Seasonal and Regional Variations of Northern Hemisphere Sea Ice as Illustrated with Satellite Passive-Microwave Data For 1974

1987 ◽  
Vol 9 ◽  
pp. 119-126 ◽  
Author(s):  
C.L. Parkinson ◽  
J.C. Comiso ◽  
H.J. Zwally ◽  
D.J. Cavalieri ◽  
P. Gloersen ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Kara Sea ◽  
Passive Microwave ◽  
The Arctic ◽  
Hudson Bay ◽  
Central Arctic Ocean ◽  
Ice Concentration ◽  
Microwave Data ◽  
Multiyear Ice

A detailed description of the seasonal cycle of Northern Hemisphere sea ice for 1974 is provided by the passive microwave data from the Nimbus 5 Electrically Scanning Microwave Radiometer (ESMR). Sea ice extent has been mapped and analyzed in eight regions of the Arctic and marginal seas. In the seasonal sea ice areas, the ice concentration is also mapped, whereas in areas of first-year and multiyear ice mixtures, the corresponding mapping is of a parameter representing a combination of ice concentration and multiyear ice fraction. The total monthly ice extent increased from a sharp minimum of 7.6 × 106 km2 in September, when the ice pack was mostly confined to the central Arctic Ocean and portions of the Greenland Sea, Kara Sea, and Canadian Archipelago, to a broad maximum of 14.4 × 106 km2 in March, when the ice cover was nearly complete in the Arctic Ocean, Hudson Bay, Kara Sea, and Canadian Archipelago and was extensive for large portions of the other peripheral seas and bays. In the areas of seasonal sea ice coverage, the average ice concentration was approximately 75% in winter, which is close to the values observed in the Southern Ocean and significantly less than the greater-than-95% concentrations observed in the central Arctic Ocean and Hudson Bay, where the ice packs are constrained by land boundaries. Midwinter decreases in ice extent for 1—2 months are noted in the regions of the Greenland Sea and the Kara and Barents Seas.

scholarly journals Seasonal and Regional Variations of Northern Hemisphere Sea Ice as Illustrated with Satellite Passive-Microwave Data For 1974

1987 ◽  
Vol 9 ◽  
pp. 119-126 ◽  
Author(s):  
C.L. Parkinson ◽  
J.C. Comiso ◽  
H.J. Zwally ◽  
D.J. Cavalieri ◽  
P. Gloersen ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Kara Sea ◽  
Passive Microwave ◽  
The Arctic ◽  
Hudson Bay ◽  
Central Arctic Ocean ◽  
Ice Concentration ◽  
Microwave Data ◽  
Multiyear Ice

A detailed description of the seasonal cycle of Northern Hemisphere sea ice for 1974 is provided by the passive microwave data from the Nimbus 5 Electrically Scanning Microwave Radiometer (ESMR). Sea ice extent has been mapped and analyzed in eight regions of the Arctic and marginal seas. In the seasonal sea ice areas, the ice concentration is also mapped, whereas in areas of first-year and multiyear ice mixtures, the corresponding mapping is of a parameter representing a combination of ice concentration and multiyear ice fraction. The total monthly ice extent increased from a sharp minimum of 7.6 × 106 km2 in September, when the ice pack was mostly confined to the central Arctic Ocean and portions of the Greenland Sea, Kara Sea, and Canadian Archipelago, to a broad maximum of 14.4 × 106 km2 in March, when the ice cover was nearly complete in the Arctic Ocean, Hudson Bay, Kara Sea, and Canadian Archipelago and was extensive for large portions of the other peripheral seas and bays. In the areas of seasonal sea ice coverage, the average ice concentration was approximately 75% in winter, which is close to the values observed in the Southern Ocean and significantly less than the greater-than-95% concentrations observed in the central Arctic Ocean and Hudson Bay, where the ice packs are constrained by land boundaries. Midwinter decreases in ice extent for 1—2 months are noted in the regions of the Greenland Sea and the Kara and Barents Seas.

scholarly journals Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression

2021 ◽  
Vol 13 (12) ◽  
pp. 2283
Author(s):  
Hyangsun Han ◽  
Sungjae Lee ◽  
Hyun-Cheol Kim ◽  
Miae Kim
Keyword(s):  
Random Forest ◽  
Sea Ice ◽  
Arctic Ocean ◽  
Open Water ◽  
The Arctic ◽  
Atmospheric Effects ◽  
Sea Ice Concentration ◽  
Air Temperatures ◽  
The Pacific ◽  
Ice Concentration

The Arctic sea ice concentration (SIC) in summer is a key indicator of global climate change and important information for the development of a more economically valuable Northern Sea Route. Passive microwave (PM) sensors have provided information on the SIC since the 1970s by observing the brightness temperature (TB) of sea ice and open water. However, the SIC in the Arctic estimated by operational algorithms for PM observations is very inaccurate in summer because the TB values of sea ice and open water become similar due to atmospheric effects. In this study, we developed a summer SIC retrieval model for the Pacific Arctic Ocean using Advanced Microwave Scanning Radiometer 2 (AMSR2) observations and European Reanalysis Agency-5 (ERA-5) reanalysis fields based on Random Forest (RF) regression. SIC values computed from the ice/water maps generated from the Korean Multi-purpose Satellite-5 synthetic aperture radar images from July to September in 2015–2017 were used as a reference dataset. A total of 24 features including the TB values of AMSR2 channels, the ratios of TB values (the polarization ratio and the spectral gradient ratio (GR)), total columnar water vapor (TCWV), wind speed, air temperature at 2 m and 925 hPa, and the 30-day average of the air temperatures from the ERA-5 were used as the input variables for the RF model. The RF model showed greatly superior performance in retrieving summer SIC values in the Pacific Arctic Ocean to the Bootstrap (BT) and Arctic Radiation and Turbulence Interaction STudy (ARTIST) Sea Ice (ASI) algorithms under various atmospheric conditions. The root mean square error (RMSE) of the RF SIC values was 7.89% compared to the reference SIC values. The BT and ASI SIC values had three times greater values of RMSE (20.19% and 21.39%, respectively) than the RF SIC values. The air temperatures at 2 m and 925 hPa and their 30-day averages, which indicate the ice surface melting conditions, as well as the GR using the vertically polarized channels at 23 GHz and 18 GHz (GR(23V18V)), TCWV, and GR(36V18V), which accounts for atmospheric water content, were identified as the variables that contributed greatly to the RF model. These important variables allowed the RF model to retrieve unbiased and accurate SIC values by taking into account the changes in TB values of sea ice and open water caused by atmospheric effects.

Increasing Multiyear Sea Ice Loss in the Beaufort Sea: A New Export Pathway for the Diminishing Multiyear Ice Cover of the Arctic Ocean

2021 ◽  
Author(s):  
David Gareth Babb ◽  
Ryan J. Galley ◽  
Stephen E. L. Howell ◽  
Jack Christopher Landy ◽  
Julienne Christine Stroeve ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Beaufort Sea ◽  
Ice Cover ◽  
The Arctic ◽  
Export Pathway ◽  
The Arctic Ocean ◽  
Multiyear Ice

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.

Sea–ice concentration at 1 km resolution in summer from merged visible and microwave radiometer observations

2021 ◽  
Author(s):  
Valentin Ludwig ◽  
Gunnar Spreen
Keyword(s):  
Spatial Resolution ◽  
Microwave Radiometer ◽  
Thermal Infrared ◽  
Passive Microwave ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Statistical Consistency ◽  
Microwave Data ◽  
Infrared Data

<p>Sea–ice concentration, the surface fraction of ice in a given area, is a key component of the Arctic climate system, governing for example the ocean–atmosphere heat exchange. Satellite–based remote sensing offers the possibility for large–scale monitoring of the sea–ice concentration. Using passive microwave measurements, it is possible to observe the sea–ice concentration all year long, almost independently of cloud coverage. The spatial resolution of these measurements is limited to 5 km and coarser. Data from the visible and thermal infrared spectrum offer finer resolutions of 250 m–1 km, but need clear–sky scenes and, in case of visible data, sunlight. In previous work, we developed and analysed a merged dataset of passive microwave and thermal infrared data, combining AMSR2 and MODIS satellite data at 1 km spatial resolution. It has benefits over passive microwave data in terms of the finer spatial resolution and an enhanced potential for lead detection. At the same time, it outperforms thermal infrared data due to its spatially continuous coverage and the statistical consistency with the extensively evaluated passive microwave data. Due to higher surface temperatures in summer, the thermal–infrared based retrieval is limited to winter and spring months. In this contribution, we present first results of extending the existing dataset to summer by using visible data instead of thermal infrared data. The reflectance contrast between ice and water is used for the sea–ice concentration retrieval and results of merging visible and microwave data at 1 km spatial resolution are presented. Difficulties for both, the microwave and visual, data are surface melt processes during summer, which make sea–ice concentration retrieval more challenging. The merged microwave, infrared and visual dataset opens the possibility for a year–long, spatially continuous sea ice concentration dataset at a spatial resolution of 1 km.</p>

scholarly journals How relevant is the deposition of mercury onto snowpacks? – Part 2: A modeling study

2012 ◽  
Vol 12 (1) ◽  
pp. 2647-2706 ◽  
Author(s):  
D. Durnford ◽  
A. Dastoor ◽  
A. Ryzhkov ◽  
L. Poissant ◽  
M. Pilote ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Atmospheric Mercury ◽  
The Arctic ◽  
Hudson Bay ◽  
Arctic Circle ◽  
Surface Level ◽  
The Arctic Ocean ◽  
Simulated Concentration ◽  
The Impact

Abstract. An unknown fraction of mercury that is deposited onto snowpacks is revolatilized to the atmosphere. Determining the revolatilized fraction is important since mercury that enters the snowpack meltwater may be converted to highly toxic bioaccumulating methylmercury. In this study, we present a new dynamic physically-based snowpack/meltwater model for mercury that is suitable for large-scale atmospheric models for mercury. It represents the primary physical and chemical processes that determine the fate of mercury deposited onto snowpacks. The snowpack/meltwater model was implemented in Environment Canada's atmospheric mercury model GRAHM. For the first time, observed snowpack-related mercury concentrations are used to evaluate and constrain an atmospheric mercury model. We find that simulated concentrations of mercury in both snowpacks and the atmosphere's surface layer agree closely with observations. The simulated concentration of mercury in both in the top 30 cm and the top 150 cm of the snowpack, averaged over 2005–2009, is predominantly below 6 ng l−1 over land south of 66.5° N but exceeds 18 ng l−1 over sea ice in extensive areas of the Arctic Ocean and Hudson Bay. The average simulated concentration of mercury in snowpack meltwater runoff tends to be higher on the Russian/European side (>20 ng l−1) of the Arctic Ocean than on the Canadian side (<10 ng l−1). The correlation coefficient between observed and simulated monthly mean atmospheric surface-level GEM concentrations increased significantly with the inclusion of the new snowpack/meltwater model at two of the three stations (midlatitude, subarctic) studied and remained constant at the third (arctic). Oceanic emissions are postulated to produce the observed summertime maximum in concentrations of surface-level atmospheric GEM at Alert in the Canadian Arctic and to generate the summertime volatility observed in these concentrations at both Alert and Kuujjuarapik on subarctic Hudson Bay, Canada. We find that the fraction of deposited mercury that is revolatilized from snowpacks increases with latitude from 28% between 30 and 45° N, to 51% from 45 to 66.5° N, to 70% polewards of 66.5° N on an annual basis. Combining this latitudinal gradient with the latitudinally increasing coverage of snowpacks causes yearly net deposition as a fraction of gross deposition to decrease from 98% between 30 and 45° N to 85% between 45 and 66.5° N to 44% within the Arctic Circle. The yearly net deposition and net accumulation of mercury at the surface within the Arctic Circle north of 66.5° N are estimated at 153 and 117 Mg, respectively. We calculate that 63 and 45 Mg of mercury are deposited annually to the Arctic Ocean directly and indirectly via melting snowpacks, respectively. For terrestrial surfaces within the Arctic Circle, we find that 24 and 21 Mg of mercury are deposited annually directly and indirectly via melting snowpacks, respectively. Within the Arctic Circle, multi-season snowpacks gained an estimated average of 136 kg of mercury annually on land but lost an average of 133 kg annually over sea ice, possibly as a result of increased melting caused by rising temperatures. The developed snowpack/meltwater model can be used for investigating the impact of climate change on the snowpack/atmosphere exchange of mercury.

scholarly journals Low-level jet characteristics over the Arctic Ocean in spring and summer

2013 ◽  
Vol 13 (1) ◽  
pp. 2125-2153
Author(s):  
L. Jakobson ◽  
T. Vihma ◽  
E. Jakobson ◽  
T. Palo ◽  
A. Männik ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Temperature Inversion ◽  
The Arctic ◽  
Jet Streams ◽  
Central Arctic Ocean ◽  
Turbulent Layer ◽  
Low Level ◽  
Low Level Jet ◽  
Jet Characteristics

Abstract. Low-level jets (LLJ) are important for turbulence in the stably stratified atmospheric boundary layer, but their occurrence, properties, and generation mechanisms in the Arctic are not well known. We analysed LLJs over the central Arctic Ocean in spring and summer 2007 on the bases of data collected in the drifting ice station Tara. Instead of traditional radiosonde soundings, data from tethersonde soundings with a high vertical resolution were used. The Tara results showed a lower occurrence of LLJs (46%) than many previous studies over polar sea ice. Strong jet core winds contributed to growth of the turbulent layer. Complex relationship between the jet core height and the temperature inversion top height were detected: substantial correlation (r = 0.72; p < 0.01) occurred when the jet core was above the turbulent layer, but inside the turbulent layer there was no correlation. The most important forcing mechanism for LLJs was baroclinicity, which was responsible for generation of strong and warm LLJs, which on average occurred at lower altitudes than other jets. Baroclinic jets were mostly associated to transient cyclones instead of the climatological air temperature gradients. Besides baroclinicity, cases related to inertial oscillations, gusts, and fronts were detected. In approximately 50% of the observed LLJs the generation mechanism remained unclear, but in most of these cases the wind speed was strong in the whole vertical profile, the jet core representing only a weak maximum. Further research needs on LLJs in the Arctic include investigation of low-level jet streams and their effects on the sea ice drift and atmospheric moisture transport.

scholarly journals Benefits of sea ice thickness initialization for the Arctic decadal climate prediction skill in EC-Earth3

2020 ◽  
Author(s):  
Tian Tian ◽  
Shuting Yang ◽  
Mehdi Pasha Karami ◽  
François Massonnet ◽  
Tim Kruschke ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Climate Prediction ◽  
Arctic Sea Ice ◽  
Prediction Skill ◽  
The Arctic ◽  
Ensemble Prediction ◽  
Central Arctic Ocean ◽  
Ice Volume ◽  
Arctic Sea

Abstract. A substantial part of Arctic climate predictability at interannual time scales stems from the knowledge of the initial sea ice conditions. Among all the variables characterizing sea ice, sea ice volume, being a product of sea ice area/concentration (SIC) and thickness (SIT), is the most sensitive parameter for climate change. However, the majority of climate prediction systems are only assimilating the observed SIC due to lack of long-term reliable global observation of SIT. In this study the EC-Earth3 Climate Prediction System with anomaly initialization to ocean, SIC and SIT states is developed. In order to evaluate the benefits of specific initialized variables at regional scales, three sets of retrospective ensemble prediction experiments are performed with different initialization strategies: ocean-only; ocean plus SIC; and ocean plus SIC and SIT initialization. The increased skill from ocean plus SIC initialization is small in most regions, compared to ocean-only initialization. In the marginal ice zone covered by seasonal ice, skills regarding winter SIC are mainly gained from the initial ocean temperature anomalies. Consistent with previous studies, the Arctic sea ice volume anomalies are found to play a dominant role for the prediction skill of September Arctic sea ice extent. Winter preconditioning of SIT for the perennial ice in the central Arctic Ocean results in increased skill of SIC in the adjacent Arctic coastal waters (e.g. the Laptev/East Siberian/Chukchi Seas) for lead time up to a decade. This highlights the importance of initializing SIT for predictions of decadal time scale in regional Arctic sea ice. Our results suggest that as the climate warming continues and the central Arctic Ocean might become seasonal ice free in the future, the controlling mechanism for decadal predictability may thus shift from being the sea ice volume playing the major role to a more ocean-related processes.

scholarly journals Impact of the Assimilation of Sea Ice Concentration Data on an Atmosphere-Ocean-Sea Ice Coupled Simulation of the Arctic Ocean Climate

SOLA ◽  
2011 ◽  
Vol 7 ◽  
pp. 37-40 ◽  
Author(s):  
Takahiro Toyoda ◽  
Toshiyuki Awaji ◽  
Nozomi Sugiura ◽  
Shuhei Masuda ◽  
Hiromichi Igarashi ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
The Arctic ◽  
Coupled Simulation ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Ocean Climate ◽  
Ice Concentration ◽  
The Arctic Ocean

scholarly journals Arctic sea ice anomalies during the MOSAiC winter 2019/20

2021 ◽  
Author(s):  
Klaus Dethloff ◽  
Wieslaw Maslowski ◽  
Stefan Hendricks ◽  
Younjoo Lee ◽  
Helge F. Goessling ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Barents Sea ◽  
Arctic Sea Ice ◽  
Heat Fluxes ◽  
The Arctic ◽  
Turbulent Heat ◽  
Central Arctic Ocean ◽  
Sea Ice Thickness ◽  
The Arctic Oscillation

Abstract. As the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) project went into effect during the winter of 2019/2020, the Arctic Oscillation (AO) has experienced some of the largest shifts from a highly negative index in November 2019 to an extremely positive index during January-February-March (JFM) 2020. Here we analyse the sea ice thickness (SIT) distribution based on CryoSat-2/SMOS satellite data augmented with results from the hindcast simulation by the fully coupled Regional Arctic System Model (RASM) for the time period from November 2019 through March 2020. A notable result of the positive AO phase during JFM 2020 were large SIT anomalies, up to 1.3 m, which emerged in the Barents-Sea (BS), along the northeastern Canadian coast and in parts of the central Arctic Ocean. These anomalies appear to be driven by nonlinear interactions between thermodynamic and dynamic processes. In particular, in the Barents- and Kara Seas (BKS) they are a result of an enhanced ice growth connected with the colder temperature anomalies and the consequence of intensified atmospheric-driven sea ice transport and deformations (i.e. divergence and shear) in this area. Low-pressure anomalies, which developed over the Eastern Arctic during JFM 2020, increased northerly winds from the cold Arctic Ocean to the BS and accelerated the southward drift of the MOSAiC ice floe. The satellite-derived and model-simulated sea ice velocity anomalies, which compared well during JFM 2020, indicate a strong acceleration of the Transpolar Drift relative to the mean for the past decade, with intensified speeds up to 6 km/day. As a consequence, sea ice transport and deformations driven by atmospheric wind forcing accounted for bulk of SIT anomalies, especially in January and February 2020. The unusual AO shift and the related sea ice anomalies during the MOSAiC winter 2019/20 are within the range of simulated states in the forecast ensemble. RASM intra-annual ensemble forecast simulations, forced with different atmospheric boundary conditions from November 1, 2019 through April 30, 2020, show a pronounced internally generated variability in the sea ice volume. A comparison of the respective SIT distribution and turbulent heat fluxes during the positive AO phase in JFM 2020 and the negative AO phase in JFM 2010 further corroborates the conclusion, that winter sea ice conditions of the Arctic Ocean can be significantly altered by AO variability.

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