scholarly journals Predictions replaced by facts: a keystone species' behavioural responses to declining arctic sea-ice

2015 ◽  
Vol 11 (11) ◽  
pp. 20150803 ◽  
Author(s):  
Charmain D. Hamilton ◽  
Christian Lydersen ◽  
Rolf A. Ims ◽  
Kit M. Kovacs
Keyword(s):  
Sea Ice ◽  
Marine Mammals ◽  
Environmental Changes ◽  
Keystone Species ◽  
Ocean Basin ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Negative Consequences ◽  
Population Declines ◽  
Arctic Sea

Since the first documentation of climate-warming induced declines in arctic sea-ice, predictions have been made regarding the expected negative consequences for endemic marine mammals. But, several decades later, little hard evidence exists regarding the responses of these animals to the ongoing environmental changes. Herein, we report the first empirical evidence of a dramatic shift in movement patterns and foraging behaviour of the arctic endemic ringed seal ( Pusa hispida ), before and after a major collapse in sea-ice in Svalbard, Norway. Among other changes to the ice-regime, this collapse shifted the summer position of the marginal ice zone from over the continental shelf, northward to the deep Arctic Ocean Basin. Following this change, which is thought to be a ‘tipping point’, subadult ringed seals swam greater distances, showed less area-restricted search behaviour, dived for longer periods, exhibited shorter surface intervals, rested less on sea-ice and did less diving directly beneath the ice during post-moulting foraging excursions. In combination, these behavioural changes suggest increased foraging effort and thus also likely increases in the energetic costs of finding food. Continued declines in sea-ice are likely to result in distributional changes, range reductions and population declines in this keystone arctic species.

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 Retention of ice-associated amphipods: possible consequences for an ice-free Arctic Ocean

2012 ◽  
Vol 8 (6) ◽  
pp. 1012-1015 ◽  
Author(s):  
J. Berge ◽  
Ø. Varpe ◽  
M. A. Moline ◽  
A. Wold ◽  
P. E. Renaud ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Marine Mammals ◽  
Deep Ocean ◽  
Arctic Sea Ice ◽  
Ocean Currents ◽  
The Arctic ◽  
Arctic Sea ◽  
The Arctic Ocean ◽  
Deep Ocean Currents

Recent studies predict that the Arctic Ocean will have ice-free summers within the next 30 years. This poses a significant challenge for the marine organisms associated with the Arctic sea ice, such as marine mammals and, not least, the ice-associated crustaceans generally considered to spend their entire life on the underside of the Arctic sea ice. Based upon unique samples collected within the Arctic Ocean during the polar night, we provide a new conceptual understanding of an intimate connection between these under-ice crustaceans and the deep Arctic Ocean currents. We suggest that downwards vertical migrations, followed by polewards transport in deep ocean currents, are an adaptive trait of ice fauna that both increases survival during ice-free periods of the year and enables re-colonization of sea ice when they ascend within the Arctic Ocean. From an evolutionary perspective, this may have been an adaptation allowing success in a seasonally ice-covered Arctic. Our findings may ultimately change the perception of ice fauna as a biota imminently threatened by the predicted disappearance of perennial sea ice.

Climate change and biological oceanography of the Arctic Ocean

1995 ◽  
Vol 352 (1699) ◽  
pp. 277-286 ◽  
Keyword(s):  
Sea Ice ◽  
Environmental Changes ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Extent ◽  
Biological Oceanography ◽  
Exchange Processes ◽  
Arctic Sea ◽  
Chemical Conditions ◽  
Physical And Chemical

Polar environments are characterized by unique physical and chemical conditions for the development of life. Low temperatures and the seasonality of light create one of the most extreme habitats on Earth. The Arctic sea ice cover not only acts as an insulator for heat and energy exchange processes between ocean and atmosphere but also serves as a unique habitat for a specialized community of organisms, consisting of bacteria, algae, protozoa and metazoa. The primary production of sea ice algae may play a crucial role in the life cycle of planktonic and benthic organisms. Thus, a reduction of the sea ice extent due to environmental changes will influence the structure and processes of communities living inside the ice and pelagic realms.

scholarly journals Arctic Sea Ice Freeboard Retrieval from Envisat Altimetry Data

2021 ◽  
Vol 13 (8) ◽  
pp. 1414
Author(s):  
Shengkai Zhang ◽  
Yue Xuan ◽  
Jiaxing Li ◽  
Tong Geng ◽  
Xiao Li ◽  
...  
Keyword(s):  
Sea Ice ◽  
Satellite Altimetry ◽  
Environmental Changes ◽  
European Space Agency ◽  
Arctic Sea Ice ◽  
Sea Surface ◽  
The Arctic ◽  
Global Changes ◽  
Altimetry Data ◽  
Arctic Sea

Arctic sea ice variations are sensitive to Arctic environmental changes and global changes. Freeboard and thickness are two important parameters in sea ice change research. Satellite altimetry can provide long-time and large-scale sea ice monitoring. We estimated the Arctic sea ice freeboard and its variations for the period from 2002 to 2012 from Envisat satellite altimetry data. To remove geoid undulations, we reprocessed the Envisat data using a newly developed mean sea surface (MSS) model, named DTU18. Residuals in the static geoid were removed by using the moving average technique. We then determined the local sea surface height and sea ice freeboard from the Envisat elevation profiles. We validated our freeboard estimates using two radar freeboard products from the European Space Agency (ESA) Climate Change Initiative (CCI) and the Alfred Wegener Institute (AWI), as well as the Operation IceBridge (OIB) sea ice freeboard product. The overall differences between our estimates and the CCI and AWI data were 0.11 ± 0.14 m and 0.12 ± 0.14 m, respectively. Our estimates show good agreement with the three products for areas of freeboard larger than 0.2 m and smaller than 0.3 m. For areas of freeboard larger than 0.3 m, our estimates correlate better with OIB freeboard than with CCI and AWI. The variations in the Arctic sea ice thickness are discussed. The ice freeboard reached its minimum in 2008 during the research period. Sharp decreases were found in the winters of 2005 and 2007.

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 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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