Articles | Volume 6, issue 2
Research article 11 Apr 2012
Research article | 11 Apr 2012
Albedo of the ice covered Weddell and Bellingshausen Seas
A. I. Weiss et al.
Related subject area
Sea IceSurface-based Ku- and Ka-band polarimetric radar for sea ice studiesStatistical predictability of the Arctic sea ice volume anomaly: identifying predictors and optimal sampling locationsRefining the sea surface identification approach for determining freeboards in the ICESat-2 sea ice productsSatellite-based sea ice thickness changes in the Laptev Sea from 2002 to 2017: comparison to mooring observationsModeling the annual cycle of daily Antarctic sea ice extentChanges of the Arctic marginal ice zone during the satellite eraAn enhancement to sea ice motion and age products at the National Snow and Ice Data Center (NSIDC)Accuracy and inter-analyst agreement of visually estimated sea ice concentrations in Canadian Ice Service ice charts using single-polarization RADARSAT-2Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networksVariability scaling and consistency in airborne and satellite altimetry measurements of Arctic sea iceAn inter-comparison of the mass budget of the Arctic sea ice in CMIP6 modelsSea ice volume variability and water temperature in the Greenland SeaSea ice export through the Fram Strait derived from a combined model and satellite data setEstimating early-winter Antarctic sea ice thickness from deformed ice morphologyOn the multi-fractal scaling properties of sea ice deformationBrief communication: Pancake ice floe size distribution during the winter expansion of the Antarctic marginal ice zoneWhat historical landfast ice observations tell us about projected ice conditions in Arctic archipelagoes and marginal seas under anthropogenic forcingInterannual sea ice thickness variability in the Bay of BothniaImproving Met Office seasonal predictions of Arctic sea ice using assimilation of CryoSat-2 thicknessBrief communication: Solar radiation management not as effective as CO2 mitigation for Arctic sea ice loss in hitting the 1.5 and 2 °C COP climate targetsReflective properties of melt ponds on sea iceThe color of melt ponds on Arctic sea iceOn the retrieval of sea ice thickness and snow depth using concurrent laser altimetry and L-band remote sensing dataA network model for characterizing brine channels in sea iceImpact of rheology on probabilistic forecasts of sea ice trajectories: application for search and rescue operations in the ArcticArctic sea ice signatures: L-band brightness temperature sensitivity comparison using two radiation transfer modelsMechanisms influencing seasonal to inter-annual prediction skill of sea ice extent in the Arctic Ocean in MIROCFloe-size distributions in laboratory ice broken by wavesThe Arctic sea ice cover of 2016: a year of record-low highs and higher-than-expected lowsConsistent biases in Antarctic sea ice concentration simulated by climate modelsFrazil-ice growth rate and dynamics in mixed layers and sub-ice-shelf plumesHow much should we believe correlations between Arctic cyclones and sea ice extent?Optical properties of sea ice doped with black carbon – an experimental and radiative-transfer modelling comparisonRelationships between Arctic sea ice drift and strength modelled by NEMO-LIM3.6Wave-induced stress and breaking of sea ice in a coupled hydrodynamic discrete-element wave–ice modelIntercomparison of snow depth retrievals over Arctic sea ice from radar data acquired by Operation IceBridgeWinter sea ice export from the Laptev Sea preconditions the local summer sea ice cover and fast ice decaySea ice assimilation into a coupled ocean–sea ice model using its adjointModelling radiative transfer through ponded first-year Arctic sea ice with a plane-parallel modelBrief communication: Increasing shortwave absorption over the Arctic Ocean is not balanced by trends in the AntarcticWave–ice interactions in the neXtSIM sea-ice modelIce bridges and ridges in the Maxwell-EB sea ice rheologyComparison of CryoSat-2 and ENVISAT radar freeboard over Arctic sea ice: toward an improved Envisat freeboard retrievalOpen-source sea ice drift algorithm for Sentinel-1 SAR imagery using a combination of feature tracking and pattern matchingMethod to characterize directional changes in Arctic sea ice drift and associated deformation due to synoptic atmospheric variations using Lagrangian dispersion statisticsA weekly Arctic sea-ice thickness data record from merged CryoSat-2 and SMOS satellite dataSea-ice deformation in a coupled ocean–sea-ice model and in satellite remote sensing dataImpacts of freshwater changes on Antarctic sea ice in an eddy-permitting sea-ice–ocean modelA simple model for the evolution of melt pond coverage on permeable Arctic sea iceBrief communication: Impacts of ocean-wave-induced breakup of Antarctic sea ice via thermodynamics in a stand-alone version of the CICE sea-ice model
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
The Cryosphere, 14, 4405–4426,Short summary
This study provides a first look at the data collected by a new dual-frequency Ka- and Ku-band in situ radar over winter sea ice in the Arctic Ocean. The instrument shows potential for using both bands to retrieve snow depth over sea ice, as well as sensitivity of the measurements to changing snow and atmospheric conditions.
Leandro Ponsoni, François Massonnet, David Docquier, Guillian Van Achter, and Thierry Fichefet
The Cryosphere, 14, 2409–2428,Short summary
The continuous melting of the Arctic sea ice observed in the last decades has a significant impact at global and regional scales. To understand the amplitude and consequences of this impact, the monitoring of the total sea ice volume is crucial. However, in situ monitoring in such a harsh environment is hard to perform and far too expensive. This study shows that four well-placed sampling locations are sufficient to explain about 70 % of the inter-annual changes in the pan-Arctic sea ice volume.
Ron Kwok, Alek A. Petty, Marco Bagnardi, Nathan T. Kurtz, Glenn F. Cunningham, and Alvaro Ivanoff
The Cryosphere Discuss.,
Revised manuscript accepted for TC
H. Jakob Belter, Thomas Krumpen, Stefan Hendricks, Jens Hoelemann, Markus A. Janout, Robert Ricker, and Christian Haas
The Cryosphere, 14, 2189–2203,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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.
Ann Keen, Ed Blockley, David 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
The Cryosphere Discuss.,
Revised manuscript accepted for TCShort summary
We compare the mass budget of the Arctic sea ice in a number of the latest climate models. New output has been defined that allows us to compare the processes of sea ice growth and loss in a more detailed way than has previously been possible. We find that that the models are strikingly similar in terms of the major processes causing the annual growth and loss of Arctic sea ice, and that the budget terms respond in a broadly consistent way as the climate warms during the 21st century.
Valeria Selyuzhenok, Igor Bashmachnikov, Robert Ricker, Anna Vesman, and Leonid Bobylev
The Cryosphere, 14, 477–495,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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.
The Cryosphere, 11, 2711–2725,Short summary
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,Short summary
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.
Polona Itkin and Thomas Krumpen
The Cryosphere, 11, 2383–2391,Short summary
By means of airborne sea ice thickness surveys, remote sensing data and results from a numerical model, we show that winter ice dynamic in the Laptev Sea has a preconditioning effect on local summer ice extent in addition to atmospheric processes acting on the ice cover between May and September. We conclude that the observed tendency towards an increased ice export further accelerates pack ice retreat in summer and fast ice decay.
Nikolay V. Koldunov, Armin Köhl, Nuno Serra, and Detlef Stammer
The Cryosphere, 11, 2265–2281,Short summary
The paper describes one of the first attempts to use the so-called adjoint data assimilation method to bring Arctic Ocean model simulations closer to observation, especially in terms of the sea ice. It is shown that after assimilation the model bias in simulating the Arctic sea ice is considerably reduced. There is also additional improvement in the sea ice thickens representation that is not assimilated directly.
Torbjørn Taskjelle, Stephen R. Hudson, Mats A. Granskog, and Børge Hamre
The Cryosphere, 11, 2137–2148,
Christian Katlein, Stefan Hendricks, and Jeffrey Key
The Cryosphere, 11, 2111–2116,Short summary
In the public debate, increasing sea ice extent in the Antarctic is often highlighted as counter-indicative of global warming. Here we show that the slight increases in Antarctic sea ice extent are not able to counter Arctic losses. Using bipolar satellite observations, we demonstrate that even in the Antarctic polar ocean solar shortwave energy absorption is increasing in accordance with strongly increasing shortwave energy absorption in the Arctic Ocean rather than compensating Arctic losses.
Timothy D. Williams, Pierre Rampal, and Sylvain Bouillon
The Cryosphere, 11, 2117–2135,Short summary
As the Arctic sea ice extent drops, more ship traffic seeks to take advantage of this, and a need for better wave and sea ice forecasts arises. One aspect of this is the location of the sea ice edge. The waves here can be quite large, but they die away as they travel into the ice. This causes momentum to be transferred from the waves to the ice, causing ice drift. However, our study found that the effect of the wind drag had more impact on the ice edge position than the waves.
Véronique Dansereau, Jérôme Weiss, Pierre Saramito, Philippe Lattes, and Edmond Coche
The Cryosphere, 11, 2033–2058,Short summary
A new mechanical framework is used to model the drift of sea ice in a narrow channel between Greenland and Ellesmere Island. It is able to reproduce its main features : curved cracks, ice “bridges” that stop the flow of ice for several months of the year and some thick, strongly localized ridged ice. The simulations suggest that a mechanical weakening of the sea ice cover can shorten the lifespan of ice bridges and result in an increased export of ice through the narrow channels of the Arctic.
Kevin Guerreiro, Sara Fleury, Elena Zakharova, Alexei Kouraev, Frédérique Rémy, and Philippe Maisongrande
The Cryosphere, 11, 2059–2073,Short summary
We analyse CryoSat-2 and Envisat freeboard height discrepancy over Arctic sea ice and we study the potential role of ice roughness. Based on our results, we build a CryoSat-2-like version of Envisat freeboard height. The improved Envisat freeboard is converted to sea ice draught and compared to in situ mooring observations to demonstrate the potential of our methodology to produce accurate ice thickness estimates over the 2002–2012 period.
Stefan Muckenhuber and Stein Sandven
The Cryosphere, 11, 1835–1850,Short summary
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.
Jennifer V. Lukovich, Cathleen A. Geiger, and David G. Barber
The Cryosphere, 11, 1707–1731,Short summary
In this study we develop a framework to characterize directional changes in sea ice drift and associated deformation in response to atmospheric forcing. Lagrangian dispersion statistics applied to ice beacons deployed in a triangular configuration in the Beaufort Sea capture a shift in ice dynamical regimes and local differences in deformation. This framework contributes to diagnostic development relevant for ice hazard assessments and forecasting required by indigenous communities and industry.
Robert Ricker, Stefan Hendricks, Lars Kaleschke, Xiangshan Tian-Kunze, Jennifer King, and Christian Haas
The Cryosphere, 11, 1607–1623,Short summary
We developed the first merging of CryoSat-2 and SMOS sea-ice thickness retrievals. ESA’s Earth Explorer SMOS satellite can detect thin sea ice, whereas its companion CryoSat-2, designed to observe thicker perennial sea ice, lacks sensitivity. Using these satellite missions together completes the picture of the changing Arctic sea ice and provides a more accurate and comprehensive view on the actual state of Arctic sea-ice thickness.
Gunnar Spreen, Ron Kwok, Dimitris Menemenlis, and An T. Nguyen
The Cryosphere, 11, 1553–1573,
Verena Haid, Doroteaciro Iovino, and Simona Masina
The Cryosphere, 11, 1387–1402,Short summary
Since the Antarctic sea ice extent shows a recent increase, we investigate the sea ice response to changed amount and distribution of surface freshwater addition in the Southern Ocean with the ocean–sea ice model NEMO/LIM2. We find that freshwater addition within the range of current estimates increases the ice extent, but higher amounts could have an opposing effect. The freshwater distribution is of great influence on the ice dynamics and the ice thickness is strongly influenced by it.
Predrag Popović and Dorian Abbot
The Cryosphere, 11, 1149–1172,Short summary
During summer, a large portion of sea ice in the Arctic is typically covered with meltwater. We present a simple model for the evolution of melt ponds on permeable sea ice that both fits observations and allows us to understand the behavior of melt ponds in a way that is often not possible with more complex models. We use this model to show that pond coverage will increase under global warming. This work is important as melt ponds affect the overall reflectance of sea ice.
Luke G. Bennetts, Siobhan O'Farrell, and Petteri Uotila
The Cryosphere, 11, 1035–1040,Short summary
A numerical model is used to investigate how Antarctic sea ice concentration and volume are affected by increased melting caused by ocean-wave breakup of the ice. When temperatures are high enough to melt the ice, concentration and volume are reduced for ~ 100 km into the ice-covered ocean. When temperatures are low enough for ice growth, the concentration recovers, but the reduced volume persists.
Allison, I., Brandt, R. E., and Warren, S. G.: East Antarctic sea ice: albedo, thickness distribution, and snow cover, J. Geophys. Res., 98, 12417–12429, 1993.
Bannehr, L. and Glover, V.: Preprocessing of airborne Pyranometer data, NCAR Technical Note, 364, 1–35, 1991.
Bracegirdle, T. J., Connolley, W. M., and Turner, J.: Antarctic climate change over the twenty first century, J. Geophys. Res., 113, D03103, https://doi.org/10.1029/2007JD008933, 2008.
Brandt, R. E., Warren, S. G., Worby, A. P., and Grenfell, T. C.: Surface Albedo of the Antarctic sea ice zone, J. Climate, 18, 3606–3622, 2005.
Burns, S. P., Khelif, D., Friehe, C. A., Hignett, P., Williams, A. G., Grant, A. L. M., Hacker, J. M., Hagan, D. E., Serra, Y. L., Rodgers, D. P., Bradley, E. F., Weller, R. A., Fairall, C. W., Anderson, S. P., Paulson, C. A., and Coppin, P. A.: Comparison of aircraft, ship, and buoy radiation and SST measurements from TOGA COARE, J. Geophys. Res., 105, 15627–15652, 2000.
Cavalieri, D. J. and Parkinson, C. L.: Antarctic sea ice variability and trends, 1979–2006, J. Geophys. Res., 113, C07004, https://doi.org/10.1029/2007JC004564, 2008.
Christensen, J. H., Christensen, O. B., Lopez, P., van Meijgaard, E., and Botzet, M.: The HIRHAM4 Regional Atmospheric Climate Model, Scientific Report, Danish Meteorological Institute, Copenhagen, 1996.
Curry, J. A., Schramm, J., Perovich, D. K., and Pinto, J. O.: Applications of SHEBA/FIRE data to evaluation of snow/ice albedo parameterizations, J. Geophys. Res., 106, 15345–15355, 2001.
Flato, G. M. and Brown, R. D.: Variability and climate sensitivity of landfast Arctic sea ice, J. Geophys. Res., 101, 25767–25777, 1996.
Gardner, A. S. and Sharp, M. J.: A review of snow and ice albedo and the development of a new physically based broadband albedo parameterization, J. Geophys. Res., 115, F01009, https://doi.org/10.1029/2009JF001444, 2010.
Gordon, C., Cooper, C., Senior C. A., Banks, H., Gregory, J. M., Johns, T. C., Mitchell, J. F. B., and Wood, R.: The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments, Clim. Dynam., 16, 147–168, 2000.
Grenfell, T. C. and Perovich, D. K.: Spectral albedos of sea ice and incident solar irradiance in the southern Beaufort Sea, J. Geophys. Res., 89, 3573–3580, 1984.
Grenfell, T. C., Warren, S. G., and Mullen, P. C.: Reflection of solar radiation by the Antarctic snow surface at ultraviolet, visible and near-infrared wavelengths, J. Geophys. Res., 99, 18669–18684, 1994.
Hanesiak, J. M., Barber, D. G., De Abreu, R. A., and Yackel, J. J.: Local and regional albedo observations of arctic first-year sea ice during melt ponding, J. Geophys. Res., 106, 1005–1016, https://doi.org/10.1029/1999JC000068, 2001.
Hansen, J., Russell, G., Rind, D., Stone, P., Lacis, A., Lebedeff, S., Ruedy, R., and Travis, L.: Efficient three-dimensional global models for climate studies: models I and II, Mon. Weather Rev., 111, 609–662, 1983.
Hibler III, W. D. and Bryan, K.: A diagnostic ice-ocean model, J. Phys. Oceanogr., 17, 987–1015, 1987.
Hunke, E. C. and Lipscomb, W. H.: CICE: The Los Alamos Sea Ice Model, Documentation and Software User's Manual, Version 4.0, T-3 Fluid Dynamics Group, Los Alamos National Laboratory, Tech. Rep. LA-CC-06-012, 2008.
Ingram, W. J., Wilson, C. A., and Mitchell, J. F. B.: Modeling climate change: An assessment of sea ice and surface albedo feedbacks, J. Geophys. Res., 94, 8609–8622, 1989.
Jacobson, M. Z.: Climate response of fossil fuel and biofuel soot, accounting for soot's feedback to snow and sea ice albedo and emissivity, J. Geophys. Res., 109, D21201, https://doi.org/10.1029/2004JD004945, 2004.
Jenkins, A. and Jacobs, S.: Circulation and melting beneath George VI Ice Self, Antarctica, J. Geophys. Res., 113, C04013, https://doi.org/10.1029/2007JC004449, 2008.
King, J. C., Lachlan-Cope, T. A., Ladkin R. S., and Weiss A.: Airborne measurements in a stable boundary layer over the Larsen Ice Shelf, Antarctica, Bound.-Lay. Meteorol., 127, 413–428, 2008.
Køltzow, M.: The effect of a new snow and sea ice albedo scheme on regional climate model simulations, J. Geophys. Res., 112, DO7110, https://doi.org/10.1029/2006JD007693, 2007.
Ledley, T.: Sensitivity of a thermodynamic sea ice model with leads to time step size, J. Geophys. Res., 90, 2251–2260, 1985.
Liu, J., Curry, J. A., and Martinson, D. G.: Interpretation of recent Antarctic sea ice variability, Geophys. Res. Lett., 31, L02205, https://doi.org/10.1029/2003GL018732, 2004.
Liu, J., Zhang, Z., Inoue, J., and Horton, R. M: Evaluation of snow/ice albedo parameterizations and their impacts on sea ice simulations, Int. J. Climatol., 27, 81–91, 2007.
Lynch, A. H., Chapman, W. L., Walsh, J. E., and Weller, G.: Development of a regional climate model of the Western Arctic, J. Climate, 8, 1555–1570, 1995.
Manabe, S., Spelman, M. J., and Stouffer, R. J.: Transient responses of a coupled ocean-atmosphere model to gradual changes of atmospheric CO2. Part II: Seasonal response, J. Climate, 5, 105–126, 1992.
Mellor, G. L. and Kantha, L.: An ice-ocean coupled model, J. Geophys. Res., 94, 10937–10954, 1989.
Nicholls, K. W., Østerhus, S., Makinson, K., and Johnson, M. R.: Oceanographic conditions south of Berkner Island, beneath Filchner-Ronne Ice Shelf, Antarctica, J. Geophys. Res., 106, 11481–11492, 2001.
Nicholls, K. W., Boeme, S. L., Biuw, M., and Fedak, M. A.: Wintertime ocean conditions over the Southern Weddell Sea continental shelf north of Ronne Ice Shelf, Antarctica, Geophys. Res. Lett., 35, L21605, https://doi.org/10.1029/2008GL035742, 2008.
Parkinson, C. L. and Washington, W. M.: A large-scale numerical model of sea ice, J. Geophys. Res., 84, 311–337, 1979.
Pedersen, C. A., Röckner, E., Lüthje, M., and Winther, J.-G.: A new sea ice albedo scheme including melt ponds for ECHAM5 general circulation model, J. Geophys. Res., 114, DO8101, https://doi.org/10.1029/2008JD010440, 2009.
Perovich, D. K. and Grenfell, T. C.: Laboratory studies of the optical properties of young sea ice, J. Glaciol., 27, 331–346, 1981.
Perovich, D. K., Tucker III, W. B., and Ligett, K. A.: Aerial observation of the evolution of ice surface conditions during summer, J. Geophys. Res., 107, 8048, https://doi.org/10.1029/2000JC000449, 2002.
Pirazzini, R.: Surface albedo measurements over Antarctic sites in summer, J. Geophys. Res., 109, D20118, https://doi.org/10.1029/2004JD004617, 2007.
Pirazzini, R. and Räisänen , P.: A method to account for surface albedo heterogeneity in single-column radiative transfer calculations under overcast conditions, J. Geophys. Res., 113, D20108, https://doi.org/10.1029/2008JD009815, 2008.
Predoehl, M. C. and Spano, A. F.: Airborne albedo measurements over the Ross Sea, October–November 1962, Mon. Weather Rev., 93, 687–696, 1965.
Randall, D. A., Wood, R. A., Bony, S., Colman, R., Fichefet, T., Fyfe, J., Kattsov, V., Pitman, A., Shukla, J., Srinivasan, J., Stouffer, R. J., Sumi, A., and Taylor, K. E.: Climate Models and Their Evaluation, in: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B., Tignor, M., and Miller, H. L., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2007.
Renfrew, I. A., King, J. C., and Markus, T.: Coastal polynyas in the southern Weddell Sea: variability of the surface energy budget, J. Geophys. Res., 107, 3063, https://doi.org/10.1029/2000JC000720, 2002.
Röckner, E., Bäumel, G., Bonaventura, L., Brotkopf, R., Esch, M., Giorgetta, M., Hagemann, S., Irchner, I., Kornblüh, L., Manzini, E., Rhodin, A., Schlese, U., Schulzweida, U., and Tomkins, A.: The atmospheric general circulation model ECHAM5-Part1 Tech. Rep. 349, Max-Planck-Institute for Meteorology, Hamburg, 2003.
Ross, B. and Walsh, J. E.: A comparison of simulated and observed fluctuations in summertime Arctic surface albedo, J. Geophys. Res., 92, 13115–13125, 1987.
Schnack-Schiel, S.: Die Winter-Expedition mit FS Polarstern in die Antarktis (ANT V/1-3) (The Winter-Expedition of RV Polarstern to the Antarctic (ANT V/1-3)), Berichte zur Polarforschung, 39, Alfred-Wegener Institut für Polarforschung, 259 pp., 1987.
Turner, J., Lachlan-Cope, T. A., Colwell, S. T., and Marshall, G. J.: Significant warming of the Antarctic winter troposphere, Science, 311, 1914–1917, 2006.
Vashisth, P.: Effect of clouds and free water on snow albedo, Bull. Glaciol. Res., 22, 63–67, 2005.
Vihma, T., Johansson, M. M., and Launiainen, J.: Radiative and turbulent surface heat fluxes over sea ice in the western Weddell Sea in early summer, J. Geophys. Res., 114, C04019, https://doi.org/10.1029/2008JC004995, 2009.
Wang, X. and Zender, C. S.: Arctic and Antarctic diurnal and seasonal variations of snow albedo from multiyear Baseline Surface Radiation Network measurements, J. Geophys. Res., 116, F03008, https://doi.org/10.1029/2010JF001864, 2011.
Weiss, A. I., King, J. C., Lachlan-Cope, T. A., and Ladkin, R.: On the effective aerodynamic and scalar roughness length of Weddell Sea ice, J. Geophys. Res., 116, D19119, https://doi.org/10.1029/2011JD015949, 2011.
Wiscombe, W. J., and Warren, S. G.: A model of the spectral albedo of snow, I: pure snow, J. Atmos. Sci., 37, 2712–2733, 1980.
Zhou, X., Li, S., Morris, K., and Jeffries, M. O.: Albedo of summer snow on sea ice, Ross Sea, Antarctica, J. Geophys. Res., 112, D16105, https://doi.org/10.1029/2006JD007907, 2007.
Zwally, H. J., Comiso, J. C., Parkinson, C. L., Cavalieri, D. J., and Gloersen, P.: Variability of Antarctic sea ice 1979–1998, J. Geophys. Res., 107, 3041, https://doi.org/10.1029/2000JC000733, 2002.