Sympagohydra tuuli gen. nov. and sp. nov. (Cnidaria: Hydrozoa) a cool hydroid from the Arctic sea ice

2008 ◽  
Vol 88 (8) ◽  
pp. 1637-1641 ◽  
Author(s):  
S. Piraino ◽  
B.A. Bluhm ◽  
R. Gradinger ◽  
F. Boero
Keyword(s):  
New Species ◽  
Sea Ice ◽  
Three Dimensional ◽  
Typical Feature ◽  
Arctic Sea Ice ◽  
The Body ◽  
The Arctic ◽  
Systematic Account ◽  
Aboral Side ◽  
Fast Ice

A new interstitial hydroid, Sympagohydra tuuli, has been collected from the three-dimensional brine channel system of the coastal fast ice off Barrow, Alaska (71°N 156ºW) in 2003 to 2006. This is the first cnidarian species described from the sea ice interstitial habitat. A morphological and systematic account of the new species is provided here. The hydroid stage is represented by solitary naked polyps, which become almost spherical under contraction. Body length can vary from 200 µm to >1 mm according to the degree of relaxation. The aboral side is extensible into a tubular foot-like projection made by epidermal tissue only, a typical feature of representatives of Protohydridae, order Capitata. However, in contrast to the other two known Protohydridae species, S. tuuli is characterized by three to four solid filiform tentacles, armed with microbasic mastigophore and desmoneme cnidocysts, and located at the base of a short hypostome bearing stenoteles. A small number of mastigophores are also scattered along the body column. Due to its unique combination of features, this taxon is designated here as a new species, and the new genus Sympagohydra is established as a new member of the class Hydroidomedusa, subclass Anthomedusae, order Capitata, family Protohydridae, to accommodate S. tuuli.

Dirty sea ice drives higher Mn concentrations in the Canada Basin

2021 ◽  
Author(s):  
Birgit Rogalla ◽  
Susan E. Allen ◽  
Manuel Colombo ◽  
Paul G. Myers ◽  
Kristin J. Orians
Keyword(s):  
Sea Ice ◽  
Long Range ◽  
Sediment Resuspension ◽  
Three Dimensional ◽  
Arctic Sea Ice ◽  
Canada Basin ◽  
The Arctic ◽  
Long Range Transport ◽  
Basin Surface ◽  
Range Transport

<p>The rapidly changing conditions of the Arctic sea ice system have cascading impacts on the biogeochemical cycles of the ocean. Sea ice transports sediments, nutrients, trace metals, pollutants, and gases from the extensive continental shelves into the more isolated central basins. However, it is difficult to assess the net contribution of this supply mechanism on nutrients in the surface ocean. In this study, we used Manganese (Mn), a micronutrient and tracer which can integrate source fluctuations in space and time, to understand the net impact of the long range transport of sea ice for Mn.</p><p>We developed a three-dimensional dissolved Mn model within a subdomain of the 1/12 degree Arctic and Northern Hemispheric Atlantic (ANHA12) configuration of NEMO centred on the Canadian Arctic Archipelago, and evaluated this model with in situ observations from the 2015 Canadian GEOTRACES cruises. The Mn model incorporates parameterizations for the contributions from river discharge, sediment resuspension, atmospheric deposition of aerosols directly to the ocean and via melt from sea ice, release of sediment from sea ice, and reversible scavenging, while the NEMO-TOP engine takes care of the advection and diffusion of the tracers. </p><p>Simulations with this model from 2002 to 2019 indicate that the majority of external Mn contributed annually to the Canada Basin surface is released by sediment from sea ice, much of which originates from the Siberian shelves. Reduced sea ice longevity in the Siberian shelf regions has been postulated to result in the disruption of the long range transport of sea ice by the transpolar drift. This reduced sea ice supply has the potential to decrease the Canada Basin Mn surface maximum and downstream Mn supply, with implications for other nutrients (such as Fe) contained in ice-rafted sediments as well. These results demonstrate some of the many changes to the biogeochemical supply mechanisms expected in the near-future in the Arctic Ocean and the subpolar seas.</p>

Fast ice characteristics, with special reference to the eastern Canadian Arctic

Polar Record ◽  
1975 ◽  
Vol 17 (110) ◽  
pp. 521-536 ◽  
Author(s):  
John D. Jacobs ◽  
Roger G. Barry ◽  
Ronald L. Weaver
Keyword(s):  
Sea Ice ◽  
Canadian Arctic ◽  
The United States ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
National Academy Of Sciences ◽  
Ice Dynamics ◽  
Pack Ice ◽  
Fast Ice ◽  
Eastern Canadian Arctic

Arctic sea ice is currently receiving increasing attention, both in relation to technological problems associated with resources development and shipping (Walker and Penney, 1973), and to basic research questions. The polar pack ice in the Beaufort Sea, for example, is the focus of the Arctic Ice Dynamics Joint Experiment (AIDJEX) (Untersteiner, 1974), while an analysis of physical links between the characteristics of polar surfaces and climate is to be the crux of the United States contribution to the Polar Experiment (POLEX) (Weller and Bierly, 1973; National Academy of Sciences, 1974). A general discussion of sea ice, with emphasis on pack ice, has been presented recently by Wittman and Burkhart (1973), but another aspect of the sea ice regime deserving separate attention, particularly in the light of Arctic offshore oil developments, is the landfast or fast ice, ie that part of the sea ice which remains attached to the shore (see “Sea ice terminology”). This paper attempts to provide a broad picture of fast ice characteristics in the context of our field experience in the eastern Canadian Arctic.

Arctic sea ice extent and thickness

1995 ◽  
Vol 352 (1699) ◽  
pp. 301-319 ◽  
Keyword(s):  
Sea Ice ◽  
Open Water ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Extent ◽  
Arctic Sea ◽  
Ice Conditions ◽  
Fast Ice ◽  
Arctic Sea Ice Extent

Current knowledge on Arctic sea ice extent and thickness variability is reviewed, and we examine whether measurements to date provide evidence for the impact of climate change. The total Arctic ice extent has shown a small but significant reduction of (2.1 ± 0.9)% during the period 1978-87, after apparently increasing from a lower level in the early 1970s. However, open water within the pack ice limit has also diminished, so that the reduction of sea ice area is only (1.8 ± 1.2)%. This stability conceals large interannual variations and trends in individual regions of the Arctic Ocean and sub-Arctic seas, which are out of phase with one another and so have little net impact on the overall hemispheric ice extent. The maximum annual global extent (occurring during the Antarctic winter) shows a more significant decrease of 5% during 1972-87. Ice thickness distribution has been measured by submarine sonar profiling, moored upward sonars, airborne laser prohlometry, airborne electromagnetic techniques and drilling. Promising new techniques include: sonar mounted on an AUV or neutrally buoyant float; acoustic tomography or thermometry; and inference from a combination of microwave sensors. In relation to climate change, the most useful measurement has been repeated submarine sonar profiling under identical parts of the Arctic, which offers some evidence of a decline in mean ice thickness in the 1980s compared to the 1970s. The link between mean ice thickness and climatic warming is complex because of the effects of dynamics and deformation. Only fast ice responds primarily to air temperature changes and one can predict thinning of fast ice and extension of the open water season in fast ice areas. Another region of increasingly mild ice conditions is the central Greenland Sea where winter thermohaline convection is triggered by cyclic growth and melt of local young ice. In recent years convection to the bottom has slowed or ceased, possibly related to moderation of ice conditions.

Prévoir les variations saisonnières de la glace de mer arctique et leurs impacts sur le climat

2020 ◽  
pp. 024
Author(s):  
Rym Msadek ◽  
Gilles Garric ◽  
Sara Fleury ◽  
Florent Garnier ◽  
Lauriane Batté ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Region ◽  
Arctic Sea Ice ◽  
Climate Impacts ◽  
The Arctic ◽  
Recent Effort ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Ice Conditions ◽  
Upper Level

L'Arctique est la région du globe qui s'est réchauffée le plus vite au cours des trente dernières années, avec une augmentation de la température de surface environ deux fois plus rapide que pour la moyenne globale. Le déclin de la banquise arctique observé depuis le début de l'ère satellitaire et attribué principalement à l'augmentation de la concentration des gaz à effet de serre aurait joué un rôle important dans cette amplification des températures au pôle. Cette fonte importante des glaces arctiques, qui devrait s'accélérer dans les décennies à venir, pourrait modifier les vents en haute altitude et potentiellement avoir un impact sur le climat des moyennes latitudes. L'étendue de la banquise arctique varie considérablement d'une saison à l'autre, d'une année à l'autre, d'une décennie à l'autre. Améliorer notre capacité à prévoir ces variations nécessite de comprendre, observer et modéliser les interactions entre la banquise et les autres composantes du système Terre, telles que l'océan, l'atmosphère ou la biosphère, à différentes échelles de temps. La réalisation de prévisions saisonnières de la banquise arctique est très récente comparée aux prévisions du temps ou aux prévisions saisonnières de paramètres météorologiques (température, précipitation). Les résultats ayant émergé au cours des dix dernières années mettent en évidence l'importance des observations de l'épaisseur de la glace de mer pour prévoir l'évolution de la banquise estivale plusieurs mois à l'avance. Surface temperatures over the Arctic region have been increasing twice as fast as global mean temperatures, a phenomenon known as arctic amplification. One main contributor to this polar warming is the large decline of Arctic sea ice observed since the beginning of satellite observations, which has been attributed to the increase of greenhouse gases. The acceleration of Arctic sea ice loss that is projected for the coming decades could modify the upper level atmospheric circulation yielding climate impacts up to the mid-latitudes. There is considerable variability in the spatial extent of ice cover on seasonal, interannual and decadal time scales. Better understanding, observing and modelling the interactions between sea ice and the other components of the climate system is key for improved predictions of Arctic sea ice in the future. Running operational-like seasonal predictions of Arctic sea ice is a quite recent effort compared to weather predictions or seasonal predictions of atmospheric fields like temperature or precipitation. Recent results stress the importance of sea ice thickness observations to improve seasonal predictions of Arctic sea ice conditions during summer.

scholarly journals Retrieval of Snow Depth over Arctic Sea Ice Using a Deep Neural Network

2019 ◽  
Vol 11 (23) ◽  
pp. 2864 ◽  
Author(s):  
Jiping Liu ◽  
Yuanyuan Zhang ◽  
Xiao Cheng ◽  
Yongyun Hu
Keyword(s):  
Neural Network ◽  
Sea Ice ◽  
Snow Depth ◽  
Deep Neural Network ◽  
Arctic Basin ◽  
Temporal Variations ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Thickness ◽  
Arctic Sea

The accurate knowledge of spatial and temporal variations of snow depth over sea ice in the Arctic basin is important for understanding the Arctic energy budget and retrieving sea ice thickness from satellite altimetry. In this study, we develop and validate a new method for retrieving snow depth over Arctic sea ice from brightness temperatures at different frequencies measured by passive microwave radiometers. We construct an ensemble-based deep neural network and use snow depth measured by sea ice mass balance buoys to train the network. First, the accuracy of the retrieved snow depth is validated with observations. The results show the derived snow depth is in good agreement with the observations, in terms of correlation, bias, root mean square error, and probability distribution. Our ensemble-based deep neural network can be used to extend the snow depth retrieval from first-year sea ice (FYI) to multi-year sea ice (MYI), as well as during the melting period. Second, the consistency and discrepancy of snow depth in the Arctic basin between our retrieval using the ensemble-based deep neural network and two other available retrievals using the empirical regression are examined. The results suggest that our snow depth retrieval outperforms these data sets.

scholarly journals Observation-based selection of climate models projects Arctic ice-free summers around 2035

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
David Docquier ◽  
Torben Koenigk
Keyword(s):  
Model Selection ◽  
Sea Ice ◽  
Climate Models ◽  
Global Climate ◽  
Coupled Model ◽  
Arctic Sea Ice ◽  
Global Climate Models ◽  
The Arctic ◽  
Arctic Sea ◽  
Area And Volume

AbstractArctic sea ice has been retreating at an accelerating pace over the past decades. Model projections show that the Arctic Ocean could be almost ice free in summer by the middle of this century. However, the uncertainties related to these projections are relatively large. Here we use 33 global climate models from the Coupled Model Intercomparison Project 6 (CMIP6) and select models that best capture the observed Arctic sea-ice area and volume and northward ocean heat transport to refine model projections of Arctic sea ice. This model selection leads to lower Arctic sea-ice area and volume relative to the multi-model mean without model selection and summer ice-free conditions could occur as early as around 2035. These results highlight a potential underestimation of future Arctic sea-ice loss when including all CMIP6 models.

scholarly journals Polar cod in jeopardy under the retreating Arctic sea ice

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Mats Brockstedt Olsen Huserbråten ◽  
Elena Eriksen ◽  
Harald Gjøsæter ◽  
Frode Vikebø
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Keystone Species ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
Biophysical Model ◽  
The Arctic ◽  
Polar Cod ◽  
Arctic Sea ◽  
Shelf Sea

Abstract The Arctic amplification of global warming is causing the Arctic-Atlantic ice edge to retreat at unprecedented rates. Here we show how variability and change in sea ice cover in the Barents Sea, the largest shelf sea of the Arctic, affect the population dynamics of a keystone species of the ice-associated food web, the polar cod (Boreogadus saida). The data-driven biophysical model of polar cod early life stages assembled here predicts a strong mechanistic link between survival and variation in ice cover and temperature, suggesting imminent recruitment collapse should the observed ice-reduction and heating continue. Backtracking of drifting eggs and larvae from observations also demonstrates a northward retreat of one of two clearly defined spawning assemblages, possibly in response to warming. With annual to decadal ice-predictions under development the mechanistic physical-biological links presented here represent a powerful tool for making long-term predictions for the propagation of polar cod stocks.

scholarly journals On the Link between Arctic Sea Ice Decline and the Freshwater Content of the Beaufort Gyre: Insights from a Simple Process Model

2014 ◽  
Vol 27 (21) ◽  
pp. 8170-8184 ◽  
Author(s):  
Peter E. D. Davis ◽  
Camille Lique ◽  
Helen L. Johnson
Keyword(s):  
Sea Ice ◽  
Momentum Transfer ◽  
Momentum Flux ◽  
Process Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Simple Process ◽  
Beaufort Gyre ◽  
Arctic Sea ◽  
The Arctic Ocean

Abstract Recent satellite and hydrographic observations have shown that the rate of freshwater accumulation in the Beaufort Gyre of the Arctic Ocean has accelerated over the past decade. This acceleration has coincided with the dramatic decline observed in Arctic sea ice cover, which is expected to modify the efficiency of momentum transfer into the upper ocean. Here, a simple process model is used to investigate the dynamical response of the Beaufort Gyre to the changing efficiency of momentum transfer, and its link with the enhanced accumulation of freshwater. A linear relationship is found between the annual mean momentum flux and the amount of freshwater accumulated in the Beaufort Gyre. In the model, both the response time scale and the total quantity of freshwater accumulated are determined by a balance between Ekman pumping and an eddy-induced volume flux toward the boundary, highlighting the importance of eddies in the adjustment of the Arctic Ocean to a change in forcing. When the seasonal cycle in the efficiency of momentum transfer is modified (but the annual mean momentum flux is held constant), it has no effect on the accumulation of freshwater, although it does impact the timing and amplitude of the annual cycle in Beaufort Gyre freshwater content. This suggests that the decline in Arctic sea ice cover may have an impact on the magnitude and seasonality of the freshwater export into the North Atlantic.

Modeling and assessing impact of artificially enhancing the Arctic Sea Ice Albedo

2019 ◽  
Author(s):  
Subarna Bhattacharyya ◽  
Detelina Ivanova ◽  
Velimir Mlaker ◽  
Leslie Field
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Arctic Sea

scholarly journals Deep Learning Convolutional Neural Network Applying for the Arctic Acoustic Tomography Current Inversion Accuracy Improvement

2021 ◽  
Vol 9 (7) ◽  
pp. 755
Author(s):  
Kangkang Jin ◽  
Jian Xu ◽  
Zichen Wang ◽  
Can Lu ◽  
Long Fan ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sea Ice ◽  
Convolutional Neural Network ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Small Scale ◽  
Acoustic Tomography ◽  
Arctic Sea ◽  
Current Inversion

Warm current has a strong impact on the melting of sea ice, so clarifying the current features plays a very important role in the Arctic sea ice coverage forecasting study field. Currently, Arctic acoustic tomography is the only feasible method for the large-range current measurement under the Arctic sea ice. Furthermore, affected by the high latitudes Coriolis force, small-scale variability greatly affects the accuracy of Arctic acoustic tomography. However, small-scale variability could not be measured by empirical parameters and resolved by Regularized Least Squares (RLS) in the inverse problem of Arctic acoustic tomography. In this paper, the convolutional neural network (CNN) is proposed to enhance the prediction accuracy in the Arctic, and especially, Gaussian noise is added to reflect the disturbance of the Arctic environment. First, we use the finite element method to build the background ocean model. Then, the deep learning CNN method constructs the non-linear mapping relationship between the acoustic data and the corresponding flow velocity. Finally, the simulation result shows that the deep learning convolutional neural network method being applied to Arctic acoustic tomography could achieve 45.87% accurate improvement than the common RLS method in the current inversion.

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