polar sea ice thickness. Through this study, we find that it seems difficult to obtain reasonable results by using conventional methods. So we use the 15 lowest points per 25 km to estimate SSHA to retrieve more reasonable Arctic radar freeboard and thickness. This study also provides reference for reprocessing L1 data. We will release products that are more reasonable and suitable for polar sea ice thickness retrieval to better evaluate HY-2B.
Wenkai Guo, Polona Itkin, Suman Singha, Anthony P. Doulgeris, Malin Johansson, and Gunnar Spreen
The Cryosphere, 17, 1279–1297, https://doi.org/10.5194/tc-17-1279-2023, https://doi.org/10.5194/tc-17-1279-2023, 2023
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Sea ice maps are produced to cover the MOSAiC Arctic expedition (2019–2020) and divide sea ice into scientifically meaningful classes. We use a high-resolution X-band synthetic aperture radar dataset and show how image brightness and texture systematically vary across the images. We use an algorithm that reliably corrects this effect and achieve good results, as evaluated by comparisons to ground observations and other studies. The sea ice maps are useful as a basis for future MOSAiC studies.
Elie Dumas-Lefebvre and Dany Dumont
The Cryosphere, 17, 827–842, https://doi.org/10.5194/tc-17-827-2023, https://doi.org/10.5194/tc-17-827-2023, 2023
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By changing the shape of ice floes, wave-induced sea ice breakup dramatically affects the large-scale dynamics of sea ice. As this process is also the trigger of multiple others, it was deemed relevant to study how breakup itself affects the ice floe size distribution. To do so, a ship sailed close to ice floes, and the breakup that it generated was recorded with a drone. The obtained data shed light on the underlying physics of wave-induced sea ice breakup.
Felix L. Müller, Stephan Paul, Stefan Hendricks, and Denise Dettmering
The Cryosphere, 17, 809–825, https://doi.org/10.5194/tc-17-809-2023, https://doi.org/10.5194/tc-17-809-2023, 2023
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Thinning sea ice has significant impacts on the energy exchange between the atmosphere and the ocean. In this study we present visual and quantitative comparisons of thin-ice detections obtained from classified Cryosat-2 radar reflections and thin-ice-thickness estimates derived from MODIS thermal-infrared imagery. In addition to good comparability, the results of the study indicate the potential for a deeper understanding of sea ice in the polar seas and improved processing of altimeter data.
Maxim L. Lamare, John D. Hedley, and Martin D. King
The Cryosphere, 17, 737–751, https://doi.org/10.5194/tc-17-737-2023, https://doi.org/10.5194/tc-17-737-2023, 2023
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The reflectivity of sea ice is crucial for modern climate change and for monitoring sea ice from satellites. The reflectivity depends on the angle at which the ice is viewed and the angle illuminated. The directional reflectivity is calculated as a function of viewing angle, illuminating angle, thickness, wavelength and surface roughness. Roughness cannot be considered independent of thickness, illumination angle and the wavelength. Remote sensors will use the data to image sea ice from space.
Yufang Ye, Yanbing Luo, Yan Sun, Mohammed Shokr, Signe Aaboe, Fanny Girard-Ardhuin, Fengming Hui, Xiao Cheng, and Zhuoqi Chen
The Cryosphere, 17, 279–308, https://doi.org/10.5194/tc-17-279-2023, https://doi.org/10.5194/tc-17-279-2023, 2023
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Arctic sea ice type (SITY) variation is a sensitive indicator of climate change. This study gives a systematic inter-comparison and evaluation of eight SITY products. Main results include differences in SITY products being significant, with average Arctic multiyear ice extent up to 1.8×106 km2; Ku-band scatterometer SITY products generally performing better; and factors such as satellite inputs, classification methods, training datasets and post-processing highly impacting their performance.
James Anheuser, Yinghui Liu, and Jeffrey R. Key
The Cryosphere, 16, 4403–4421, https://doi.org/10.5194/tc-16-4403-2022, https://doi.org/10.5194/tc-16-4403-2022, 2022
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A prominent part of the polar climate system is sea ice, a better understanding of which would lead to better understanding Earth's climate. Newly published methods for observing the temperature of sea ice have made possible a new method for estimating daily sea ice thickness growth from space using an energy balance. The method compares well with existing sea ice thickness observations.
Mikko Lensu and Markku Similä
The Cryosphere, 16, 4363–4377, https://doi.org/10.5194/tc-16-4363-2022, https://doi.org/10.5194/tc-16-4363-2022, 2022
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Ice ridges form a compressing ice cover. From above they appear as walls of up to few metres in height and extend even kilometres across the ice. Below they may reach tens of metres under the sea surface. Ridges need to be observed for the purposes of ice forecasting and ice information production. This relies mostly on ridging signatures discernible in radar satellite (SAR) images. New methods to quantify ridging from SAR have been developed and are shown to agree with field observations.
Julienne Stroeve, Vishnu Nandan, Rosemary Willatt, Ruzica Dadic, Philip Rostosky, Michael Gallagher, Robbie Mallett, Andrew Barrett, Stefan Hendricks, Rasmus Tonboe, Michelle McCrystall, Mark Serreze, Linda Thielke, Gunnar Spreen, Thomas Newman, John Yackel, Robert Ricker, Michel Tsamados, Amy Macfarlane, Henna-Reetta Hannula, and Martin Schneebeli
The Cryosphere, 16, 4223–4250, https://doi.org/10.5194/tc-16-4223-2022, https://doi.org/10.5194/tc-16-4223-2022, 2022
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Impacts of rain on snow (ROS) on satellite-retrieved sea ice variables remain to be fully understood. This study evaluates the impacts of ROS over sea ice on active and passive microwave data collected during the 2019–20 MOSAiC expedition. Rainfall and subsequent refreezing of the snowpack significantly altered emitted and backscattered radar energy, laying important groundwork for understanding their impacts on operational satellite retrievals of various sea ice geophysical variables.
Benjamin Heikki Redmond Roche and Martin D. King
The Cryosphere, 16, 3949–3970, https://doi.org/10.5194/tc-16-3949-2022, https://doi.org/10.5194/tc-16-3949-2022, 2022
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Sea ice is bright, playing an important role in reflecting incoming solar radiation. The reflectivity of sea ice is affected by the presence of pollutants, such as crude oil, even at low concentrations. Modelling how the brightness of three types of sea ice is affected by increasing concentrations of crude oils shows that the type of oil, the type of ice, the thickness of the ice, and the size of the oil droplets are important factors. This shows that sea ice is vulnerable to oil pollution.
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.
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.
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.
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.
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.
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.
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.
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.
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).
Céline Heuzé, Lu Zhou, Martin Mohrmann, and Adriano Lemos
The Cryosphere, 15, 3401–3421, https://doi.org/10.5194/tc-15-3401-2021, https://doi.org/10.5194/tc-15-3401-2021, 2021
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For navigation or science planning, knowing when sea ice will open in advance is a prerequisite. Yet, to date, routine spaceborne microwave observations of sea ice are unable to do so. We present the first method based on spaceborne infrared that can forecast an opening several days ahead. We develop it specifically for the Weddell Polynya, a large hole in the Antarctic winter ice cover that unexpectedly re-opened for the first time in 40 years in 2016, and determine why the polynya opened.
Marcel Kleinherenbrink, Anton Korosov, Thomas Newman, Andreas Theodosiou, Alexander S. Komarov, Yuanhao Li, Gert Mulder, Pierre Rampal, Julienne Stroeve, and Paco Lopez-Dekker
The Cryosphere, 15, 3101–3118, https://doi.org/10.5194/tc-15-3101-2021, https://doi.org/10.5194/tc-15-3101-2021, 2021
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Harmony is one of the Earth Explorer 10 candidates that has the chance of being selected for launch in 2028. The mission consists of two satellites that fly in formation with Sentinel-1D, which carries a side-looking radar system. By receiving Sentinel-1's signals reflected from the surface, Harmony is able to observe instantaneous elevation and two-dimensional velocity at the surface. As such, Harmony's data allow the retrieval of sea-ice drift and wave spectra in sea-ice-covered regions.
Zhixiang Yin, Xiaodong Li, Yong Ge, Cheng Shang, Xinyan Li, Yun Du, and Feng Ling
The Cryosphere, 15, 2835–2856, https://doi.org/10.5194/tc-15-2835-2021, https://doi.org/10.5194/tc-15-2835-2021, 2021
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MODIS thermal infrared (TIR) imagery provides promising data to study the rapid variations in the Arctic turbulent heat flux (THF). The accuracy of estimated THF, however, is low (especially for small leads) due to the coarse resolution of the MODIS TIR data. We train a deep neural network to enhance the spatial resolution of estimated THF over leads from MODIS TIR imagery. The method is found to be effective and can generate a result which is close to that derived from Landsat-8 TIR imagery.
Joan Antoni Parera-Portell, Raquel Ubach, and Charles Gignac
The Cryosphere, 15, 2803–2818, https://doi.org/10.5194/tc-15-2803-2021, https://doi.org/10.5194/tc-15-2803-2021, 2021
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We describe a new method to map sea ice and water at 500 m resolution using data acquired by the MODIS sensors. The strength of this method is that it achieves high-accuracy results and is capable of attenuating unwanted resolution-breaking effects caused by cloud masking. Our resulting March and September monthly aggregates reflect the loss of sea ice in the European Arctic during the 2000–2019 period and show the algorithm's usefulness as a sea ice monitoring tool.
Robbie D. C. Mallett, Julienne C. Stroeve, Michel Tsamados, Jack C. Landy, Rosemary Willatt, Vishnu Nandan, and Glen E. Liston
The Cryosphere, 15, 2429–2450, https://doi.org/10.5194/tc-15-2429-2021, https://doi.org/10.5194/tc-15-2429-2021, 2021
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We re-estimate pan-Arctic sea ice thickness (SIT) values by combining data from the Envisat and CryoSat-2 missions with data from a new, reanalysis-driven snow model. Because a decreasing amount of ice is being hidden below the waterline by the weight of overlying snow, we argue that SIT may be declining faster than previously calculated in some regions. Because the snow product varies from year to year, our new SIT calculations also display much more year-to-year variability.
Renée Mie Fredensborg Hansen, Eero Rinne, Sinéad Louise Farrell, and Henriette Skourup
The Cryosphere, 15, 2511–2529, https://doi.org/10.5194/tc-15-2511-2021, https://doi.org/10.5194/tc-15-2511-2021, 2021
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Ice navigators rely on timely information about ice conditions to ensure safe passage through ice-covered waters, and one parameter, the degree of ice ridging (DIR), is particularly useful. We have investigated the possibility of estimating DIR from the geolocated photons of ICESat-2 (IS2) in the Bay of Bothnia, show that IS2 retrievals from different DIR areas differ significantly, and present some of the first steps in creating sea ice applications beyond e.g. thickness retrieval.
Luisa von Albedyll, Christian Haas, and Wolfgang Dierking
The Cryosphere, 15, 2167–2186, https://doi.org/10.5194/tc-15-2167-2021, https://doi.org/10.5194/tc-15-2167-2021, 2021
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Convergent sea ice motion produces a thick ice cover through ridging. We studied sea ice deformation derived from high-resolution satellite imagery and related it to the corresponding thickness change. We found that deformation explains the observed dynamic thickness change. We show that deformation can be used to model realistic ice thickness distributions. Our results revealed new relationships between thickness redistribution and deformation that could improve sea ice models.
Rasmus T. Tonboe, Vishnu Nandan, John Yackel, Stefan Kern, Leif Toudal Pedersen, and Julienne Stroeve
The Cryosphere, 15, 1811–1822, https://doi.org/10.5194/tc-15-1811-2021, https://doi.org/10.5194/tc-15-1811-2021, 2021
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A relationship between the Ku-band radar scattering horizon and snow depth is found using a radar scattering model. This relationship has implications for (1) the use of snow climatology in the conversion of satellite radar freeboard into sea ice thickness and (2) the impact of variability in measured snow depth on the derived ice thickness. For both 1 and 2, the impact of using a snow climatology versus the actual snow depth is relatively small.
Stephan Paul and Marcus Huntemann
The Cryosphere, 15, 1551–1565, https://doi.org/10.5194/tc-15-1551-2021, https://doi.org/10.5194/tc-15-1551-2021, 2021
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Cloud cover in the polar regions is difficult to identify at night when using only thermal-infrared data. This is due to occurrences of warm clouds over cold sea ice and cold clouds over warm sea ice. Especially the standard MODIS cloud mask frequently tends towards classifying open water and/or thin ice as cloud cover. Using a neural network, we present an improved discrimination between sea-ice, open-water and/or thin-ice, and cloud pixels in nighttime MODIS thermal-infrared satellite data.
Stephen E. L. Howell, Randall K. Scharien, Jack Landy, and Mike Brady
The Cryosphere, 14, 4675–4686, https://doi.org/10.5194/tc-14-4675-2020, https://doi.org/10.5194/tc-14-4675-2020, 2020
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Melt ponds form on the surface of Arctic sea ice during spring and have been shown to exert a strong influence on summer sea ice area. Here, we use RADARSAT-2 satellite imagery to estimate the predicted peak spring melt pond fraction in the Canadian Arctic Archipelago from 2009–2018. Our results show that RADARSAT-2 estimates of peak melt pond fraction can be used to provide predictive information about summer sea ice area within certain regions of the Canadian Arctic Archipelago.
Hoyeon Shi, Byung-Ju Sohn, Gorm Dybkjær, Rasmus Tage Tonboe, and Sang-Moo Lee
The Cryosphere, 14, 3761–3783, https://doi.org/10.5194/tc-14-3761-2020, https://doi.org/10.5194/tc-14-3761-2020, 2020
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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 sea ice thickness and snow depth simultaneously from freeboards by imposing a thermodynamic constraint. Obtained ice thicknesses and snow depths were consistent with airborne measurements, suggesting that uncertainty of ice thickness caused by uncertain snow depth can be reduced.
Nicholas C. Wright, Chris M. Polashenski, Scott T. McMichael, and Ross A. Beyer
The Cryosphere, 14, 3523–3536, https://doi.org/10.5194/tc-14-3523-2020, https://doi.org/10.5194/tc-14-3523-2020, 2020
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This work presents a new dataset of sea ice surface fractions along NASA Operation IceBridge flight tracks created by processing hundreds of thousands of aerial images. These results are then analyzed to investigate the behavior of meltwater on first-year ice in comparison to multiyear ice. We find preliminary evidence that first-year ice frequently has a lower melt pond fraction than adjacent multiyear ice, contrary to established knowledge in the sea ice community.
Wolfgang Dierking, Harry L. Stern, and Jennifer K. Hutchings
The Cryosphere, 14, 2999–3016, https://doi.org/10.5194/tc-14-2999-2020, https://doi.org/10.5194/tc-14-2999-2020, 2020
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Monitoring deformation of sea ice is useful for studying effects of ice compression and divergent motion on the ice mass balance and ocean–ice–atmosphere interactions. In calculations of deformation parameters not only the measurement uncertainty of drift vectors has to be considered. The size of the area and the time interval used in the calculations have to be chosen within certain limits to make sure that the uncertainties of deformation parameters are smaller than their real magnitudes.
Igor E. Kozlov, Evgeny V. Plotnikov, and Georgy E. Manucharyan
The Cryosphere, 14, 2941–2947, https://doi.org/10.5194/tc-14-2941-2020, https://doi.org/10.5194/tc-14-2941-2020, 2020
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Here we demonstrate a recently emerged opportunity to retrieve high-resolution surface current velocities from sequential spaceborne radar images taken over low-concentration ice regions of polar oceans. Such regularly available data uniquely resolve complex surface ocean dynamics even at small scales and can be used in operational applications to assess and predict transport and distribution of biogeochemical substances and pollutants in ice-covered waters.
Jeong-Won Park, Anton Andreevich Korosov, Mohamed Babiker, Joong-Sun Won, Morten Wergeland Hansen, and Hyun-Cheol Kim
The Cryosphere, 14, 2629–2645, https://doi.org/10.5194/tc-14-2629-2020, https://doi.org/10.5194/tc-14-2629-2020, 2020
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A new Sentinel-1 radar-based sea ice classification algorithm is proposed. We show that the readily available ice charts from operational ice services can reduce the amount of manual work in preparation of large amounts of training/testing data and feed highly reliable data to the trainer in an efficient way. Test results showed that the classifier is capable of retrieving three generalized cover types with overall accuracy of 87 % and 67 % in the winter and summer seasons, respectively.
Marcel König and Natascha Oppelt
The Cryosphere, 14, 2567–2579, https://doi.org/10.5194/tc-14-2567-2020, https://doi.org/10.5194/tc-14-2567-2020, 2020
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We used data that we collected on RV Polarstern cruise PS106 in summer 2017 to develop a model for the derivation of melt pond depth on Arctic sea ice from reflectance measurements. We simulated reflectances of melt ponds of varying color and water depth and used the sun zenith angle and the slope of the log-scaled reflectance at 710 nm to derive pond depth. We validated the model on the in situ melt pond data and found it to derive pond depth very accurately.
Stefan Kern, Thomas Lavergne, Dirk Notz, Leif Toudal Pedersen, and Rasmus Tonboe
The Cryosphere, 14, 2469–2493, https://doi.org/10.5194/tc-14-2469-2020, https://doi.org/10.5194/tc-14-2469-2020, 2020
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Arctic sea-ice concentration (SIC) estimates based on satellite passive microwave observations are highly inaccurate during summer melt. We compare 10 different SIC products with independent satellite data of true SIC and melt pond fraction (MPF). All products disagree with the true SIC. Regional and inter-product differences can be large and depend on the MPF. An inadequate treatment of melting snow and melt ponds in the products’ algorithms appears to be the main explanation for our findings.
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.
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.
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.
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...
