Sea Ice Characteristics Research Articles

Machine learning-based temporal mixture analysis of hypertemporal Antarctic sea ice data

ABSTRACTHypertemporal image (HTI) is often used to exploit the seasonal characteristics of environmental phenomena such as sea ice concentration (SIC). However, it is difficult to analyse the long-term time series acquired at high temporal frequencies and over extensive areas. This study performed temporal mixture analysis (TMA), which is algebraically similar to spectral mixture analysis (SMA), but occurs in the time domain instead of the spectral domain. TMA was used to investigate the temporal characteristics of Antarctic sea ice. Because endmember (EM) selection is critical to the success of both SMA and TMA, it is important to select proper EMs from large quantities of HTI. In this study, a machine learning (ML) technique is incorporated in identifying EMs without prior information to address the limitations of previous research. A fully linear mixing model was then implemented in an attempt to produce more robust and physically meaningful abundance estimates. Experiments that quantitatively and qualitatively evaluated the proposed approaches were conducted. A TMA of high-temporal-dimensional data provides a unique summary of long-term Antarctic sea ice and noise-whitened reconstruction images via inverse processing. Furthermore, comparisons of regional sea ice fractions from experimental results enhance the understanding of the overall Antarctic sea ice changes.

Processes controlling surface, bottom and lateral melt of Arctic sea ice in a state of the art sea ice model.

We present a modelling study of processes controlling the summer melt of the Arctic sea ice cover. We perform a sensitivity study and focus our interest on the thermodynamics at the ice-atmosphere and ice-ocean interfaces. We use the Los Alamos community sea ice model CICE, and additionally implement and test three new parametrization schemes: (i) a prognostic mixed layer; (ii) a three equation boundary condition for the salt and heat flux at the ice-ocean interface; and (iii) a new lateral melt parametrization. Recent additions to the CICE model are also tested, including explicit melt ponds, a form drag parametrization and a halodynamic brine drainage scheme. The various sea ice parametrizations tested in this sensitivity study introduce a wide spread in the simulated sea ice characteristics. For each simulation, the total melt is decomposed into its surface, bottom and lateral melt components to assess the processes driving melt and how this varies regionally and temporally. Because this study quantifies the relative importance of several processes in driving the summer melt of sea ice, this work can serve as a guide for future research priorities.

Skill metrics for evaluation and comparison of sea ice models

AbstractFive quantitative methodologies (metrics) that may be used to assess the skill of sea ice models against a control field are analyzed. The methodologies are Absolute Deviation, Root‐Mean‐Square Deviation, Mean Displacement, Hausdorff Distance, and Modified Hausdorff Distance. The methodologies are employed to quantify similarity between spatial distribution of the simulated and control scalar fields providing measures of model performance. To analyze their response to dissimilarities in two‐dimensional fields (contours), the metrics undergo sensitivity tests (scale, rotation, translation, and noise). Furthermore, in order to assess their ability to quantify resemblance of three‐dimensional fields, the metrics are subjected to sensitivity tests where tested fields have continuous random spatial patterns inside the contours. The Modified Hausdorff Distance approach demonstrates the best response to tested differences, with the other methods limited by weak responses to scale and translation. Both Hausdorff Distance and Modified Hausdorff Distance metrics are robust to noise, as opposed to the other methods. The metrics are then employed in realistic cases that validate sea ice concentration fields from numerical models and sea ice mean outlook against control data and observations. The Modified Hausdorff Distance method again exhibits high skill in quantifying similarity between both two‐dimensional (ice contour) and three‐dimensional (ice concentration) sea ice fields. The study demonstrates that the Modified Hausdorff Distance is a mathematically tractable and efficient method for model skill assessment and comparison providing effective and objective evaluation of both two‐dimensional and three‐dimensional sea ice characteristics across data sets.

Object-based detection of Arctic sea ice and melt ponds using high spatial resolution aerial photographs

High resolution aerial photographs used to detect and classify sea ice features can provide accurate physical parameters to refine, validate, and improve climate models. However, manually delineating sea ice and melt ponds is time-consuming and labor-intensive. In this study, an object-based classification algorithm is developed to automatically extract sea ice and melt ponds efficiently from 163 aerial photographs taken during the Chinese National Arctic Research Expedition in summer 2010 (CHINARE 2010) in the Arctic Pacific Sector. The photographs are selected from 599 cloud-free photographs based on their image quality and representativeness in the marginal ice zone (MIZ). The algorithm includes three major steps: (1) the image segmentation groups the neighboring pixels into objects according to the similarity of spectral and textural information; (2) the random forest ensemble classifier distinguishes four general classes: water, general submerged ice (GSI, including melt ponds and submerged ice along ice edges), shadow, and ice/snow; and (3) the polygon neighbor analysis further separates melt ponds and submerged ice from the GSI according to their spatial relationships. The overall classification accuracy for the four general classes is 95.5% based on 178 ground reference objects. Furthermore, the producer's accuracy of 90.8% and user's accuracy of 91.8% are achieved for melt pond detection through 98 independent reference objects. For the 163 photos examined, a total of 19,438 melt ponds larger than 1m2 are detected, with a pond density of 867.2km−2, mean pond size of 32.6±0.03m2, and mean pond fraction of 0.06±0.006; a total of 42,468 ice floes are detected, with the mean floe size of 173.3±0.1m2 (majority in 1–30m2) and mean ice concentration of 46.1±0.5% (ranging from 18.6–98.6%). These results matched well with ship-based visual observations in the MIZ in the same area and time. The method presented in the paper can be applied to data sets of high spatial resolution Arctic sea ice photographs for deriving detailed sea ice concentration, floe size, and melt pond distributions over wider regions, and extracting sea ice physical parameters and their corresponding changes between years.

Image Processing for Identification of Sea-Ice Floes and the Floe Size Distributions

An unmanned aerial vehicle was used as a mobile sensor platform to collect sea-ice features at Ny-Alesund in early May 2011, and several image processing algorithms have been applied to samples of sea-ice images to extract useful information about sea ice. The sea-ice statistics given by the floe size distribution, being an important parameter for climate and wave- and structure-ice analysis, is challenging to calculate due to difficulties in ice floe identification, particularly the separation of seemingly connected ice floes. In this paper, the gradient vector flow (GVF) snake algorithm is applied to solve this problem. To evolve the GVF snake algorithm automatically, an initialization based on the distance transform is proposed to detect individual ice floes, and the morphological cleaning is afterward applied to smoothen the shape of each identified ice floe. Based on the identification result, the image is separated into four different layers: ice floes, brash pieces, slush, and water. This makes it further possible to present a color map of the ice floes and brash pieces based on sizes, and the corresponding ice floe size distribution histogram. The proposed algorithm yields an acceptable identification result, and its effectiveness is demonstrated in a case study. A discussion on the methods and results concludes the paper.

A stability‐dependent parametrization of transfer coefficients for momentum and heat over polar sea ice to be used in climate models

AbstractThe interaction between sea ice and atmosphere depends strongly on the near‐surface transfer coefficients for momentum and heat. A parametrization of these coefficients is developed on the basis of an existing parametrization of drag coefficients for neutral stratification that accounts for form drag caused by the edges of ice floes and melt ponds. This scheme is extended to better account for the dependence of surface wind on limiting cases of high and low ice concentration and to include near‐surface stability effects over open water and ice on form drag. The stability correction is formulated on the basis of stability functions from Monin‐Obukhov similarity theory and also using the Louis concept with stability functions depending on the bulk Richardson numbers. Furthermore, a parametrization is proposed that includes the effect of edge‐related turbulence also on heat transfer coefficients. The parametrizations are available in different levels of complexity. The lowest level only needs sea ice concentration and surface temperature as input, while the more complex level needs additional sea ice characteristics. An important property of our parametrization is that form drag caused by ice edges depends on the stability over both ice and water which is in contrast to the skin drag over ice. Results of the parametrization show that stability has a large impact on form drag and, thereby, determines the value of sea ice concentration for which the transfer coefficients reach their maxima. Depending on the stratification, these maxima can occur anywhere between ice concentrations of 20 and 80%.

On sea-ice dynamical regimes in the Arctic Ocean

AbstractCentral to an understanding of evolution in sea-ice characteristics in response to climate change is an understanding of sea-ice dynamics. In this study, we investigate regional differences in ice dynamics in the Beaufort Sea and High Arctic using high-frequency ice buoy (beacon) data deployed during the SEDNA and IPY-CFL field campaigns from spring 2007 to winter 2008. Examined in particular are scaling laws determined from absolute dispersion statistics. We create temporal scaling maps to determine whether distinct dynamical regimes can be identified with differing scaling properties. The results from this analysis provide an alternative characterization to changes in sea ice based on dynamics rather than concentration and thickness, and thus insight into, and improved understanding of, the connections between sea-ice drift and morphology.

Optimality of piecewise thermal conductivity in a snow-ice thermodynamic system

This article is intended to provide the optimality of piecewise thermal conductivity in a snow-ice thermodynamic system. Based on the temperature distribution characteristics of snow and sea ice, we construct a piecewise smooth thermodynamic system coupled by snow and sea ice. Taking the piecewise thermal conductivities of snow and sea ice as control variables and the temperature deviations obtained from the system and the observations as the performance criterion, an identification model with state constraints is given. The dependency relationship between state and control variables is proven, and the existence of the optimal control is discussed. The work can provide a theoretical foundation for simulating temperature distributions of snow and sea ice.

Sea-ice conditions in the Adélie Depression, Antarctica, during besetment of the icebreaker RV Xuelong

AbstractDuring the 30th Chinese Antarctic Expedition in 2013/14, the Chinese icebreaker RV Xuelong answered a rescue call from the Russian RV Akademik Shokalskiy. While assisting the repatriation of personnel from the Russian vessel to the Australian RV Aurora Australis, RV Xuelong itself became entrapped within the compacted ice in the Adélie Depression region. Analysis of MODIS and SAR imagery provides a detailed description of the regional sea-ice conditions which led to the 6 day long besetment of RV Xuelong. The remotely sensed imagery revealed four stages of sea-ice characteristics during the entrapment: the gathering, compaction, dispersion and calving stages. Four factors characterizing the local sea-ice conditions during late December 2013 and early January 2014 were identified: surface component of the coastal current; near-surface wind; ocean tides; and surface air temperature. This study demonstrates that shipping activity in ice-invested waters should be underpinned by general knowledge of the ice situation. In addition, during such activity high spatiotemporal resolution remotely sensed data should be acquired regularly to monitor local and regional sea-ice changes with a view to avoiding the besetment of vessels.

Modelling the Antarctic marine cryosphere at the Last Glacial Maximum

AbstractWe estimate the sea-ice extent and basal melt of Antarctic ice shelves at the Last Glacial Maximum (LGM) using a coupled ice-shelf-sea-ice-ocean model. The shape of Antarctic ice shelves, ocean conditions and atmospheric surface conditions at the LGM are different from those in the present day; these are derived from an ice-shelf-ice-sheet model, a sea-ice-ocean model and a climate model for glacial simulations, respectively. The winter sea ice in the LGM is shown to extend up to ∼7° of latitude further equatorward than in the present day. For the LGM summer, the model shows extensive sea-ice cover in the Atlantic sector and little sea ice in the other sectors. These modelled sea-ice features are consistent with those reconstructed from sea-floor sedimentary records. Total basal melt of Antarctic ice shelves in the LGM was ∼2147 Gt a–1, which is much larger than the present-day value. More warm waters originating from Circumpolar Deep Water could be easily transported into ice-shelf cavities during the LGM because the full glacial grounding line extended to shelf break regions and ice shelves overhung continental slopes. This increased transport of warm water masses underneath an ice shelf and into their basal cavities led to the high basal melt of ice shelves in the LGM.

On the Estimation of Melt Pond Fraction on the Arctic Sea Ice With ENVISAT WSM Images

The accuracy of microwave radiometer ice concentration (IC) retrievals in the Arctic is degraded by melt ponds on sea ice during the melting season. For the development of IC retrieval algorithms and for the quantification of their uncertainties, data sets on the area fraction of melt ponds $(f_{mp})$ are needed. $f_{mp}$ retrieval with optical satellite data is limited by clouds. Thus, we have studied $f_{mp}$ retrieval with ENVISAT wide swath mode (WSM) synthetic aperture radar (SAR) images which have large daily coverage over the Arctic Sea ice in 2007–2012. The WSM images used here were acquired north of the Fram Strait in June–August 2009. Data on $f_{mp}$ were available from the Integrated Climate Data Center's daily Moderate Resolution Imaging Spectroradiometer (MODIS) $f_{mp}$ product in a 12.5-km grid. Relationships between SAR $\sigma^{\circ}$ and MODIS $f_{mp}$ were studied visually by comparing daily SAR mosaics and $f_{mp}$ charts and by analyzing $f_{mp}$ and $\sigma^{\circ}$ time series and spatially and temporally coincident $f_{mp}$ and $\sigma^{\circ}$ data. The correspondence between the changes of $f_{mp}$ and the $\sigma^{\circ}$ statistics is too low to suggest $f_{mp}$ estimation from the WSM images. In some cases, there was a 2–3-dB $\sigma^{\circ}$ increase during the ponding period. It is assumed that the variation of snow and sea ice characteristics diminishes $\sigma^{\circ}$ changes due to the melt ponding and drainage. Good correlation between $\sigma^{\circ}$ and $f_{mp}$ has only been observed for smooth landfast first-year ice in previous studies. A very interesting observation was the large temporal $\sigma^{\circ}$ variations during the late melting season, which are likely linked to the atmospherically forced freezing–melting events. These events may also influence radiometer IC retrievals.

Formalizing the semantics of sea ice

We have initiated a project aimed at enhancing interdisciplinary understanding and usability of polar data by diverse communities. We have produced computer- and human-understandable models of sea ice that can be used to support the interoperability of a wide range of sea ice data. This has the potential to improve scientific predictive analyses and increase usage of the data by scientists, modelers, and forecasters as well as residents of communities that rely on sea ice. We have developed a family of ontologies, leveraging existing best in class models, including one module describing physical characteristics of sea ice, another describing sea ice charts, and a third modeling “egg codes” - an internationally accepted standard for symbolically representing sea ice within geographic regions. We used a semantic Web methodology to rapidly gather and refine requirements, design and iterate over the ontologies, and to evaluate the ontologies with respect to the use cases. We gathered requirements from a wide range of potential stakeholders reflecting the interests of operational ice centers, ice researchers, and indigenous people. We introduce the driving use case and provide an overview of the resulting open source ontologies. We also introduce some key technical considerations including the prominent role of provenance, terms of use, and credit in the model. We describe how the ontologies are being employed and highlight their compatibility with a wide range of existing standards previously developed by many of the stakeholder communities.

Potential influences on the United Kingdom's floods of winter 2013/14

During the winter of 2013/14, much of the UK experienced repeated intense rainfall events and flooding. This had a considerable impact on property and transport infrastructure. A key question is whether the burning of fossil fuels is changing the frequency of extremes, and if so to what extent. We assess the scale of the winter flooding before reviewing a broad range of Earth system drivers affecting UK rainfall. Some drivers can be potentially disregarded for these specific storms whereas others are likely to have increased their risk of occurrence. We discuss the requirements of hydrological models to transform rainfall into river flows and flooding. To determine any general changing flood risk, we argue that accurate modelling needs to capture evolving understanding of UK rainfall interactions with a broad set of factors. This includes changes to multiscale atmospheric, oceanic, solar and sea-ice features, and land-use and demographics. Ensembles of such model simulations may be needed to build probability distributions of extremes for both pre-industrial and contemporary concentration levels of atmospheric greenhouse gases.

Climate change and Southern Ocean ecosystems I: how changes in physical habitats directly affect marine biota.

Antarctic and Southern Ocean (ASO) marine ecosystems have been changing for at least the last 30 years, including in response to increasing ocean temperatures and changes in the extent and seasonality of sea ice; the magnitude and direction of these changes differ between regions around Antarctica that could see populations of the same species changing differently in different regions. This article reviews current and expected changes in ASO physical habitats in response to climate change. It then reviews how these changes may impact the autecology of marine biota of this polar region: microbes, zooplankton, salps, Antarctic krill, fish, cephalopods, marine mammals, seabirds, and benthos. The general prognosis for ASO marine habitats is for an overall warming and freshening, strengthening of westerly winds, with a potential pole-ward movement of those winds and the frontal systems, and an increase in ocean eddy activity. Many habitat parameters will have regionally specific changes, particularly relating to sea ice characteristics and seasonal dynamics. Lower trophic levels are expected to move south as the ocean conditions in which they are currently found move pole-ward. For Antarctic krill and finfish, the latitudinal breadth of their range will depend on their tolerance of warming oceans and changes to productivity. Ocean acidification is a concern not only for calcifying organisms but also for crustaceans such as Antarctic krill; it is also likely to be the most important change in benthic habitats over the coming century. For marine mammals and birds, the expected changes primarily relate to their flexibility in moving to alternative locations for food and the energetic cost of longer or more complex foraging trips for those that are bound to breeding colonies. Few species are sufficiently well studied to make comprehensive species-specific vulnerability assessments possible. Priorities for future work are discussed.

Influence of stochastic sea ice parametrization on climate and the role of atmosphere-sea ice-ocean interaction.

The influence of a stochastic sea ice strength parametrization on the mean climate is investigated in a coupled atmosphere-sea ice-ocean model. The results are compared with an uncoupled simulation with a prescribed atmosphere. It is found that the stochastic sea ice parametrization causes an effective weakening of the sea ice. In the uncoupled model this leads to an Arctic sea ice volume increase of about 10-20% after an accumulation period of approximately 20-30 years. In the coupled model, no such increase is found. Rather, the stochastic perturbations lead to a spatial redistribution of the Arctic sea ice thickness field. A mechanism involving a slightly negative atmospheric feedback is proposed that can explain the different responses in the coupled and uncoupled system. Changes in integrated Antarctic sea ice quantities caused by the stochastic parametrization are generally small, as memory is lost during the melting season because of an almost complete loss of sea ice. However, stochastic sea ice perturbations affect regional sea ice characteristics in the Southern Hemisphere, both in the uncoupled and coupled model. Remote impacts of the stochastic sea ice parametrization on the mean climate of non-polar regions were found to be small.

On the surface roughness characteristics of the land fast sea-ice in the Bohai Sea

The surface roughness characteristics (e.g., height and slope) of sea ice are critical for determining the parameters of an electromagnetic scattering, a surface emission and a surface drag coefficients. It is also important in identifying various ice types, retrieval ice thickness, surface temperature and drag coefficients from remote sensing data. The point clouds (a set of points which are usually defined by X, Y, and Z coordinates that represents the external surface of an object on earth) of land fast ice in five in situ sites in the eastern coast Bohai Sea were measured using a laser scanner-Trimble GX during 2011–2012 winter season. Two hundred and fifty profiles selected from the point clouds of different samples have been used to calculate the height root mean square, height skewness, height kurtosis, slope root mean square, slope skewness and slope kurtosis of them. The root mean square of the height, the root mean square of the slope and the correlation length are about 0.090, 0.075 and 11.74 m, respectively. The heights of 150 profiles in three sites manifest the Gaussian distribution and the slopes of total 250 profiles distributed exponentially. In addition, the fractal dimension and power spectral density profiles were calculated. The results show that the fractal dimension of land fast ice in the Bohai Sea is about 1.132. The power spectral densities of 250 profiles can be expressed through an exponential autocorrelation function.

MICROWAVE MODEL OF RADIATION FROM THE MULTILAYER ``OCEAN-ATMOSPHERE'' SYSTEM FOR REMOTE SENSING STUDIES OF THE POLAR REGIONS

Microwave model for simulation of radiation from the multilayer system \sea surface-sea ice- snow cover-atmosphere is introduced. In the general case, ice and snow cover is modelled by multilayer medium, where every layer is characterized by its speciflc physical parameters. Electrodynamical properties of each layer are determined from the original authors' model of the efiective permittivity of heterogeneous medium. This model takes into account efiects of radiation scattering on irregularities of environment. Measurable physical characteristics of sea ice and snow are used as the model input data. This advantage allows using this model for interpretation of remote sensing images of the ice cover in the Polar Regions. Major attention is drawn to comparison of model calculations with satellite data and visual observations from ships. The collection of SSM/I and SSMIS images from GLOBAL-RT data base, and processed visual observations from ships in Arctic cruises were used. Observations data served as the input parameters for electrodynamical model. Comparison of model results with SSM/I images demonstrated good coincidence at various frequencies.

A modified discrete element model for sea ice dynamics

Considering the discontinuous characteristics of sea ice on various scales, a modified discrete element model (DEM) for sea ice dynamics is developed based on the granular material rheology. In this modified DEM, a soft sea ice particle element is introduced as a self-adjustive particle size function. Each ice particle can be treated as an assembly of ice floes, with its concentration and thickness changing to variable sizes under the conservation of mass. In this model, the contact forces among ice particles are calculated using a viscous-elastic-plastic model, while the maximum shear forces are described with the Mohr-Coulomb friction law. With this modified DEM, the ice flow dynamics is simulated under the drags of wind and current in a channel of various widths. The thicknesses, concentrations and velocities of ice particles are obtained, and then reasonable dynamic process is analyzed. The sea ice dynamic process is also simulated in a vortex wind field. Taking the influence of thermodynamics into account, this modified DEM will be improved in the future work.

Study of Specific energy of mechanical destruction of ice for calculation of ice load on ships and offshore structures

Analysis of scenarios of transportation oil and gas which produced in the Arctic and others cold seas shows that in the near-term there will be a significant increase of tonnage of tankers for oil and gas and number of ships which should be exploited in difficult ice conditions. For the construction of ice-resistant structures (IRS) intended for production of oil and gas and transportation of these products at ice-class vessels, calculating the load from ice to board the ship and on surface of supports of the platforms are the actuality and urgent tasks. These tasks have one basis in both cases: at beginning of the contact occurs fracture of edge of ice, then occurs compressing of rubble shattered of ice, then they extruding from contact area, after this next layer of ice begin to destruct. At calculating the strength of plating and elements construct of vessels, icebreakers and ice-resistant platforms the specific energy of mechanical destruction ice ? cr is an important parameter. For the whole period of study of physical and mechanical characteristics of sea ice have been not many experimental studies various researchers to obtain numerical values of this energetic characteristic of the strength of ice by a method called Ball Drop Test. This study shows that the destruction of the ice from dynamic loading in zone of contact occurs in several cycles, and the ice destructed with a minimum numerical values of ? cr . The author offer this energy characteristic to take as a base value for the calculation of ice load on ships and offshore structures.

Fatty acid and stable isotope characteristics of sea ice and pelagic particulate organic matter in the Bering Sea: tools for estimating sea ice algal contribution to Arctic food web production

We determined fatty acid (FA) profiles and carbon stable isotopic composition of individual FAs (δ(13)CFA values) from sea ice particulate organic matter (i-POM) and pelagic POM (p-POM) in the Bering Sea during maximum ice extent, ice melt, and ice-free conditions in 2010. Based on FA biomarkers, differences in relative composition of diatoms, dinoflagellates, and bacteria were inferred for i-POM versus p-POM and for seasonal succession stages in p-POM. Proportions of diatom markers were higher in i-POM (16:4n-1, 6.6-8.7%; 20:5n-3, 19.6-25.9%) than in p-POM (16:4n-1, 1.2-4.0%; 20:5n-3, 5.5-14.0%). The dinoflagellate marker 22:6n-3/20:5n-3 was highest in p-POM. Bacterial FA concentration was higher in the bottom 1 cm of sea ice (14-245 μg L(-1)) than in the water column (0.6-1.7 μg L(-1)). Many i-POM δ(13)C(FA) values were higher (up to ~10‰) than those of p-POM, and i-POM δ(13)C(FA) values increased with day length. The higher i-POM δ(13)C(FA) values are most likely related to the reduced dissolved inorganic carbon (DIC) availability within the semi-closed sea ice brine channel system. Based on a modified Rayleigh equation, the fraction of sea ice DIC fixed in i-POM ranged from 12 to 73%, implying that carbon was not limiting for primary productivity in the sympagic habitat. These differences in FA composition and δ(13)C(FA) values between i-POM and p-POM will aid efforts to track the proportional contribution of sea ice algal carbon to higher trophic levels in the Bering Sea and likely other Arctic seas.