Internal Waves in the Arctic: Influence of Ice Concentration, Ice Roughness, and Surface Layer Stratification

The interplay between sea ice concentration, sea ice roughness, ocean stratification, and momentum transfer to the ice and ocean is subject to seasonal and decadal variations that are crucial to understanding the present and future air-ice-ocean system in the Arctic. In this study, continuous observations in the Canada Basin from March through December 2014 were used to investigate spatial differences and temporal changes in under-ice roughness and momentum transfer as the ice cover evolved seasonally. Observations of wind, ice, and ocean properties from four clusters of drifting instrument systems were complemented by direct drill-hole measurements and instrumented overhead flights by NASA operation IceBridge in March, as well as satellite remote sensing imagery about the instrument clusters. Spatially, directly estimated ice-ocean drag coefficients varied by a factor of three with rougher ice associated with smaller multi-year ice floe sizes embedded within the first-year-ice/multi-year-ice conglomerate. Temporal differences in the ice-ocean drag coefficient of 20–30% were observed prior to the mixed layer shoaling in summer and were associated with ice concentrations falling below 100%. The ice-ocean drag coefficient parameterization was found to be invalid in September with low ice concentrations and small ice floe sizes. Maximum momentum transfer to the ice occurred for moderate ice concentrations, and transfer to the ocean for the lowest ice concentrations and shallowest stratification. Wind work and ocean work on the ice were the dominant terms in the kinetic energy budget of the ice throughout the melt season, consistent with free drift conditions. Overall, ice topography, ice concentration, and the shallow summer mixed layer all influenced mixed layer currents and the transfer of momentum within the air-ice-ocean system. The observed changes in momentum transfer show that care must be taken to determine appropriate parameterizations of momentum transfer, and imply that the future Arctic system could become increasingly seasonal.

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Sea Ice Concentration Products over Polar Regions with Chinese FY3C/MWRI Data

Remote Sensing ◽

10.3390/rs13112174 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2174

Author(s):

Lijian Shi ◽

Sen Liu ◽

Yingni Shi ◽

Xue Ao ◽

Bin Zou ◽

...

Keyword(s):

Time Series ◽

Sea Ice ◽

Ocean Circulation ◽

The Arctic ◽

Retrieval Algorithm ◽

Atmospheric Effects ◽

Observation Data ◽

Long Time Series ◽

Ice Concentration ◽

Long Time

Polar sea ice affects atmospheric and ocean circulation and plays an important role in global climate change. Long time series sea ice concentrations (SIC) are an important parameter for climate research. This study presents an SIC retrieval algorithm based on brightness temperature (Tb) data from the FY3C Microwave Radiation Imager (MWRI) over the polar region. With the Tb data of Special Sensor Microwave Imager/Sounder (SSMIS) as a reference, monthly calibration models were established based on time–space matching and linear regression. After calibration, the correlation between the Tb of F17/SSMIS and FY3C/MWRI at different channels was improved. Then, SIC products over the Arctic and Antarctic in 2016–2019 were retrieved with the NASA team (NT) method. Atmospheric effects were reduced using two weather filters and a sea ice mask. A minimum ice concentration array used in the procedure reduced the land-to-ocean spillover effect. Compared with the SIC product of National Snow and Ice Data Center (NSIDC), the average relative difference of sea ice extent of the Arctic and Antarctic was found to be acceptable, with values of −0.27 ± 1.85 and 0.53 ± 1.50, respectively. To decrease the SIC error with fixed tie points (FTPs), the SIC was retrieved by the NT method with dynamic tie points (DTPs) based on the original Tb of FY3C/MWRI. The different SIC products were evaluated with ship observation data, synthetic aperture radar (SAR) sea ice cover products, and the Round Robin Data Package (RRDP). In comparison with the ship observation data, the SIC bias of FY3C with DTP is 4% and is much better than that of FY3C with FTP (9%). Evaluation results with SAR SIC data and closed ice data from RRDP show a similar trend between FY3C SIC with FTPs and FY3C SIC with DTPs. Using DTPs to present the Tb seasonal change of different types of sea ice improved the SIC accuracy, especially for the sea ice melting season. This study lays a foundation for the release of long time series operational SIC products with Chinese FY3 series satellites.

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Sea Ice Concentration and Sea Ice Extent Mapping with L-Band Microwave Radiometry and GNSS-R Data from the FFSCat Mission Using Neural Networks

Remote Sensing ◽

10.3390/rs13061139 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1139

Author(s):

David Llaveria ◽

Juan Francesc Munoz-Martin ◽

Christoph Herbert ◽

Miriam Pablos ◽

Hyuk Park ◽

...

Keyword(s):

Sea Ice ◽

Microwave Radiometer ◽

Cost Effective ◽

The Arctic ◽

Sea Ice Concentration ◽

Sea Ice Extent ◽

Neural Network Approach ◽

Ice Concentration ◽

L Band ◽

Moderate Cost

CubeSat-based Earth Observation missions have emerged in recent times, achieving scientifically valuable data at a moderate cost. FSSCat is a two 6U CubeSats mission, winner of the ESA S3 challenge and overall winner of the 2017 Copernicus Masters Competition, that was launched in September 2020. The first satellite, 3Cat-5/A, carries the FMPL-2 instrument, an L-band microwave radiometer and a GNSS-Reflectometer. This work presents a neural network approach for retrieving sea ice concentration and sea ice extent maps on the Arctic and the Antarctic oceans using FMPL-2 data. The results from the first months of operations are presented and analyzed, and the quality of the retrieved maps is assessed by comparing them with other existing sea ice concentration maps. As compared to OSI SAF products, the overall accuracy for the sea ice extent maps is greater than 97% using MWR data, and up to 99% when using combined GNSS-R and MWR data. In the case of Sea ice concentration, the absolute errors are lower than 5%, with MWR and lower than 3% combining it with the GNSS-R. The total extent area computed using this methodology is close, with 2.5% difference, to those computed by other well consolidated algorithms, such as OSI SAF or NSIDC. The approach presented for estimating sea ice extent and concentration maps is a cost-effective alternative, and using a constellation of CubeSats, it can be further improved.

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

Remote Sensing ◽

10.3390/rs13122283 ◽

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.

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MOSAiC simulator in CMIP6

10.5194/egusphere-egu21-15993 ◽

2021 ◽

Author(s):

Rajka Juhrbandt ◽

Suvarchal Cheedela ◽

Nikolay Koldunov ◽

Thomas Jung

Keyword(s):

Surface Layer ◽

Arctic Ocean ◽

Ease Of Use ◽

Arctic Climate ◽

The Arctic ◽

Early Results ◽

The Arctic Ocean ◽

Virtual Field ◽

Field Campaigns ◽

Type Code

The recently completed Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) can serve as reference to evaluate current and future ocean state of the Arctic Ocean. With this premise, we perform a virtual MOSAiC expedition in historical and ssp370-scenario experiments in data generated by CMIP6 models. The timespan covered ranges from preindustrial times (1851-1860) through present-day up to a 4K world (2091-2100). Early results using AWI-CM model, suggest that for scenario simulations a thinning of the colder surface layer and a warming of the layer between 200 and 1200 m along the MOSAiC path can be expected, while there is no significant change in temperature below this depth. Results from other models will be presented. The Python-centric tool used for the analysis simplifies preprocessing of a pool of CMIP6 data and selecting data on space-time trajectory. It exposes an interface that is agnostic to underlying model or its grid type. Code snippets are presented along to demonstrate the tool's ease of use with a hope to inspire such virtual field campaigns using other past observations or arbitrary trajectories.

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Correlation and trend studies of the sea-ice cover and surface temperatures in the Arctic

Annals of Glaciology ◽

10.3189/172756402781818067 ◽

2002 ◽

Vol 34 ◽

pp. 420-428 ◽

Cited By ~ 14

Author(s):

Josefino C. Comiso

Keyword(s):

Sea Ice ◽

Surface Temperature ◽

Ice Cover ◽

The Arctic ◽

Temperature Anomalies ◽

Ice Concentration ◽

Arctic Ice ◽

Highly Correlated ◽

Surface Ice ◽

Perennial Ice

AbstractCo-registered and continuous satellite data of sea-ice concentrations and surface ice temperatures from 1981 to 2000 are analyzed to evaluate relationships between these two critical climate parameters and what they reveal in tandem about the changing Arctic environment. During the 19 year period, the Arctic ice extent and actual ice area are shown to be declining at a rate of –2.0±0.3% dec –1 and 3.1 ±0.4% dec–1, respectively, while the surface ice temperature has been increasing at 0.4 ±0.2 K dec–1, where dec is decade. The extent and area of the perennial ice cover, estimated from summer minimum values, have been declining at a much faster rate of –6.7±2.4% dec–1 and –8.3±2.4% dec–1, respectively, while the surface ice temperature has been increasing at 0.9 ±0.6K dec–1. This unusual rate of decline is accompanied by a very variable summer ice cover in the 1990s compared to the 1980s, suggesting increases in the fraction of the relatively thin second-year, and hence a thinning in the perennial, ice cover during the last two decades. Yearly anomaly maps show that the ice-concentration anomalies are predominantly positive in the 1980s and negative in the 1990s, while surface temperature anomalies were mainly negative in the 1980s and positive in the 1990s. The yearly ice-concentration and surface temperature anomalies are highly correlated, indicating a strong link especially in the seasonal region and around the periphery of the perennial ice cover. The surface temperature anomalies also reveal the spatial scope of each warming (or cooling) phenomenon that usually extends beyond the boundaries of the sea-ice cover.

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

Remote Sensing ◽

10.3390/rs12071060 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1060 ◽

Cited By ~ 1

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.

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Cryospheric Impacts of Soviet River Diversion Schemes

Annals of Glaciology ◽

10.3189/1984aog5-1-61-68 ◽

1984 ◽

Vol 5 ◽

pp. 61-68 ◽

Cited By ~ 4

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.

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TO THE 75th ANNIVERSARY OF E.G. Morozov

Journal of Oceanological Research ◽

10.29006/1564-2291.jor-2021.49(2).13 ◽

2021 ◽

Vol 49 (2) ◽

pp. 155-163

Author(s):

S. M. Shapovalov

Keyword(s):

Internal Waves ◽

Executive Committee ◽

International Association ◽

World Ocean ◽

Hydrological Processes ◽

The Arctic ◽

Physical Sciences ◽

Russian Academy Of Sciences ◽

Wide Range ◽

Academy Of Sciences

March 15, 2021 Chief Researcher, Head of the Laboratory of Hydrological Processes of the P.P. Shirshov Institute of Oceanology of the Russian Academy of Sciences, DSc, ex-president of the International Association for Physical Ocean Sciences (IAPSO) Evgeny Morozov is 75 years old. E.G. Morozov is a prominent scientist and organizer of world-class science in the field of studying the temporal and spatial variability of hydrological processes and internal waves in a wide range of scales. He was the first to build a map of the amplitudes of tidal internal waves of the World Ocean. His monograph “Oceanic Internal Waves” published in 1985 in Russian, as well as his article “Semidiurnal internal wave global field”, published in the Deep Sea Research in 1995, are among the most cited on the problem of internal tidal waves. Unique results were obtained by E.G. Morozov in the study of internal waves in the Arctic, including under the ice and near the front of glaciers sliding into the ocean on Spitsbergen. He made a significant contribution to the study of various currents: the Gulf Stream, the Kuroshio and their rings, the Antarctic Circumpolar Current, the California Current, the Falkland Current, the Lomonosov and Tareev subsurface equatorial currents. Since 1999 he has been a member of the Executive Committee of the International Association for the Physical Sciences of the Ocean (IAPSO) and since 2011 he has been elected President of the IAPSO, represented the IAPSO in this capacity on the Executive Committee of the International Geodetic and Geophysical Union (IUGG) and on the Executive Committee of the Scientific Committee on Oceanic research (SCOR). E.G. Morozov is the chairman of the Ocean Physical Sciences Section of the National Geophysical Committee of the Russian Academy of Sciences.

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Diversity and biogeography of SAR11 bacteria from the Arctic Ocean

10.1101/517433 ◽

2019 ◽

Cited By ~ 1

Author(s):

Susanne Kraemer ◽

Arthi Ramachandran ◽

David Colatriano ◽

Connie Lovejoy ◽

David A. Walsh

Keyword(s):

Surface Layer ◽

Arctic Ocean ◽

Adaptive Evolution ◽

Brackish Water ◽

Water Mass ◽

The Arctic ◽

High Latitudes ◽

Bacterial Group ◽

The Arctic Ocean ◽

Globally Distributed

AbstractThe Arctic Ocean is relatively isolated from other oceans and consists of strongly stratified water masses with distinct histories, nutrient, temperature and salinity characteristics, therefore providing an optimal environment to investigate local adaptation. The globally distributed SAR11 bacterial group consists of multiple ecotypes that are associated with particular marine environments, yet relatively little is known about Arctic SAR11 diversity. Here, we examined SAR11 diversity using ITS analysis and metagenome-assembled genomes (MAGs). Arctic SAR11 assemblages were comprised of the S1a, S1b, S2, and S3 clades, and structured by water mass and depth. The fresher surface layer was dominated by an ecotype (S3-derived P3.2) previously associated with Arctic and brackish water. In contrast, deeper waters of Pacific origin were dominated by the P2.3 ecotype of the S2 clade, within which we identified a novel subdivision (P2.3s1) that was rare outside the Arctic Ocean. Arctic S2-derived SAR11 MAGs were restricted to high latitudes and included MAGs related to the recently defined S2b subclade, a finding consistent with bi-polar ecotypes and Arctic endemism. These results place the stratified Arctic Ocean into the SAR11 global biogeography and have identified SAR11 lineages for future investigation of adaptive evolution in the Arctic Ocean.

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