scholarly journals Potential Consequences Of “Dirty” Arctic Sea Ice

1990 ◽  
Vol 14 ◽  
pp. 355
Author(s):  
Stephanie Pfirman ◽  
Manfred A. Lange ◽  
Tamara S. Ledley
Keyword(s):  
Sea Ice ◽  
Arctic Basin ◽  
Surface Melting ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Melting ◽  
Large Area ◽  
Sea Ice Concentration ◽  
East Greenland Current ◽  
Ice Surface

Observations of high particulate loads on Eurasian Basin sea ice in 1987 raise questions of consequence for sediment budgets, ice melting, ice modeling and remote sensing. Biogenic and lithogenic particles were observed in concentrations high enough to color the ice surface brown over large area (greater than 15 × 15 km2) within the Siberian branch of the Transpolar Drift stream. The sediment is most likely incorporated when ice forms on the Siberian shelf seas, and is concentrated at the ice surface after several years of summer surface melting and biological growth within the Arctic basin. Much of the particle-laden multi-year ice appears to leave the Arctic basin via Fram Strait, depositing its sediment load along the axis of the East Greenland Current. To date, variation in sea-ice particle load has not been taken into consideration when modeling ice thickness or distribution for past or future environmental scenarios, with the exception of soot deposited from nuclear war. Naturally elevated surface-particle concentration may occur if there is increased deposition from long-range or coastal transport of aeolian material, increased sediment input into sea ice which is then exposed to surface melting, and/or increased biogenic productivity on the ice surface. Such conditions may have prevailed during the Younger Dryas. If particle loads become high enough to cause extensive sea-ice melting, changes may be expected in sea-ice concentration and distribution, sea-floor sedimentation rates, and oceanic productivity.

scholarly journals Potential Consequences Of “Dirty” Arctic Sea Ice

1990 ◽  
Vol 14 ◽  
pp. 355-355
Author(s):  
Stephanie Pfirman ◽  
Manfred A. Lange ◽  
Tamara S. Ledley
Keyword(s):  
Sea Ice ◽  
Arctic Basin ◽  
Surface Melting ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Melting ◽  
Large Area ◽  
Sea Ice Concentration ◽  
East Greenland Current ◽  
Ice Surface

Observations of high particulate loads on Eurasian Basin sea ice in 1987 raise questions of consequence for sediment budgets, ice melting, ice modeling and remote sensing. Biogenic and lithogenic particles were observed in concentrations high enough to color the ice surface brown over large area (greater than 15 × 15 km2) within the Siberian branch of the Transpolar Drift stream. The sediment is most likely incorporated when ice forms on the Siberian shelf seas, and is concentrated at the ice surface after several years of summer surface melting and biological growth within the Arctic basin. Much of the particle-laden multi-year ice appears to leave the Arctic basin via Fram Strait, depositing its sediment load along the axis of the East Greenland Current.To date, variation in sea-ice particle load has not been taken into consideration when modeling ice thickness or distribution for past or future environmental scenarios, with the exception of soot deposited from nuclear war. Naturally elevated surface-particle concentration may occur if there is increased deposition from long-range or coastal transport of aeolian material, increased sediment input into sea ice which is then exposed to surface melting, and/or increased biogenic productivity on the ice surface. Such conditions may have prevailed during the Younger Dryas. If particle loads become high enough to cause extensive sea-ice melting, changes may be expected in sea-ice concentration and distribution, sea-floor sedimentation rates, and oceanic productivity.

Impact of local atmospheric intraseasonal variability on mean sea ice state in the Arctic Ocean

2021 ◽  
pp. 1-52
Keyword(s):  
Sea Ice ◽  
Intraseasonal Variability ◽  
Arctic Sea Ice ◽  
Heat Fluxes ◽  
The Arctic ◽  
Ice Melting ◽  
Sea Ice Concentration ◽  
Budget Analysis ◽  
Ice Concentration ◽  
Arctic Atmosphere

Abstract The Arctic atmosphere shows significant variability on intraseasonal timescales of 10-90 days. The intraseasonal variability in the Arctic sea ice is clearly related to that in the Arctic atmosphere. It is well-known that the Arctic mean sea ice state is governed by the local mean atmospheric state. However, the response of the Arctic mean sea ice state to the local atmospheric intraseasonal variability is unclear. The Arctic atmospheric intraseasonal variability exists in both the thermodynamical and dynamical variables. Based on a sea ice-ocean coupled simulation with a quantitative sea ice budget analysis, this study finds that: 1) the intraseasonal atmospheric thermodynamical variability tends to reduce sea ice melting through changing the downward heat flux on the open water area in the marginal sea ice zone, and the intraseasonal atmospheric dynamical variability tends to increase sea ice melting by a combination of modified air-ocean, ice-ocean heat fluxes and sea ice deformation. 2) The intraseasonal atmospheric dynamical variability increases summertime sea ice concentration in the Beaufort Sea and the Greenland Sea but decreases summertime sea ice concentration along the Eurasian continent in the East Siberia-Laptev-Kara Seas, resulting from the joint effects of the modified air-ocean, ice-ocean heat fluxes, the sea ice deformation, as well as the mean sea ice advection due to the changes of sea ice drift. The large spread in sea ice in the CMIP models may be partly attributed to the different model performances in representing the observed atmospheric intraseasonal variability. Reliable modeling of atmospheric intraseasonal variability is an essential condition in correctly projecting future sea ice evolution.

scholarly journals Arctic sea-ice variability revisited

2008 ◽  
Vol 48 ◽  
pp. 71-81 ◽  
Author(s):  
Julienne Stroeve ◽  
Allan Frei ◽  
James McCreight ◽  
Debjani Ghatak
Keyword(s):  
Sea Ice ◽  
Arctic Basin ◽  
Warming Trend ◽  
Atmospheric Temperature ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Near Surface ◽  
Arctic Sea ◽  
Ice Concentration

AbstractThis paper explores spatial and temporal relationships between variations in Arctic sea-ice concentration (summer and winter) and near-surface atmospheric temperature and atmospheric pressure using multivariate statistical techniques. Trend, empirical orthogonal function (EOF) and singular value decomposition (SVD) analyses are used to identify spatial patterns associated with covariances and correlations between these fields. Results show that (1) in winter, the Arctic Oscillation still explains most of the variability in sea-ice concentration from 1979 to 2006; and (2) in summer, a decreasing sea-ice trend centered in the Pacific sector of the Arctic basin is clearly correlated to an Arctic-wide air temperature warming trend. These results demonstrate the applicability of multivariate methods, and in particular SVD analysis, which has not been used in earlier studies for assessment of changes in the Arctic sea-ice cover. Results are consistent with the interpretation that a warming signal has now emerged from the noise in the Arctic sea-ice record during summer. Our analysis indicates that such a signal may also be forthcoming during winter.

Inter-comparison of melt pond products with melt/freeze-up dates and sea ice concentration data

2020 ◽  
Author(s):  
Sanggyun Lee ◽  
Julienne Stroeve ◽  
Michel Tsamados
Keyword(s):  
Machine Learning ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Solar Heat ◽  
Melt Pond ◽  
Ice Surface ◽  
Melt Ponds ◽  
Ice Concentration

<p> Melt ponds are a dominant feature on the Arctic sea ice surface in summer, occupying up to about 50 – 60% of the sea ice surface during advanced melt. Melt ponds normally begin to form around mid-May in the marginal ice zone and expand northwards as the summer melt season progresses. Once melt ponds emerge, the scattering characteristics of the ice surface changes, dramatically lowering the sea ice albedo. Since 96% of the total annual solar heat into the ocean through sea ice occurs between May and August, the presence of melt ponds plays a significant role in this transfer of solar heat, influencing not only the sea ice energy balance, but also the amount of light available under the sea ice and ocean primary productivity. Given the importance melt ponds play in the coupled Arctic climate-ecosystem, mapping and quantification of melt pond variability on a Pan-Arctic basin scale are needed. Satellite-based observations are the only way to map melt ponds and albedo changes on a pan-Arctic scale. Rösel et al. (2012) utilized a MODIS 8-day average product to map melt ponds on a pan-Arctic scale and over several years. In another approach, melt pond fraction and surface albedo were retrieved based on the physical and optical characteristics of sea ice and melt ponds without a priori information using MERIS.Here, we propose a novel machine learning-based methodology to map Arctic melt ponds from MODIS 500m resolution data. We provide a merging procedure to create the first pan-Arctic melt pond product spanning a 20-year period at a weekly temporal resolution. Specifically, we use MODIS data together with machine learning, including multi-layer neural network and logistic regression to test our ability to map melt ponds from the start to the end of the melt season. Since sea ice reflectance is strongly dependent on the viewing and solar geometry (i.e. sensor and solar zenith and azimuth angles), we attempt to minimize this dependence by using normalized band ratios in the machine learning algorithms. Each melt pond retrieval algorithm is different and validation ways are different as well producing somewhat dissimilar melt pond results. In this study, we inter-compare melt ponds products from different institutes, including university of Hamburg, university of Bremen, and university college London. The melt pond maps are compared with melt onset and freeze-up dates data and sea ice concentration. The melt pond maps are evaluated by melt pond fraction statistics from high resolution satellite (MEDEA) images that have not been used for the evaluation in melt pond products. </p>

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 Stationary Wave Interference and Its Relation to Tropical Convection and Arctic Warming

2016 ◽  
Vol 29 (4) ◽  
pp. 1369-1389 ◽  
Author(s):  
Michael Goss ◽  
Steven B. Feldstein ◽  
Sukyoung Lee
Keyword(s):  
Sea Ice ◽  
Stationary Wave ◽  
Warm Pool ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Boreal Winter ◽  
Destructive Interference ◽  
Constructive Interference ◽  
Sea Ice Concentration ◽  
Stratospheric Polar Vortex

Abstract The interference between transient eddies and climatological stationary eddies in the Northern Hemisphere is investigated. The amplitude and sign of the interference is represented by the stationary wave index (SWI), which is calculated by projecting the daily 300-hPa streamfunction anomaly field onto the 300-hPa climatological stationary wave. ERA-Interim data for the years 1979 to 2013 are used. The amplitude of the interference peaks during boreal winter. The evolution of outgoing longwave radiation, Arctic temperature, 300-hPa streamfunction, 10-hPa zonal wind, Arctic sea ice concentration, and the Arctic Oscillation (AO) index are examined for days of large SWI values during the winter. Constructive interference during winter tends to occur about one week after enhanced warm pool convection and is followed by an increase in Arctic surface air temperature along with a reduction of sea ice in the Barents and Kara Seas. The warming of the Arctic does occur without prior warm pool convection, but it is enhanced and prolonged when constructive interference occurs in concert with enhanced warm pool convection. This is followed two weeks later by a weakening of the stratospheric polar vortex and a decline of the AO. All of these associations are reversed in the case of destructive interference. Potential climate change implications are briefly discussed.

scholarly journals Ice Concentration Retrieval from the Analysis of Microwaves: A New Methodology Designed for the Copernicus Imaging Microwave Radiometer

2020 ◽  
Vol 12 (7) ◽  
pp. 1060 ◽  
Author(s):  
Lise Kilic ◽  
Catherine Prigent ◽  
Filipe Aires ◽  
Georg Heygster ◽  
Victor Pellet ◽  
...  
Keyword(s):  
Sea Ice ◽  
Spatial Resolution ◽  
Microwave Radiometer ◽  
Optimal Estimation ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Special Focus ◽  
Polar Regions ◽  
Sea Ice Concentration ◽  
Ice Concentration

Over the last 25 years, the Arctic sea ice has seen its extent decline dramatically. Passive microwave observations, with their ability to penetrate clouds and their independency to sunlight, have been used to provide sea ice concentration (SIC) measurements since the 1970s. The Copernicus Imaging Microwave Radiometer (CIMR) is a high priority candidate mission within the European Copernicus Expansion program, with a special focus on the observation of the polar regions. It will observe at 6.9 and 10.65 GHz with 15 km spatial resolution, and at 18.7 and 36.5 GHz with 5 km spatial resolution. SIC algorithms are based on empirical methods, using the difference in radiometric signatures between the ocean and sea ice. Up to now, the existing algorithms have been limited in the number of channels they use. In this study, we proposed a new SIC algorithm called Ice Concentration REtrieval from the Analysis of Microwaves (IceCREAM). It can accommodate a large range of channels, and it is based on the optimal estimation. Linear relationships between the satellite measurements and the SIC are derived from the Round Robin Data Package of the sea ice Climate Change Initiative. The 6 and 10 GHz channels are very sensitive to the sea ice presence, whereas the 18 and 36 GHz channels have a better spatial resolution. A data fusion method is proposed to combine these two estimations. Therefore, IceCREAM will provide SIC estimates with the good accuracy of the 6+10GHz combination, and the high spatial resolution of the 18+36GHz combination.

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 Cryospheric Impacts of Soviet River Diversion Schemes

1984 ◽  
Vol 5 ◽  
pp. 61-68 ◽  
Author(s):  
T. Holt ◽  
P. M. Kelly ◽  
B. S. G. Cherry
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
River Discharge ◽  
Large Scale ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
The Arctic Ocean ◽  
The Impact

Soviet plans to divert water from rivers flowing into the Arctic Ocean have led to research into the impact of a reduction in discharge on Arctic sea ice. We consider the mechanisms by which discharge reductions might affect sea-ice cover and then test various hypotheses related to these mechanisms. We find several large areas over which sea-ice concentration correlates significantly with variations in river discharge, supporting two particular hypotheses. The first hypothesis concerns the area where the initial impacts are likely to which is the Kara Sea. Reduced riverflow is associated occur, with decreased sea-ice concentration in October, at the time of ice formation. This is believed to be the result of decreased freshening of the surface layer. The second hypothesis concerns possible effects on the large-scale current system of the Arctic Ocean and, in particular, on the inflow of Atlantic and Pacific water. These effects occur as a result of changes in the strength of northward-flowing gradient currents associated with variations in river discharge. Although it is still not certain that substantial transfers of riverflow will take place, it is concluded that the possibility of significant cryospheric effects and, hence, large-scale climate impact should not be neglected.

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.

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