Articles | Volume 11, issue 6
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Optical properties of sea ice doped with black carbon – an experimental and radiative-transfer modelling comparison
Amelia A. Marks
Department of Earth Sciences, Royal Holloway University of London, Egham, Surrey, TW20 0EX, UK
Maxim L. Lamare
Department of Earth Sciences, Royal Holloway University of London, Egham, Surrey, TW20 0EX, UK
A. A. Marks and M. D. King
The Cryosphere, 8, 1625–1638,
A. A. Marks and M. D. King
The Cryosphere, 7, 1193–1204,
Maxim L. Lamare, John D. Hedley, and Martin D. King
The Cryosphere, 17, 737–751,Short summary
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.
Benjamin Heikki Redmond Roche and Martin D. King
The Cryosphere, 16, 3949–3970,Short summary
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.
François Tuzet, Marie Dumont, Ghislain Picard, Maxim Lamare, Didier Voisin, Pierre Nabat, Mathieu Lafaysse, Fanny Larue, Jesus Revuelto, and Laurent Arnaud
The Cryosphere, 14, 4553–4579,Short summary
This study presents a field dataset collected over 30 d from two snow seasons at a Col du Lautaret site (French Alps). The dataset compares different measurements or estimates of light-absorbing particle (LAP) concentrations in snow, highlighting a gap in the current understanding of the measurement of these quantities. An ensemble snowpack model is then evaluated for this dataset estimating that LAPs shorten each snow season by around 10 d despite contrasting meteorological conditions.
Maxim Lamare, Marie Dumont, Ghislain Picard, Fanny Larue, François Tuzet, Clément Delcourt, and Laurent Arnaud
The Cryosphere, 14, 3995–4020,Short summary
Terrain features found in mountainous regions introduce large errors into the calculation of the physical properties of snow using optical satellite images. We present a new model performing rapid calculations of solar radiation over snow-covered rugged terrain that we tested over a site in the French Alps. The results of the study show that all the interactions between sunlight and the terrain should be accounted for over snow-covered surfaces to correctly estimate snow properties from space.
Fanny Larue, Ghislain Picard, Laurent Arnaud, Inès Ollivier, Clément Delcourt, Maxim Lamare, François Tuzet, Jesus Revuelto, and Marie Dumont
The Cryosphere, 14, 1651–1672,Short summary
The effect of surface roughness on snow albedo is often overlooked, although a small change in albedo may strongly affect the surface energy budget. By carving artificial roughness in an initially smooth snowpack, we highlight albedo reductions of 0.03–0.04 at 700 nm and 0.06–0.10 at 1000 nm. A model using photon transport is developed to compute albedo considering roughness and applied to understand the impact of roughness as a function of snow properties and illumination conditions.
Ghislain Picard, Marie Dumont, Maxim Lamare, François Tuzet, Fanny Larue, Roberta Pirazzini, and Laurent Arnaud
The Cryosphere, 14, 1497–1517,Short summary
Surface albedo is an essential variable of snow-covered areas. The measurement of this variable over a tilted terrain with levelled sensors is affected by artefacts that need to be corrected. Here we develop a theory of spectral albedo measurement over slopes from which we derive four correction algorithms. The comparison to in situ measurements taken in the Alps shows the adequacy of the theory, and the application of the algorithms shows systematic improvements.
Francois Tuzet, Marie Dumont, Laurent Arnaud, Didier Voisin, Maxim Lamare, Fanny Larue, Jesus Revuelto, and Ghislain Picard
The Cryosphere, 13, 2169–2187,Short summary
Here we present a novel method to estimate the impurity content (e.g. black carbon or mineral dust) in Alpine snow based on measurements of light extinction profiles. This method is proposed as an alternative to chemical measurements, allowing rapid retrievals of vertical concentrations of impurities in the snowpack. In addition, the results provide a better understanding of the impact of impurities on visible light extinction in snow.
Ghislain Picard, Laurent Arnaud, Romain Caneill, Eric Lefebvre, and Maxim Lamare
The Cryosphere, 13, 1983–1999,Short summary
To study how snow accumulates in Antarctica, we analyze daily surface elevation recorded by an automatic laser scanner. We show that new snow often accumulates in thick patches covering a small fraction of the surface. Most patches are removed by erosion within weeks, implying that only a few contribute to the snowpack. This explains the heterogeneity on the surface and in the snowpack. These findings are important for surface mass and energy balance, photochemistry, and ice core interpretation.
Alexander Kokhanovsky, Maxim Lamare, Biagio Di Mauro, Ghislain Picard, Laurent Arnaud, Marie Dumont, François Tuzet, Carsten Brockmann, and Jason E. Box
The Cryosphere, 12, 2371–2382,Short summary
This work presents a new technique with which to derive the snow microphysical and optical properties from snow spectral reflectance measurements. The technique is robust and easy to use, and it does not require the extraction of snow samples from a given snowpack. It can be used in processing satellite imagery over extended fresh dry, wet and polluted snowfields.
Rosalie H. Shepherd, Martin D. King, Amelia A. Marks, Neil Brough, and Andrew D. Ward
Atmos. Chem. Phys., 18, 5235–5252,Short summary
The refractive index of atmospheric extracts sourced from urban (London), remote (Antarctica), and woodsmoke aerosol was determined by applying optical trapping simultaneously with Mie spectroscopy. In addition, owing to the absorbing nature of woodsmoke and an aqueous humic acid aerosol extract, the absorption Ångström exponent could be determined.The refractive index and absorption Ångström exponent were then applied in a top-of-the-atmosphere albedo radiation transfer model.
Hoi Ga Chan, Markus M. Frey, and Martin D. King
Atmos. Chem. Phys., 18, 1507–1534,Short summary
Emissions of reactive nitrogen from snowpacks influence remote air quality. Two physical air–snow models for nitrate were developed. One assumes that below a threshold temperature the air–snow grain interface is pure ice and above it a disordered interface emerges. The other assumes an air–ice interface below melting and that any liquid present is concentrated in micropockets. Only the latter matches observations at two Antarctic lcoations covering a wide range of environmental conditions.
M. L. Lamare, J. Lee-Taylor, and M. D. King
Atmos. Chem. Phys., 16, 843–860,Short summary
The decrease in reflectivity (albedo) of sea ice and snow containing mineral dusts and volcanic ashes is calculated. The type of snow and sea ice, the thickness and the layering of mineral aerosol deposits are varied. The results show that the response of the albedo of snow and sea ice to mineral aerosol deposits is more sensitive to the type of snow or sea ice than to the properties of the mineral aerosol deposits themselves.
M. M. Frey, H. K. Roscoe, A. Kukui, J. Savarino, J. L. France, M. D. King, M. Legrand, and S. Preunkert
Atmos. Chem. Phys., 15, 7859–7875,Short summary
Surprisingly large concentrations and flux of atmospheric nitrogen oxides were measured at Dome C, East Antarctica. It was found that the surface snow holds a significant reservoir of photochemically produced NOx and is a sink of gas-phase ozone. Main drivers of NOx snow emissions were large snow nitrate concentrations, with contributions of increased UV from decreases in stratospheric ozone. Observed halogen and hydroxyl radical concentrations were too low to explain large NO2:NO ratios.
H. G. Chan, M. D. King, and M. M. Frey
Atmos. Chem. Phys., 15, 7913–7927,
S. Preunkert, M. Legrand, M. M. Frey, A. Kukui, J. Savarino, H. Gallée, M. King, B. Jourdain, W. Vicars, and D. Helmig
Atmos. Chem. Phys., 15, 6689–6705,Short summary
During two austral summers HCHO was investigated in air, snow, and interstitial air at the Concordia site located on the East Antarctic Plateau. Snow emission fluxes were estimated to be around 1 to 2 and 3 to 5 x 10^12 molecules m-2 s-1 at night and at noon, respectively. Shading experiments suggest that the photochemical HCHO production in the snowpack at Concordia remains negligible. The mean HCHO level of 130pptv observed at 1m above the surface is quite well reproduced by 1-D simulations.
A. Kukui, M. Legrand, S. Preunkert, M. M. Frey, R. Loisil, J. Gil Roca, B. Jourdain, M. D. King, J. L. France, and G. Ancellet
Atmos. Chem. Phys., 14, 12373–12392,Short summary
Concentrations of OH radicals and the sum of peroxy radicals, RO2, were measured in the boundary layer for the first time on the East Antarctic Plateau at the Concordia Station during the austral summer 2011/2012. The concentrations of radicals were comparable to those observed at the South Pole, confirming that the elevated oxidative capacity of the Antarctic atmospheric boundary layer found at the South Pole is not restricted to the South Pole but common over the high Antarctic plateau.
M. Legrand, S. Preunkert, M. Frey, Th. Bartels-Rausch, A. Kukui, M. D. King, J. Savarino, M. Kerbrat, and B. Jourdain
Atmos. Chem. Phys., 14, 9963–9976,
A. A. Marks and M. D. King
The Cryosphere, 8, 1625–1638,
Q. Libois, G. Picard, J. L. France, L. Arnaud, M. Dumont, C. M. Carmagnola, and M. D. King
The Cryosphere, 7, 1803–1818,
A. A. Marks and M. D. King
The Cryosphere, 7, 1193–1204,
M. M. Frey, N. Brough, J. L. France, P. S. Anderson, O. Traulle, M. D. King, A. E. Jones, E. W. Wolff, and J. Savarino
Atmos. Chem. Phys., 13, 3045–3062,
Related subject area
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Nikolas O. Aksamit, Randall K. Scharien, Jennifer K. Hutchings, and Jennifer V. Lukovich
The Cryosphere, 17, 1545–1566,Short summary
Coherent flow patterns in sea ice have a significant influence on sea ice fracture and refreezing. We can better understand the state of sea ice, and its influence on the atmosphere and ocean, if we understand these structures. By adapting recent developments in chaotic dynamical systems, we are able to approximate ice stretching surrounding individual ice buoys. This illuminates the state of sea ice at much higher resolution and allows us to see previously invisible ice deformation patterns.
Robert Ricker, Steven Fons, Arttu Jutila, Nils Hutter, Kyle Duncan, Sinead L. Farrell, Nathan T. Kurtz, and Renée Mie Fredensborg Hansen
The Cryosphere, 17, 1411–1429,Short summary
Information on sea ice surface topography is important for studies of sea ice as well as for ship navigation through ice. The ICESat-2 satellite senses the sea ice surface with six laser beams. To examine the accuracy of these measurements, we carried out a temporally coincident helicopter flight along the same ground track as the satellite and measured the sea ice surface topography with a laser scanner. This showed that ICESat-2 can see even bumps of only few meters in the sea ice cover.
Ludovic Moreau, Léonard Seydoux, Jérôme Weiss, and Michel Campillo
The Cryosphere, 17, 1327–1341,Short summary
In the perspective of an upcoming seasonally ice-free Arctic, understanding the dynamics of sea ice in the changing climate is a major challenge in oceanography and climatology. It is therefore essential to monitor sea ice properties with fine temporal and spatial resolution. In this paper, we show that icequakes recorded on sea ice can be processed with artificial intelligence to produce accurate maps of sea ice thickness with high temporal and spatial resolutions.
Sasan Tavakoli and Alexander V. Babanin
The Cryosphere, 17, 939–958,Short summary
We have tried to develop some new wave–ice interaction models by considering two different types of forces, one of which emerges in the ice and the other of which emerges in the water. We have checked the ability of the models in the reconstruction of wave–ice interaction in a step-wise manner. The accuracy level of the models is acceptable, and it will be interesting to check whether they can be used in wave climate models or not.
Christian Melsheimer, Gunnar Spreen, Yufang Ye, and Mohammed Shokr
The Cryosphere, 17, 105–126,Short summary
It is necessary to know the type of Antarctic sea ice present – first-year ice (grown in one season) or multiyear ice (survived one summer melt) – to understand and model its evolution, as the ice types behave and react differently. We have adapted and extended an existing method (originally for the Arctic), and now, for the first time, daily maps of Antarctic sea ice types can be derived from microwave satellite data. This will allow a new data set from 2002 well into the future to be built.
Katarzyna Bradtke and Agnieszka Herman
The frazil streaks are one of the visible signs of complex interactions between the mixed layer dynamics and the forming sea ice. Using high-resolution visible satellite imagery we characterize their spatial properties, relationship with the meteorological forcing and role in modifying wind-wave growth in the Terra Nova Bay Polynya. We provide a simple statistical tool for estimating the extent and ice coverage of the region of high ice production under given wind speed and air temperature.
Heather Christine Regan, Pierre Rampal, Einar Ólason, Guillaume Boutin, and Anton Korosov
The Cryosphere Discuss.,
Revised manuscript accepted for TCShort summary
Multiyear ice (MYI), sea ice that survives the summer, is more resistant to changes than younger ice in the Arctic, so is a good indicator of sea ice resilience. We use a model with a new way of tracking MYI to assess the contribution of different processes affecting MYI. We find two important years for MYI decline: 2007, when dynamics are important, and 2012, when melt is important. These affect MYI volume and area in different ways, which is important for interpretation of observations.
Nazanin Asadi, Philippe Lamontagne, Matthew King, Martin Richard, and K. Andrea Scott
The Cryosphere, 16, 3753–3773,Short summary
Machine learning approaches are deployed to provide accurate daily spatial maps of sea ice presence probability based on ERA5 data as input. Predictions are capable of predicting freeze-up/breakup dates within a 7 d period at specific locations of interest to shipping operators and communities. Forecasts of the proposed method during the breakup season have skills comparing to Climate Normal and sea ice concentration forecasts from a leading subseasonal-to-seasonal forecasting system.
Simon Felix Reifenberg and Helge Friedrich Goessling
The Cryosphere, 16, 2927–2946,Short summary
Using model simulations, we analyze the impact of chaotic error growth on Arctic sea ice drift predictions. Regarding forecast uncertainty, our results suggest that it matters in which season and where ice drift forecasts are initialized and that both factors vary with the model in use. We find ice velocities to be slightly more predictable than near-surface wind, a main driver of ice drift. This is relevant for future developments of ice drift forecasting systems.
Agathe Serripierri, Ludovic Moreau, Pierre Boue, Jérôme Weiss, and Philippe Roux
The Cryosphere, 16, 2527–2543,Short summary
As a result of global warming, the sea ice is disappearing at a much faster rate than predicted by climate models. To better understand and predict its ongoing decline, we deployed 247 geophones on the fast ice in Van Mijen Fjord in Svalbard, Norway, in March 2019. The analysis of these data provided a precise daily evolution of the sea-ice parameters at this location with high spatial and temporal resolution and accuracy. The results obtained are consistent with the observations made in situ.
Laura L. Landrum and Marika M. Holland
The Cryosphere, 16, 1483–1495,Short summary
High-latitude Arctic wintertime sea ice and snow insulate the relatively warmer ocean from the colder atmosphere. As the climate warms, wintertime Arctic conductive heat fluxes increase even when the sea ice concentrations remain high. Simulations from the Community Earth System Model Large Ensemble (CESM1-LE) show how sea ice and snow thicknesses, as well as the distribution of these thicknesses, significantly impact large-scale calculations of wintertime surface heat budgets in the Arctic.
Yunhe Wang, Xiaojun Yuan, Haibo Bi, Mitchell Bushuk, Yu Liang, Cuihua Li, and Haijun Huang
The Cryosphere, 16, 1141–1156,Short summary
We develop a regional linear Markov model consisting of four modules with seasonally dependent variables in the Pacific sector. The model retains skill for detrended sea ice extent predictions for up to 7-month lead times in the Bering Sea and the Sea of Okhotsk. The prediction skill, as measured by the percentage of grid points with significant correlations (PGS), increased by 75 % in the Bering Sea and 16 % in the Sea of Okhotsk relative to the earlier pan-Arctic model.
Charles Brunette, L. Bruno Tremblay, and Robert Newton
The Cryosphere, 16, 533–557,Short summary
Sea ice motion is a versatile parameter for monitoring the Arctic climate system. In this contribution, we use data from drifting buoys, winds, and ice thickness to parameterize the motion of sea ice in a free drift regime – i.e., flowing freely in response to the forcing from the winds and ocean currents. We show that including a dependence on sea ice thickness and taking into account a climatology of the surface ocean circulation significantly improves the accuracy of sea ice motion estimates.
Madison M. Smith, Marika Holland, and Bonnie Light
The Cryosphere, 16, 419–434,Short summary
Climate models represent the atmosphere, ocean, sea ice, and land with equations of varying complexity and are important tools for understanding changes in global climate. Here, we explore how realistic variations in the equations describing how sea ice melt occurs at the edges (called lateral melting) impact ice and climate. We find that these changes impact the progression of the sea-ice–albedo feedback in the Arctic and so make significant changes to the predicted Arctic sea ice.
Arttu Jutila, Stefan Hendricks, Robert Ricker, Luisa von Albedyll, Thomas Krumpen, and Christian Haas
The Cryosphere, 16, 259–275,Short summary
Sea-ice thickness retrieval from satellite altimeters relies on assumed sea-ice density values because density cannot be measured from space. We derived bulk densities for different ice types using airborne laser, radar, and electromagnetic induction sounding measurements. Compared to previous studies, we found high bulk density values due to ice deformation and younger ice cover. Using sea-ice freeboard, we derived a sea-ice bulk density parameterisation that can be applied to satellite data.
Mathieu Plante and L. Bruno Tremblay
The Cryosphere, 15, 5623–5638,Short summary
We propose a generalized form for the damage parameterization such that super-critical stresses can return to the yield with different final sub-critical stress states. In uniaxial compression simulations, the generalization improves the orientation of sea ice fractures and reduces the growth of numerical errors. Shear and convergence deformations however remain predominant along the fractures, contrary to observations, and this calls for modification of the post-fracture viscosity formulation.
Joey J. Voermans, Qingxiang Liu, Aleksey Marchenko, Jean Rabault, Kirill Filchuk, Ivan Ryzhov, Petra Heil, Takuji Waseda, Takehiko Nose, Tsubasa Kodaira, Jingkai Li, and Alexander V. Babanin
The Cryosphere, 15, 5557–5575,Short summary
We have shown through field experiments that the amount of wave energy dissipated in landfast ice, sea ice attached to land, is much larger than in broken ice. By comparing our measurements against predictions of contemporary wave–ice interaction models, we determined which models can explain our observations and which cannot. Our results will improve our understanding of how waves and ice interact and how we can model such interactions to better forecast waves and ice in the polar regions.
Marika M. Holland, David Clemens-Sewall, Laura Landrum, Bonnie Light, Donald Perovich, Chris Polashenski, Madison Smith, and Melinda Webster
The Cryosphere, 15, 4981–4998,Short summary
As the most reflective and most insulative natural material, snow has important climate effects. For snow on sea ice, its high reflectivity reduces ice melt. However, its high insulating capacity limits ice growth. These counteracting effects make its net influence on sea ice uncertain. We find that with increasing snow, sea ice in both hemispheres is thicker and more extensive. However, the drivers of this response are different in the two hemispheres due to different climate conditions.
Don Perovich, Madison Smith, Bonnie Light, and Melinda Webster
The Cryosphere, 15, 4517–4525,Short summary
During summer, Arctic sea ice melts on its surface and bottom and lateral edges. Some of this fresh meltwater is stored on the ice surface in features called melt ponds. The rest flows into the ocean. The meltwater flowing into the upper ocean affects ice growth and melt, upper ocean properties, and ocean ecosystems. Using field measurements, we found that the summer meltwater was equal to an 80 cm thick layer; 85 % of this meltwater flowed into the ocean and 15 % was stored in melt ponds.
Sönke Maus, Martin Schneebeli, and Andreas Wiegmann
The Cryosphere, 15, 4047–4072,Short summary
As the hydraulic permeability of sea ice is difficult to measure, observations are sparse. The present work presents numerical simulations of the permeability of young sea ice based on a large set of 3D X-ray tomographic images. It extends the relationship between permeability and porosity available so far down to brine porosities near the percolation threshold of a few per cent. Evaluation of pore scales and 3D connectivity provides novel insight into the percolation behaviour of sea ice.
Cyril Palerme and Malte Müller
The Cryosphere, 15, 3989–4004,Short summary
Methods have been developed for calibrating sea ice drift forecasts from an operational prediction system using machine learning algorithms. These algorithms use predictors from sea ice concentration observations during the initialization of the forecasts, sea ice and wind forecasts, and some geographical information. Depending on the calibration method, the mean absolute error is reduced between 3.3 % and 8.0 % for the direction and between 2.5 % and 7.1 % for the speed of sea ice drift.
Dongyang Fu, Bei Liu, Yali Qi, Guo Yu, Haoen Huang, and Lilian Qu
The Cryosphere, 15, 3797–3811,Short summary
Our results show three main sea ice drift patterns have different multiscale variation characteristics. The oscillation period of the third sea ice transport pattern is longer than the other two, and the ocean environment has a more significant influence on it due to the different regulatory effects of the atmosphere and ocean environment on sea ice drift patterns on various scales. Our research can provide a basis for the study of Arctic sea ice dynamics parameterization in numerical models.
Andrii Murdza, Arttu Polojärvi, Erland M. Schulson, and Carl E. Renshaw
The Cryosphere, 15, 2957–2967,Short summary
The strength of refrozen floes or piles of ice rubble is an important factor in assessing ice-structure interactions, as well as the integrity of an ice cover itself. The results of this paper provide unique data on the tensile strength of freeze bonds and are the first measurements to be reported. The provided information can lead to a better understanding of the behavior of refrozen ice floes and better estimates of the strength of an ice rubble pile.
H. Jakob Belter, Thomas Krumpen, Luisa von Albedyll, Tatiana A. Alekseeva, Gerit Birnbaum, Sergei V. Frolov, Stefan Hendricks, Andreas Herber, Igor Polyakov, Ian Raphael, Robert Ricker, Sergei S. Serovetnikov, Melinda Webster, and Christian Haas
The Cryosphere, 15, 2575–2591,Short summary
Summer sea ice thickness observations based on electromagnetic induction measurements north of Fram Strait show a 20 % reduction in mean and modal ice thickness from 2001–2020. The observed variability is caused by changes in drift speeds and consequential variations in sea ice age and number of freezing-degree days. Increased ocean heat fluxes measured upstream in the source regions of Arctic ice seem to precondition ice thickness, which is potentially still measurable more than a year later.
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
The Cryosphere, 15, 951–982,Short 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.
Ron Kwok, Alek A. Petty, Marco Bagnardi, Nathan T. Kurtz, Glenn F. Cunningham, Alvaro Ivanoff, and Sahra Kacimi
The Cryosphere, 15, 821–833,
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.
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.
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.
Abramoff, M., Magalhaes, P., and Ram, S.: Image Processing with ImageJ, Biophotonics Int., 11, 36–42, 2004.
Atkinson, M. and Bingman, C.: Elemental composition of commercial seasalts, J. Aquariculture Aquatic Sci., 8, 39–43, 1997.
Ball, C., Levick, A., Woolliams, E., Green, P., Dury, M., Winkler, R., Deadman, A., Fox, N., and King, M.: Effect of polytetrafluoroethylene (PTFE) phase transition at 19 °C on the use of Spectralon as a reference standard for reflectance, Appl. Optics, 52, 4806–4812, 2013.
Bond, T., Doherty, S., Fahey, D., Forster, P., Berntsen, T., DeAngelo, B., Flanner, M., Ghan, S., Kärcher, B., and Koch, D.: Bounding the role of black carbon in the climate system: A scientific assessment, J. Geophys. Res.-Atmos., 118, 5380–5552, 2013.
Boyer, T. P., Antonov, J. I., Baranova, O. K., Coleman, C., Garcia, H. E., Grodsky, A., Johnson, D. R., Locarnini, R. A., Mishonov, A. V., O'Brien, T. D., Paver, C. R., Reagan, J. R., Seidov, D., Smolyar, I. V., Zweng, M. M.: World Ocean Database 2013, Sydney Levitus, edited by: Alexey, M., NOAA Atlas NESDIS 72, 209 pp., https://doi.org/10.7289/V5NZ85MT, 2013.
Brandt, R. E., Warren, S. G., and Clarke, A. D.: A controlled snowmaking experiment testing the relation between black carbon content and reduction of snow albedo, J. Geophys. Res.-Atmos., 116, D08109, https://doi.org/10.1029/2010JD015330, 2011.
Bricaud, A., Claustre, H., Ras, J., and Oubelkheir, K.: Natural variability of phytoplanktonic absorption in oceanic waters: Influence of the size structure of algal populations, J. Geophys. Res., 109, C11010, https://doi.org/10.1029/2004JC002419, 2004.
Buist, I., Potter, S., Nedwed, T., and Mullin, J.: Herding surfactants to contract and thicken oil spills in pack ice for in situ burning, Cold Reg. Sci. Technol., 67, 3–23, https://doi.org/10.1016/j.coldregions.2011.02.004, 2011.
Clarke, A.: Integrating sandwich: a new method of measurement of the light absorption coefficient for atmospheric particles, Appl. Optics, 21, 3011–3020, https://doi.org/10.1364/AO.21.003011, 1982.
Clarke, A. and Noone, K.: Soot in the Arctic snowpack: A cause for perturbations in radiative transfer, Atmos. Environ., 19, 2045–2053, 1985.
Dang, C., Brandt, R. E., and Warren, S. G.: Parameterizations for narrowband and broadband albedo of pure snow and snow containing mineral dust and black carbon., J. Geophys. Res.-Atmos., 120, 5446–5468, 2015.
Doherty, S. J., Warren, S. G., Grenfell, T. C., Clarke, A. D., and Brandt, R. E.: Light-absorbing impurities in Arctic snow, Atmos. Chem. Phys., 10, 11647–11680, https://doi.org/10.5194/acp-10-11647-2010, 2010.
Eicken, H.: Sea ice-an introduction to its physics, biology, chemistry and geology, chap. From the microscopic, to the macroscopic, to the regional scale: growth, microstructure and properties of sea ice, Blackwell Science, London, 2003.
Flanner, M., Zender, C., Randerson, J., and Rasch, P.: Present-day climate forcing and response from black carbon in snow, J. Geophys. Res., 112, D11202, https://doi.org/10.1029/2006JD008003, 2007.
France, J. and King, M.: The effect of measurement geometry on recording solar radiation attenuation in snowpack (e-folding depth) using fibre-optic probes, J. Glaciol., 58, 417–418, https://doi.org/10.3189/2012JoG11J227, 2012.
France, J. L., King, M. D., Frey, M. M., Erbland, J., Picard, G., Preunkert, S., MacArthur, A., and Savarino, J.: Snow optical properties at Dome C (Concordia), Antarctica; implications for snow emissions and snow chemistry of reactive nitrogen, Atmos. Chem. Phys., 11, 9787–9801, https://doi.org/10.5194/acp-11-9787-2011, 2011.
France, J., Reay, H., King, M., Voison, D., Jacobi, H., Domine, F., Beine, H., Anastasio, C., MacArthur, A., and Lee-Taylor, J.: Hydroxyl Radical and NOx producion rates, black carbon concentrations and light-absorbing impurities in the snow from field measurements of light penetration and nadir reflectivity of on-shore and off-shore coastal Alaskan snow, J. Geophys. Res., 117, D00R12, https://doi.org/10.1029/2011JD016639, 2012.
Goldenson, N., Doherty, S. J., Bitz, C. M., Holland, M. M., Light, B., and Conley, A. J.: Arctic climate response to forcing from light-absorbing particles in snow and sea ice in CESM, Atmos. Chem. Phys., 12, 7903–7920, https://doi.org/10.5194/acp-12-7903-2012, 2012.
Grenfell, T. and Maykut, G.: The optical properties of ice and snow in the Arctic Basin, J. Glaciol., 18, 445–463, https://doi.org/10.1017/S0022143000021122, 1977.
Grenfell, T. and Perovich, D.: Radiation absorption coefficients of polycrystalline ice from 400–1400 nm, J. Geophys. Res., 86, 7447–7450, 1981.
Grenfell, T., Light, B., and Sturm, M.: Spatial distribution and radiative effects of soot in the snow and sea ice during the SHEBA experiment: The surface heat budget of Arctic ocen (SHEBA), J. Geophys. Res., 107, C10, https://doi.org/10.1029/2000JC000414, 2002.
Grenfell, T., Doherty, S., Clarke, A., and Warren, S.: Light absorption from particulate impurities in snow and ice determined by spectrophotometric analysis of filters, Appl. Optics, 50, 2037–2048, 2011.
Haas, C., Cottier, F., Smedsrud, L., and Thomas, D.: Multidisciplinary ice tank study shedding new light on sea ice growth processes, EOS, Transactions, 80, 507–513, 1999.
Hadley, O. and Kirchstetter, T.: Black carbon reduction of snow albedo, Nature Clim. Change, 2, 437–440, 2012.
Hansen, J. and Nazarenko, L.: Soot climate forcing via snow and ice albedos, P. Natl. Acad. Sci. USA, 101, 423, https://doi.org/10.1073/pnas.2237157100, 2004.
Hare, A., Wang, F., Barber, D., Geilfus, N., and Galley, R.: pH evolution in sea ice grown at an outdoor experimental facility, Marine Chem., 154, 46–54, https://doi.org/10.1016/j.marchem.2013.04.007, 2013.
Heilbronner, R. and Barrett, S.: Image analysis in Earth Sciences: microstructures and textures of earth materials, Springer Science and Business Media, 129, 520 pp., 2013.
Highwood, E. and Kinnersley, R.: When smoke gets in our eyes: The multiple impacts of atmospheric black carbon on climate, air quality and health, Environ. Int., 32, 560–566, 2006.
Holland, M., Bailey, D., Breigleb, B., Light, B., and Hunke, E.: Improved sea ice short wave radiation physics in CCSM4: The impact of melt ponds and aerosols on Arctic sea ice, J. Climate, 25, 1413–1430, 2012.
IPCC: IPCC Working Group 1 Fifth Assessment Report Summary for Policy Makers - Climate Change 2013: The Physical Science Basis., Tech. rep., IPCC, 2013.
Jacobson, M.: Strong radiative heating due to the mixing state of black carbon in atmospheric aerosols, Nature, 409, 695–697, 2001.
Jacobson, M.: 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.
Johnston, M. and Timco, G.: Temperature Changes in First Year Arctic Sea Ice During the Decay Process., in: Proceedings of the 16th IAHR International Symposium on Ice, 2, 194–202, 2002.
King, M., France, J., Fisher, F., and Beine, H.: Measurement and modelling of UV radiation penetration and photolysis rates of nitrate and hydrogen peroxide in Antarctic sea ice: An estimate of the production rate of hydroxyl radicals in first-year sea ice, J. Photoch. Photobio. A, 176, 39–49, 2005.
Krembs, C., Mock, T., and Gradinger, R.: A mesocosm study of physical-biological interactions in artificial sea ice: effects of brine channel surface evolution and brine movement on algal biomass, Polar Biology, 24, 356–364, 2001.
Lamare, M. L., Lee-Taylor, J., and King, M. D.: The impact of atmospheric mineral aerosol deposition on the albedo of snow & sea ice: are snow and sea ice optical properties more important than mineral aerosol optical properties?, Atmos. Chem. Phys., 16, 843–860, https://doi.org/10.5194/acp-16-843-2016, 2016.
Ledley, T. and Thompson, S.: Potential effect of nuclear war smokefall on sea ice, Clim. Change, 8, 155–171, 1986.
Lee-Taylor, J. and Madronich, S.: Calculation of actinic fluxes with a coupled atmosphere-snow radiative transfer model, J. Geophys. Res., 107, 4796, https://doi.org/10.1029/2002JD002084, 2002.
Libois, Q., Picard, G., France, J. L., Arnaud, L., Dumont, M., Carmagnola, C. M., and King, M. D.: Influence of grain shape on light penetration in snow, The Cryosphere, 7, 1803–1818, https://doi.org/10.5194/tc-7-1803-2013, 2013.
Light, B., Eicken, H., Maykut, G., and Grenfell, T.: The effect of included particulates on the spectral albedo of sea ice, J. Geophys. Res., 103, 27739–27752, 1998.
Light, B., Carns, R., and Warren, S.: “Albedo dome”: A method for measuring spectral flux-reflectance in a laboratory for media with long optical paths, Appl. Optics, 54, 5260–5269, 2015.
Malgrem, F., Institutt, G., Forskningsfond, S., and Fond, N.: The Norwegian Polar Expedition `Maud,' 1918–1925: Scientific Results, vol. 1, chap. On the properties of sea-ice, Gofys. Inst., Bergen, Norway, 1–67, 1927.
Marks, A. A. and King, M. D.: The effects of additional black carbon on the albedo of Arctic sea ice: variation with sea ice type and snow cover, The Cryosphere, 7, 1193–1204, https://doi.org/10.5194/tc-7-1193-2013, 2013.
Marks, A. A. and King, M. D.: The effect of snow/sea ice type on the response of albedo and light penetration depth (e-folding depth) to increasing black carbon, The Cryosphere, 8, 1625–1638, https://doi.org/10.5194/tc-8-1625-2014, 2014.
Marks, A., Lamare, M., and King, M.: Data for “Optical properties of sea ice doped with black carbon – an experimental and radiative-transfer modelling comparison”, available at: https://doi.org/10.5281/zenodo.1067150, 2017.
Mitchell, J.: Visual range in the polar regions with particular reference to the Alaskan Arctic, J. Atmos. Terr. Phys. Special Supplement, 195–211, 1957.
Mobley, C., Cota, G., Grenfell, T., Maffione, R., Pegau, W., and Perovich, D.: Modeling light propagation in sea ice, Geoscience and Remote Sensing, IEEE Transactions on, 36, 1743–1749, 1998.
Mock, T., Dieckmann, G., Haas, C., Krell, A., Tison, J., Belem, A., Papadimitriou, S., and Thomas, D.: Micro-optodes in sea ice: a new approach to investigate oxygen dynamics during sea ice formation, Aquat. Microb. Ecol., 29, 297–306, https://doi.org/10.3354/ame029297, 2002.
Mundy, C., Gosselin, M., Ehn, J., Belzile, C., Poulin, M., Alou, E., Roy, S., Hop, H., Lessard, S., Papakyriakau, T., Barber, D., and Stewart, J.: Characteristics of two distinct high-light acclimated algal communities during advanced stages of sea ice melt, Polar Biology, 34, 1869–1886, 2011.
Nomurai, D., Yoshikawa-Inoue, H., and Toyota, T.: The effect of sea-ice growth on air–sea CO2 flux in a tank experiment, Tellus B, 58, 2006.
Papadimitriou, S., Kennedy, H., Kattner, G., Dieckmann, G., and Thomas, D.: Experimental evidence for carbonate precipitation and CO2 degassing during sea ice formation, Geochem. Cosmochim. Ac., 68, 1749–1761, 2003.
Peltoniemi, J. I., Gritsevich, M., Hakala, T., Dagsson-Waldhauserová, P., Arnalds, Ø., Anttila, K., Hannula, H.-R., Kivekäs, N., Lihavainen, H., Meinander, O., Svensson, J., Virkkula, A., and de Leeuw, G.: Soot on Snow experiment: bidirectional reflectance factor measurements of contaminated snow, The Cryosphere, 9, 2323–2337, https://doi.org/10.5194/tc-9-2323-2015, 2015.
Perovich, D.: The Optical Properties of Sea Ice., US Army Corps of Engineers: Cold Regions Research and Engineering Laboratory, 96, 1996.
Perovich, D. and Grenfell, T.: Laboratory studies of the optical properties of young sea ice, J. Glaciol., 27, 331–346, https://doi.org/10.1017/S0022143000015410, 1981.
Perovich, D., Roesler, C., and Pegau, W.: Variability in Arctic sea ice optical properties, J. Geophys. Res., 103, 1193–1208, 1998.
Phillips, G. and Simpson, W.: Verification of snowpack radiation transfer models using actinometry, J. Geophys. Res., 110, D08306, https://doi.org/10.1029/2004JD005552, 2005.
Polach, R., Ehlers, S., and Kujala, P.: Model-Scale Ice-Part A: Experiments, Cold Reg. Sci. Technol., 94, 74–81, 2013.
Rabus, B. and Echelmeyer, K.: Increase of 10 m ice temperature: climate warming or glacier thinning?, J. Glaciol., 48, 279–286, https://doi.org/10.3189/172756502781831430, 2002.
Ramanathan, V. and Carmichael, G.: Global and regional climate changes due to black carbon, Nature Geosci., 1, 221–227, 2008.
Rand, J. and Mellor, M.: Ice-coring augers for shallow depth sampling, Tech. Rep. 85-21, CRREL, Washington, 1985.
Reay, H., France, J., and King, M.: Decreased albedo, efolding depth and photolytic OH radical and NO2 production with increasing black carbon content in Arctic snow., J. Geophys. Res., 117, D00R20, https://doi.org/10.1029/2011JD016630, 2012.
Stamnes, K., Tsay, S., Jayaweera, K., and Wiscombe, W.: Numerically stable algorithm for discrete-ordinate-method radiative transfer in multiple scattering and emitting layered media, Appl. Optics, 27, 2502–2509, 1988.
Timco, G. and Frederking, R.: A review of sea ice density, Cold Reg. Sci. Technol., 24, 1–6, 1996.
Weeks, W.: On sea ice, chap. Snow, pp. 417–433, University of Alaska Press, 2010.
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.
Arctic sea ice extent is declining rapidly. Prediction of sea ice trends relies on sea ice...