scholarly journals The Influence of Sea Ice Dynamics on the Climate Sensitivity and Memory to Increased Antarctic Sea Ice

2015 ◽  
Vol 28 (24) ◽  
pp. 9642-9668 ◽  
Author(s):  
Claudia K. Parise ◽  
Luciano P. Pezzi ◽  
Kevin I. Hodges ◽  
Flavio Justino
Keyword(s):  
Sea Ice ◽  
Climate Sensitivity ◽  
Southern Annular Mode ◽  
Fast Response ◽  
Heat Fluxes ◽  
Positive Phase ◽  
Ice Dynamics ◽  
Antarctic Sea Ice ◽  
Current Climate ◽  
Subsurface Layers

Abstract The study analyzes the sensitivity and memory of the Southern Hemisphere coupled climate system to increased Antarctic sea ice (ASI), taking into account the persistence of the sea ice maxima in the current climate. The mechanisms involved in restoring the climate balance under two sets of experiments, which differ in regard to their sea ice models, are discussed. The experiments are perturbed with extremes of ASI and integrated for 10 yr in a large 30-member ensemble. The results show that an ASI maximum is able to persist for ~4 yr in the current climate, followed by a negative sea ice phase. The sea ice insulating effect during the positive phase reduces heat fluxes south of 60°S, while at the same time these are intensified at the sea ice edge. The increased air stability over the sea ice field strengthens the polar cell while the baroclinicity increases at midlatitudes. The mean sea level pressure is reduced (increased) over high latitudes (midlatitudes), typical of the southern annular mode (SAM) positive phase. The Southern Ocean (SO) becomes colder and fresher as the sea ice melts mainly through sea ice lateral melting, the consequence of which is an increase in the ocean stability by buoyancy and mixing changes. The climate sensitivity is triggered by the sea ice insulating process and the resulting freshwater pulse (fast response), while the climate equilibrium is restored by the heat stored in the SO subsurface layers (long response). It is concluded that the time needed for the ASI anomaly to be dissipated and/or melted is shortened by the sea ice dynamical processes.

scholarly journals Antarctic Sea-ice velocity as derived from SSM/I imagery

2006 ◽  
Vol 44 ◽  
pp. 361-366 ◽  
Author(s):  
P. Heil ◽  
C.W. Fowler ◽  
S.E. Lake
Keyword(s):  
Sea Ice ◽  
Sea Level ◽  
Southern Annular Mode ◽  
Ice Dynamics ◽  
Sea Level Pressure ◽  
Level Pressure ◽  
Antarctic Sea Ice ◽  
Sea Ice Dynamics ◽  
Ice Velocity ◽  
Microwave Imager

AbstractSea-ice velocities derived from remotely Sensed microwave imagery of the Special Sensor Microwave/Imager (SSM/I) have been analyzed for changes over time in Antarctic Sea-ice velocity, for the period 1988–2004. Year-to-year variability in mean Antarctic annual SSM/I-derived ice Speed is Small (17 year Standard deviation (SD) = 0.008 ms–1), with greater interannual variability in the zonal (eastward positive) velocity components (17 year SD = 0.016ms–1). Seasonally, minimum ice Speed is encountered during Summer, when nearly all Antarctic Sea ice is within the marginal ice zone. Ice motion peaks during winter and Spring, due to high velocities encountered in the outer pack of the Seasonal Sea-ice zone. The correlation (R2 = 0.47) between winter Southern Annular Mode (SAM) and mean winter ice Speed highlights the importance of atmospheric forcing on Sea-ice dynamics. The Spatial pattern of the correlation of the Standardized SAM index with the June–November ice Speed exhibits a wave-3 pattern, which matches the Sea-level pressure distribution. Sea-ice Speed in the upstream regions of quasi-stationary centres of low Sea-level pressure is likely to increase (decrease) during high (low) SAMyears, and the opposite for Sea-ice Speed in the downstream regions of the centres.

Dominant Covarying Climate Signals in the Southern Ocean and Antarctic Sea Ice Influence during the Last Three Decades

2017 ◽  
Vol 30 (8) ◽  
pp. 3055-3072 ◽  
Author(s):  
D. Cerrone ◽  
G. Fusco ◽  
I. Simmonds ◽  
G. Aulicino ◽  
G. Budillon
Keyword(s):  
Southern Ocean ◽  
Sea Ice ◽  
Surface Air Temperature ◽  
Southern Annular Mode ◽  
Heat Fluxes ◽  
Sea Ice Concentration ◽  
Antarctic Sea Ice ◽  
Zonal Wavenumber ◽  
Temporal Variance ◽  
Climate Signals

A composite dataset (comprising geopotential height, sea surface temperature, zonal and meridional surface winds, precipitation, cloud cover, surface air temperature, latent plus sensible heat fluxes, and sea ice concentration) has been investigated with the aim of revealing the dominant time scales of variability from 1982 to 2013. Three covarying climate signals associated with variations in the sea ice distribution around Antarctica have been detected through the application of the multiple-taper method with singular value decomposition (MTM-SVD). Features of the established patterns of variation over the Southern Hemisphere extratropics have been identified in each of these three climate signals in the form of coupled or individual oscillations. The climate patterns considered here are the southern annular mode (SAM), the Pacific–South American (PSA) teleconnection, the semiannual oscillation (SAO), and the zonal wavenumber-3 (ZW3) mode. It is shown that most of the sea ice temporal variance is concentrated at the quasi-triennial scale resulting from the constructive superposition of the PSA and ZW3 patterns. In addition, the combination of the SAM and SAO patterns is found to promote the interannual sea ice variations underlying a general change in the Southern Ocean atmospheric and oceanic circulations. These two modes of variability are also found to be consistent with the occurrence of the positive SAM/negative PSA (SAM+/PSA−) or negative SAM/positive PSA (SAM−/PSA+) combinations, which could have favored the cooling of the sub-Antarctic region and important changes in the Antarctic sea ice distribution since 2000.

scholarly journals Brief communication: lack of agreement in remote sensing detection of cyclonic drift caused by Atlantic weather in Antarctic sea ice

2021 ◽  
Author(s):  
Wayne de Jager ◽  
Marcello Vichi
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Alternative Methods ◽  
Polar Regions ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Ice Dynamics ◽  
Antarctic Sea Ice ◽  
Ice Drift ◽  
The Impact

Abstract. Sea-ice extent variability, a measure based on satellite-derived sea ice concentration measurements, has traditionally been used as an essential climate variable to evaluate the impact of climate change on polar regions. However, concentration- based measurements of ice variability do not allow to discriminate the relative contributions made by thermodynamic and dynamic processes, prompting the need to use sea-ice drift products and develop alternative methods to quantify changes in sea ice dynamics that would indicate trends in Antarctic ice characteristics. Here, we present a new method to automate the detection of rotational drift features in Antarctic sea ice at daily timescales using currently available remote sensing ice motion products from EUMETSAT OSI SAF. Results show that there is a large discrepancy in the detection of cyclonic drift features between products, both in terms of intensity and year-to-year distributions, thus diminishing the confidence at which ice drift variability can be further analysed. Product comparisons showed that there was good agreement in detecting anticyclonic drift, and cyclonic drift features were measured to be 1.5–2.2 times more intense than anticyclonic features. The most intense features were detected by the merged product, suggesting that the processing chain used for this product could be injecting additional rotational momentum into the resultant drift vectors. We conclude that it is therefore necessary to better understand why the products lack agreement before further trend analysis of these drift features and their climatic significance can be assessed.

Does CMIP6 better constrain projections of 21st century Antarctic sea ice loss?

2021 ◽  
Author(s):  
Caroline Holmes ◽  
Tom Bracegirdle ◽  
Paul Holland
Keyword(s):  
Model Selection ◽  
Sea Ice ◽  
Climate Sensitivity ◽  
Linear Constraint ◽  
Antarctic Sea Ice ◽  
Historical Climatology ◽  
Regression Techniques ◽  
The Mean ◽  
Global Surface ◽  
Global Mean

<p>Results from CMIP5 have previously suggested that ensemble regression techniques or model selection may provide solutions to the challenge of making projections of future Antarctic sea ice area (SIA) in the presence of large historical biases. Here, we revisit and extend such analysis incorporating the CMIP6 ensemble, which shows modest improvements in some aspects of sea ice simulation and in particular a reduction of inter-model spread in historical SIA. We focus on the strongest forcing scenarios analysed, CMIP5 RCP85 and CMIP6 SSP5.85.</p><p>In summer (February) the historical climatology of SIA is a strong linear constraint on projections of SIA in both generations. This is because the strong forcing leads to the loss of the majority of summer SIA in each model, so that the models that start with greater SIA exhibit greater reductions. Differences between CMIP5 and CMIP6 are largely explained by the fact that, compared to CMIP6, CMIP5 contains many more models that have very large positive biases in historical SIA and do not lose the majority of ice.</p><p>In winter (September), a much smaller proportion of SIA is lost, but inter-model spread in SIA climatology still explains just under half the variance in projections of SIA change, in both CMIP5 and CMIP6. The mean historical winter climatology is similar between generations, as is the regression slope of SIA change against SIA climatology.  However, there is a greater reduction of SIA in CMIP6 than CMIP5. We find this to be statistically related to greater global mean warming in CMIP6 than CMIP5, and therefore potentially to the larger climate sensitivity in the CMIP6 ensemble.</p><p>These findings imply that, depending on season, a different balance of local (SIA climatology) and global (GMST change) drivers can be used to explain the inter-model and inter-generation spread in projections of SIA loss. They also firmly tie our ability to project Antarctic SIA loss to our understanding of the fidelity of higher CMIP6 climate sensitivity. Questions remain about whether models are correct in their simulation of Antarctic SIA sensitivity to global surface temperature.</p>

scholarly journals Positive Trend in the Antarctic Sea Ice Cover and Associated Changes in Surface Temperature

2017 ◽  
Vol 30 (6) ◽  
pp. 2251-2267 ◽  
Author(s):  
Josefino C. Comiso ◽  
Robert A. Gersten ◽  
Larry V. Stock ◽  
John Turner ◽  
Gay J. Perez ◽  
...  
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Climate Models ◽  
Ice Cover ◽  
Southern Annular Mode ◽  
Positive Trend ◽  
Growth Season ◽  
Sea Ice Extent ◽  
Antarctic Sea Ice ◽  
The Antarctic

Abstract The Antarctic sea ice extent has been slowly increasing contrary to expected trends due to global warming and results from coupled climate models. After a record high extent in 2012 the extent was even higher in 2014 when the magnitude exceeded 20 × 106 km2 for the first time during the satellite era. The positive trend is confirmed with newly reprocessed sea ice data that addressed inconsistency issues in the time series. The variability in sea ice extent and ice area was studied alongside surface ice temperature for the 34-yr period starting in 1981, and the results of the analysis show a strong correlation of −0.94 during the growth season and −0.86 during the melt season. The correlation coefficients are even stronger with a one-month lag in surface temperature at −0.96 during the growth season and −0.98 during the melt season, suggesting that the trend in sea ice cover is strongly influenced by the trend in surface temperature. The correlation with atmospheric circulation as represented by the southern annular mode (SAM) index appears to be relatively weak. A case study comparing the record high in 2014 with a relatively low ice extent in 2015 also shows strong sensitivity to changes in surface temperature. The results suggest that the positive trend is a consequence of the spatial variability of global trends in surface temperature and that the ability of current climate models to forecast sea ice trend can be improved through better performance in reproducing observed surface temperatures in the Antarctic region.

scholarly journals Evaluation of Polar WRF from Modeling the Atmospheric Boundary Layer over Antarctic Sea Ice in Autumn and Winter

2012 ◽  
Vol 140 (12) ◽  
pp. 3919-3935 ◽  
Author(s):  
Esa-Matti Tastula ◽  
Timo Vihma ◽  
Edgar L Andreas
Keyword(s):  
Boundary Layer ◽  
Sea Ice ◽  
Surface Temperature ◽  
Atmospheric Boundary Layer ◽  
Inversion Layer ◽  
Temperature Inversion ◽  
Heat Fluxes ◽  
Poor Correlation ◽  
Antarctic Sea Ice ◽  
Autumn And Winter

Abstract Regional simulations of the atmospheric boundary layer over Antarctic sea ice that have been adequately validated are rare. To address this gap, the authors use the doubly nested Polar Weather Research and Forecasting (Polar WRF) mesoscale model to simulate conditions during Ice Station Weddell (ISW) in the austral autumn and winter of 1992. The WRF simulations test two boundary layer schemes: Mellor–Yamada–Janjic and the Asymmetric Convective Model. Validation is against surface-layer and sounding observations from ISW. Simulated latent and sensible heat fluxes for both boundary layer schemes had poor correlation with the observed fluxes. Simulated surface temperature had better correlation with the observations, with a typical bias of 0–2 K and a root-mean-square error of 6–7 K. For surface temperature and wind speed, the Polar WRF yielded better results than the ECMWF Re-Analysis Interim (ERA-Interim). A more challenging test of the simulations is to reproduce features of the low-level jet and the temperature inversion, which were observed, respectively, in 80% and 96% of the ISW radiosoundings. Both boundary layer schemes produce only about half as many jets as were observed. Moreover, the simulated jet coincided with an observed jet only about 30% of the time. The number of temperature inversions and the height at the inversion base were better reproduced, although this was not the case with the depth of the inversion layer. Simulations of the temperature inversion improved when forecasts of cloud fraction agreed to within 0.3 with observations. The modeled inversions were strongest when the incoming longwave radiation was smallest, but this relationship was not observed at ISW.

scholarly journals Interactions between Antarctic sea ice and large-scale atmospheric modes in CMIP5 models

2016 ◽  
Author(s):  
Serena Schroeter ◽  
Will Hobbs ◽  
Nathaniel L. Bindoff
Keyword(s):  
Sea Ice ◽  
Large Scale ◽  
Coupled Model ◽  
Southern Annular Mode ◽  
Cmip5 Models ◽  
Atmospheric Variability ◽  
Annular Mode ◽  
Antarctic Sea Ice ◽  
Ice Retreat ◽  
Sea Ice Retreat

Abstract. The response of Antarctic sea ice to large-scale patterns of atmospheric variability varies according to sea ice sector and season. In this study, interannual atmosphere-sea ice interactions were explored using observation-based data and compared with simulated interactions by models in the Coupled Model Intercomparison Project Phase 5. Simulated relationships between atmospheric variability and sea ice variability generally reproduced the observed relationships, though more closely during the season of sea ice advance than the season of sea ice retreat. Atmospheric influence on sea ice is known to be strongest during its advance, with the ocean emerging as a dominant driver of sea ice retreat; therefore, while it appears that models are able to capture the dominance of the atmosphere during advance, simulations of ocean-atmosphere-sea ice interactions during retreat require further investigation. A large proportion of model ensemble members overestimated the relative importance of the Southern Annular Mode compared with other modes on high southern latitude climate, while the influence of tropical forcing was underestimated. This result emerged particularly strongly during the season of sea ice retreat. The amplified zonal patterns of the Southern Annular Mode in many models and its exaggerated influence on sea ice overwhelm the comparatively underestimated meridional influence, suggesting that simulated sea ice variability would become more zonally symmetric as a result. Across the seasons of sea ice advance and retreat, 3 of the 5 sectors did not reveal a strong relationship with a pattern of large-scale atmospheric variability in one or both seasons, indicating that sea ice in these sectors may be influenced more strongly by atmospheric variability unexplained by the major atmospheric modes, or by heat exchange in the ocean.

scholarly journals Pack-Ice Drift off East Antarctica and some Implications

1989 ◽  
Vol 12 ◽  
pp. 1-8 ◽  
Author(s):  
Allison Ian
Keyword(s):  
Sea Ice ◽  
Bottom Topography ◽  
Regional Climate ◽  
Qualitative Assessment ◽  
Ocean Bottom ◽  
Ice Dynamics ◽  
Antarctic Sea Ice ◽  
Ice Drift ◽  
Region Data ◽  
Autumn And Winter

Three satellite-tracked data buoys were deployed between 70° and 80°E south of 65°S in February-March 1985. These buoys were subsequently trapped within the expanding seasonal sea ice and drifted with the ice. The buoys measured air temperature and pressure, and water temperatures to 100 m depth. Data from the buoys are used to describe the ice drift and environment within the winter sea-ice zone in this region, both north and south of the Antarctic Divergence. Additional preliminary data from a further six buoys deployed in the same area in March 1987 are also presented. Besides providing information on the broad-scale drift of the ice in the Prydz Bay region, data from the buoys have shown: (i) the important role that ice drift plays in determining the autumn and winter expansion of Antarctic sea ice; (ii) the highly mobile nature of the ice, even hundreds of kilometres from the ice edge; (iii) the role that ocean-bottom topography has in determining ice drift over the continental shelf; and (iv) the modifying influence that an ice cover has on regional climate. A qualitative assessment is made of the relative importance of the major forces driving the ice, although the data are insufficient for a detailed study of the ice dynamics.

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