Weekly Mapping of Sea Ice Freeboard in the Ross Sea from ICESat-2

NASA’s ICESat-2 has been providing sea ice freeboard measurements across the polar regions since October 2018. In spite of the outstanding spatial resolution and precision of ICESat-2, the spatial sparsity of the data can be a critical issue for sea ice monitoring. This study employs a geostatistical approach (i.e., ordinary kriging) to characterize the spatial autocorrelation of the ICESat-2 freeboard measurements (ATL10) to estimate weekly freeboard variations in 2019 for the entire Ross Sea area, including where ICESat-2 tracks are not directly available. Three variogram models (exponential, Gaussian, and spherical) are compared in this study. According to the cross-validation results, the kriging-estimated freeboards show correlation coefficients of 0.56–0.57, root mean square error (RMSE) of ~0.12 m, and mean absolute error (MAE) of ~0.07 m with the actual ATL10 freeboard measurements. In addition, the estimated errors of the kriging interpolation are low in autumn and high in winter to spring, and low in southern regions and high in northern regions of the Ross Sea. The effective ranges of the variograms are 5–10 km and the results from the three variogram models do not show significant differences with each other. The southwest (SW) sector of the Ross Sea shows low and consistent freeboard over the entire year because of the frequent opening of wide polynya areas generating new ice in this sector. However, the southeast (SE) sector shows large variations in freeboard, which demonstrates the advection of thick multiyear ice from the Amundsen Sea into the Ross Sea. Thus, this kriging-based interpolation of ICESat-2 freeboard can be used in the future to estimate accurate sea ice production over the Ross Sea by incorporating other remote sensing data.

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The Amundsen Sea Low: Variability, Change, and Impact on Antarctic Climate

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-14-00018.1 ◽

2016 ◽

Vol 97 (1) ◽

pp. 111-121 ◽

Cited By ~ 119

Author(s):

M. N. Raphael ◽

G. J. Marshall ◽

J. Turner ◽

R. L. Fogt ◽

D. Schneider ◽

...

Keyword(s):

Sea Ice ◽

Climate Model ◽

Ross Sea ◽

Ice Core ◽

Meridional Wind ◽

West Antarctica ◽

Sea Ice Extent ◽

Amundsen Sea ◽

West Antarctic ◽

Climate Model Simulations

Abstract The Amundsen Sea low (ASL) is a climatological low pressure center that exerts considerable influence on the climate of West Antarctica. Its potential to explain important recent changes in Antarctic climate, for example, in temperature and sea ice extent, means that it has become the focus of an increasing number of studies. Here, the authors summarize the current understanding of the ASL, using reanalysis datasets to analyze recent variability and trends, as well as ice-core chemistry and climate model projections, to examine past and future changes in the ASL, respectively. The ASL has deepened in recent decades, affecting the climate through its influence on the regional meridional wind field, which controls the advection of moisture and heat into the continent. Deepening of the ASL in spring is consistent with observed West Antarctic warming and greater sea ice extent in the Ross Sea. Climate model simulations for recent decades indicate that this deepening is mediated by tropical variability while climate model projections through the twenty-first century suggest that the ASL will deepen in some seasons in response to greenhouse gas concentration increases.

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Implementation of a simple thermodynamic sea ice scheme, SICE version 1.0-38h1, within the ALADIN–HIRLAM numerical weather prediction system version 38h1

Geoscientific Model Development ◽

10.5194/gmd-11-3347-2018 ◽

2018 ◽

Vol 11 (8) ◽

pp. 3347-3368 ◽

Cited By ~ 6

Author(s):

Yurii Batrak ◽

Ekaterina Kourzeneva ◽

Mariken Homleid

Keyword(s):

Sea Ice ◽

Numerical Weather Prediction ◽

Mean Absolute Error ◽

Weather Prediction ◽

Absolute Error ◽

The Arctic ◽

Polar Regions ◽

Form Drag ◽

Numerical Weather ◽

Ice Surface Temperature

Abstract. Sea ice is an important factor affecting weather regimes, especially in polar regions. A lack of its representation in numerical weather prediction (NWP) systems leads to large errors. For example, in the HARMONIE–AROME model configuration of the ALADIN–HIRLAM NWP system, the mean absolute error in 2 m temperature reaches 1.5 ∘C after 15 forecast hours for Svalbard. A possible reason for this is that the sea ice properties are not reproduced correctly (there is no prognostic sea ice temperature in the model). Here, we develop a new simple sea ice scheme (SICE) and implement it in the ALADIN–HIRLAM NWP system in order to improve the forecast quality in areas influenced by sea ice. The new parameterization is evaluated using HARMONIE–AROME experiments covering the Svalbard and Gulf of Bothnia areas for a selected period in March–April 2013. It is found that using the SICE scheme improves the forecast, decreasing the value of the 2 m temperature mean absolute error on average by 0.5 ∘C in areas that are influenced by sea ice. The new scheme is sensitive to the representation of the form drag. The 10 m wind speed bias increases on average by 0.4 m s−1 when the form drag is not taken into account. Also, the performance of SICE in March–April 2013 and December 2015–December 2016 was studied by comparing modelling results with the sea ice surface temperature products from MODIS and VIIRS. The warm bias (of approximately 5 ∘C) of the new scheme is indicated for areas of thick ice in the Arctic. Impacts of the SICE scheme on the modelling results and possibilities for future improvement of sea ice representation in the ALADIN–HIRLAM NWP system are discussed.

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Tropical Pacific SST Drivers of Recent Antarctic Sea Ice Trends

Journal of Climate ◽

10.1175/jcli-d-16-0440.1 ◽

2016 ◽

Vol 29 (24) ◽

pp. 8931-8948 ◽

Cited By ~ 31

Author(s):

Ariaan Purich ◽

Matthew H. England ◽

Wenju Cai ◽

Yoshimitsu Chikamoto ◽

Axel Timmermann ◽

...

Keyword(s):

Sea Ice ◽

Climate Models ◽

Climate Model ◽

Ross Sea ◽

Surface Wind ◽

Tropical Pacific ◽

Sea Ice Concentration ◽

Amundsen Sea ◽

Model Simulations ◽

Antarctic Sea Ice

Abstract A strengthening of the Amundsen Sea low from 1979 to 2013 has been shown to largely explain the observed increase in Antarctic sea ice concentration in the eastern Ross Sea and decrease in the Bellingshausen Sea. Here it is shown that while these changes are not generally seen in freely running coupled climate model simulations, they are reproduced in simulations of two independent coupled climate models: one constrained by observed sea surface temperature anomalies in the tropical Pacific and the other by observed surface wind stress in the tropics. This analysis confirms previous results and strengthens the conclusion that the phase change in the interdecadal Pacific oscillation from positive to negative over 1979–2013 contributed to the observed strengthening of the Amundsen Sea low and the associated pattern of Antarctic sea ice change during this period. New support for this conclusion is provided by simulated trends in spatial patterns of sea ice concentrations that are similar to those observed. These results highlight the importance of accounting for teleconnections from low to high latitudes in both model simulations and observations of Antarctic sea ice variability and change.

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Bromine, iodine and sodium in surface snow along the 2013 Talos Dome–GV7 traverse (northern Victoria Land, East Antarctica)

The Cryosphere ◽

10.5194/tc-11-693-2017 ◽

2017 ◽

Vol 11 (2) ◽

pp. 693-705 ◽

Cited By ~ 3

Author(s):

Niccolò Maffezzoli ◽

Andrea Spolaor ◽

Carlo Barbante ◽

Michele Bertò ◽

Massimo Frezzotti ◽

...

Keyword(s):

Sea Ice ◽

Inductively Coupled Plasma ◽

Ross Sea ◽

Chemical Species ◽

Polar Regions ◽

Austral Spring ◽

Sea Ice Extent ◽

Inductively Coupled ◽

Halogen Chemistry ◽

Polar Snow

Abstract. Halogen chemistry in the polar regions occurs through the release of halogen elements from different sources. Bromine is primarily emitted from sea salt aerosols and other saline condensed phases associated with sea ice surfaces, while iodine is affected by the release of organic compounds from algae colonies living within the sea ice environment. Measurements of halogen species in polar snow samples are limited to a few sites although there is some evidence that they are related to sea ice extent. We examine here total bromine, iodine and sodium concentrations in a series of 2 m cores collected during a traverse from Talos Dome (72°48' S, 159°06' E) to GV7 (70°41' S, 158°51' E) analyzed by inductively coupled plasma-sector field mass spectrometry (ICP-SFMS) at a resolution of 5 cm. We find a distinct seasonality of the bromine enrichment signal in most of the cores, with maxima during the austral spring. Iodine shows average concentrations of 0.04 ppb with little variability. No distinct seasonality is found for iodine and sodium. The transect reveals homogeneous air-to-snow fluxes for the three chemical species along the transect due to competing effects of air masses originating from the Ross Sea and the Southern Ocean.

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Limited use of sea ice by the Ross seal (Ommatophoca rossii), in Amundsen Sea, Antarctica, using telemetry and remote sensing data

Polar Biology ◽

10.1007/s00300-014-1602-y ◽

2014 ◽

Vol 38 (4) ◽

pp. 445-461 ◽

Cited By ~ 9

Author(s):

Anna Arcalís-Planas ◽

Signe Sveegaard ◽

Olle Karlsson ◽

Karin C. Harding ◽

Anna Wåhlin ◽

...

Keyword(s):

Remote Sensing ◽

Sea Ice ◽

Remote Sensing Data ◽

Amundsen Sea ◽

Sensing Data

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Recent changes in pan-Antarctic region surface snowmelt detected by AMSR-E and AMSR2

The Cryosphere ◽

10.5194/tc-14-3811-2020 ◽

2020 ◽

Vol 14 (11) ◽

pp. 3811-3827

Author(s):

Lei Zheng ◽

Chunxia Zhou ◽

Tingjun Zhang ◽

Qi Liang ◽

Kang Wang

Keyword(s):

Sea Ice ◽

Remote Sensing Data ◽

Microwave Remote Sensing ◽

Open Water ◽

Southern Annular Mode ◽

Temporal Variations ◽

Polar Regions ◽

Antarctic Region ◽

Marginal Sea ◽

The Antarctic

Abstract. Surface snowmelt in the pan-Antarctic region, including the Antarctic ice sheet (AIS) and sea ice, is crucial to the mass and energy balance in polar regions and can serve as an indicator of climate change. In this study, we investigate the spatial and temporal variations in surface snowmelt over the entire pan-Antarctic region from 2002 to 2017 by using passive microwave remote sensing data. The stable orbits and appropriate acquisition times of the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and the Advanced Microwave Scanning Radiometer 2 (AMSR2) enable us to take full advantage of daily brightness temperature (Tb) variations to detect surface snowmelt. The difference between AMSR-E/2 ascending and descending 36.5 GHz Tb values in vertical polarization (DAV36) was utilized to map the pan-Antarctic region snowmelt, as this method is unaffected by snow metamorphism. We evaluated the DAV36 algorithm against ground-based measurements and further improved the method over the marginal sea ice zone by excluding the effect of open water. Snowmelt detected by AMSR-E/2 data was more extensive and persistent than that detected by the Special Sensor Microwave/Imager (SSM/I) data. Continuous melt onset (CMO) ranged from August in the marginal sea ice zone to January in the Antarctic inland, and the early transient melt events occurred several days to more than 2 months earlier. The pan-Antarctic region CMO was significantly correlated (R=0.54, p<0.05) with the summer Southern Annular Mode (SAM). The decreased AIS melt extent was very likely linked (R=-0.82, p<0.01) with the enhanced summer SAM.

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Semi-automated tracking of iceberg B43 using Sentinel-1 SAR images via Google Earth Engine

10.5194/tc-2021-131 ◽

2021 ◽

Author(s):

YoungHyun Koo ◽

Hongjie Xie ◽

Stephen F. Ackley ◽

Alberto M. Mestas-Nuñez ◽

Grant J. Macdonald ◽

...

Keyword(s):

Southern Ocean ◽

Sea Ice ◽

Ross Sea ◽

Google Earth ◽

Coastal Current ◽

Threshold Method ◽

Sar Images ◽

Sea Ice Concentration ◽

Amundsen Sea ◽

Google Earth Engine

Abstract. Sentinel-1 C-band synthetic aperture radar (SAR) images can be used to observe the drift of icebergs over the Southern Ocean with around 1–3 days of temporal resolution and 10–40 m of spatial resolution. The Google Earth Engine (GEE) cloud-based platform allows processing of a large quantity of Sentinel-1 images, saving time and computational resources. In this study, we process Sentinel-1 data via GEE to detect and track the drift of iceberg B43 during its lifespan of 3 years (2017–2020) in the Southern Ocean. First, to detect all candidate icebergs in Sentinel-1 images, we employ an object-based image segmentation (simple non-iterative clustering – SNIC) and a traditional backscatter threshold method. Next, we automatically choose and trace the location of the target iceberg by comparing the centroid distance histograms (CDHs) of all detected icebergs in subsequent days with the CDH of the reference target iceberg. Using this approach, we successfully track the iceberg B43 from the Amundsen Sea to the Ross Sea, and examine its changes in area, speed, and direction. Three periods with sudden losses of area (i.e. split-offs) coincide with periods of low sea ice concentration, warm air temperature, and high waves. This implies that these variables may be related to mechanisms causing the split-off of the iceberg. Since the iceberg is generally surrounded by compacted sea ice, its drift correlates in part with sea ice motion and wind velocity. Given that the bulk of the iceberg is under water (~30–60 m freeboard and ~150–400 m thickness), its motion is predominantly driven by the westward-flowing Antarctic Coastal Current (ACoC) which dominates the circulation of the region. Considering the complexity of modeling icebergs, there is a demand for a large iceberg database to better understand the behavior of icebergs and their interactions with surrounding environments. The GEE-based semi-automated iceberg tracking method presented here can be used for this purpose.

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Implementation of a simple thermodynamic sea ice scheme, SICE version 1.0-38h1, within the ALADIN-HIRLAM numerical weather prediction system version 38h1

10.5194/gmd-2018-22 ◽

2018 ◽

Author(s):

Yurii Batrak ◽

Ekaterina Kourzeneva ◽

Mariken Homleid

Keyword(s):

Sea Ice ◽

Numerical Weather Prediction ◽

Mean Absolute Error ◽

Weather Prediction ◽

Absolute Error ◽

The Arctic ◽

Polar Regions ◽

Form Drag ◽

Numerical Weather ◽

General Evaluation

Abstract. Sea ice is an important factor affecting weather regimes, especially in polar regions. A lack of its representation in numerical weather prediction (NWP) systems leads to large errors. For example, in the HARMONIE-AROME model configuration of the ALADIN-HIRLAM NWP system, the mean absolute error in 2 metre temperature reaches 1.5 °C after 15 forecast hours for Svalbard. A possible reason for that is that the sea ice properties are not reproduced correctly (there is no prognostic sea ice temperature in the model). Here, we develop a new SImple sea iCE scheme (SICE) and implement it into the ALADIN-HIRLAM NWP system in order to improve the quality of its forecasts in areas influenced by sea ice. General evaluation of the new parameterization is performed within HARMONIE-AROME by experiments covering the Svalbard and Gulf of Bothnia areas for a selected period in March–April 2013. It is found that using the SICE scheme improves the forecast, decreasing the value of the 2 metre temperature mean absolute error on average by 0.5 °C in areas that are influenced by sea ice. The new scheme is sensitive to the representation of the form drag: it may increase the 10 metre wind speed bias on average by 0.4 m s−1 when the form drag is not taken into account. Also, the modelling results are compared with the sea ice surface temperature observations from MODIS. The warm bias (of approximately 5 °C) of the new scheme is indicated for the areas of thick ice in the Arctic. Impacts of the SICE scheme on the modelling results and possibilities for future improvement of sea ice representation in the ALADIN-HIRLAM NWP system are discussed.

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Semi-automated tracking of iceberg B43 using Sentinel-1 SAR images via Google Earth Engine

The Cryosphere ◽

10.5194/tc-15-4727-2021 ◽

2021 ◽

Vol 15 (10) ◽

pp. 4727-4744

Author(s):

YoungHyun Koo ◽

Hongjie Xie ◽

Stephen F. Ackley ◽

Alberto M. Mestas-Nuñez ◽

Grant J. Macdonald ◽

...

Keyword(s):

Southern Ocean ◽

Sea Ice ◽

Ross Sea ◽

Google Earth ◽

Coastal Current ◽

Threshold Method ◽

Sar Images ◽

Sea Ice Concentration ◽

Amundsen Sea ◽

Google Earth Engine

Abstract. Sentinel-1 C-band synthetic aperture radar (SAR) images can be used to observe the drift of icebergs over the Southern Ocean with around 1–3 d of temporal resolution and 10–40 m of spatial resolution. The Google Earth Engine (GEE) cloud-based platform allows processing of a large quantity of Sentinel-1 images, saving time and computational resources. In this study, we process Sentinel-1 data via GEE to detect and track the drift of iceberg B43 during its lifespan of 3 years (2017–2020) in the Southern Ocean. First, to detect all candidate icebergs in Sentinel-1 images, we employ an object-based image segmentation (simple non-iterative clustering – SNIC) and a traditional backscatter threshold method. Next, we automatically choose and trace the location of the target iceberg by comparing the centroid distance histograms (CDHs) of all detected icebergs in subsequent days with the CDH of the reference target iceberg. Using this approach, we successfully track iceberg B43 from the Amundsen Sea to the Ross Sea and examine its changes in area, speed, and direction. Three periods with sudden losses of area (i.e., split-offs) coincide with periods of low sea ice concentration, warm air temperature, and high waves. This implies that these variables may be related to mechanisms causing the split-off of the iceberg. Since the iceberg is generally surrounded by compacted sea ice, its drift correlates in part with sea ice motion and wind velocity. Given that the bulk of the iceberg is under water (∼30–60 m freeboard and ∼150–400 m thickness), its motion is predominantly driven by the westward-flowing Antarctic Coastal Current, which dominates the circulation of the region. Considering the complexity of modeling icebergs, there is a demand for a large iceberg database to better understand the behavior of icebergs and their interactions with surrounding environments. The semi-automated iceberg tracking based on the storage capacity and computing power of GEE can be used for this purpose.

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Links between the Amundsen Sea Low and sea ice in the Ross Sea: seasonal and interannual relationships

Climate Dynamics ◽

10.1007/s00382-018-4258-4 ◽

2018 ◽

Vol 52 (3-4) ◽

pp. 2333-2349 ◽

Cited By ~ 1

Author(s):

Marilyn N. Raphael ◽

Marika M. Holland ◽

Laura Landrum ◽

William R. Hobbs

Keyword(s):

Sea Ice ◽

Ross Sea ◽

Amundsen Sea

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