Research article
08 Sep 2017
Research article
| 08 Sep 2017
Modelling radiative transfer through ponded first-year Arctic sea ice with a plane-parallel model
Torbjørn Taskjelle et al.
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Muhammed Fatih Sert, Helge Niemann, Eoghan P. Reeves, Mats A. Granskog, Kevin P. Hand, Timo Kekäläinen, Janne Jänis, Pamela E. Rossel, Bénédicte Ferré, Anna Silyakova, and Friederike Gründger
Biogeosciences, 19, 2101–2120, https://doi.org/10.5194/bg-19-2101-2022, https://doi.org/10.5194/bg-19-2101-2022, 2022
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We investigate organic matter composition in the Arctic Ocean water column. We collected seawater samples from sea ice to deep waters at six vertical profiles near an active hydrothermal vent and its plume. In comparison to seawater, we found that the organic matter in waters directly affected by the hydrothermal plume had different chemical composition. We suggest that hydrothermal processes may influence the organic matter distribution in the deep ocean.
Tristan Petit, Børge Hamre, Håkon Sandven, Rüdiger Röttgers, Piotr Kowalczuk, Monika Zablocka, and Mats A. Granskog
Ocean Sci., 18, 455–468, https://doi.org/10.5194/os-18-455-2022, https://doi.org/10.5194/os-18-455-2022, 2022
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We provide the first insights on bio-optical processes in Storfjorden (Svalbard). Information on factors controlling light propagation in the water column in this arctic fjord becomes crucial in times of rapid sea ice decline. We find a significant contribution of dissolved matter to light absorption and a subsurface absorption maximum linked to phytoplankton production. Dense bottom waters from sea ice formation carry elevated levels of dissolved and particulate matter.
Stephen M. Platt, Øystein Hov, Torunn Berg, Knut Breivik, Sabine Eckhardt, Konstantinos Eleftheriadis, Nikolaos Evangeliou, Markus Fiebig, Rebecca Fisher, Georg Hansen, Hans-Christen Hansson, Jost Heintzenberg, Ove Hermansen, Dominic Heslin-Rees, Kim Holmén, Stephen Hudson, Roland Kallenborn, Radovan Krejci, Terje Krognes, Steinar Larssen, David Lowry, Cathrine Lund Myhre, Chris Lunder, Euan Nisbet, Pernilla B. Nizzetto, Ki-Tae Park, Christina A. Pedersen, Katrine Aspmo Pfaffhuber, Thomas Röckmann, Norbert Schmidbauer, Sverre Solberg, Andreas Stohl, Johan Ström, Tove Svendby, Peter Tunved, Kjersti Tørnkvist, Carina van der Veen, Stergios Vratolis, Young Jun Yoon, Karl Espen Yttri, Paul Zieger, Wenche Aas, and Kjetil Tørseth
Atmos. Chem. Phys., 22, 3321–3369, https://doi.org/10.5194/acp-22-3321-2022, https://doi.org/10.5194/acp-22-3321-2022, 2022
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Here we detail the history of the Zeppelin Observatory, a unique global background site and one of only a few in the high Arctic. We present long-term time series of up to 30 years of atmospheric components and atmospheric transport phenomena. Many of these time series are important to our understanding of Arctic and global atmospheric composition change. Finally, we discuss the future of the Zeppelin Observatory and emerging areas of future research on the Arctic atmosphere.
Caixin Wang, Robert M. Graham, Keguang Wang, Sebastian Gerland, and Mats A. Granskog
The Cryosphere, 13, 1661–1679, https://doi.org/10.5194/tc-13-1661-2019, https://doi.org/10.5194/tc-13-1661-2019, 2019
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A warm bias and higher total precipitation and snowfall were found in ERA5 compared with ERA-Interim (ERA-I) over Arctic sea ice. The warm bias in ERA5 was larger in the cold season when 2 m air temperature was < −25 °C and smaller in the warm season than in ERA-I. Substantial anomalous Arctic rainfall in ERA-I was reduced in ERA5, particularly in summer and autumn. When using ERA5 and ERA-I to force a 1-D sea ice model, the effects on ice growth are very small (cm) during the freezing period.
Anna Makarewicz, Piotr Kowalczuk, Sławomir Sagan, Mats A. Granskog, Alexey K. Pavlov, Agnieszka Zdun, Karolina Borzycka, and Monika Zabłocka
Ocean Sci., 14, 543–562, https://doi.org/10.5194/os-14-543-2018, https://doi.org/10.5194/os-14-543-2018, 2018
Daiki Nomura, Mats A. Granskog, Agneta Fransson, Melissa Chierici, Anna Silyakova, Kay I. Ohshima, Lana Cohen, Bruno Delille, Stephen R. Hudson, and Gerhard S. Dieckmann
Biogeosciences, 15, 3331–3343, https://doi.org/10.5194/bg-15-3331-2018, https://doi.org/10.5194/bg-15-3331-2018, 2018
D. V. Divine, M. A. Granskog, S. R. Hudson, C. A. Pedersen, T. I. Karlsen, S. A. Divina, A. H. H. Renner, and S. Gerland
The Cryosphere, 9, 255–268, https://doi.org/10.5194/tc-9-255-2015, https://doi.org/10.5194/tc-9-255-2015, 2015
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Regional melt pond coverage and albedo of thin (70-90cm) first year Arctic sea ice in advanced stage of melt was estimated from a combination of low-altitude imagery and in situ measurements north of Svalbard in summer 2012. The study revealed a homogeneous melt across the study area with a typical pond fraction of 0.29 and sea-ice albedo of 0.44. A decrease in pond fraction was, however, observed in the 30km marginal ice zone, occurring in parallel with an increase in open-water coverage.
M. Nicolaus, C. Petrich, S. R. Hudson, and M. A. Granskog
The Cryosphere, 7, 977–986, https://doi.org/10.5194/tc-7-977-2013, https://doi.org/10.5194/tc-7-977-2013, 2013
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Ann Keen, Ed Blockley, David A. Bailey, Jens Boldingh Debernard, Mitchell Bushuk, Steve Delhaye, David Docquier, Daniel Feltham, François Massonnet, Siobhan O'Farrell, Leandro Ponsoni, José M. Rodriguez, David Schroeder, Neil Swart, Takahiro Toyoda, Hiroyuki Tsujino, Martin Vancoppenolle, and Klaus Wyser
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Leandro Ponsoni, François Massonnet, David Docquier, Guillian Van Achter, and Thierry Fichefet
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H. Jakob Belter, Thomas Krumpen, Stefan Hendricks, Jens Hoelemann, Markus A. Janout, Robert Ricker, and Christian Haas
The Cryosphere, 14, 2189–2203, https://doi.org/10.5194/tc-14-2189-2020, https://doi.org/10.5194/tc-14-2189-2020, 2020
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The validation of satellite sea ice thickness (SIT) climate data records with newly acquired moored sonar SIT data shows that satellite products provide modal rather than mean SIT in the Laptev Sea region. This tendency of satellite-based SIT products to underestimate mean SIT needs to be considered for investigations of sea ice volume transports. Validation of satellite SIT in the first-year-ice-dominated Laptev Sea will support algorithm development for more reliable SIT records in the Arctic.
Mark S. Handcock and Marilyn N. Raphael
The Cryosphere, 14, 2159–2172, https://doi.org/10.5194/tc-14-2159-2020, https://doi.org/10.5194/tc-14-2159-2020, 2020
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Traditional methods of calculating the annual cycle of sea ice extent disguise the variation of amplitude and timing (phase) of the advance and retreat of the ice. We present a multiscale model that explicitly allows them to vary, resulting in a much improved representation of the cycle. We show that phase is the dominant contributor to the variability in the cycle and that the anomalous decay of Antarctic sea ice in 2016 was due largely to a change of phase.
Rebecca J. Rolph, Daniel L. Feltham, and David Schröder
The Cryosphere, 14, 1971–1984, https://doi.org/10.5194/tc-14-1971-2020, https://doi.org/10.5194/tc-14-1971-2020, 2020
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It is well known that the Arctic sea ice extent is declining, and it is often assumed that the marginal ice zone (MIZ), the area of partial sea ice cover, is consequently increasing. However, we find no trend in the MIZ extent during the last 40 years from observations that is consistent with a widening of the MIZ as it moves northward. Differences of MIZ extent between different satellite retrievals are too large to provide a robust basis to verify model simulations of MIZ extent.
Mark A. Tschudi, Walter N. Meier, and J. Scott Stewart
The Cryosphere, 14, 1519–1536, https://doi.org/10.5194/tc-14-1519-2020, https://doi.org/10.5194/tc-14-1519-2020, 2020
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A new version of a set of data products that contain the velocity of sea ice and the age of this ice has been developed. We provide a history of the product development and discuss the improvements to the algorithms that create these products. We find that changes in sea ice motion and age show a significant shift in the Arctic ice cover, from a pack with a high concentration of older ice to a sea ice cover dominated by younger ice, which is more susceptible to summer melt.
Angela Cheng, Barbara Casati, Adrienne Tivy, Tom Zagon, Jean-François Lemieux, and L. Bruno Tremblay
The Cryosphere, 14, 1289–1310, https://doi.org/10.5194/tc-14-1289-2020, https://doi.org/10.5194/tc-14-1289-2020, 2020
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Sea ice charts by the Canadian Ice Service (CIS) contain visually estimated ice concentration produced by analysts. The accuracy of manually derived ice concentrations is not well understood. The subsequent uncertainty of ice charts results in downstream uncertainties for ice charts users, such as models and climatology studies, and when used as a verification source for automated sea ice classifiers. This study quantifies the level of accuracy and inter-analyst agreement for ice charts by CIS.
Young Jun Kim, Hyun-Cheol Kim, Daehyeon Han, Sanggyun Lee, and Jungho Im
The Cryosphere, 14, 1083–1104, https://doi.org/10.5194/tc-14-1083-2020, https://doi.org/10.5194/tc-14-1083-2020, 2020
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In this study, we proposed a novel 1-month sea ice concentration (SIC) prediction model with eight predictors using a deep-learning approach, convolutional neural networks (CNNs). The proposed CNN model was evaluated and compared with the two baseline approaches, random-forest and simple-regression models, resulting in better performance. This study also examined SIC predictions for two extreme cases in 2007 and 2012 in detail and the influencing factors through a sensitivity analysis.
Shiming Xu, Lu Zhou, and Bin Wang
The Cryosphere, 14, 751–767, https://doi.org/10.5194/tc-14-751-2020, https://doi.org/10.5194/tc-14-751-2020, 2020
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Sea ice thickness parameters are key to polar climate change studies and forecasts. Airborne and satellite measurements provide complementary observational capabilities. The study analyzes the variability in freeboard and snow depth measurements and its changes with scale in Operation IceBridge, CryoVEx, CryoSat-2 and ICESat. Consistency between airborne and satellite data is checked. Analysis calls for process-oriented attribution of variability and covariability features of these parameters.
Valeria Selyuzhenok, Igor Bashmachnikov, Robert Ricker, Anna Vesman, and Leonid Bobylev
The Cryosphere, 14, 477–495, https://doi.org/10.5194/tc-14-477-2020, https://doi.org/10.5194/tc-14-477-2020, 2020
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This study explores a link between the long-term variations in the integral sea ice volume in the Greenland Sea and oceanic processes. We link the changes in the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) regional sea ice volume with the mixed layer, depth and upper-ocean heat content derived using the ARMOR dataset.
Chao Min, Longjiang Mu, Qinghua Yang, Robert Ricker, Qian Shi, Bo Han, Renhao Wu, and Jiping Liu
The Cryosphere, 13, 3209–3224, https://doi.org/10.5194/tc-13-3209-2019, https://doi.org/10.5194/tc-13-3209-2019, 2019
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Sea ice volume export through the Fram Strait has been studied using varied methods, however, mostly in winter months. Here we report sea ice volume estimates that extend over summer seasons. A recent developed sea ice thickness dataset, in which CryoSat-2 and SMOS sea ice thickness together with SSMI/SSMIS sea ice concentration are assimilated, is used and evaluated in the paper. Results show our estimate is more reasonable than that calculated by satellite data only.
M. Jeffrey Mei, Ted Maksym, Blake Weissling, and Hanumant Singh
The Cryosphere, 13, 2915–2934, https://doi.org/10.5194/tc-13-2915-2019, https://doi.org/10.5194/tc-13-2915-2019, 2019
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Sea ice thickness is hard to measure directly, and current datasets are very limited to sporadically conducted drill lines. However, surface elevation is much easier to measure. Converting surface elevation to ice thickness requires making assumptions about snow depth and density, which leads to large errors (and may not generalize to new datasets). A deep learning method is presented that uses the surface morphology as a direct predictor of sea ice thickness, with testing errors of < 20 %.
Pierre Rampal, Véronique Dansereau, Einar Olason, Sylvain Bouillon, Timothy Williams, Anton Korosov, and Abdoulaye Samaké
The Cryosphere, 13, 2457–2474, https://doi.org/10.5194/tc-13-2457-2019, https://doi.org/10.5194/tc-13-2457-2019, 2019
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In this article, we look at how the Arctic sea ice cover, as a solid body, behaves on different temporal and spatial scales. We show that the numerical model neXtSIM uses a new approach to simulate the mechanics of sea ice and reproduce the characteristics of how sea ice deforms, as observed by satellite. We discuss the importance of this model performance in the context of simulating climate processes taking place in polar regions, like the exchange of energy between the ocean and atmosphere.
Alberto Alberello, Miguel Onorato, Luke Bennetts, Marcello Vichi, Clare Eayrs, Keith MacHutchon, and Alessandro Toffoli
The Cryosphere, 13, 41–48, https://doi.org/10.5194/tc-13-41-2019, https://doi.org/10.5194/tc-13-41-2019, 2019
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Existing observations do not provide quantitative descriptions of the floe size distribution for pancake ice floes. This is important during the Antarctic winter sea ice expansion, when hundreds of kilometres of ice cover around the Antarctic continent are composed of pancake floes (D = 0.3–3 m). Here, a new set of images from the Antarctic marginal ice zone is used to measure the shape of individual pancakes for the first time and to infer their size distribution.
Frédéric Laliberté, Stephen E. L. Howell, Jean-François Lemieux, Frédéric Dupont, and Ji Lei
The Cryosphere, 12, 3577–3588, https://doi.org/10.5194/tc-12-3577-2018, https://doi.org/10.5194/tc-12-3577-2018, 2018
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Ice that forms over marginal seas often gets anchored and becomes landfast. Landfast ice is fundamental to the local ecosystems, is of economic importance as it leads to hazardous seafaring conditions and is also a choice hunting ground for both the local population and large predators. Using observations and climate simulations, this study shows that, especially in the Canadian Arctic, landfast ice might be more resilient to climate change than is generally thought.
Iina Ronkainen, Jonni Lehtiranta, Mikko Lensu, Eero Rinne, Jari Haapala, and Christian Haas
The Cryosphere, 12, 3459–3476, https://doi.org/10.5194/tc-12-3459-2018, https://doi.org/10.5194/tc-12-3459-2018, 2018
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We quantify the sea ice thickness variability in the Bay of Bothnia using various observational data sets. For the first time we use helicopter and shipborne electromagnetic soundings to study changes in drift ice of the Bay of Bothnia. Our results show that the interannual variability of ice thickness is larger in the drift ice zone than in the fast ice zone. Furthermore, the mean thickness of heavily ridged ice near the coast can be several times larger than that of fast ice.
Edward W. Blockley and K. Andrew Peterson
The Cryosphere, 12, 3419–3438, https://doi.org/10.5194/tc-12-3419-2018, https://doi.org/10.5194/tc-12-3419-2018, 2018
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Arctic sea-ice prediction on seasonal time scales is becoming increasingly more relevant to society but the predictive capability of forecasting systems is low. Several studies suggest initialization of sea-ice thickness (SIT) could improve the skill of seasonal prediction systems. Here for the first time we test the impact of SIT initialization in the Met Office's GloSea coupled prediction system using CryoSat-2 data. We show significant improvements to Arctic extent and ice edge location.
Jeff K. Ridley and Edward W. Blockley
The Cryosphere, 12, 3355–3360, https://doi.org/10.5194/tc-12-3355-2018, https://doi.org/10.5194/tc-12-3355-2018, 2018
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The climate change conference held in Paris in 2016 made a commitment to limiting global-mean warming since the pre-industrial era to well below 2 °C and to pursue efforts to limit the warming to 1.5 °C. Since global warming is already at 1 °C, the 1.5 °C can only be achieved at considerable cost. It is thus important to assess the risks associated with the higher target. This paper shows that the decline of Arctic sea ice, and associated impacts, can only be halted with the 1.5 °C target.
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.
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.
Lu Zhou, Shiming Xu, Jiping Liu, and Bin Wang
The Cryosphere, 12, 993–1012, https://doi.org/10.5194/tc-12-993-2018, https://doi.org/10.5194/tc-12-993-2018, 2018
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This work proposes a new data synergy method for the retrieval of sea ice thickness and snow depth by using colocating L-band passive remote sensing and active laser altimetry. Physical models are adopted for the retrieval, including L-band radiation model and buoyancy relationship. Covariability of snow depth and total freeboard is further utilized to mitigate resolution differences and improve retrievability. The method can be applied to future campaigns including ICESat-2 and WCOM.
Ross M. Lieblappen, Deip D. Kumar, Scott D. Pauls, and Rachel W. Obbard
The Cryosphere, 12, 1013–1026, https://doi.org/10.5194/tc-12-1013-2018, https://doi.org/10.5194/tc-12-1013-2018, 2018
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We imaged first-year sea ice using micro-computed tomography to visualize, capture, and quantify the 3-D complex structure of salt water channels weaving through sea ice. From these data, we then built a mathematical network to better understand the pathways transporting heat, gases, and salts between the ocean and the atmosphere. Powered with this structural knowledge, we can create new modeled brine channels for a given sea ice depth and temperature that accurately mimic field conditions.
Matthias Rabatel, Pierre Rampal, Alberto Carrassi, Laurent Bertino, and Christopher K. R. T. Jones
The Cryosphere, 12, 935–953, https://doi.org/10.5194/tc-12-935-2018, https://doi.org/10.5194/tc-12-935-2018, 2018
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Large deviations still exist between sea ice forecasts and observations because of both missing physics in models and uncertainties on model inputs. We investigate how the new sea ice model neXtSIM is sensitive to uncertainties in the winds. We highlight and quantify the role of the internal forces in the ice on this sensitivity and show that neXtSIM is better at predicting sea ice drift than a free-drift (without internal forces) ice model and is a skilful tool for search and rescue operations.
Friedrich Richter, Matthias Drusch, Lars Kaleschke, Nina Maaß, Xiangshan Tian-Kunze, and Susanne Mecklenburg
The Cryosphere, 12, 921–933, https://doi.org/10.5194/tc-12-921-2018, https://doi.org/10.5194/tc-12-921-2018, 2018
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L-band (1.4 GHz) brightness temperatures from ESA's Soil Moisture and Ocean Salinity SMOS mission have been used to derive thin sea ice thickness. However, the brightness temperature measurements can potentially be assimilated directly in forecasting systems reducing the data latency and providing a more consistent first guess. We studied the forward (observation) operator that translates geophysical sea ice parameters from the ECMWF Ocean ReAnalysis Pilot 5 (ORAP5) into brightness temperatures.
Jun Ono, Hiroaki Tatebe, Yoshiki Komuro, Masato I. Nodzu, and Masayoshi Ishii
The Cryosphere, 12, 675–683, https://doi.org/10.5194/tc-12-675-2018, https://doi.org/10.5194/tc-12-675-2018, 2018
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Sea ice in the Arctic Ocean has experienced rapid decline since the beginning of satellite observations. To assess the predictability of sea ice extent (SIE) in the Arctic Ocean and to clarify the underlying physical processes, we conducted prediction experiments using an initialized climate model (MIROC5). The present study suggests that subsurface ocean heat content originating from the North Atlantic contributes to the skillful prediction of winter SIE at lead times up to 11 months.
Agnieszka Herman, Karl-Ulrich Evers, and Nils Reimer
The Cryosphere, 12, 685–699, https://doi.org/10.5194/tc-12-685-2018, https://doi.org/10.5194/tc-12-685-2018, 2018
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In regions close to the ice edge, sea ice is composed of many separate ice floes of different sizes and shapes. Strong fragmentation is caused mainly by ice breaking by waves coming from the open ocean. At present, this process, although recognized as important for many other physical processes, is not well understood. In this study we present results of a laboratory study of ice breaking by waves, and we provide interpretation of those results that may guide analysis of other similar datasets.
Alek A. Petty, Julienne C. Stroeve, Paul R. Holland, Linette N. Boisvert, Angela C. Bliss, Noriaki Kimura, and Walter N. Meier
The Cryosphere, 12, 433–452, https://doi.org/10.5194/tc-12-433-2018, https://doi.org/10.5194/tc-12-433-2018, 2018
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There was significant scientific and media attention surrounding Arctic sea ice in 2016, due primarily to the record-warm air temperatures and low sea ice conditions observed at the start of the year. Here we quantify and assess the record-low monthly sea ice cover in winter, spring and fall, and the lack of record-low sea ice conditions in summer. We explore the primary drivers of these monthly sea ice states and explore the implications for improved summer sea ice forecasting.
Lettie A. Roach, Samuel M. Dean, and James A. Renwick
The Cryosphere, 12, 365–383, https://doi.org/10.5194/tc-12-365-2018, https://doi.org/10.5194/tc-12-365-2018, 2018
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This paper evaluates Antarctic sea ice simulated by global climate models against satellite observations. We find biases in high-concentration and low-concentration sea ice that are consistent across the population of 40 models, in spite of the differences in physics between different models. Targeted model experiments show that biases in low-concentration sea ice can be significantly reduced by enhanced lateral melt, a result that may be valuable for sea ice model development.
David W. Rees Jones and Andrew J. Wells
The Cryosphere, 12, 25–38, https://doi.org/10.5194/tc-12-25-2018, https://doi.org/10.5194/tc-12-25-2018, 2018
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Frazil or granular ice grows rapidly from turbulent water cooled beneath its freezing temperature. We analyse numerical models of a population of ice crystals to provide insight into the treatment of frazil ice in large-scale models and hence in the environment. We determine critical conditions for explosively rapid frazil growth. We show that frazil-ice processes impact whether a plume of ice shelf water beneath an Antarctic ice shelf intrudes at depth or reaches the end of the shelf.
Jamie G. L. Rae, Alexander D. Todd, Edward W. Blockley, and Jeff K. Ridley
The Cryosphere, 11, 3023–3034, https://doi.org/10.5194/tc-11-3023-2017, https://doi.org/10.5194/tc-11-3023-2017, 2017
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Several studies have highlighted links between Arctic summer storms and September sea ice extent in observations. Here we use model and reanalysis data to investigate the sensitivity of such links to the analytical methods used, in order to determine their robustness. The links were found to depend on the resolution of the model and dataset, the method used to identify storms and the time period used in the analysis. We therefore recommend caution when interpreting the results of such studies.
Amelia A. Marks, Maxim L. Lamare, and Martin D. King
The Cryosphere, 11, 2867–2881, https://doi.org/10.5194/tc-11-2867-2017, https://doi.org/10.5194/tc-11-2867-2017, 2017
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Arctic sea ice extent is declining rapidly. Prediction of sea ice trends relies on sea ice models that need to be evaluated with real data. A realistic sea ice environment is created in a laboratory by the Royal Holloway sea ice simulator and is used to show a sea ice model can replicate measured properties of sea ice, e.g. reflectance. Black carbon, a component of soot found in atmospheric pollution, is also experimentally shown to reduce the sea ice reflectance, which could exacerbate melting.
David Docquier, François Massonnet, Antoine Barthélemy, Neil F. Tandon, Olivier Lecomte, and Thierry Fichefet
The Cryosphere, 11, 2829–2846, https://doi.org/10.5194/tc-11-2829-2017, https://doi.org/10.5194/tc-11-2829-2017, 2017
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Our study provides a new way to evaluate the performance of a climate model regarding the interplay between sea ice motion, area and thickness in the Arctic against different observation datasets. We show that the NEMO-LIM model is good in that respect and that the relationships between the different sea ice variables are complex. The metrics we developed can be used in the framework of the Coupled Model Intercomparison Project 6 (CMIP6), which will feed the next IPCC report.
Agnieszka Herman
The Cryosphere, 11, 2711–2725, https://doi.org/10.5194/tc-11-2711-2017, https://doi.org/10.5194/tc-11-2711-2017, 2017
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It is often assumed that ocean waves break sea ice into floes with sizes depending on wavelength. The results of this modeling study (in agreement with some earlier observations and models) suggest that this is not the case; instead the sizes of ice floes produced by wave breaking depend only on ice thickness and mechanical properties. This may have important consequences for predicting sea ice response to oceanic and atmospheric forcing in regions where sea ice is influenced by waves.
Ron Kwok, Nathan T. Kurtz, Ludovic Brucker, Alvaro Ivanoff, Thomas Newman, Sinead L. Farrell, Joshua King, Stephen Howell, Melinda A. Webster, John Paden, Carl Leuschen, Joseph A. MacGregor, Jacqueline Richter-Menge, Jeremy Harbeck, and Mark Tschudi
The Cryosphere, 11, 2571–2593, https://doi.org/10.5194/tc-11-2571-2017, https://doi.org/10.5194/tc-11-2571-2017, 2017
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Since 2009, the ultra-wideband snow radar on Operation IceBridge has acquired data in annual campaigns conducted during the Arctic and Antarctic springs. Existing snow depth retrieval algorithms differ in the way the air–snow and snow–ice interfaces are detected and localized in the radar returns and in how the system limitations are addressed. Here, we assess five retrieval algorithms by comparisons with field measurements, ground-based campaigns, and analyzed fields of snow depth.
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