Quantifying the Influence of Atlantic Heat on Barents Sea Ice Variability and Retreat*

2012 ◽  
Vol 25 (13) ◽  
pp. 4736-4743 ◽  
Author(s):  
M. Årthun ◽  
T. Eldevik ◽  
L. H. Smedsrud ◽  
Ø. Skagseth ◽  
R. B. Ingvaldsen
Keyword(s):  
Sea Ice ◽  
Heat Transport ◽  
Barents Sea ◽  
Heat Budget ◽  
Heat Content ◽  
Ocean Model ◽  
Sea Ice Extent ◽  
The Barents Sea ◽  
Ice Retreat ◽  
Sea Ice Retreat

Abstract The recent Arctic winter sea ice retreat is most pronounced in the Barents Sea. Using available observations of the Atlantic inflow to the Barents Sea and results from a regional ice–ocean model the authors assess and quantify the role of inflowing heat anomalies on sea ice variability. The interannual variability and longer-term decrease in sea ice area reflect the variability of the Atlantic inflow, both in observations and model simulations. During the last decade (1998–2008) the reduction in annual (July–June) sea ice area was 218 × 103 km2, or close to 50%. This reduction has occurred concurrent with an increase in observed Atlantic heat transport due to both strengthening and warming of the inflow. Modeled interannual variations in sea ice area between 1948 and 2007 are associated with anomalous heat transport (r = −0.63) with a 70 × 103 km2 decrease per 10 TW input of heat. Based on the simulated ocean heat budget it is found that the heat transport into the western Barents Sea sets the boundary of the ice-free Atlantic domain and, hence, the sea ice extent. The regional heat content and heat loss to the atmosphere scale with the area of open ocean as a consequence. Recent sea ice loss is thus largely caused by an increasing “Atlantification” of the Barents Sea.

scholarly journals The Value of Sustained Ocean Observations for Sea Ice Predictions in the Barents Sea

2019 ◽  
Vol 32 (20) ◽  
pp. 7017-7035 ◽  
Author(s):  
Mitchell Bushuk ◽  
Xiaosong Yang ◽  
Michael Winton ◽  
Rym Msadek ◽  
Matthew Harrison ◽  
...  
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Initial Conditions ◽  
Heat Budget ◽  
Heat Content ◽  
Arctic Sea Ice ◽  
Heat Fluxes ◽  
Seasonal Forecasts ◽  
Surface Heat Fluxes ◽  
The Barents Sea

ABSTRACT Dynamical prediction systems have shown potential to meet the emerging need for seasonal forecasts of regional Arctic sea ice. Observationally constrained initial conditions are a key source of skill for these predictions, but the direct influence of different observation types on prediction skill has not yet been systematically investigated. In this work, we perform a hierarchy of observing system experiments with a coupled global data assimilation and prediction system to assess the value of different classes of oceanic and atmospheric observations for seasonal sea ice predictions in the Barents Sea. We find notable skill improvements due to the inclusion of both sea surface temperature (SST) satellite observations and subsurface conductivity–temperature–depth (CTD) measurements. The SST data are found to provide the crucial source of interannual variability, whereas the CTD data primarily provide climatological and trend improvements. Analysis of the Barents Sea ocean heat budget suggests that ocean heat content anomalies in this region are driven by surface heat fluxes on seasonal time scales.

Associations among temperature, sea ice and phytoplankton bloom dynamics in the Barents Sea

2020 ◽  
Vol 635 ◽  
pp. 25-36 ◽  
Author(s):  
K Dong ◽  
ØK Kvile ◽  
NC Stenseth ◽  
LC Stige
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Barents Sea ◽  
Phytoplankton Bloom ◽  
Sea Surface ◽  
The Barents Sea ◽  
Ice Retreat ◽  
Sea Ice Retreat ◽  
Time Substitution

Variations in physical conditions caused by climate change are likely to have large influences on marine organisms, including phytoplankton. Here, we investigated associations between satellite-derived chlorophyll a data from the Barents Sea and 2 key abiotic factors: sea surface temperature and sea-ice concentration. Specifically, we investigated how climate variability, through the measured physical factors, associated with phytoplankton phenology between 1998 and 2014. Associations between sea surface temperature and phytoplankton bloom dynamics differed depending on the area. The spring phytoplankton bloom occurred earlier and had higher magnitude in warm compared to cold years in the northern part of the Barents Sea, but there was no significant association in the southern part. In seasonally ice-covered regions, the association between the timing of the sea-ice retreat and the phytoplankton peak was nonlinear: sea-ice retreat time before mid-May was not associated with bloom timing, whereas the phytoplankton bloom occurred before or immediately following the ice retreat when the ice retreated after mid-May. Although drivers that are relatively constant across years, such as insolation, probably influenced the spatial gradient in chlorophyll, a space-for-time substitution captured the predicted effects of sea-ice retreat on the timing and magnitude of the phytoplankton bloom quite well.

scholarly journals Sea-ice indicators of polar bear habitat

2016 ◽  
Vol 10 (5) ◽  
pp. 2027-2041 ◽  
Author(s):  
Harry L. Stern ◽  
Kristin L. Laidre
Keyword(s):  
Sea Ice ◽  
Polar Bear ◽  
Barents Sea ◽  
Arctic Basin ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
The Barents Sea ◽  
Ice Concentration ◽  
Ice Retreat ◽  
Sea Ice Retreat

Abstract. Nineteen subpopulations of polar bears (Ursus maritimus) are found throughout the circumpolar Arctic, and in all regions they depend on sea ice as a platform for traveling, hunting, and breeding. Therefore polar bear phenology – the cycle of biological events – is linked to the timing of sea-ice retreat in spring and advance in fall. We analyzed the dates of sea-ice retreat and advance in all 19 polar bear subpopulation regions from 1979 to 2014, using daily sea-ice concentration data from satellite passive microwave instruments. We define the dates of sea-ice retreat and advance in a region as the dates when the area of sea ice drops below a certain threshold (retreat) on its way to the summer minimum or rises above the threshold (advance) on its way to the winter maximum. The threshold is chosen to be halfway between the historical (1979–2014) mean September and mean March sea-ice areas. In all 19 regions there is a trend toward earlier sea-ice retreat and later sea-ice advance. Trends generally range from −3 to −9 days decade−1 in spring and from +3 to +9 days decade−1 in fall, with larger trends in the Barents Sea and central Arctic Basin. The trends are not sensitive to the threshold. We also calculated the number of days per year that the sea-ice area exceeded the threshold (termed ice-covered days) and the average sea-ice concentration from 1 June through 31 October. The number of ice-covered days is declining in all regions at the rate of −7 to −19 days decade−1, with larger trends in the Barents Sea and central Arctic Basin. The June–October sea-ice concentration is declining in all regions at rates ranging from −1 to −9 percent decade−1. These sea-ice metrics (or indicators of habitat change) were designed to be useful for management agencies and for comparative purposes among subpopulations. We recommend that the National Climate Assessment include the timing of sea-ice retreat and advance in future reports.

scholarly journals Sea-Ice Indicators of Polar Bear Habitat

2016 ◽  
Author(s):  
Harry L. Stern ◽  
Kristin L. Laidre
Keyword(s):  
Sea Ice ◽  
Polar Bear ◽  
Barents Sea ◽  
Arctic Basin ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
The Barents Sea ◽  
Ice Concentration ◽  
Ice Retreat ◽  
Sea Ice Retreat

Abstract. Abstract. Nineteen distinct subpopulations of polar bears (Ursus maritimus) are found throughout the Arctic, and in all regions they depend on sea ice as a platform for traveling, hunting, and breeding. Therefore polar bear phenology – the cycle of biological events – is tied to the timing of sea-ice retreat in spring and advance in fall. We analyzed the dates of sea-ice retreat and advance in all 19 polar bear subpopulation regions from 1979 to 2014, using daily sea-ice concentration data from satellite passive microwave instruments. We define the dates of sea-ice retreat and advance in a region as the dates when the area of sea ice drops below a certain threshold (retreat) on its way to the summer minimum, or rises above the threshold (advance) on its way to the winter maximum. The threshold is chosen to be halfway between the historical (1979–2014) mean September and mean March sea-ice areas. In all 19 regions there is a trend toward earlier sea-ice retreat and later sea-ice advance. Trends generally range from −3 to −9 days decade−1 in spring, and from +3 to +9 days decade−1 in fall, with larger trends in the Barents Sea and central Arctic Basin. The trends are not sensitive to the threshold. We also calculated the number of days per year that the sea-ice area exceeded the threshold (termed ice-covered days), and the average sea-ice concentration from 1 June through 31 October. The number of ice-covered days is declining in all regions at the rate of −7 to −19 days decade−1, with larger trends in the Barents Sea and central Arctic Basin. The June–October sea-ice concentration is declining in all regions at rates ranging from −1 to −9 percent decade−1. These sea-ice metrics (or indicators of change in marine mammal habitat) were designed to be useful for management agencies. We recommend that the National Climate Assessment include the timing of sea-ice retreat and advance in future reports.

scholarly journals Wind-Driven Atlantic Water Flow as a Direct Mode for Reduced Barents Sea Ice Cover

2017 ◽  
Vol 30 (2) ◽  
pp. 803-812 ◽  
Author(s):  
Vidar S. Lien ◽  
Pawel Schlichtholz ◽  
Øystein Skagseth ◽  
Frode B. Vikebø
Keyword(s):  
Sea Ice ◽  
Heat Transport ◽  
Ocean Circulation ◽  
Barents Sea ◽  
Heat Content ◽  
Circulation Model ◽  
Ice Cover ◽  
Atlantic Water ◽  
Direct Role ◽  
The Barents Sea

Variability in the Barents Sea ice cover on interannual and longer time scales has previously been shown to be governed by oceanic heat transport. Based on analysis of observations and results from an ocean circulation model during an event of reduced sea ice cover in the northeastern Barents Sea in winter 1993, it is shown that the ocean also plays a direct role within seasons. Positive wind stress curl and associated Ekman divergence causes a coherent increase in the Atlantic water transport along the negative thermal gradient through the Barents Sea. The immediate response connected to the associated local winds in the northeastern Barents Sea is a decrease in the sea ice cover due to advection. Despite a subsequent anomalous ocean-to-air heat loss on the order of 100 W m−2 due to the open water, the increase in the ocean heat content caused by the circulation anomaly reduced refreezing on a time scale of order one month. Furthermore, it is found that coherent ocean heat transport anomalies occurred more frequently in the latter part of the last five decades during periods of positive North Atlantic Oscillation index, coinciding with the Barents Sea winter sea ice cover decline from the 1990s and onward.

scholarly journals Autumn Arctic Pacific Sea Ice Dipole as a Source of Predictability for Subsequent Spring Barents Sea Ice Condition

2021 ◽  
Vol 34 (2) ◽  
pp. 787-804
Author(s):  
Yu-Chiao Liang ◽  
Young-Oh Kwon ◽  
Claude Frankignoul
Keyword(s):  
Sea Ice ◽  
Heat Transport ◽  
Lead Time ◽  
Barents Sea ◽  
Correlation Coefficients ◽  
The Arctic ◽  
Ocean Heat Transport ◽  
Sea Ice Extent ◽  
Ice Retreat ◽  
Highly Correlated

AbstractThis study uses observational and reanalysis datasets in 1980–2016 to show a close connection between a boreal autumn sea ice dipole in the Arctic Pacific sector and sea ice anomalies in the Barents Sea (BS) during the following spring. The September–October Arctic Pacific sea ice dipole variations are highly correlated with the subsequent April–May BS sea ice variations (r = 0.71). The strong connection between the regional sea ice variabilities across the Arctic uncovers a new source of predictability for spring BS sea ice prediction at 7-month lead time. A cross-validated linear regression prediction model using the Arctic Pacific sea ice dipole with 7-month lead time is demonstrated to have significant prediction skills with 0.54–0.85 anomaly correlation coefficients. The autumn sea ice dipole, manifested as sea ice retreat in the Beaufort and Chukchi Seas and expansion in the East Siberian and Laptev Seas, is primarily forced by preceding atmospheric shortwave anomalies from late spring to early autumn. The spring BS sea ice increases are mostly driven by an ocean-to-sea ice heat flux reduction in preceding months, associated with reduced horizontal ocean heat transport into the BS. The dynamical linkage between the two regional sea ice anomalies is suggested to involve positive stratospheric polar cap anomalies during autumn and winter, with its center slowly moving toward Greenland. The migration of the stratospheric anomalies is followed in midwinter by a negative North Atlantic Oscillation–like pattern in the troposphere, leading to reduced ocean heat transport into the BS and sea ice extent increase.

scholarly journals Climate-driven benthic invertebrate activity and biogeochemical functioning across the Barents Sea polar front

2020 ◽  
Vol 378 (2181) ◽  
pp. 20190365 ◽  
Author(s):  
Martin Solan ◽  
Ellie R. Ward ◽  
Christina L. Wood ◽  
Adam J. Reed ◽  
Laura J. Grange ◽  
...  
Keyword(s):  
Sea Ice ◽  
Ecosystem Functioning ◽  
Barents Sea ◽  
Benthic Invertebrate ◽  
Polar Front ◽  
The Arctic ◽  
Nutrient Concentrations ◽  
Sea Ice Extent ◽  
Biological Communities ◽  
The Barents Sea

Arctic marine ecosystems are undergoing rapid correction in response to multiple expressions of climate change, but the consequences of altered biodiversity for the sequestration, transformation and storage of nutrients are poorly constrained. Here, we determine the bioturbation activity of sediment-dwelling invertebrate communities over two consecutive summers that contrasted in sea-ice extent along a transect intersecting the polar front. We find a clear separation in community composition at the polar front that marks a transition in the type and amount of bioturbation activity, and associated nutrient concentrations, sufficient to distinguish a southern high from a northern low. While patterns in community structure reflect proximity to arctic versus boreal conditions, our observations strongly suggest that faunal activity is moderated by seasonal variations in sea ice extent that influence food supply to the benthos. Our observations help visualize how a climate-driven reorganization of the Barents Sea benthic ecosystem may be expressed, and emphasize the rapidity with which an entire region could experience a functional transformation. As strong benthic-pelagic coupling is typical across most parts of the Arctic shelf, the response of these ecosystems to a changing climate will have important ramifications for ecosystem functioning and the trophic structure of the entire food web. This article is part of the theme issue ‘The changing Arctic Ocean: consequences for biological communities, biogeochemical processes and ecosystem functioning'.

scholarly journals Mechanisms influencing seasonal-to-interannual prediction skill of sea ice extent in the Arctic Ocean in MIROC

2017 ◽  
Author(s):  
Jun Ono ◽  
Hiroaki Tatebe ◽  
Yoshiki Komuro ◽  
Masato I. Nodzu ◽  
Masayoshi Ishii
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Barents Sea ◽  
Heat Content ◽  
The Arctic ◽  
Subsurface Water ◽  
Lead Times ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
The Arctic Ocean

Abstract. To assess the skill of predictions of the seasonal-to-interannual detrended sea ice extent in the Arctic Ocean (SIEAO) and to clarify the underlying physical processes, we conducted ensemble hindcasts, started on January 1st, April 1st, July 1st, and October 1st for each year from 1980 to 2011, for lead times of up three years, using the Model for Interdisciplinary Research on Climate (MIROC) version 5 initialized with the observed atmosphere and ocean anomalies and sea ice concentration. Significant skill is found for the winter months: the December SIEAO can be predicted up to 1 year ahead. This skill is attributed to the subsurface ocean heat content originating in the North Atlantic. The subsurface water flows into the Barents Sea from spring to fall and emerges at the surface in winter by vertical mixing, and eventually affects the sea ice variability there. Meanwhile, the September SIEAO predictions are skillful for lead times of up to 3 months, due to the persistence of sea ice in the Beaufort, Chukchi, and East Siberian Seas initialized in July, as suggested by previous studies.

scholarly journals Seasonal Prediction from Arctic Sea Surface Temperatures: Opportunities and Pitfalls

2018 ◽  
Vol 31 (20) ◽  
pp. 8197-8210 ◽  
Author(s):  
Erik W. Kolstad ◽  
Marius Årthun
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Arctic Sea Ice ◽  
Sea Surface ◽  
Sea Ice Extent ◽  
Norwegian Sea ◽  
The Barents Sea ◽  
Arctic Sea ◽  
Lagged Correlation ◽  
Sst Anomalies

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.

Sign in / Sign up

Export Citation Format

Share Document