On large outflows of Arctic sea ice into the Barents Sea

Arctic sea ice extent and sea surface temperature (SST) anomalies have been shown to be skillful predictors of weather anomalies in the midlatitudes on the seasonal time scale. In particular, below-normal sea ice extent in the Barents Sea in fall has sometimes preceded cold winters in parts of Eurasia. Here we explore the potential for predicting seasonal surface air temperature (SAT) anomalies in Europe from seasonal SST anomalies in the Nordic seas throughout the year. First, we show that fall SST anomalies not just in the Barents Sea but also in the Norwegian Sea have the potential to predict wintertime SAT anomalies in Europe. Norwegian Sea SST anomalies in spring are also significant predictors of European SAT anomalies in summer. Second, we demonstrate that the potential for prediction is sensitive to trends in the data. In particular, the lagged correlation between Norwegian Sea SST anomalies in spring and European SAT anomalies in summer is considerably higher for raw data than linearly detrended data, largely due to warming SST and SAT trends in recent decades. Third, we show that the potential for prediction has not been stationary in time. One key result is that, according to two twentieth-century reanalyses, the strength of the negative lagged correlation between Barents Sea SST anomalies in fall and European SAT anomalies in winter after 1979 is unprecedented since 1900.

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Possible Link Between Arctic Sea Ice and January PM10 Concentrations in South Korea

Atmosphere ◽

10.3390/atmos10100619 ◽

2019 ◽

Vol 10 (10) ◽

pp. 619 ◽

Cited By ~ 5

Author(s):

Jeong-Hun Kim ◽

Maeng-Ki Kim ◽

Chang-Hoi Ho ◽

Rokjin J. Park ◽

Minjoong J. Kim ◽

...

Keyword(s):

High Pressure ◽

South Korea ◽

Sea Ice ◽

Time Scales ◽

Korean Peninsula ◽

Barents Sea ◽

Arctic Sea Ice ◽

The Barents Sea ◽

Arctic Sea ◽

Annual Time

In this study, we investigated the possible teleconnection between PM10 concentrations in South Korea and Arctic Sea ice concentrations at inter-annual time scales using observed PM10 data from South Korea, NCEP R2 data, and NOAA Sea Ice Concentration (SIC) data from 2001 to 2018. From the empirical orthogonal function (EOF) analysis, we found that the first mode (TC1) was a large-scale mode for PM10 in South Korea and explained about 27.4% of the total variability. Interestingly, the TC1 is more dominantly influenced by the horizontal ventilation effect than the vertical atmospheric stability effect. The pollution potential index (PPI), which is defined by the weighted average of the two ventilation effects, is highly correlated with the TC1 of PM10 at a correlation coefficient of 0.75, indicating that the PPI is a good measure for PM10 in South Korea at inter-annual time scales. Regression maps show that the decrease of SIC over the Barents Sea is significantly correlated with weakening of high pressure over the Ural mountain range region, the anomalous high pressure at 500 hPa over the Korean peninsula, and the weakening of the Siberian High and Aleutian low. Moreover, these patterns are similar to the correlation pattern with the PPI, suggesting that the variability of SIC over the Barents Sea may play an important role in modulating the variability of PM10 in South Korea through teleconnection from the Barents Sea to the Korean peninsula via Eurasia.

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Arctic sea ice area in CMIP3 and CMIP5 climate model ensembles – variability and change

The Cryosphere Discussions ◽

10.5194/tcd-9-1077-2015 ◽

2015 ◽

Vol 9 (1) ◽

pp. 1077-1131 ◽

Cited By ~ 13

Author(s):

V. A. Semenov ◽

T. Martin ◽

L. K. Behrens ◽

M. Latif

Keyword(s):

Sea Ice ◽

21St Century ◽

Seasonal Cycle ◽

Climate Models ◽

Barents Sea ◽

Arctic Sea Ice ◽

The Arctic ◽

Cmip5 Models ◽

The Barents Sea ◽

Arctic Sea

Abstract. The shrinking Arctic sea ice cover observed during the last decades is probably the clearest manifestation of ongoing climate change. While climate models in general reproduce the sea ice retreat in the Arctic during the 20th century and simulate further sea ice area loss during the 21st century in response to anthropogenic forcing, the models suffer from large biases and the model results exhibit considerable spread. The last generation of climate models from World Climate Research Programme Coupled Model Intercomparison Project Phase 5 (CMIP5), when compared to the previous CMIP3 model ensemble and considering the whole Arctic, were found to be more consistent with the observed changes in sea ice extent during the recent decades. Some CMIP5 models project strongly accelerated (non-linear) sea ice loss during the first half of the 21st century. Here, complementary to previous studies, we compare results from CMIP3 and CMIP5 with respect to regional Arctic sea ice change. We focus on September and March sea ice. Sea ice area (SIA) variability, sea ice concentration (SIC) variability, and characteristics of the SIA seasonal cycle and interannual variability have been analysed for the whole Arctic, termed Entire Arctic, Central Arctic and Barents Sea. Further, the sensitivity of SIA changes to changes in Northern Hemisphere (NH) averaged temperature is investigated and several important dynamical links between SIA and natural climate variability involving the Atlantic Meridional Overturning Circulation (AMOC), North Atlantic Oscillation (NAO) and sea level pressure gradient (SLPG) in the western Barents Sea opening serving as an index of oceanic inflow to the Barents Sea are studied. The CMIP3 and CMIP5 models not only simulate a coherent decline of the Arctic SIA but also depict consistent changes in the SIA seasonal cycle and in the aforementioned dynamical links. The spatial patterns of SIC variability improve in the CMIP5 ensemble, particularly in summer. Both CMIP ensembles depict a significant link between the SIA and NH temperature changes. Our analysis suggests that, on average, the sensitivity of SIA to external forcing is enhanced in the CMIP5 models. The Arctic SIA variability response to anthropogenic forcing is different in CMIP3 and CMIP5. While the CMIP3 models simulate increased variability in March and September, the CMIP5 ensemble shows the opposite tendency. A noticeable improvement in the simulation of summer SIA by the CMIP5 models is often accompanied by worse results for winter SIA characteristics. The relation between SIA and mean AMOC changes is opposite in September and March, with March SIA changes being positively correlated with AMOC slowing. Finally, both CMIP ensembles demonstrate an ability to capture, at least qualitatively, important dynamical links of SIA to decadal variability of the AMOC, NAO and SLPG. SIA in the Barents Sea is strongly overestimated by the majority of the CMIP3 and CMIP5 models, and projected SIA changes are characterized by a large spread giving rise to high uncertainty.

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Navigation in the Russian Arctic: Sea Ice Caused Difficulties and Accidents

Volume 6: Polar and Arctic Sciences and Technology; Offshore Geotechnics; Petroleum Technology Symposium ◽

10.1115/omae2013-10546 ◽

2013 ◽

Cited By ~ 6

Author(s):

Nataliya Marchenko

Keyword(s):

Sea Ice ◽

Barents Sea ◽

Arctic Sea Ice ◽

The Arctic ◽

Russian Arctic ◽

Arctic Seas ◽

The North ◽

The Barents Sea ◽

Arctic Sea ◽

Ice Conditions

The 5 Russian Arctic Seas have common features, but differ significantly from each other in the sea ice regime and navigation specifics. Navigation in the Arctic is a big challenge, especially during the winter season. However, it is necessary, due to limited natural resources elsewhere on Earth that may be easier for exploitation. Therefore sea ice is an important issue for future development. We foresee that the Arctic may become ice free in summer as a result of global warming and even light yachts will be able to pass through the Eastern Passage. There have been several such examples in the last years. But sea ice is an inherent feature of Arctic Seas in winter, it is permanently immanent for the Central Arctic Basin. That is why it is important to get appropriate knowledge about sea ice properties and operations in ice conditions. Four seas, the Kara, Laptev, East Siberian, and Chukchi have been examined in the book “Russian Arctic Seas. Navigation Condition and Accidents”, Marchenko, 2012 [1]. The book is devoted to the eastern sector of the Arctic, with a description of the seas and accidents caused by heavy ice conditions. The traditional physical-geographical characteristics, information about the navigation conditions and the main sea routes and reports on accidents that occurred in the 20th century have reviewed. An additional investigation has been performed for more recent accidents and for the Barents Sea. Considerable attention has been paid to problems associated with sea ice caused by the present development of the Arctic. Sea ice can significantly affect shipping, drilling, and the construction and operation of platforms and handling terminals. Sea ice is present in the main part of the east Arctic Sea most of the year. The Barents Sea, which is strongly influenced and warmed by the North Atlantic Current, has a natural environment that is dramatically different from those of the other Arctic seas. The main difficulties with the Barents Sea are produced by icing and storms and in the north icebergs. The ice jet is the most dangerous phenomenon in the main straits along the Northern Sea Route and in Chukchi Seas. The accidents in the Arctic Sea have been classified, described and connected with weather and ice conditions. Behaviour of the crew is taken into consideration. The following types of the ice-induced accidents are distinguished: forced drift, forced overwintering, shipwreck, and serious damage to the hull in which the crew, sometimes with the help of other crews, could still save the ship. The main reasons for shipwrecks and damages are hits of ice floes (often in rather calm ice conditions), ice nipping (compression) and drift. Such investigation is important for safety in the Arctic.

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The Role of Atlantic Heat Transport in Future Arctic Winter Sea Ice Loss

Journal of Climate ◽

10.1175/jcli-d-18-0750.1 ◽

2019 ◽

Vol 32 (11) ◽

pp. 3327-3341 ◽

Cited By ~ 21

Author(s):

Marius Årthun ◽

Tor Eldevik ◽

Lars H. Smedsrud

Keyword(s):

Sea Ice ◽

Heat Transport ◽

Atmospheric Circulation ◽

Barents Sea ◽

Atlantic Water ◽

Arctic Sea Ice ◽

The Arctic ◽

The Barents Sea ◽

Arctic Sea ◽

Warming World

Abstract During recent decades Arctic sea ice variability and retreat during winter have largely been a result of variable ocean heat transport (OHT). Here we use the Community Earth System Model (CESM) large ensemble simulation to disentangle internally and externally forced winter Arctic sea ice variability, and to assess to what extent future winter sea ice variability and trends are driven by Atlantic heat transport. We find that OHT into the Barents Sea has been, and is at present, a major source of internal Arctic winter sea ice variability and predictability. In a warming world (RCP8.5), OHT remains a good predictor of winter sea ice variability, although the relation weakens as the sea ice retreats beyond the Barents Sea. Warm Atlantic water gradually spreads downstream from the Barents Sea and farther into the Arctic Ocean, leading to a reduced sea ice cover and substantial changes in sea ice thickness. The future long-term increase in Atlantic heat transport is carried by warmer water as the current itself is found to weaken. The externally forced weakening of the Atlantic inflow to the Barents Sea is in contrast to a strengthening of the Nordic Seas circulation, and is thus not directly related to a slowdown of the Atlantic meridional overturning circulation (AMOC). The weakened Barents Sea inflow rather results from regional atmospheric circulation trends acting to change the relative strength of Atlantic water pathways into the Arctic. Internal OHT variability is associated with both upstream ocean circulation changes, including AMOC, and large-scale atmospheric circulation anomalies reminiscent of the Arctic Oscillation.

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The Arctic Sea ice in the CMIP3 climate model ensemble – variability and anthropogenic change

The Cryosphere Discussions ◽

10.5194/tcd-6-5317-2012 ◽

2012 ◽

Vol 6 (6) ◽

pp. 5317-5344 ◽

Cited By ~ 4

Author(s):

L. K. Behrens ◽

T. Martin ◽

V. A. Semenov ◽

M. Latif

Keyword(s):

Sea Ice ◽

Observational Data ◽

Seasonal Cycle ◽

Barents Sea ◽

Arctic Sea Ice ◽

The Arctic ◽

Late 20Th Century ◽

Ice Volume ◽

The Barents Sea ◽

Arctic Sea

Abstract. The strongest manifestation of global warming is observed in the Arctic. The warming in the Arctic during the recent decades is about twice as strong as in the global average and has been accompanied by a summer sea ice decline that is very likely unprecedented during the last millennium. Here, Arctic sea ice variability is analyzed in the ensemble of CMIP3 models. Complementary to several previous studies, we focus on regional aspects, in particular on the Barents Sea. We also investigate the changes in the seasonal cycle and interannual variability. In all regions, the models predict a reduction in sea ice area and sea ice volume during 1900–2100. Toward the end of the 21st century, the models simulate higher sea ice area variability in September than in March, whereas the variability in the preindustrial control runs is higher in March. Furthermore, the amplitude and phase of the sea ice seasonal cycle change in response to enhanced greenhouse warming. The amplitude of the sea ice area seasonal cycle increases due to the very strong sea ice area decline in September. The seasonal cycle amplitude of the sea ice volume decreases due to the stronger reduction of sea ice volume in March. Multi-model mean estimates for the late 20th century are comparable with observational data only for the entire Arctic and the Central Arctic. In the Barents Sea, differences between the multi-model mean and the observational data are more pronounced. Regional sea ice sensitivity to Northern Hemisphere average surface warming has been investigated.

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The Impact of Stratospheric Circulation Extremes on Minimum Arctic Sea Ice Extent

Journal of Climate ◽

10.1175/jcli-d-17-0495.1 ◽

2018 ◽

Vol 31 (18) ◽

pp. 7169-7183 ◽

Cited By ~ 10

Author(s):

Karen L. Smith ◽

Lorenzo M. Polvani ◽

L. Bruno Tremblay

Keyword(s):

Sea Ice ◽

Barents Sea ◽

Arctic Sea Ice ◽

The Arctic ◽

Sea Ice Extent ◽

The Barents Sea ◽

Arctic Sea ◽

Stratospheric Circulation ◽

The Impact ◽

Arctic Sea Ice Extent

Given the rapidly changing Arctic climate, there is an urgent need for improved seasonal predictions of Arctic sea ice. Yet, Arctic sea ice prediction is inherently complex. Among other factors, wintertime atmospheric circulation has been shown to be predictive of summertime Arctic sea ice extent. Specifically, many studies have shown that the interannual variability of summertime Arctic sea ice extent (SIE) is anticorrelated with the leading mode of extratropical atmospheric variability, the Arctic Oscillation (AO), in the preceding winter. Given this relationship, the potential predictive role of stratospheric circulation extremes and stratosphere–troposphere coupling in linking the AO and Arctic SIE variability is examined. It is shown that extremes in the stratospheric circulation during the winter season, namely, stratospheric sudden warming (SSW) and strong polar vortex (SPV) events, are associated with significant anomalies in sea ice concentration in the Barents Sea in spring and along the Eurasian coastline in summer in both observations and a fully coupled, stratosphere-resolving general circulation model. Consistent with previous work on the AO, it is shown that SSWs, which are followed by the negative phase of the AO at the surface, result in sea ice growth, whereas SPVs, which are followed by the positive phase of the AO at the surface, result in sea ice loss, although the mechanisms in the Barents Sea and along the Eurasian coastline are different. The analysis suggests that the presence or absence of stratospheric circulation extremes in winter may play a nontrivial role in determining total September Arctic SIE when combined with other factors.

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Polar cod in jeopardy under the retreating Arctic sea ice

Communications Biology ◽

10.1038/s42003-019-0649-2 ◽

2019 ◽

Vol 2 (1) ◽

Cited By ~ 6

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.

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Multi-aspect Assessment of CMIP6 Models for Arctic Sea Ice Simulation

Journal of Climate ◽

10.1175/jcli-d-20-0522.1 ◽

2020 ◽

pp. 1-37

Author(s):

Mengyuan Long ◽

Lujun Zhang ◽

Siyu Hu ◽

Shimeng Qian

Keyword(s):

Sea Ice ◽

Temporal Variation ◽

Spatial Patterns ◽

Barents Sea ◽

Linear Trend ◽

Arctic Sea Ice ◽

Sea Ice Concentration ◽

Sea Ice Extent ◽

East Greenland ◽

Arctic Sea

AbstractThis paper evaluates the ability of 35 models from the 6th phase of the Coupled Model Intercomparison Project (CMIP6) to simulate Arctic sea ice by comparing simulated results with observation from the aspects of spatial patterns and temporal variation. The simulation ability of each model is also quantified by Taylor score and e score from these two aspects. Results show that biases between observed and simulated Arctic sea ice concentration (SIC) are mainly located in the East Greenland, Barents, Bering Sea and Sea of Okhotsk. The largest difference between the observed and simulated SIC spatial patterns occurs in September. Since the beginning of the 21st century, the ability of most models to simulate summer SIC spatial patterns has decreased. We also find that models with Sea Ice Simulator (SIS) sea-ice component in CMIP6 show a consistent larger positive simulation biases of SIC in the East Greenland and Barents Sea. In addition, for most models, the higher the model resolution is, the better the match between the simulated and observed spatial patterns of winter Arctic SIC is. Furthermore, this paper makes a detailed assessment for temporal variation of Arctic sea ice extent (SIE) with regard to climatological average, seasonal SIE, multi-year linear trend and detrended standard deviation of SIE. The sensitivity of September Arctic SIE to a given change of Arctic surface air temperature (SAT) over 1979-2014 in each model has also been investigated. Most models simulate a smaller loss of September Arctic SIE per degree of warming than observed (1.37×106 km2 K-1).

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Model-data comparison and data assimilation of mid-Holocene Arctic sea-ice concentration

Climate of the Past Discussions ◽

10.5194/cpd-9-6515-2013 ◽

2013 ◽

Vol 9 (6) ◽

pp. 6515-6549 ◽

Cited By ~ 1

Author(s):

F. Klein ◽

H. Goosse ◽

A. Mairesse ◽

A. de Vernal

Keyword(s):

Data Assimilation ◽

Sea Ice ◽

Atmospheric Circulation ◽

Barents Sea ◽

Arctic Sea Ice ◽

The Arctic ◽

Model Data ◽

Sea Ice Concentration ◽

Arctic Sea ◽

Ice Concentration

Abstract. The consistency between a new quantitative reconstruction of Arctic sea-ice concentration based on dinocyst assemblages and the results of climate models has been investigated for the mid-Holocene. The comparison shows that the simulated sea-ice changes are weaker and spatially more homogeneous than the recorded ones. Furthermore, although the model-data agreement is relatively good in some regions such as the Labrador Sea, the skill of the models at local scale is low. The response of the models follows mainly the increase in summer insolation at large scale. This is modulated by changes in atmospheric circulation leading to differences between regions in the models that are albeit smaller than in the reconstruction. Performing simulations with data assimilation using the model LOVECLIM amplifies those regional differences, mainly through a reduction of the southward winds in the Barents Sea and an increase in the westerly winds in the Canadian Basin of the Arctic. This leads to an increase in the ice concentration in the Barents and Chukchi Seas and a better agreement with the reconstructions. This underlines the potential role of atmospheric circulation to explain the reconstructed changes during the Holocene.

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