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 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.

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.

Gridded Arctic sea ice concentration reconstruction for the first half of the 20th century based on different proxy data

2021 ◽  
Author(s):  
Vladimir Semenov ◽  
Tatiana Matveeva
Keyword(s):  
Sea Ice ◽  
20Th Century ◽  
Climate Model ◽  
Surface Air Temperature ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
The North ◽  
Arctic Sea ◽  
Ice Concentration

<p>Global warming in the recent decades has been accompanied by a rapid recline of the Arctic sea ice area most pronounced in summer (10% per decade). To understand the relative contribution of external forcing and natural variability to the modern and future sea ice area changes, it is necessary to evaluate a range of long-term variations of the Arctic sea ice area in the period before a significant increase in anthropogenic emissions of greenhouse gases into the atmosphere. Available observational data on the spatiotemporal dynamics of Arctic sea ice until 1950s are characterized by significant gaps and uncertainties. In the recent years, there have appeared several reconstructions of the early 20<sup>th</sup> century Arctic sea ice area that filled the gaps by analogue methods or utilized combined empirical data and climate model’s output. All of them resulted in a stronger that earlier believed negative sea ice area anomaly in the 1940s concurrent with the early 20<sup>th</sup> century warming (ETCW) peak. In this study, we reconstruct the monthly average gridded sea ice concentration (SIC) in the first half of the 20th century using the relationship between the spatiotemporal features of SIC variability, surface air temperature over the Northern Hemisphere extratropical continents, sea surface temperature in the North Atlantic and North Pacific, and sea level pressure. In agreement with a few previous results, our reconstructed data also show a significant negative anomaly of the Arctic sea ice area in the middle of the 20th century, however with some 15% to 30% stronger amplitude, about 1.5 million km<sup>2</sup> in September and 0.7 million km<sup>2</sup> in March. The reconstruction demonstrates a good agreement with regional Arctic sea ice area data when available and suggests that ETWC in the Arctic has been accompanied by a concurrent sea ice area decline of a magnitude that have been exceeded only in the beginning of the 21<sup>st</sup> century.</p>

Convolutional Neural Networks for Sea Ice Concentration Charting for Maritime Navigation in the Arctic

2021 ◽  
Author(s):  
Andreas Stokholm ◽  
Leif Pedersen ◽  
René Forsberg ◽  
Sine Hvidegaard
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Arctic Seas ◽  
Sea Ice Concentration ◽  
Data Set ◽  
Architecture Model ◽  
Radar Images ◽  
North East ◽  
Ice Concentration

<p>In recent years the Arctic has seen renewed political and economic interest, increased maritime traffic and desire for improved sea ice navigational tools. Despite a rise in digital technology, maps of sea ice concentration used for Arctic maritime operations are still today created by humans manually interpreting radar images. This process is slow with low map release frequency, uncertainties up to 20 % and discrepancies up to 60 %. Utilizing emerging AI Convolutional Neural Network (CNN) semantic image segmentation techniques to automate this process is drastically changing navigation in the Arctic seas, with better resolution, accuracy, release frequency and coverage. Automatic Arctic sea ice products may contribute to enabling the disruptive Northern Sea Route connecting North East Asia to Europe via the Arctic oceans.</p><p>The AI4Arctic/ASIP V2 data set, that combines 466 Sentinel-1 HH and HV SAR images from Greenland, Passive Microwave Radiometry from the AMSR2 instrument, and an equivalent sea ice concentration chart produced by ice analysts at the Danish Meteorological Institute, have been used to train a CNN U-Net Architecture model. The model shows robust capabilities in producing highly detailed sea ice concentration maps with open water, intermediate sea ice concentrations as well as full sea ice cover, which resemble those created by professional sea ice analysts. Often cited obstacles in automatic sea ice concentration models are wind-roughened sea ambiguities resembling sea ice. Final inference scenes show robustness towards such ambiguities.</p>

Sensitivity of the Arctic sea ice concentration forecasts to different atmospheric forcing: a case study

2014 ◽  
Vol 33 (12) ◽  
pp. 15-23
Author(s):  
Qinghua Yang ◽  
Jiping Liu ◽  
Zhanhai Zhang ◽  
Cuijuan Sui ◽  
Jianyong Xing ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Atmospheric Forcing ◽  
Sea Ice Concentration ◽  
Arctic Sea ◽  
Ice Concentration

scholarly journals Step changes in persistent organic pollutants over the Arctic and their implications

2015 ◽  
Vol 15 (6) ◽  
pp. 3479-3495 ◽  
Author(s):  
Y. Zhao ◽  
T. Huang ◽  
L. Wang ◽  
H. Gao ◽  
J. Ma
Keyword(s):  
Sea Ice ◽  
Step Change ◽  
Arctic Sea Ice ◽  
Change Points ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Arctic Sea ◽  
Ice Concentration ◽  
Exchange Flux ◽  
Air Exchange

Abstract. While some persistent organic pollutants (POPs) have been declining globally due to their worldwide ban since the 1980s, the declining trends of many of these toxic chemicals become less significant and in some cases their ambient air concentrations, e.g., polychlorinated biphenyls (PCBs), showed observable increase during the 2000s, disagreeing with their declining global emissions and environmental degradation. As part of the efforts to assess the influences of environmental factors on the long-term trend of POPs in the Arctic, step change points in the time series of ambient POP atmospheric concentrations collected from four arctic monitoring sites were examined using various statistical techniques. Results showed that the step change points of these POP data varied in different years and at different sites. Most step change points were found in 2001–2002 and 2007–2008. In particular, the step change points of many PCBs for 2007–2008 were coincident with the lowest arctic sea ice concentration occurring during the 2000s. The perturbations of air concentration and water–air exchange fluxes of several selected POPs averaged over the Arctic, simulated by a POP mass balance perturbation model, switched from negative to positive during the early 2000s, indicating a tendency for reversal of POPs from deposition to volatilization which coincides with a positive to negative reversal of arctic sea ice extent anomalies from 2001. Perturbed ice–air exchange flux of PCB 28 and 153 showed an increasing trend and a negative to positive reversal in 2007, the year with the lowest arctic sea ice concentration. On the other hand, perturbed ice–air exchange flux of α-hexachlorocyclohexane decreased over the period of 1995 to 2012, likely owing to its lower Henry's law constant which indicates its relatively lower tendency for volatilization from ice to air.

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.

scholarly journals Presentation and evaluation of the Arctic sea ice forecasting system neXtSIM-F

2019 ◽  
Author(s):  
Timothy Williams ◽  
Anton Korosov ◽  
Pierre Rampal ◽  
Einar Ólason
Keyword(s):  
Sea Ice ◽  
Initial Conditions ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Forecast System ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Daily Data ◽  
Ice Concentration ◽  
Forecasting System

Abstract. The neXtSIM-F forecast system consists of a stand-alone sea ice model, neXtSIM, forced by the TOPAZ ocean forecast and the ECMWF atmospheric forecast, combined with daily data assimilation. It was tested for the northern winter of 2018–2019 with different data being assimilated and was found to perform well. Despite drift not being assimilated in our system, we obtain quite good agreement between observations, comparing well to more sophisticated coupled ice-ocean forecast systems. The RMSE in drift speed is around 3 km/day for the first three days, climbing to about 4 km/day for the next day or two; computing the RMSE in the total drift adds about 1 km/day to the error in speed. The drift bias remains close to zero over the whole period from Nov 2018–Apr 2019. The neXtSIM-F forecast system assimilates OSISAF sea ice concentration products (both SSMI and AMSR2) and SMOS sea ice thickness by modifying the initial conditions daily and adding a compensating heat flux to prevent removed ice growing back too quickly. This greatly improved the agreement of these quantities with observations for the first 3–4 days of the forecast.

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