scholarly journals Interannual predictability of Arctic sea ice in a global climate model: regional contrasts and temporal evolution

2014 ◽  
Vol 43 (9-10) ◽  
pp. 2519-2538 ◽  
Author(s):  
Agathe Germe ◽  
Matthieu Chevallier ◽  
David Salas y Mélia ◽  
Emilia Sanchez-Gomez ◽  
Christophe Cassou
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Temporal Evolution ◽  
Global Climate ◽  
Global Climate Model ◽  
Arctic Sea Ice ◽  
Arctic Sea

Interactions between ocean heat transport and Arctic sea ice

2020 ◽  
Author(s):  
David Docquier ◽  
Ramon Fuentes-Franco ◽  
Klaus Wyser ◽  
Torben Koenigk
Keyword(s):  
Sea Ice ◽  
Heat Transport ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Arctic Sea Ice ◽  
Ocean Heat Transport ◽  
Arctic Sea ◽  
Fast Pace ◽  
Area And Volume

<p>Arctic sea ice has been retreating at fast pace in the last decades, with potential impacts on the weather and climate at mid and high latitudes, as well as the biosphere and society. Sea-ice loss is driven by anthropogenic global warming, atmospheric circulation changes, climate feedbacks, and ocean heat transport. To date, no clear consensus regarding the detailed impact of ocean heat transport on Arctic sea ice exists. Previous observational and modeling studies show that the poleward Atlantic Ocean heat transport and Arctic sea-ice area and volume are generally anti-correlated, suggesting a decrease in sea-ice area and volume with larger ocean heat transport. In turn, the changing sea ice may also affect ocean heat transport, but this effect has been much less studied. Our study explores the two-way interactions between ocean heat transport and Arctic sea ice. We use the EC-Earth global climate model, coupling the atmosphere and ocean, and perform different sensitivity experiments to gain insights into these interactions. The mechanisms by which ocean heat transport and Arctic sea ice interact are analyzed, and compared to observations. This study provides a way to better constrain model projections of Arctic sea ice, based on the relationships between ocean heat transport and Arctic sea ice.</p>

scholarly journals Probability Assessments of an Ice-Free Arctic: Comparing Statistical and Climate Model Projections

2021 ◽  
Author(s):  
Glenn Rudebusch ◽  
Francis Diebold
Keyword(s):  
Sea Ice ◽  
Carbon Emissions ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Arctic Sea Ice ◽  
Global Climate Models ◽  
Sea Ice Extent ◽  
Arctic Sea ◽  
Arctic Sea Ice Loss

<p>Based on several decades of satellite data, we provide statistical forecasts of Arctic sea ice extent during the rest of this century. The best fitting statistical model indicates that overall sea ice coverage is declining at an increasing rate. By contrast, average projections from the CMIP5 global climate models foresee a gradual slowing of Arctic sea ice loss even in scenarios with high amounts of carbon emissions. Our long-range statistical projections also deliver <em>probability</em> assessments of the timing of an ice-free Arctic. These results indicate almost a 60 percent chance of an effectively ice-free Arctic Ocean sometime during the 2030s—much earlier than the average projection from the global climate models. Our results are also consistent with projections from bivariate regressions of sea ice extent and carbon emissions. </p>

scholarly journals How reversible is sea ice loss?

2012 ◽  
Vol 6 (1) ◽  
pp. 193-198 ◽  
Author(s):  
J. K. Ridley ◽  
J. A. Lowe ◽  
H. T. Hewitt
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
Rate Of Change ◽  
Atmospheric Co2 ◽  
Asymmetric Behaviour ◽  
The Impact

Abstract. It is well accepted that increasing atmospheric CO2 results in global warming, leading to a decline in polar sea ice area. Here, the specific question of whether there is a tipping point in the sea ice cover is investigated. The global climate model HadCM3 is used to map the trajectory of sea ice area under idealised scenarios. The atmospheric CO2 is first ramped up to four times pre-industrial levels (4 × CO2), then ramped down to pre-industrial levels. We also examine the impact of stabilising climate at 4 × CO2 prior to ramping CO2 down to pre-industrial levels. Against global mean temperature, Arctic sea ice area is reversible, while the Antarctic sea ice shows some asymmetric behaviour – its rate of change slower, with falling temperatures, than its rate of change with rising temperatures. However, we show that the asymmetric behaviour is driven by hemispherical differences in temperature change between transient and stabilisation periods. We find no irreversible behaviour in the sea ice cover.

scholarly journals The Seasonality and Interannual Variability of Arctic Sea Ice Reemergence

2017 ◽  
Vol 30 (12) ◽  
pp. 4657-4676 ◽  
Author(s):  
Mitchell Bushuk ◽  
Dimitrios Giannakis
Keyword(s):  
Sea Ice ◽  
Interannual Variability ◽  
Climate Model ◽  
Global Climate Model ◽  
Arctic Sea Ice ◽  
Coupled Modes ◽  
Growth Season ◽  
Sea Ice Concentration ◽  
Potential Predictability ◽  
Arctic Sea

There is a significant gap between the potential predictability of Arctic sea ice area and the current forecast skill of operational prediction systems. One route to closing this gap is improving understanding of the physical mechanisms, such as sea ice reemergence, which underlie this inherent predictability. Sea ice reemergence refers to the tendency of melt-season sea ice area anomalies to recur the following growth season and growth-season anomalies to recur the following melt season. This study builds on earlier work, providing a mode-based analysis of the seasonality and interannual variability of three distinct reemergence mechanisms. These mechanisms are studied using a common set of coupled modes of variability obtained via coupled nonlinear Laplacian spectral analysis, a data analysis technique for high-dimensional multivariate datasets. The coupled modes capture the covariability of sea ice concentration (SIC), sea surface temperature (SST), sea level pressure (SLP), and sea ice thickness (SIT) in a control integration of a global climate model. Using a parsimonious reemergence mode family, the spatial characteristics of growth-to-melt reemergence are studied, and an SIT–SIC reemergence mechanism is examined. A set of reemergence metrics to quantify the amplitude and phase of growth-to-melt reemergence are introduced. Metrics quantifying SST–SIC and SLP–SIC mechanisms for melt-to-growth reemergence are also computed. A simultaneous comparison of the three reemergence mechanisms, with focus on their seasonality and interannual variability, is performed. Finally, the conclusions are tested in a model hierarchy, consisting of models that share the same sea ice component but differ in their atmospheric and oceanic formulation.

scholarly journals How reversible is sea ice loss?

2011 ◽  
Vol 5 (5) ◽  
pp. 2349-2363 ◽  
Author(s):  
J. K. Ridley ◽  
J. A. Lowe ◽  
H. T. Hewitt
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
Rate Of Change ◽  
Atmospheric Co2 ◽  
Antarctic Sea Ice ◽  
The Impact

Abstract. It is well accepted that increasing atmospheric CO2 results in global warming, leading to a decline in polar sea ice area. Here, the specific question of whether there is a tipping point in the sea ice cover is investigated. The global climate model HadCM3, is used to map the trajectory of sea ice area under idealised scenarios. The atmospheric CO2 is first ramped up to four times pre-industrial levels (4 × CO2) then ramped down back to pre-industrial levels. We also examine the impact of stabilising climate at 4 × CO2 prior to ramping CO2 down to pre-industrial levels. Against global mean temperature Arctic sea ice area has little hysteresis while the Antarctic sea ice shows significant hysteresis – its rate of change slower, with falling temperatures, than its rate of change with rising temperatures. However, we show that the driver of the hysteresis is the hemispherical differences in temperature change between transient and stabilisation periods. We find no irreversible behaviour in the sea ice cover.

scholarly journals Probability Assessments of an Ice-Free Arctic: Comparing Statistical and Climate Model Projections

2020 ◽  
Author(s):  
Glenn Rudebusch ◽  
Francis Diebold
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Arctic Sea Ice ◽  
Global Climate Models ◽  
Economic Consequences ◽  
Sea Ice Extent ◽  
Wide Range ◽  
Arctic Sea

<p>The downward trend in the amount of Arctic sea ice is a key factor determining the pace and intensity of future global climate change. Diminished sea ice also has a wide range of other environmental and economic consequences. Based on several decades of satellite data, we provide statistical forecasts of Arctic sea ice extent during the rest of this century. The best fitting statistical model indicates that overall sea ice coverage is declining at an increasing rate. By contrast, average projections from the CMIP5 global climate models foresee a gradual slowing of Arctic sea ice loss even in scenarios with high carbon emissions. Our long-range statistical projections also deliver probability assessments of the timing of an ice-free Arctic. These results indicate almost a 60 percent chance of an effectively ice-free Arctic Ocean during some summer in the 2030s -- much earlier than the average projection from global climate models.</p>

scholarly journals Observation-based selection of climate models projects Arctic ice-free summers around 2035

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
David Docquier ◽  
Torben Koenigk
Keyword(s):  
Model Selection ◽  
Sea Ice ◽  
Climate Models ◽  
Global Climate ◽  
Coupled Model ◽  
Arctic Sea Ice ◽  
Global Climate Models ◽  
The Arctic ◽  
Arctic Sea ◽  
Area And Volume

AbstractArctic sea ice has been retreating at an accelerating pace over the past decades. Model projections show that the Arctic Ocean could be almost ice free in summer by the middle of this century. However, the uncertainties related to these projections are relatively large. Here we use 33 global climate models from the Coupled Model Intercomparison Project 6 (CMIP6) and select models that best capture the observed Arctic sea-ice area and volume and northward ocean heat transport to refine model projections of Arctic sea ice. This model selection leads to lower Arctic sea-ice area and volume relative to the multi-model mean without model selection and summer ice-free conditions could occur as early as around 2035. These results highlight a potential underestimation of future Arctic sea-ice loss when including all CMIP6 models.

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