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neXtSIM: a new Lagrangian sea ice model

neXtSIM: a new Lagrangian sea ice model

portant as they both drive large scale sea ice drift and deformation patterns (e.g. Weiss et al., 2009). These processes are the expression of the mechanical damage of the ice pack which, as a result, looks more like an assembly of plates (> O(1 km)) and floes (< O(100 m)) than an intact solid plate. In addition to the quick damaging processes the slow formation of new ice is also important. Indeed, new ice formed in fractures

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The Finite Element Sea Ice-Ocean Model (FESOM) v.1.4: formulation of an ocean general circulation model

The Finite Element Sea Ice-Ocean Model (FESOM) v.1.4: formulation of an ocean general circulation model

Both Laplacian and biharmonic momentum friction op- erators are used in large-scale ocean simulations, and there is no first principle motivating either form. With respect to the dissipation scale-selectivity, the biharmonic operator is favourable compared to the Laplacian operator as it induces less dissipation at the resolved scales and concentrates dissi- pation at the grid scale (Griffies and Hallberg, 2000; Griffies, 2004). Large et al. (2001) and Smith and McWilliams (2003) proposed an anisotropic viscosity scheme by distinguishing the along and cross-flow directions in strong jets in order to reduce horizontal dissipation while satisfying the numerical constraints. Larger zonal viscosities were used in the equa- torial band to maintain numerical stability in the presence of strong zonal currents, and larger meridional viscosities were employed along the western boundaries to resolve the Munk boundary layer (Munk, 1950), while the meridional viscos- ity remained small in the equatorial band to better capture the magnitude and structure of the equatorial current. This approach was adopted in the previous GFDL climate model (Griffies et al., 2005), while isotropic viscosities are restored in a new GFDL Earth System Model to “allow more vigor- ous tropical instability wave activity at the expense of adding zonal grid noise, particularly in the tropics” (Dunne et al., 2012).
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A method for sea ice thickness and concentration analysis based on SAR data and a thermodynamic model

A method for sea ice thickness and concentration analysis based on SAR data and a thermodynamic model

HIGHTSI simulations were carried out at each of the 3111 grid cells. Each model run starts 1 December and lasts until 31 May. The simulations are initialized with a thin ice layer (0.01 m) at each grid cell. The ice thickness is calculated as a mean over the ice-covered area in the grid cell. During the simulation, if the external weather data do not favor ice growth, HIGHTSI maintains the ice thickness of the previ- ous time step. Off the coastal land-fast ice zone, ice drifts significantly in the GSL. The impact of ice drift is taken into account by incorporating sea ice concentration (A) derived from SAR data into the HIGHTSI model (see Sect. 4). When a grid cell is at least partly covered by ice (A > 10 %), the ice growth is calculated by applying the atmospheric forcing at that particular grid cell. If the ice concentration at a certain grid cell is reduced below 10 %, the ice thickness will remain at the value of the previous time step. The calculation of ice growth is resumed once the ice concentration is again larger than 10 %. This approach implicitly excludes new ice forma- tion in open leads. In winter season, however, open leads or polynyas cover a small fraction of the GSL. In the GSL, the fresh water runoff drives circulation in the Gulf, and the up- per layer ocean tends to be less saline and cold in early winter (approximately as in the estuary). As a first order estimation, we assume the oceanic heat flux is 2 W m −2 in early winter. Far from the cost, the oceanic heat flux is parametrized as a function of the SAR-based ice concentration. It is assumed to increase from 2 W m −2 to 6 W m −2 when ice concentra- tion decreases from 100 to 10 %. This assumption is related to the low latitudes of 46–52 ◦ N, where increasing amounts
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Sea ice biogeochemistry: a guide for modellers.

Sea ice biogeochemistry: a guide for modellers.

Sea ice is a fundamental component of the climate system and plays a key role in polar trophic food webs. Nonetheless sea ice biogeochemical dynamics at large temporal and spatial scales are still rarely described. Numerical models may potentially contribute integrating among sparse observations, but available models of sea ice biogeochemistry are still scarce, whether their relevance for properly describing the current and future state of the polar oceans has been recently addressed. A general methodology to develop a sea ice biogeochemical model is presented, deriving it from an existing validated model application by extension of generic pelagic biogeochemistry model parameterizations. The described methodology is flexible and considers different levels of ecosystem complexity and vertical representation, while adopting a strategy of coupling that ensures mass conservation. We show how to apply this methodology step by step by building an intermediate complexity model from a published realistic application and applying it to analyze theoretically a typical season of first-year sea ice in the Arctic, the one currently needing the most urgent understanding. The aim is to (1) introduce sea ice biogeochemistry and address its relevance to ocean modelers of polar regions, supporting them in adding a new sea ice component to their modelling framework for a more adequate representation of the sea ice-covered ocean ecosystem as a whole, and (2) extend our knowledge on the relevant controlling factors of sea ice algal production, showing that beyond the light and nutrient availability, the duration of the sea ice season may play a key-role shaping the algal production during the on going and upcoming projected changes.
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Quantification of ice production in Laptev Sea polynyas and its sensitivity to thin-ice parameterizations in a regional climate model

Quantification of ice production in Laptev Sea polynyas and its sensitivity to thin-ice parameterizations in a regional climate model

The rate of sea-ice growth strongly depends on the energy fluxes at the ice or ocean surface. If the total atmospheric heat flux is negative, the ocean loses heat either directly to the atmosphere or via conduction through an existing sea-ice cover. In the former case frazil ice forms, which aggregates subsequently to a new thin-ice layer under calm conditions. In the latter case basal freezing occurs to balance this heat loss. Most of the heat loss from the ocean occurs over open water or thin-ice areas, such as leads and polynyas, within an otherwise compact sea-ice cover (Smith et al., 1990; Morales Maqueda et al., 2004). Although the fraction of such areas in polar oceans is relatively small during winter, they are of ma- jor importance for the heat budget of the atmospheric bound- ary layer (ABL) (Heinemann and Rose, 1990; Haid et al., 2015) and the ocean circulation, such as the Arctic circumpo- lar boundary current (e.g. Aksenov et al., 2011; Rudels et al., 1999).
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Capabilities and performance of Elmer/Ice, a new-generation ice sheet model

Capabilities and performance of Elmer/Ice, a new-generation ice sheet model

Abstract. The Fourth IPCC Assessment Report concluded that ice sheet flow models, in their current state, were un- able to provide accurate forecast for the increase of polar ice sheet discharge and the associated contribution to sea level rise. Since then, the glaciological community has un- dertaken a huge effort to develop and improve a new genera- tion of ice flow models, and as a result a significant number of new ice sheet models have emerged. Among them is the parallel finite-element model Elmer/Ice, based on the open- source multi-physics code Elmer. It was one of the first full- Stokes models used to make projections for the evolution of the whole Greenland ice sheet for the coming two cen- turies. Originally developed to solve local ice flow problems of high mechanical and physical complexity, Elmer/Ice has today reached the maturity to solve larger-scale problems, earning the status of an ice sheet model. Here, we summarise almost 10 yr of development performed by different groups. Elmer/Ice solves the full-Stokes equations, for isotropic but also anisotropic ice rheology, resolves the grounding line dynamics as a contact problem, and contains various basal friction laws. Derived fields, like the age of the ice, the strain rate or stress, can also be computed. Elmer/Ice in- cludes two recently proposed inverse methods to infer badly known parameters. Elmer is a highly parallelised code thanks to recent developments and the implementation of a block
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Sea ice inertial oscillations in the Arctic Basin

Sea ice inertial oscillations in the Arctic Basin

In this paper, we propose a method to quantify the magni- tude of inertial oscillations from Lagrangian (buoys) trajecto- ries (Sect. 3). We then apply this methodology to the Interna- tional Arctic Buoy Program (IABP) dataset covering 30 yr of data in the Arctic Ocean (Sect. 4). As shown below, this anal- ysis is in full agreement with the above expectations: inertial oscillations are very weak or absent in a highly cohesive ice cover, such as in winter within the central Arctic, but are well developed in summer at the periphery of the basin, i.e. in re- gions of less concentrated, loose ice. In addition, a signifi- cant strengthening (on average) of these oscillations is ob- served, suggesting a mechanical weakening of the Arctic sea ice cover. This is confirmed in Gimbert et al. (2012), where a simple ocean-sea ice coupled dynamical model explains these seasonal, geographical and multi-annual variations of inertial oscillation magnitude in term of changes within sea ice internal mechanical properties, through an associating de- crease of sea ice internal friction in recent years.
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Polynyas in a dynamic-thermodynamic sea-ice model

Polynyas in a dynamic-thermodynamic sea-ice model

Here we expand on the work done by Bjornsson et al. (2001) and compare the granular model to the more com- mon viscous-plastic model of Hibler (1979) and the lesser known modified Coulombic yield curve by Hibler and Schul- son (2000) in a setting similar to that used by Bjornsson et al. (2001). The granular model results are used to assess the out- come from the other two yield curves. Secondly, we consider formulations by Hibler (1979) and Mellor and Kantha (1989) for the thickness of newly formed ice. The former formula- tion was used by Kern et al. (2005) and Marsland et al. (2004) and the latter by Smedsrud et al. (2006). Finally, we use the collection depth parametrisation of Winsor and Bj¨ork (2000) to parametrise the new-ice thickness. Thus we address the important points of a polynya simulation; first the behaviour of the consolidated ice, which is determined by the rheology, and secondly ice formation inside the polynya, determined by the new-ice thickness parametrisation.
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Greenland Ice Sheet contribution to sea-level rise from a new-generation ice-sheet model

Greenland Ice Sheet contribution to sea-level rise from a new-generation ice-sheet model

marginal extent between the three perturbation experiments detailed below lead to only small differences in the total SMB after one century (Fig. 8c). These differences are one order of magnitude lower than those between the various climate models (Fettweis et al., 2008). Other retro-actions could arise from surface elevation changes but this could be constrained more precisely only by coupling ice-sheet and climate models.

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The land-ice contribution to 21st-century dynamic sea level rise

The land-ice contribution to 21st-century dynamic sea level rise

In this study we develop projections of DSL change asso- ciated with new plausible scenarios of land-based ice melt. We assess two ice melt scenarios developed under the aus- pices of the European Union ice2sea project which include updated projections of the Glacier and Ice Cap (G&IC) contribution and Greenland and Antarctic ice sheet fresh- water contributions. The ice sheet components are derived from simplified simulations which include information about likely regions of glacial dynamic instability. The spatially and temporally varying glacial freshwater fluxes are applied in simulations with the HadCM3-coupled climate model (Gordon et al., 2000). The objective is to determine the de- tectability of DSL changes from the addition of these rel- atively small freshwater flux anomalies. We consider the role of this additional freshwater under both pre-industrial radiative forcing and under the Special Report on Emis- sions Scenarios (SRES) A1B greenhouse gas warming sce- nario (IPCC, 2000), which is usually regarded as a medium business-as-usual emissions scenario.
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The southern hemisphere at glacial terminations: insights from the Dome C ice core

The southern hemisphere at glacial terminations: insights from the Dome C ice core

around Antarctica, extending to over a thousand kilometres from the coast into the Southern Ocean (Gersonde et al., 2005). Sea salt aerosol produced at the distant margin of the sea ice cover will need to be transported over such long distances before reaching Dome C. However, it has been shown that the atmospheric sea salt aerosol concentration rapidly decreases with increasing transport distance, with only a small

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Sea ice detection with space-based LIDAR

Sea ice detection with space-based LIDAR

With the finer resolution of the CALIPSO footprint (90 m diameter, spaced 335 m apart) and its ability to acquire measurements during both daytime and nighttime orbit seg- ments and in the presence of clouds, the CALIPSO sea ice product provides fine-scale information on mixed phase scenes and can be used to assess/validate the estimates of sea-ice concentration currently provided by passive sensors. This paper describes

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Constraining projections of summer Arctic sea ice

Constraining projections of summer Arctic sea ice

Table 1 lists the 29 ESMs and GCMs used for this study, selected on the requirement that they archive sea ice fields up to 2100 (a final sample of ∼ 35 models is expected when all simulations are uploaded onto the repository). Out of the existing climate forcing scenarios, we only retain two “representative concentration pathways” (RCPs, Moss et al., 2010): RCP4.5 and RCP8.5. The radiative forcing in RCP8.5 increases nearly steadily over the 21st century to peak at +8.5 W m −2 in 2100 relative to pre-industrial levels. In RCP4.5, the increase is also nearly linear up to 2060, and then eventually flattens out so that a net value of +4.5 W m −2 is reached in 2100 (van Vuuren et al., 2011). Because of the much smaller population of available models under RCP2.6 and RCP6.0, these two other scenarios are not discused here. For each simulation, we derive three quantities from the monthly sea ice fields on the model native grid: the sea ice extent (calculated as the area of grid cells comprising at least 15 % of ice); the total sea ice volume (sum, over the grid cells, of the grid cell area multiplied by the mean thickness including open water), and the thin ice extent, which is the extent of sea ice with mean grid cell thickness between 0.01 and 0.5 m. Working on the original grid is a well-founded choice, (1) because the grid is part of the model experimental design, and (2) because no ice is artificially created/removed due to interpolation onto a common grid, with a prescribed land-sea mask. However, as the area covered by ocean in the Arctic (defined here north of 65 ◦ N) is different on each model grid (∼ 1.8 million km 2 difference between the ex- tremes), care must be taken when the output is analysed: for example, a model may misrepresent the observed sea ice ex- tent due to too coarse a grid resolution or to an inaccurate representation of coastlines and land distribution. We there- fore consider the land-sea mask as an important property of the CMIP5 simulations.
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Insolation and gacial meltwater influence on sea‐ice and circulation variability in the Northeastern Labrador Sea during the last glacial period

Insolation and gacial meltwater influence on sea‐ice and circulation variability in the Northeastern Labrador Sea during the last glacial period

Regime 1 is defined by overall very low IRD fluxes and can be divided into two sub‐intervals; A) the older sub‐interval spanning late MIS4 across the MIS4/3 transition to GI‐17.2 to GI‐16; and B) the younger sub‐ interval encompassing the interval from GI‐16 to GI‐13 (Figure 4). The older sub‐interval is characterized by relatively low foraminifer fluxes, a shift from depleted (GS‐18) to high δ 18 O values (MIS4/3 transition) and %Np values around 98 %. Values between 98 and 94 % of N. pachyderma in the planktic assemblage are associated with the location of the oceanic polar front nearby which likely represents the extent of the summer seaice, as known from the Nordic Seas (Johannessen et al., 1994; Pflaumann et al., 1996). Today, the polar waters along the East ‐Greenland margin are dominated by the EGC which exports icebergs and seaice from the Arctic Ocean and Nordic Seas. South of the Denmark Strait, the polar waters are seasonally seaice free allowing for plankton productivity. Combining the %Np data and the foraminifer flux we inter- pret that our core location was covered by near ‐perennial seaice during the older part of regime 1 although a few icebergs were transported by the EGC which discharged some IRD. Although the foraminifer and IRD concentrations are high (Figure 4) the very low sedimentation rates smear those signals out in terms of their flux. Low sedimentation rates probably reflect a closed seaice cover, which limited vertical particle flux and iceberg transport (Dowdeswell et al., 1998). It might also be indicative for times with a weak boundary cur- rent at the depth of the site. The enhanced Arctic freshwater export by the EGC associated with H6, indicated by the depleted δ 18 O values, could be another reason for the lower surface productivity and hence relatively low foraminifer flux. Just before and after the H6 meltwater peak (GI‐18 and GI‐17), the 22CC foraminifer flux increases to moderate levels when the δ 18 O values increase, apparently as a result of diminished fresh-
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On the influence of model physics on simulations of Arctic and Antarctic sea ice

On the influence of model physics on simulations of Arctic and Antarctic sea ice

We have investigated the sensitivity of an ocean-sea ice model to the representation of physics in its sea ice com- ponent: two hindcast simulations have been studied over the period 1983–2007, for both Arctic and Antarctic sea ice, with an ocean General Circulation Model driven by atmospheric reanalyses and various climatologies. For the purposes of this study, we have developed a set of comprehensive met- rics designed for sea ice. These metrics involve the main sea ice characteristics (i.e. concentration, thickness and drift), fo- cus both on regional and global scales, and take mean state as well as variability into account. We chose to define all our metrics as the ratio between the actual model versus ob- servations error, and a typical, or acceptable error. The use of our metrics can extend beyond the purpose of this study and could be full of interest for assessing the performance of fully coupled GCMs in the polar regions in terms of mean sea ice cover and variability.
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Pacific walrus (Odobenus rosmarus divergens) resource selection in the Northern Bering Sea.

Pacific walrus (Odobenus rosmarus divergens) resource selection in the Northern Bering Sea.

Walruses selected lower ice concentrations within the mostly high concentrations available to them in the choice sets (quartiles: 76%, 93%, and 99%). The need for sufficient ice concentration and thickness to rest upon, and simultaneously, for open water that allows access to feeding on the underlying benthic fauna, likely influences this pattern. The location of low ice concentration in early spring is subject to regional weather events, wind direction, and current patterns, all combining to influence resource partitioning for predator access to prey patches. During our studies in March–April, sea ice was thinner in 2008 and 2009 than in 2006 (unpublished plots of ice thickness using data from the U.S. National Ice Center, http://www.natice.noaa.gov/, accessed 22 Sep 2011), which coincided with the lack of movement of tagged walruses across the northern part of our study area in those years (Fig. 2). However, in 2006, a year when it was relatively cold and thick ice prevailed, most of the tagged walruses moved throughout the northern region. Ice concentration can vary Figure 4. Mean (top row) and sample standard deviation (bottom row) daily sea ice concentration (%). Means and standard deviations were calculated for each 2 km62 km cell within the benthic sampling areas in 2006 and 2008–2009 during walrus tracking periods (tracking periods indicated in Table 1).
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Impact of Albufeira bay outfall plumes in bathing water quality, a modelling approach

Impact of Albufeira bay outfall plumes in bathing water quality, a modelling approach

The rapid development of computing technology has furnished a large number of models to be employed in coastal hydrodynamic problems. A variety of coastal models are available and the modelling techniques have become quite mature. The numerical technique can be based, among others, in the finite element method, finite difference method, boundary element method, finite volume method and Eulerian-Lagrangian method. The time-stepping algorithm can be implicit, semi-implicit, explicit, or characteristic-based. In finite element models the shape functions can be of the first order, second order, or a higher order. The spatial discretization can be based in different spatial dimensions, i.e., a one-dimensional (1D) model, two-dimensional (2D) depth- integrated model, 2D lateral-integrated model, 2D layered model and 3D model (Prinos, 2016). An analysis of coastal hydraulics and water quality often demands the application of heuristics and empirical experience, and is accomplished through some simplifications and modelling techniques according to the experience of specialists. However, the accuracy of the prediction is to a great extent dependent on open boundary conditions, model parameters, and the numerical scheme. The adoption of a proper numerical model for a practical coastal problem is a highly specialized task. These predictive tools inevitably involve certain assumptions and/or limitations, and can be applied only by experienced engineers who possess a comprehensive understanding of the problem domain. This leads to severe constraints on the use of models and large gaps in understanding and expectations between the developers and practitioners of a model (Prinos, 2016).
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The CSIRO Mk3L climate system model version 1.0 – Part 1: Description and evaluation

The CSIRO Mk3L climate system model version 1.0 – Part 1: Description and evaluation

The land/sea mask used by the ocean model differs from that used by the atmosphere model. The tips of South America and the Antarctic Peninsula are removed, ensur- ing that Drake Passage accommodates three horizontal ve- locity gridpoints. To ensure adequate resolution of the Greenland-Scotland sill, Iceland is also removed; likewise, a re-arrangement of the land/sea mask ensures adequate reso- lution of the flows through the Indonesian archipelago. Sval- bard, which occupies a single isolated gridpoint on the at- mosphere model grid, is not represented in the ocean model. Any straits that have a width of only one gridpoint on the tracer grid are closed, as these will not contain any horizon- tal velocity gridpoints. The Bass, Bering, Gibraltar, Hudson and Torres Straits, the Mozambique Channel and the Sea of Japan are therefore removed, while the Canadian archipelago becomes a land bridge.
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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

calm. As the echo level is highly variable and not every sin- gle peak above the threshold represents open water, the echo signal was filtered as a 10-points running mean. This guaran- tees that the threshold is passed only by those signals that on average remain high for a longer time period, which is typ- ical for leads. Within these time windows the times of open water were then defined as the points of the unfiltered echo level that lie above the echo threshold (Fig. 6). The perfor- mance of this method can be assessed with the respective pseudo draft plot. Leads within the ice appear as rectangular- shaped gaps in the draft record. Intervals with strongly wind- disturbed open water were excluded to avoid a bias of the surface level offset. Leads that were correctly captured by the search algorithm – i.e. the tie points of the interpolated zero line – have zero draft. Thus, their error is zero. The re- sulting elevation distribution of the leads that were not de- tected is approximately Gaussian shaped. In this time series the statistical open water draft mode was detected similarly to the method of Strass (1998): single modes were detected for leads observed long enough to remove the noise result- ing from short surface gravity waves. The final mode was then calculated as the mean of the open water draft modes from those leads in the six data sets that were identified by their echo level but not detected by the search algorithm or the operator. The mean open water draft mode found in this way is 4 cm. The standard deviation of the mean open water draft mode is ±6 cm. The mode represents a bias, whereas the standard deviation represents the dispersion (precision) of the corrected draft data around the location of undetected leads. The overall accuracy is then calculated as the root mean square (rms) error, which accounts for both types of errors (Hauck et al., 2008). The accuracy found in this way is about ±7 cm.
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Sea ice dynamics influence halogen deposition to Svalbard

Sea ice dynamics influence halogen deposition to Svalbard

the last few centuries at Law Dome in Antarctica (Curran et al., 2003) and from the Lomonosovfonna ice core from Svalbard (Isaksson et al., 2005). Although a positive cor- relation was found between MSA and sea ice for the Law Dome site, the Lomonosovfonna MSA record showed a neg- ative correlation between sea ice extent and MSA concentra- tion. It has been proposed that the reduced ice cover in the Barents Sea allowed a greater area for primary production as well as warmer water temperatures, thereby promoting DMS production (O’Dwyer et al., 2000). MSA is suscepti- ble to post-depositional mobility and loss which renders it unsuitable for millennial-scale sea ice reconstruction (Smith et al., 2004). Sea salt sodium (ss-Na) has also been discussed to estimate past sea ice variations (Wolff et al., 2006). The sea ice surface is salt enriched mainly because of the salt expelled in high salinity frost flowers and brine (Rankin et al., 2000) which can be lofted into the atmosphere and de- posited on surface snow. However, a recent paper suggests that frost flowers are very stable in the presence of wind and no aerosol emission was observed in laboratory studies (Roscoe et al., 2011). Despite the suggested links between sodium and sea ice (Wolff et al., 2006), the contribution of open-ocean sea spray and wind transport, as well as the role of the frost flower–brine production and sodium dust deposi- tion, must be taken into account (Petit et al., 1999). There has been much recent progress in satellite-based measurements of trace gases in the atmosphere (Saiz-Lopez et al., 2007). In particular, satellite images show that high levels of BrO and IO in the Antarctic atmosphere are located above sea- sonal or first-year sea ice (Schönhardt et al., 2012; Kaleschke et al., 2004). The increase of BrO concentration during the austral spring is likely due to the reaction of bromine with ozone which is one of the main mechanisms of ozone de- pletion events (ODEs) (Fan and Jacob, 1992) triggered by the injection of gas-phase bromine into the polar atmosphere during bromine explosions (Simpson et al., 2007). Simpson et al. (2007) and Kaleschke et al. (2004) revealed that the occurrence of bromine explosions are linked with the pres- ence of first-year sea ice and in particular with the fresh snow above first year sea ice (Pratt et al., 2013). These observa- tions are also detected above Arctic sea ice where the atmo- spheric content of BrO is enhanced in spring time (Sihler et al., 2012).
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