scholarly journals Seasonal and Regional Manifestation of Arctic Sea Ice Loss

2018 ◽  
Vol 31 (12) ◽  
pp. 4917-4932 ◽  
Author(s):  
Ingrid H. Onarheim ◽  
Tor Eldevik ◽  
Lars H. Smedsrud ◽  
Julienne C. Stroeve
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
The Barents Sea ◽  
East Siberian Sea ◽  
Shelf Seas ◽  
Perennial Ice

The Arctic Ocean is currently on a fast track toward seasonally ice-free conditions. Although most attention has been on the accelerating summer sea ice decline, large changes are also occurring in winter. This study assesses past, present, and possible future change in regional Northern Hemisphere sea ice extent throughout the year by examining sea ice concentration based on observations back to 1950, including the satellite record since 1979. At present, summer sea ice variability and change dominate in the perennial ice-covered Beaufort, Chukchi, East Siberian, Laptev, and Kara Seas, with the East Siberian Sea explaining the largest fraction of September ice loss (22%). Winter variability and change occur in the seasonally ice-covered seas farther south: the Barents Sea, Sea of Okhotsk, Greenland Sea, and Baffin Bay, with the Barents Sea carrying the largest fraction of loss in March (27%). The distinct regions of summer and winter sea ice variability and loss have generally been consistent since 1950, but appear at present to be in transformation as a result of the rapid ice loss in all seasons. As regions become seasonally ice free, future ice loss will be dominated by winter. The Kara Sea appears as the first currently perennial ice-covered sea to become ice free in September. Remaining on currently observed trends, the Arctic shelf seas are estimated to become seasonally ice free in the 2020s, and the seasonally ice-covered seas farther south to become ice free year-round from the 2050s.

Assessing trends in Arctic sea-ice distribution in the Barents and Kara seas using the Kosmos–Okean satellite series

Polar Record ◽  
1995 ◽  
Vol 31 (177) ◽  
pp. 129-134 ◽  
Author(s):  
Gennady I. Belchansky ◽  
Ilia N. Mordvintsev ◽  
Gregory K. Ovchinnikov ◽  
David C. Douglas
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Satellite Image ◽  
Kara Sea ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Published Data ◽  
Sea Ice Extent ◽  
The Barents Sea ◽  
Barents And Kara Seas

AbstractTrends in the annual minimum sea-ice extent, determined by three criteria (absolute annual minimum, minimum monthly mean, and the extent at the end of August), were investigated for the Barents and western Kara seas and adjacent parts of the Arctic Ocean during 1984–1993. Four definitions of ice extent were examined, based on thresholds of ice concentration: >90%, >70%, >40%, and >10% (El, E2, E3, and E4, respectively). Trends were studied using ice maps produced by the Russian Hydro-Meteorological Service, Kosmos and Okean satellite imagery, and data extracted from published literature. During 1984–1993, an increasing trend in the extent of minimum sea-ice cover was observed in the Barents, Kara, and combined Barents–Kara seas, for all ice-extent definitions. Root-mean-square differences between hydro-meteorological ice maps and satellite-image ice classifications for coincident areas and dates were 15.5%, 19.3%, 18.8%, and 11.5%, for ice extensions El–E4, respectively. The differences were subjected to Monte Carlo analyses to construct confidence intervals for the 10-year ice-map trends. With probability p = 0.8, the average 10-year increase in the minimum monthly mean sea-ice extent (followed in brackets by the average increase in the absolute annual minimum ice extent) was 12–46% [26–96%], 31–71% [55–140%], 30–69% [26–94%], and 48–94% [35–108%] in the Barents Sea; 20–60% [32–120%], 10–45% [20–92%], 2–36% [13–78%], and 10–47% [8–69%] in the Kara Sea; and 9–43% [26–59%], 9–41% [30–63%], 8–41% [22–52%] and 15–51% [21–51%] in the combined Barents–Kara seas, for ice concentrations El–E4, respectively. Including published data from 1966–1983, the trend in minimum monthly mean sea-ice extent for the combined 28-year period showed an average reduction of 8% in the Barents Sea and a 55% reduction in the western Kara Sea; ice extent at the end of August showed an average reduction of 33% in the Barents Sea.

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

2018 ◽  
Vol 31 (18) ◽  
pp. 7169-7183 ◽  
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.

Mechanisms for and Predictability of a Drastic Reduction in the Arctic Sea Ice: APPOSITE Data with Climate Model MIROC

2019 ◽  
Vol 32 (5) ◽  
pp. 1361-1380 ◽  
Author(s):  
J. Ono ◽  
H. Tatebe ◽  
Y. Komuro
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ensemble Prediction ◽  
Forecast Errors ◽  
Drastic Reduction ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Arctic Sea

Abstract The mechanisms for and predictability of a drastic reduction in the Arctic sea ice extent (SIE) are investigated using the Model for Interdisciplinary Research on Climate (MIROC) version 5.2. Here, a control (CTRL) with forcing fixed at year 2000 levels and perfect-model ensemble prediction (PRED) experiments are conducted. In CTRL, three (model years 51, 56, and 57) drastic SIE reductions occur during a 200-yr-long integration. In year 56, the sea ice moves offshore in association with a positive phase of the summer Arctic dipole anomaly (ADA) index and melts due to heat input through the increased open water area, and the SIE drastically decreases. This provides the preconditioning for the lowest SIE in year 57 when the Arctic Ocean interior is in a warm state and the spring sea ice volume has a large negative anomaly due to drastic ice reduction in the previous year. Although the ADA is one of the key mechanisms behind sea ice reduction, it does not always cause a drastic reduction. Our analysis suggests that wind direction favoring offshore ice motion is a more important factor for drastic ice reduction events. In years experiencing drastic ice reduction events, the September SIE can be skillfully predicted in PRED started from July, but not from April. This is because the forecast errors for the July sea level pressure and those for the sea ice concentration and sea ice thickness along the ice edge are large in PRED started from April.

scholarly journals Changes of the Arctic marginal ice zone during the satellite era

2020 ◽  
Vol 14 (6) ◽  
pp. 1971-1984 ◽  
Author(s):  
Rebecca J. Rolph ◽  
Daniel L. Feltham ◽  
David Schröder
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Satellite Observations ◽  
The Arctic ◽  
Upper And Lower Bounds ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Ice Concentration ◽  
Marginal Ice Zone ◽  
Definition Of

Abstract. Many studies have shown a decrease in Arctic sea ice extent. It does not logically follow, however, that the extent of the marginal ice zone (MIZ), here defined as the area of the ocean with ice concentrations from 15 % to 80 %, is also changing. Changes in the MIZ extent has implications for the level of atmospheric and ocean heat and gas exchange in the area of partially ice-covered ocean and for the extent of habitat for organisms that rely on the MIZ, from primary producers like sea ice algae to seals and birds. Here, we present, for the first time, an analysis of satellite observations of pan-Arctic averaged MIZ extent. We find no trend in the MIZ extent over the last 40 years from observations. Our results indicate that the constancy of the MIZ extent is the result of an observed increase in width of the MIZ being compensated for by a decrease in the perimeter of the MIZ as it moves further north. We present simulations from a coupled sea ice–ocean mixed layer model using a prognostic floe size distribution, which we find is consistent with, but poorly constrained by, existing satellite observations of pan-Arctic MIZ extent. We provide seasonal upper and lower bounds on MIZ extent based on the four satellite-derived sea ice concentration datasets used. We find a large and significant increase (>50 %) in the August and September MIZ fraction (MIZ extent divided by sea ice extent) for the Bootstrap and OSI-450 observational datasets, which can be attributed to the reduction in total sea ice extent. Given the results of this study, we suggest that references to “rapid changes” in the MIZ should remain cautious and provide a specific and clear definition of both the MIZ itself and also the property of the MIZ that is changing.

scholarly journals Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes

2020 ◽  
Vol 35 (3) ◽  
pp. 793-806
Author(s):  
William Gregory ◽  
Michel Tsamados ◽  
Julienne Stroeve ◽  
Peter Sollich
Keyword(s):  
Sea Ice ◽  
Time Series Data ◽  
Gaussian Process Regression ◽  
Forecast Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Series Data ◽  
Lead Times ◽  
Sea Ice Concentration ◽  
Sea Ice Extent

Abstract Reliable predictions of the Arctic sea ice cover are becoming of paramount importance for Arctic communities and industry stakeholders. In this study pan-Arctic and regional September mean sea ice extents are forecast with lead times of up to 3 months using a complex network statistical approach. This method exploits relationships within climate time series data by constructing regions of spatiotemporal homogeneity (i.e., nodes), and subsequently deriving teleconnection links between them. Here the nodes and links of the networks are generated from monthly mean sea ice concentration fields in June, July, and August; hence, individual networks are constructed for each respective month. Network information is then utilized within a linear Gaussian process regression forecast model, a Bayesian inference technique, in order to generate predictions of sea ice extent. Pan-Arctic forecasts capture a significant amount of the variability in the satellite observations of September sea ice extent, with detrended predictive skills of 0.53, 0.62, and 0.81 at 3-, 2-, and 1-month lead times, respectively. Regional forecasts are also performed for nine Arctic regions. On average, the highest predictive skill is achieved in the Canadian Archipelago, Beaufort, Chukchi, East Siberian, Laptev, and Kara Seas, although the ability to accurately predict many of these regions appears to be changing over time.

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'.

The Role of Moist Intrusions in Winter Arctic Warming and Sea Ice Decline

2016 ◽  
Vol 29 (12) ◽  
pp. 4473-4485 ◽  
Author(s):  
Cian Woods ◽  
Rodrigo Caballero
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Surface Air Temperature ◽  
The Arctic ◽  
Positive Trend ◽  
Sea Ice Concentration ◽  
Local Conditions ◽  
The Barents Sea ◽  
Strong Inversion ◽  
Ice Concentration

Abstract This paper examines the trajectories followed by intense intrusions of moist air into the Arctic polar region during autumn and winter and their impact on local temperature and sea ice concentration. It is found that the vertical structure of the warming associated with moist intrusions is bottom amplified, corresponding to a transition of local conditions from a “cold clear” state with a strong inversion to a “warm opaque” state with a weaker inversion. In the marginal sea ice zone of the Barents Sea, the passage of an intrusion also causes a retreat of the ice margin, which persists for many days after the intrusion has passed. The authors find that there is a positive trend in the number of intrusion events crossing 70°N during December and January that can explain roughly 45% of the surface air temperature and 30% of the sea ice concentration trends observed in the Barents Sea during the past two decades.

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

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).

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

2013 ◽  
Vol 9 (6) ◽  
pp. 6515-6549 ◽  
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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