scholarly journals Tropical Pacific SST Drivers of Recent Antarctic Sea Ice Trends

2016 ◽  
Vol 29 (24) ◽  
pp. 8931-8948 ◽  
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.

scholarly journals Impact of surface wind biases on the Antarctic sea ice concentration budget in climate models

2016 ◽  
Vol 105 ◽  
pp. 60-70 ◽  
Author(s):  
O. Lecomte ◽  
H. Goosse ◽  
T. Fichefet ◽  
P.R. Holland ◽  
P. Uotila ◽  
...  
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Surface Wind ◽  
Sea Ice Concentration ◽  
Antarctic Sea Ice ◽  
Ice Concentration ◽  
The Antarctic

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

2016 ◽  
Vol 97 (1) ◽  
pp. 111-121 ◽  
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.

scholarly journals Antarctic sea ice decline delayed well into the 21st century in a high-resolution climate projection

2020 ◽  
Author(s):  
Thomas Rackow ◽  
Sergey Danilov ◽  
Helge F. Goessling ◽  
Hartmut H. Hellmer ◽  
Dmitry V. Sein ◽  
...  
Keyword(s):  
Global Warming ◽  
High Resolution ◽  
Sea Ice ◽  
Climate Models ◽  
Climate Model ◽  
The Arctic ◽  
Alternative Mechanism ◽  
Sea Ice Extent ◽  
Antarctic Sea Ice ◽  
Sea Ice Decline

<p>Despite ongoing global warming and strong sea ice decline in the Arctic, the sea ice extent around the Antarctic continent has not declined during the satellite era since 1979. This is in stark contrast to existing climate models that tend to show a strong negative sea ice trend for the same period; hence the confidence in projected Antarctic sea-ice changes is considered to be low. In the years since 2016, there has been significantly lower Antarctic sea ice extent, which some consider a sign of imminent change; however, others have argued that sea ice extent is expected to regress to the weak decadal trend in the near future.</p><p>In this presentation, we show results from climate change projections with a new climate model that allows the simulation of mesoscale eddies in dynamically active ocean regions in a computationally efficient way. We find that the high-resolution configuration (HR) favours periods of stable Antarctic sea ice extent in September as observed over the satellite era. Sea ice is not projected to decline well into the 21<sup>st</sup> century in the HR simulations, which is similar to the delaying effect of, e.g., added glacial melt water in recent studies. The HR ocean configurations simulate an ocean heat transport that responds differently to global warming and is more efficient at moderating the anthropogenic warming of the Southern Ocean. As a consequence, decrease of Antarctic sea ice extent is significantly delayed, in contrast to what existing coarser-resolution climate models predict.</p><p>Other explanations why current models simulate a non-observed decline of Antarctic sea-ice have been put forward, including the choice of included sea ice physics and underestimated simulated trends in westerly winds. Our results provide an alternative mechanism that might be strong enough to explain the gap between modeled and observed trends alone.</p>

The relative roles of Arctic and Antarctic sea-ice loss in the response to greenhouse warming

2021 ◽  
Author(s):  
Stephanie Hay ◽  
Paul Kusnher
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Climate Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Extent ◽  
Antarctic Sea Ice ◽  
Arctic Sea ◽  
Climate Model Simulations ◽  
Arctic Sea Ice Loss

<p>Antarctic sea ice has gradually increased in extent over the forty-year-long satellite record, in contrast with the clear decrease in sea-ice extent seen in the Arctic over the same time period. However, state-of-the-art climate models ubiquitously project Antarctic sea-ice to decrease over the coming century, much as they do for Arctic sea-ice. Several recent years have also seen record low Antarctic sea-ice. It is therefore of interest to understand what the climate response to Antarctic sea-ice loss will be. </p><p>We have carried out new fully coupled climate model simulations to assess the response to sea-ice loss in either hemisphere separately or coincidentally under different albedo parameter settings to determine the relative importance of each. By perturbing the albedo of the snow overlying the sea ice and the albedo of the bare sea ice, we obtain a suite of simulations to assess the linearity and additivity of sea-ice loss. We find the response to sea-ice loss in each hemisphere exhibits a high degree of additivity, and can simply be decomposed into responses due to loss in each hemisphere separately. We find that the response to Antarctic sea-ice loss exceeds that of Arctic sea-ice loss in the tropics, and that Antarctic sea-ice loss leads to statistically significant Arctic warming, while the opposite is not true.</p><p>With these new simulations and one in which CO<sub>2</sub> is instantaneously doubled , we can further characterize the response to sea-ice loss from each hemisphere using an extension to classical pattern scaling that includes three controlling parameters. This allows us to simultaneously compute the sensitivity patterns to Arctic sea-ice loss, Antarctic sea-ice loss, and to tropical warming. The statistically significant response to Antarctic sea-ice loss in the Northern Hemisphere extratropics is found to be mediated by tropical warming and small amounts of Arctic sea-ice loss.</p>

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

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.

scholarly journals Consistent biases in Antarctic sea ice concentration simulated by climate models

2017 ◽  
Author(s):  
Lettie A. Roach ◽  
Samuel M. Dean ◽  
James A. Renwick
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Regional Distribution ◽  
Global Climate Models ◽  
Satellite Observations ◽  
High Concentration ◽  
Sea Ice Concentration ◽  
Low Concentration ◽  
Antarctic Sea Ice ◽  
Ice Concentration

Abstract. The simulation of Antarctic sea ice in global climate models often does not agree with observations. In this study, we examine the compactness of sea ice, as well as the regional distribution of sea ice concentration, in climate models from the latest Coupled Model Intercomparison Project (CMIP5) and in satellite observations. We find substantial differences in concentration values between different sets of satellite observations, requiring careful treatment when comparing to models. As a fraction of total sea ice extent, models simulate too much loose, low-concentration sea ice cover throughout the year, and too little compact, high-concentration cover in the summer. In spite of the differences in physics between models, these tendencies are broadly consistent across the population of 27 CMIP5 simulations, a result not previously highlighted. Targeted model experiments with a coupled ocean – sea ice model show that over-estimation of low-concentration cover is partially determined by choice of constant floe diameter in the lateral melt scheme. This suggests that current sea ice thermodynamics contribute to the inadequate simulation of the low-concentration regime.

scholarly journals Arctic sea ice sensitivity to lateral melting representation in a coupled climate model

2021 ◽  
Author(s):  
Madison Smith ◽  
Marika Holland ◽  
Bonnie Light
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Climate Model ◽  
Open Water ◽  
Arctic Sea Ice ◽  
Coupled Climate Model ◽  
Sea Ice Concentration ◽  
Arctic Sea ◽  
Ice Concentration ◽  
Ice Floes

Abstract. The melting of sea ice floes from the edges (lateral melting) results in open water formation and subsequently increases absorption of solar shortwave energy. However, lateral melt plays a small role in the sea ice mass budget in both hemispheres in most climate models (Keen et al., 2020). This is likely influenced by simple parameterizations of this process in sea ice models that are constrained by limited observations. Here we use a coupled climate model (CESM2.0) to assess the sensitivity of modeled sea ice state to the lateral melt parameterization. The results show that sea ice is sensitive both to the parameters determining the effective lateral melt rate, as well as the nuances in how lateral melting is applied to the ice pack. Increasing the lateral melt rate within the range of reasonable values is largely compensated by decreases in the basal melt rate, but can still result in a significant decrease in sea ice concentration and thickness, particularly in the marginal ice zone. We suggest that it is important to consider the efficiency of melt processes at forming open water, which drives the majority of the ice-albedo feedback. Melt processes are more efficient at forming open water in thinner ice scenarios (as we are likely to see in the future), suggesting the importance of well representing thermodynamic evolution. Revisiting model parameterizations of lateral melting with observations will require finding new ways to represent important physical processes.

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

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.

scholarly journals Recent changes in Antarctic Sea Ice

2015 ◽  
Vol 373 (2045) ◽  
pp. 20140163 ◽  
Author(s):  
John Turner ◽  
J. Scott Hosking ◽  
Thomas J. Bracegirdle ◽  
Gareth J. Marshall ◽  
Tony Phillips
Keyword(s):  
Southern Ocean ◽  
Sea Ice ◽  
Climate Models ◽  
Surface Wind ◽  
The Arctic ◽  
Cmip5 Models ◽  
Intrinsic Variability ◽  
Sea Ice Extent ◽  
Amundsen Sea ◽  
Near Surface

In contrast to the Arctic, total sea ice extent (SIE) across the Southern Ocean has increased since the late 1970s, with the annual mean increasing at a rate of 186×10 3  km 2 per decade (1.5% per decade; p <0.01) for 1979–2013. However, this overall increase masks larger regional variations, most notably an increase (decrease) over the Ross (Amundsen–Bellingshausen) Sea. Sea ice variability results from changes in atmospheric and oceanic conditions, although the former is thought to be more significant, since there is a high correlation between anomalies in the ice concentration and the near-surface wind field. The Southern Ocean SIE trend is dominated by the increase in the Ross Sea sector, where the SIE is significantly correlated with the depth of the Amundsen Sea Low (ASL), which has deepened since 1979. The depth of the ASL is influenced by a number of external factors, including tropical sea surface temperatures, but the low also has a large locally driven intrinsic variability, suggesting that SIE in these areas is especially variable. Many of the current generation of coupled climate models have difficulty in simulating sea ice. However, output from the better-performing IPCC CMIP5 models suggests that the recent increase in Antarctic SIE may be within the bounds of intrinsic/internal variability.

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