Sea ice concentration in response to weather systems in the Weddell Sea: comparison between SSM/I data and model simulations

2002 ◽  
Author(s):  
H. Fischer ◽  
C. Oelke
Keyword(s):  
Sea Ice ◽  
Weddell Sea ◽  
Sea Ice Concentration ◽  
Model Simulations ◽  
Ice Concentration

Testing for Dynamical Dependence: Application to the Surface Mass Balance Over Antarctica

2020 ◽  
Author(s):  
Quentin Dalaiden ◽  
Stephane Vannitsem ◽  
Hugues Goosse
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Mass Balance ◽  
Information Transfer ◽  
Climate Models ◽  
Weddell Sea ◽  
Surface Mass Balance ◽  
Sea Ice Concentration ◽  
Surface Mass ◽  
Ice Concentration

<p>Dynamical dependence between key observables and the surface mass balance (SMB) over Antarctica is analyzed in two historical runs performed with the MPI‐ESM‐P and the CESM1‐CAM5 climate models. The approach used is a novel method allowing for evaluating the rate of information transfer between observables that goes beyond the classical correlation analysis and allows for directional characterization of dependence. It reveals that a large proportion of significant correlations do not lead to dependence. In addition, three coherent results concerning the dependence of SMB emerge from the analysis of both models: (i) The SMB over the Antarctic Plateau is mostly influenced by the surface temperature and sea ice concentration and not by large‐scale circulation changes; (ii) the SMB of the Weddell Sea and the Dronning Maud Land coasts are not influenced significantly by the surface temperature; and (iii) the Weddell Sea coast is not significantly influenced by the sea ice concentration.</p>

Polynya area and frequency in the Weddell Sea in CMIP6 climate models

2020 ◽  
Author(s):  
Martin Mohrmann ◽  
Céline Heuzé ◽  
Sebastiaan Swart
Keyword(s):  
Sea Ice ◽  
Deep Water ◽  
Climate Models ◽  
Numerical Models ◽  
Open Water ◽  
Weddell Sea ◽  
Threshold Method ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Ice Concentration

<p>The presence of polynyas has a large effect on air-sea fluxes and deep water production, therefore impacting climate-relevant properties such as heat and carbon exchange between the atmosphere and ocean interior. One of the key areas of deep water formation is in the Weddell Sea, where much attention has recently been placed in the reoccurance of the open ocean Maud Rise polynya. In this study, two methods are presented to track the number, area and spatial distribution of polynyas with a focus on the Weddell Sea. The analysis is applied to a set of 10 Coupled Model Intercomparison Project phase 6 (CMIP6) models and to satellite sea ice concentration data. The first approach is a sea ice threshold method applied to the CMIP6 sea ice data at the original model grid. Open water areas surrounded by sea ice are classified as polynyas. Without requiring any remapping or interpolation, this method preserves the area information of all grid cells and is well suited to compute the combined area of the polynyas in the Weddell Sea. The second approach makes use of an image analysis technique to outline areas with low sea ice concentration. This method is preferable for counting the absolute number of polynyas and obtaining statistical information about their position. Satellite sea ice concentration is used as a reference to compare the performance of the models representing polynya area and to indicate model biases in the location of polynyas. All analyzed CMIP6 models show coastal polynyas, while only about half of the models regularly form open water polynyas. The resolution (about one degree for most models) sets a limit for the number of the polynyas in the numerical models.</p>

scholarly journals Characterization of Moisture Sources for Austral Seas and Relationship with Sea Ice Concentration

Atmosphere ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 627
Author(s):  
Michelle Simões Reboita ◽  
Raquel Nieto ◽  
Rosmeri P. da Rocha ◽  
Anita Drumond ◽  
Marta Vázquez ◽  
...  
Keyword(s):  
Sea Ice ◽  
Negative Temperature ◽  
Southern Annular Mode ◽  
Weddell Sea ◽  
Lagrangian Model ◽  
Sea Ice Concentration ◽  
Moisture Sources ◽  
Ice Concentration ◽  
Backward Analysis

In this study, the moisture sources acting over each sea (Weddell, King Haakon VII, East Antarctic, Amundsen-Bellingshausen, and Ross-Amundsen) of the Southern Ocean during 1980–2015 are identified with the FLEXPART Lagrangian model and by using two approaches: backward and forward analyses. Backward analysis provides the moisture sources (positive values of Evaporation minus Precipitation, E − P > 0), while forward analysis identifies the moisture sinks (E − P < 0). The most important moisture sources for the austral seas come from midlatitude storm tracks, reaching a maximum between austral winter and spring. The maximum in moisture sinks, in general, occurs in austral end-summer/autumn. There is a negative correlation (higher with 2-months lagged) between moisture sink and sea ice concentration (SIC), indicating that an increase in the moisture sink can be associated with the decrease in the SIC. This correlation is investigated by focusing on extremes (high and low) of the moisture sink over the Weddell Sea. Periods of high (low) moisture sinks show changes in the atmospheric circulation with a consequent positive (negative) temperature anomaly contributing to decreasing (increasing) the SIC over the Weddell Sea. This study also suggests possible relationships between the positive (negative) phase of the Southern Annular Mode with the increase (decrease) in the moisture that travels from the midlatitude sources to the Weddell Sea.

scholarly journals Spatial fields of Antarctic sea-ice concentration anomalies for summer–autumn and their relationship to Southern Hemisphere atmospheric circulation during the period 1979–2009

2011 ◽  
Vol 52 (57) ◽  
pp. 140-150 ◽  
Author(s):  
Sandra Barreira ◽  
Rosa Hilda Compagnucci
Keyword(s):  
Sea Ice ◽  
Atmospheric Circulation ◽  
Ross Sea ◽  
Opposite Sign ◽  
Surface Air Temperature ◽  
Principal Component ◽  
Weddell Sea ◽  
Equal Sign ◽  
Sea Ice Concentration ◽  
Ice Concentration

AbstractSummer–autumn monthly sea-ice concentration anomaly (SICA) fields in Antarctica obtained from satellite data for the period 1979–2009 were analysed with Varimax-rotated T-mode principal component analysis (PCA). the first three PCA scores described the SICA spatial behaviour and explained 38.07% of the total variance. the related atmospheric circulation characteristics were analysed using 850 hPa height and surface air-temperature anomalies for the months clustered by the corresponding SICA composites, which were based on PCA loadings above a ±0.3 threshold. the principal characteristics of SICA can be seen between the Ross and Weddell Seas, areas that remained ice-covered during the analysis period. Elsewhere around Antarctica, small distinct characteristics occur mostly in embayments. the leading summer–autumn SICA pattern shows a structure with two centres of equal sign located one over the Weddell and the other over the Ross Sea–southwest Pacific Ocean sector and a centre of opposite sign over the Bellingshausen and Amundsen Seas. the second SICA pattern is represented by a dipole over the Weddell Sea as a result of an increase (decrease) in sea-ice concentration in the northern sector (positive phase) and a decrease (increase) in the southern region, together with a positive (negative) centre over the Ross and Amundsen Seas. the latter pattern is characterized by equal-sign anomalies on both sides of the Antarctic Peninsula and opposite-sign centres all around Antarctica with the highest intensity over the Ross Sea.

scholarly journals The Impact of Sea Ice Concentration Accuracies on Climate Model Simulations with the GISS GCM

2001 ◽  
Vol 14 (12) ◽  
pp. 2606-2623 ◽  
Author(s):  
Claire L. Parkinson ◽  
David Rind ◽  
Richard J. Healy ◽  
Douglas G. Martinson
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Sea Ice Concentration ◽  
Model Simulations ◽  
Ice Concentration ◽  
Climate Model Simulations ◽  
The Impact

Comparison of sea ice classification methods based on satellite scatterometer and radiometer data in the Weddell Sea, Antarctica

2019 ◽  
Vol 31 (3) ◽  
pp. 150-164
Author(s):  
Xiaoping Pang ◽  
Xiang Gao ◽  
Qing Ji
Keyword(s):  
Sea Ice ◽  
Satellite Remote Sensing ◽  
Remote Sensing Data ◽  
Weddell Sea ◽  
First Year ◽  
Sea Ice Concentration ◽  
Antarctic Sea Ice ◽  
Ice Concentration ◽  
Type Information

AbstractInformation on sea ice type is an important factor for deriving sea ice parameters from satellite remote sensing data, such as sea ice concentration, extent and thickness. In this study, sea ice in the Weddell Sea was classified by the histogram threshold (HT) method, the Spreen model (SM) method from satellite scatterometer data and the strong contrast (SC) method from radiometer data, and this information was compared with Antarctic Sea Ice Processes and Climate (ASPeCt) sea ice-type ship-based observations. The results show that all three methods can distinguish the multi-year (MY) ice and first-year (FY) ice using Ku-band scatterometer data and radiometer data during the ice growth season, while C-band scatterometer data are not suitable for MY ice and FY ice discrimination using HT and SM methods. The SM model has a smaller MY ice classification extent than the HT method from scatterometer data. The classification accuracy of the SM method is the higher compared to ship-based observations. It can be concluded that the SM method is a promising method for discriminating MY ice from FY ice. These results provide a reference for further retrieval of long-term sea ice-type information for the whole of Antarctica.

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