Sea-Ice Concentration Derived From GNSS Reflection Measurements in Fram Strait

Abstract. Arctic Ocean surface sea-ice conditions are linked with the deep sea benthic oxygen fluxes via a cascade of interdependencies across ecosystem components such as primary production, food supply, activity of the benthic community, and their functions. Additionally, each ecosystem component is influenced by abiotic factors such as light availability, temperature, water depth, and grain size structure. In this study, we investigated the coupling between surface sea-ice conditions and deep-sea benthic remineralization processes through a cascade of interdependencies in the Fram Strait. We measured sea-ice concentrations, a variety of different sediment characteristics, benthic community parameters, and oxygen fluxes at 12 stations of the LTER HAUSGARTEN observatory, Fram Strait, at water depths of 275–2500 m. Our investigations reveal that the Fram Strait is bisected into two long-lasting and stable regions: (i) a permanently and highly sea-ice-covered area and (ii) a seasonally and low sea-ice-covered area. Within the Fram Strait ecosystem, sea-ice concentration and water depth are two independent abiotic factors, controlling the deep-sea benthos. Sea-ice concentration correlated with the available food and water depth with the oxygen flux. In addition, both abiotic factors sea-ice concentration and water depth correlate with the macrofauna biomass. However, at water depths > 1500 m the influence of the surface sea-ice cover is minimal with water depth becoming more dominant. Benthic remineralization across the Fram Strait on average is ∼ 1 mmol C m−2 d−1. Our data indicate that the portion of newly produced carbon that is remineralized by the benthos is 5 % in the seasonally low sea-ice-covered eastern part of Fram Strait but can be 14 % in the permanently high sea-ice-covered western part of Fram Strait. Here, by comparing a permanently sea-ice-covered area with a seasonally sea-ice-covered area, we discuss a potential scenario for the deep-sea benthic ecosystem in the future Arctic Ocean, in which an increased surface primary production may lead to increasing benthic remineralization at water depths < 1500 m.

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Deep-sea benthic communities and oxygen fluxes in the Arctic Fram Strait controlled by sea-ice cover and water depth

10.5194/bg-2017-537 ◽

2018 ◽

Author(s):

Ralf Hoffmann ◽

Ulrike Braeckman ◽

Christiane Hasemann ◽

Frank Wenzhöfer

Keyword(s):

Sea Ice ◽

Primary Production ◽

Water Depth ◽

Deep Sea ◽

Abiotic Factors ◽

Fram Strait ◽

Sea Ice Concentration ◽

Covered Area ◽

Ice Concentration ◽

Ice Conditions

Abstract. Arctic Ocean surface sea-ice conditions are linked with the deep sea benthic oxygen fluxes via a cascade of dependencies across ecosystem components like primary production, food supply, the activity of the benthic community, and their functions. Additionally, each of the ecosystem components is influenced by abiotic factors like light availability, temperature, water depth or grain size structure. In this study, we investigated the coupling between surface sea-ice conditions and deep-sea benthic remineralization processes through a cascade of dependencies in the Fram Strait. We measured sea-ice concentrations, nutrient profiles, different sediment compounds, benthic community parameters, and oxygen fluxes at 12 stations in the HAUSGARTEN area of the Fram Strait in water depth between 275–2500 m. Our investigations reveal that the Fram Strait is bisected in a permanently and highly sea-ice covered area and a seasonally and low sea-ice covered area, which both are long-lasting and stable. Within the Fram Strait ecosystem, sea-ice concentration and water depth are two independent abiotic factors, controlling the deep-sea benthos. Sea-ice concentration correlates well with the available food, water depth with the oxygen flux, and both abiotic factors correlate with the macrofauna biomass. However, in water depths >1500 m the influence of the surface sea-ice cover fades out and water depth effect becomes more dominant. Remineralisation across the Fram Strait is ~ 1 mmol C m−2 d−1. Owing to the contrasting primary production pattern, our data indicate that the portion of newly produced carbon that is remineralised by the benthos is ~2.6 % in the seasonally low sea-ice covered Fram Strait but can be >15 % in the permanently high sea-ice covered Fram Strait. Furthermore, by comparing a permanently sea-ice covered area with a seasonally sea-ice covered area, we discuss a potential scenario for the deep-sea benthic ecosystem in the future Arctic Ocean, in which an increased surface primary production can lead to increasing benthic remineralisation in water depths <1500 m.

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Comparing the Satellite Microwave and Visual Shipborne Sea Ice Concentration

Исследования Земли из космоса ◽

10.31857/s020596140003369-6 ◽

2018 ◽

pp. 65-76

Author(s):

T. Alekseeva ◽

◽

E. Sharkov ◽

V. Tikhonov ◽

S. Frolov ◽

...

Keyword(s):

Sea Ice ◽

Sea Ice Concentration ◽

Ice Concentration ◽

Satellite Microwave

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DASSO: a data assimilation system for the Southern Ocean that utilizes both sea-ice concentration and thickness observations

Journal of Glaciology ◽

10.1017/jog.2021.57 ◽

2021 ◽

pp. 1-6

Author(s):

Hao Luo ◽

Qinghua Yang ◽

Longjiang Mu ◽

Xiangshan Tian-Kunze ◽

Lars Nerger ◽

...

Keyword(s):

Data Assimilation ◽

Southern Ocean ◽

Sea Ice ◽

Coupled Model ◽

Sea Ice Concentration ◽

Model Free ◽

Antarctic Sea Ice ◽

Ice Concentration ◽

Data Assimilation System ◽

Assimilation System

Abstract To improve Antarctic sea-ice simulations and estimations, an ensemble-based Data Assimilation System for the Southern Ocean (DASSO) was developed based on a regional sea ice–ocean coupled model, which assimilates sea-ice thickness (SIT) together with sea-ice concentration (SIC) derived from satellites. To validate the performance of DASSO, experiments were conducted from 15 April to 14 October 2016. Generally, assimilating SIC and SIT can suppress the overestimation of sea ice in the model-free run. Besides considering uncertainties in the operational atmospheric forcing data, a covariance inflation procedure in data assimilation further improves the simulation of Antarctic sea ice, especially SIT. The results demonstrate the effectiveness of assimilating sea-ice observations in reconstructing the state of Antarctic sea ice, but also highlight the necessity of more reasonable error estimation for the background as well as the observation.

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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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