Sea Ice

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Better constraints on the sea-ice state using global sea-ice data assimilation

Better constraints on the sea-ice state using global sea-ice data assimilation

close to each other. Furthermore, these simulations show the same large scale geo- graphical distribution of ice thickness. However, during ON07, as for the MA07 cam- paign (Figs. 8, 7), ice is thinner in the centre of the Arctic Basin, by up to 70 cm near the North Pole (unobserved area due to satellite orbit) (Fig. 6b, d). The intrusion of thick sea ice in the Beaufort sea is also less pronounced in FB, which is more realistic.

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Sea ice dynamics influence halogen deposition to Svalbard

Sea ice dynamics influence halogen deposition to Svalbard

Abstract. Sea ice is an important parameter in the climate system and its changes impact upon the polar albedo and at- mospheric and oceanic circulation. Iodine (I) and bromine (Br) have been measured in a shallow firn core drilled at the summit of the Holtedahlfonna glacier (Northwest Spitsber- gen, Svalbard). Changing I concentrations can be linked to the March–May maximum sea ice extension. Bromine en- richment, indexed to the Br / Na sea water mass ratio, appears to be influenced by changes in the seasonal sea ice area. I is emitted from marine biota and so the retreat of March–May sea ice coincides with enlargement of the open-ocean surface which enhances marine primary production and consequent I emission. The observed Br enrichment could be explained by greater Br emissions during the Br explosions that have been observed to occur mainly above first year sea ice during the early springtime. In this work we present the first compari- son between halogens in surface snow and Arctic sea ice ex- tension. Although further investigation is required to charac- terize potential depositional and post-depositional processes, these preliminary findings suggest that I and Br can be linked to variability in the spring maximum sea ice extension and seasonal sea ice surface area.
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Numerical modelling of thermodynamics and dynamics of sea ice in the Baltic Sea

Numerical modelling of thermodynamics and dynamics of sea ice in the Baltic Sea

Because of the high spatial and temporal variability of the Baltic sea ice properties, observational data representative of larger areas, and thus appropriate for verifying the results of numerical models are difficult to acquire. As opposed to single-point measurements (at stations that are usually lo- cated directly at the coast, i.e., within the fast-ice zone) that very often do not meet this requirement, application of re- mote sensing techniques supplies valuable spatially resolved information. However, the use of satellite or airborne data in combination with numerical sea-ice modelling in the Baltic Sea has been rather limited to date. Moreover, verification of modelling results is usually qualitative only, and/or lim- ited to a few arbitrarily selected situations. For example, Zhang (2000) visually compared results provided by his nu- merical model with the Finnish Institute of Marine Research (FIMR) ice charts and the Synthetic Aperture Radar (SAR) images. Meier et al. (1999) and Meier (2002) qualitatively compared their modelled ice concentrations and thickness with the Swedish Meteorological and Hydrological Institute (SMHI) ice charts for a number of selected situations. In a similar manner, Rudolph and Lehmann (2006) used ice
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The effects of additional black carbon on Arctic sea ice surface albedo: variation with sea ice type and snow cover

The effects of additional black carbon on Arctic sea ice surface albedo: variation with sea ice type and snow cover

To establish the extent to which a snow cover will mitigate the albedo e ff ect of black carbon in sea ice two di ff erent Arctic snow types (also described by Grenfell and Maykut, 1977)[r]

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Laboratory study of initial sea-ice growth: properties of grease ice and nilas

Laboratory study of initial sea-ice growth: properties of grease ice and nilas

To gain a better understanding of initial sea-ice formation in quiescent and turbulent water, we conducted a tank study in which we simulated the early stages of sea-ice formation under quiescent conditions, in a wave field and under the in- fluence of wind and a current. These experiments allowed us to identify differences and similarities of initial sea-ice for- mation in quiescent and turbulent water. Because of the rel- atively low turbulence that we were able to produce in the tank, in our experiments grease ice only prevailed for a few hours. Our measurements are therefore most closely resem- bling field conditions with rather weak turbulent mixing, as are for example observed in nature once a grease-ice layer has become thick enough to effectively dampen mechanical mixing to greater depth.
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Sea ice inertial oscillations in the Arctic Basin

Sea ice inertial oscillations in the Arctic Basin

To further test the link between the state of the sea ice cover and its cohesiveness, as expressed by the am- plitude of the inertial oscillations, we perform a correla- tion analysis between the M values and the open water concentration 1 − α, where α is the sea ice concentration dataset collected by NSIDC (http://nsidc.org/data/seaice/ index.html) (Comiso, 1990, updated 2012). This dataset has a spatial resolution of 25 km and consists of ice concentration values sampled every two days from 1979 to 1987 (SMMR), and every day since 1987 (SSM/I). For each value of M, we search for the corresponding value of open water concentra- tion as the closest sample in time and space: a 1 − α value is associated with a given M value if we can find, for the same day of record, within one day during the period 1979–1987, a sample that is closer than 25 km. The corresponding corre- lation coefficient R is equal to 0.245( ±0.002). This positive correlation is statistically significant as R is more than 120 times greater than the standard deviation obtained in the null hypothesis at no correlation (numerically computed by ran- domly reshuffling the M and 1 −α values). This is consistent with stronger oscillations characteristic of a less compact sea ice cover.
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Parameterization of atmosphere–surface exchange of CO<sub>2</sub> over sea ice

Parameterization of atmosphere–surface exchange of CO<sub>2</sub> over sea ice

For fluxes over terrestrial areas Rc can be estimated from simple parameterizations based on a constant resistant to complex parameterizations taking soil texture, surface wet- ness and leave stomatal opening or closure and processes within the stomatal into account (Duyzer and Fowler, 1994; Hicks et al., 1987; Sutton et al., 2001). The Rc for sea ice surfaces could be parameterized in a similar way using brine opening instead of stomatal opening and processes within the brine similar to processes within the stomatal. However in or- der to carry out a thorough analysis of Rc for sea ice and to suggest a complex parameterization, detailed information of the surface properties with a higher time resolution is needed. Nevertheless below we look in to some of the controlling pa- rameters and suggest more detailed studies on these parame- ters in order to start developing a parameterization of Rc. 6.1 Parameters influencing the ice surface pCO 2
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Simulation of the satellite radar altimeter sea ice thickness retrieval uncertainty

Simulation of the satellite radar altimeter sea ice thickness retrieval uncertainty

Snow depths on Antarctic sea ice are large compared to the Arctic, and temporary melt may occur even in winter, which means that the snow packs can have large vertical diversity. However, liquid water and layering in the snow-pack are also relevant for Arctic sea ice, especially during spring. Three measured snow profiles in the Weddell Sea were selected due to their different backscatter intensity in QuikScat SeaWinds

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Sea ice draft in the Weddell Sea, measured by upward looking sonars

Sea ice draft in the Weddell Sea, measured by upward looking sonars

Abstract. The presented database contains time-referenced sea ice draft values from upward looking sonar (ULS) measurements in the Weddell Sea, Antarctica. The sea ice draft data can be used to infer the thickness of the ice. They were collected during the period 1990–2008. In total, the database includes measurements from 13 locations in the Weddell Sea and was generated from more than 3.7 million measurements of sea ice draft. The files contain uncorrected raw drafts, corrected drafts and the basic parameters measured by the ULS. The measurement principle, the data processing procedure and the quality control are described in detail. To account for the unknown speed of sound in the water column above the ULS, two correction methods were applied to the draft data. The first method is based on defining a reference level from the identification of open water leads. The second method uses a model of sound speed in the oceanic mixed layer and is applied to ice draft in austral winter. Both methods are discussed and their accuracy is estimated. Finally, selected results of the processing are presented. The data can be downloaded from doi:10.1594/PANGAEA.785565.
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A record of Antarctic sea ice extent in the Southern Indian Ocean for the past 300 yr and its relationship with global mean temperature

A record of Antarctic sea ice extent in the Southern Indian Ocean for the past 300 yr and its relationship with global mean temperature

2010) and the differences have been linked to large scale atmospheric circulation pat- terns. For example, Raphael (2007) shows a relationship between atmospheric zonal wave three and variability of Antarctic sea ice concentration, with a strong positive cor- relation between the two occurring in our study area off the Amery ice shelf (70–80 ◦ E). The proxy record for the 70–100 ◦ E sector also shows a subsequent increase in SIE

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Morphology and distribution of liquid inclusions in young sea ice as imaged by magnetic resonance

Morphology and distribution of liquid inclusions in young sea ice as imaged by magnetic resonance

the extended warm period in the middle of the experiment (Fig. 2). However, it may be that brine contained in this region of the core sample resides between transitional and/or columnar sea ice crystals (e.g. Eicken et al., 2000) though the MR images presented here may not be high resolution enough to show them as a result of the very short scanning time employed. The increase in bulk salinity and brine volume

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Polynyas in a dynamic-thermodynamic sea-ice model

Polynyas in a dynamic-thermodynamic sea-ice model

The model domain is a bay, 135 km long and 75 km wide at 2.5 km resolution (see Fig. 3), similar to the setup Bjornsson et al. (2001) used. At such a high resolution we must assume that on average the ice floes being modelled are no larger than 250 m in diameter. This is because a scale of approximately 10 grain widths can generally be modelled without resolv- ing each individual element using a granular model (Savage, 1998). McNutt and Overland (2003) state that at the multi- floe scale (approximately 2–10 km) sea ice behaves like a granular material, so the granular model should be ideal for a simulation at that scale. We can assume the ice floes in the polynya interior are no larger than pancake ice, which is not much larger than 3 m in diameter. The ice in the polynya in- terior, and by extension the consolidated ice, is therefore well within the maximum allowed floe size. However, assuming that individual floes are no larger than 250 m in diameter may not always be valid for the thick initial ice, depending on the geographical location of the polynya as well as the time of year. This is only a minor concern for this study since it fo- cuses on the steady state solution where the thick initial ice does not play a role (as it has drifted out of the model do- main).
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High-resolution mineral dust and sea ice proxy records from the Talos Dome ice core

High-resolution mineral dust and sea ice proxy records from the Talos Dome ice core

centennial resolution reaching back 150 thousand years be- fore present (ka BP), where present refers to 1950 AD. The new proxy data from TALDICE based on continuous flow analysis (CFA) techniques is compared to three other East Antarctic ice core records in the following, all covering at least the last 150 ka (a map of Antarctica with all investi- gated ice core sites is shown in Fig. 1; Table 1 provides fur- ther information about the sites). These are the two deep ice cores drilled within the European Project for Ice Coring in Antarctica (EPICA) at Dome C (EDC) (EPICA, 2004) and at Kohnen station in Dronning Maud Land (EDML) (EPICA, 2006), as well as the Vostok ice core (Petit et al., 1999). Fischer et al. (2007a) have already shown that a comparison of nssCa 2+ records of different ice cores can reveal substan- tial information about the sources and transport mechanisms of dust deposited on the East Antarctic plateau. Here we ex- tend the findings by Fischer et al. (2007a) with a multi-site comparison of nssCa 2+ ice core records in centennial res- olution, complemented with model data of dust deposition on the East Antarctic plateau available for the Last Glacial Maximum (LGM) and the Holocene. In addition, the ssNa + records of the different drill sites give indications on the evo- lution of local sea ice coverage in the Atlantic Ocean, In- dian Ocean, and Ross Sea sector of the SO during the past 150 ka BP.
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Comparison of automatic segmentation of full polarimetric SAR sea ice images with manually drawn ice charts

Comparison of automatic segmentation of full polarimetric SAR sea ice images with manually drawn ice charts

Acknowledgements. The authors acknowledge Vera Lund and Trond Robertsen of the Norwe- gian Ice Service for their analysis of sea ice stage of development and Thomas Kræmer at the University of Tromsø for advice in the geocoding process. We are grateful to the captain and crew onboard the Norwegian coast guard vessel Svalbard and the Airlift pilots and technicians onboard AS 350 and Dauphin for their assistance during the research cruise. This project was

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Seasonal cycle of solar energy fluxes through Arctic sea ice

Seasonal cycle of solar energy fluxes through Arctic sea ice

ble between October and March, (2) solar surface radiation dominates the under-ice light conditions from April to June, because transmittance increases only slowly, while surface irradiance determines most of the observed changes and variability, (3) dur- ing summer (July to September), energy fluxes depend mainly on the sea ice type, showing large differences in transmittance between FYI and MYI.

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The origin of sea salt in snow on Arctic sea ice and in coastal regions

The origin of sea salt in snow on Arctic sea ice and in coastal regions

We could never come to the conclusion that most of the ions in surface snow came from upward migration from sea ice. This is in part because our selection of sampling sites was biased towards thicker snow banks, subjectively viewed as more interesting. Furthermore, dry deposition of SO 2− 4 and Ca 2+ onto surface layers can rapidly obscure any sea salt chemical signature and make the detection of upward migra- tion in surface layers difficult. More case studies are needed, and we suggest that time series of vertical profiles on sea ice, over several weeks to months, would shed additional light on the actual impact of upward migration on surface snow composition.
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The melt pond fraction and spectral sea ice albedo retrieval from MERIS data: validation and trends of sea ice albedo and melt pond fraction in the Arctic for years 2002&ndash;2011

The melt pond fraction and spectral sea ice albedo retrieval from MERIS data: validation and trends of sea ice albedo and melt pond fraction in the Arctic for years 2002&ndash;2011

not allocate a specific field for details of this kind and they were sometimes (but not al- ways) mentioned in the comments, and for HOTRAX cruise such information was not available at all. At the same time, these details are helpful for the validation of the MPD algorithm. Spectral reflectance of frozen and snow covered ponds are close to that of sea ice within the MERIS spectral range, and melted through ponds have the spectral

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Decadal trends in the Antarctic sea ice extent ultimately controlled by ice–ocean feedback

Decadal trends in the Antarctic sea ice extent ultimately controlled by ice–ocean feedback

Abstract. The large natural variability of the Antarctic sea ice is a key characteristic of the system that might be re- sponsible for the small positive trend in sea ice extent ob- served since 1979. In order to gain insight of the processes responsible for this variability, we have analysed in a con- trol simulation performed with a coupled climate model a positive ice–ocean feedback that amplifies sea ice variations. When sea ice concentration increases in a region, in particu- lar close to the ice edge, the mixed layer depth tends to de- crease. This can be caused by a net inflow of ice, and thus of freshwater, that stabilizes the water column. A second stabi- lizing mechanism at interannual timescales is associated with the downward salt transport due to the seasonal cycle of ice formation: brine is released in winter and mixed over a deep layer while the freshwater flux caused by ice melting is in- cluded in a shallow layer, resulting in a net vertical transport of salt. Because of this stronger stratification due to the pres- ence of sea ice, more heat is stored at depth in the ocean and the vertical oceanic heat flux is reduced, which contributes to maintaining a higher ice extent. This positive feedback is not associated with a particular spatial pattern. Consequently, the spatial distribution of the trend in ice concentration is largely imposed by the wind changes that can provide the initial per- turbation. A positive freshwater flux could alternatively be the initial trigger but the amplitude of the final response of the sea ice extent is finally set up by the amplification related to the ice–ocean feedback. Initial conditions also have an in- fluence as the chance to have a large increase in ice extent is higher if starting from a state characterized by a low value.
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High-resolution ice nucleation spectra of sea-ice bacteria: implications for cloud formation and life in frozen environments

High-resolution ice nucleation spectra of sea-ice bacteria: implications for cloud formation and life in frozen environments

the atmosphere. In this paper, we examined the ice nucleation activity (INA) of sev- eral representative Arctic and Antarctic sea-ice bacterial isolates and a polar Colwellia phage virus. High-resolution ice nucleation spectra were obtained for droplets contain- ing bacterial cells or virus particles using a free-fall freezing tube technique. The frac- tion of frozen droplets at a particular droplet temperature was determined by measuring

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Corrigendum to "A combined approach of remote sensing and airborne electromagnetics to determine the volume of polynya sea ice in the Laptev Sea" published in The Cryosphere, 7, 947&minus;959, 2013

Corrigendum to "A combined approach of remote sensing and airborne electromagnetics to determine the volume of polynya sea ice in the Laptev Sea" published in The Cryosphere, 7, 947&minus;959, 2013

Fig. 5. Map with all HEM sea-ice thickness profiles. Northern and southern profiles flown on 14 and 16 April are coded 14a–m and 16a–h, respectively. Circle colors refer to the polynya events in which the respective surveyed sea ice formed (for color code, see Table 1). The yellow lines on the SAR map show a classification of the survey area in zones of the same age. The blue colors refer to the mean thickness of the corresponding HEM cross profile.

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