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 Aspects of intraseasonal variability of Antarctic sea ice in austral winter related to ENSO and SAM events

2017 ◽  
Vol 63 (241) ◽  
pp. 838-846 ◽  
Author(s):  
KENJI BABA ◽  
JAMES RENWICK
Keyword(s):  
Sea Ice ◽  
El Nino ◽  
Intraseasonal Variability ◽  
Southern Annular Mode ◽  
Wave Number ◽  
Austral Winter ◽  
Sea Ice Concentration ◽  
Annular Mode ◽  
Antarctic Sea Ice ◽  
Ice Concentration

ABSTRACTWe performed an Empirical Orthogonal Function (EOF) analysis to assess the intraseasonal variability of 5–60 day band-pass filtered Antarctic sea-ice concentration in austral winter using a 20-year daily dataset from 1995 to 2014. Zonal wave number 3 dominated in the Antarctic, especially so across the west Antarctic. Results showed the coexistence of stationary and propagating wave components. A spectral analysis of the first two principal components (PCs) showed a similar structure for periods up to 15 days but generally more power in PC1 at longer periods. Regression analysis upon atmospheric fields using the first two PCs of sea-ice concentration showed a coherent wave number 3 pattern. The spatial phase delay between the sea-ice and mean sea-level pressure patterns suggests that meridional flow and associated temperature advection are important for modulating the sea-ice field. EOF analyses carried out separately for El Niño, La Niña and neutral years, and for Southern Annular Mode positive, negative and neutral periods, suggest that the spatial patterns of wave number 3 shift between subsets. The results also indicate that El Niño-Southern Oscillation and Southern Annular Mode affect stationary wave interactions between sea-ice and atmospheric fields on intraseasonal timescales.

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

&lt;p&gt;Dynamical dependence between key observables and the surface mass balance (SMB) over Antarctica is analyzed in two historical runs performed with the MPI&amp;#8208;ESM&amp;#8208;P and the CESM1&amp;#8208;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&amp;#8208;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.&lt;/p&gt;

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

&lt;p&gt;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.&lt;/p&gt;

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.

Seasonal Relationships between Large-Scale Climate Variability and Antarctic Sea Ice Concentration

2012 ◽  
Vol 25 (16) ◽  
pp. 5451-5469 ◽  
Author(s):  
Graham R. Simpkins ◽  
Laura M. Ciasto ◽  
David. W. J. Thompson ◽  
Matthew H. England
Keyword(s):  
Climate Variability ◽  
Sea Ice ◽  
Large Scale ◽  
Southern Oscillation ◽  
Principal Component ◽  
Southern Annular Mode ◽  
Enso Cycle ◽  
Sea Ice Concentration ◽  
Antarctic Sea Ice ◽  
Ice Concentration

Abstract The observed relationships between anomalous Antarctic sea ice concentration (SIC) and the leading patterns of Southern Hemisphere (SH) large-scale climate variability are examined as a function of season over 1980–2008. Particular emphasis is placed on 1) the interactions between SIC, the southern annular mode (SAM), and El Niño–Southern Oscillation (ENSO); and 2) the contribution of these two leading modes to the 29-yr trends in sea ice. Regression, composite, and principal component analyses highlight a seasonality in SH sea ice–atmosphere interactions, whereby Antarctic sea ice variability exhibits the strongest linkages to the SAM and ENSO during the austral cold season months. As noted in previous work, a dipole in SIC anomalies emerges in relation to the SAM, characterized by centers of action located near the Bellingshausen/Weddell and Amundsen/eastern Ross Seas. The structure and magnitude of this SIC dipole is found to vary considerably as a function of season, consistent with the seasonality of the overlying atmospheric circulation anomalies. Relative to the SAM, the pattern of sea ice anomalies linked to ENSO exhibits a similar seasonality but tends to be weaker in amplitude and more diffuse in structure. The relationships between ENSO and sea ice also exhibit a substantial nonlinear component, highlighting the need to consider both season and phase of the ENSO cycle when diagnosing ENSO–SIC linkages. Trends in SIC over 1980–2008 are not significantly related to trends in either the SAM or ENSO during any season, including austral summer when the trend in the SAM is most pronounced.

scholarly journals Characterizing the Sea-Ice Floe Size Distribution in the Canada Basin from High-Resolution Optical Satellite Imagery

2021 ◽  
Author(s):  
Alexis Anne Denton ◽  
Mary-Louise Timmermans
Keyword(s):  
High Resolution ◽  
Sea Ice ◽  
Size Distribution ◽  
Satellite Images ◽  
Threshold Value ◽  
Canada Basin ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Optical Satellite Images

Abstract. The sea-ice floe size distribution (FSD) characterizes the sea-ice response to atmosphere and ocean forcing and is important for understanding and modeling the evolving ice pack in a warming Arctic. FSDs are evaluated from 78 floe- segmented high-resolution (1-m) optical satellite images capturing a range of settings and sea-ice states during spring through fall from 1999 to 2014 in the Canada Basin. For any given image, the structure of the FSD is found to be sensitive to a classification threshold value (i.e., to specify an image pixel as being either water or ice) used in image segmentation, and an objective approach to minimize this sensitivity is presented. The FSDs are found to exhibit a single power-law regime between floe areas 50 m2 and 5 km2, characterized by exponents (slopes in log-log space) in the range −2.03 to −1.65. A distinct linear relationship between slopes and sea-ice concentrations is found, with steeper slopes (i.e., a larger proportion of smaller to larger floes) corresponding to lower sea-ice concentrations. Further, a seasonal variation in slopes is found for fixed sites in the Canada Basin that undergo a seasonal cycle in sea-ice concentration, while sites with extensive sea-ice cover year-round do not exhibit any seasonal change in FSD properties. Our results suggest that sea-ice concentration should be considered in any characterization of a time-varying FSD (for use in sea-ice models, for example).

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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