Javascript must be enabled to continue!
Simulated last deglaciation of the Barents Sea Ice Sheet primarily driven by oceanic conditions
View through CrossRef
<p>An interconnected complex of ice sheets, collectively referred to as the Eurasian ice sheets, covered north-westernmost Europe, Russia and the Barents Sea during the Last Glacial Maximum (around 21 ky BP), connecting to the Scandinavian Ice Sheet to the south. Due to common geological features, the Barents Sea component of this ice complex is seen as a paleo-analogue for the present-day West Antarctic Ice Sheet. Investigating key processes driving the last deglaciation of the Barents Sea Ice Sheet represents an important tool to interpret recent observations in Antarctica over the multi-millennial temporal scale of glaciological changes. We present results from a statistical ensemble of ice sheet model simulations of the last deglaciation of the Barents Sea Ice Sheet, all forced with transient atmospheric and oceanic conditions derived from AOGCM simulations. The ensemble of transient simulations is evaluated against the data-based DATED-1 reconstruction. We find that the simulated deglaciation of the Barents Sea Ice Sheet is primarily driven by the oceanic forcing, with sea level rise and surface melting amplifying the ice sheet sensitivity to ocean warming over relatively short intervals. Despite a large model/data mismatch at the western and eastern ice sheet margins, the simulated and DATED-1 deglaciation scenarios agree well on the timing of the deglaciation of the central and northern Barents Sea. The primary role played by ocean forcing in our simulations suggests that the long-term stability of the West Antarctic Ice Sheet could be at stake if the current trend in ocean warming will continue.</p>
Title: Simulated last deglaciation of the Barents Sea Ice Sheet primarily driven by oceanic conditions
Description:
<p>An interconnected complex of ice sheets, collectively referred to as the Eurasian ice sheets, covered north-westernmost Europe, Russia and the Barents Sea during the Last Glacial Maximum (around 21 ky BP), connecting to the Scandinavian Ice Sheet to the south.
Due to common geological features, the Barents Sea component of this ice complex is seen as a paleo-analogue for the present-day West Antarctic Ice Sheet.
Investigating key processes driving the last deglaciation of the Barents Sea Ice Sheet represents an important tool to interpret recent observations in Antarctica over the multi-millennial temporal scale of glaciological changes.
We present results from a statistical ensemble of ice sheet model simulations of the last deglaciation of the Barents Sea Ice Sheet, all forced with transient atmospheric and oceanic conditions derived from AOGCM simulations.
The ensemble of transient simulations is evaluated against the data-based DATED-1 reconstruction.
We find that the simulated deglaciation of the Barents Sea Ice Sheet is primarily driven by the oceanic forcing, with sea level rise and surface melting amplifying the ice sheet sensitivity to ocean warming over relatively short intervals.
Despite a large model/data mismatch at the western and eastern ice sheet margins, the simulated and DATED-1 deglaciation scenarios agree well on the timing of the deglaciation of the central and northern Barents Sea.
The primary role played by ocean forcing in our simulations suggests that the long-term stability of the West Antarctic Ice Sheet could be at stake if the current trend in ocean warming will continue.
</p>.
Related Results
Ground ice detection and implications for permafrost geomorphology
Ground ice detection and implications for permafrost geomorphology
Most permafrost contains ground ice, often as pore ice or thin veins or lenses of ice. In certain circumstance, larger bodies of ice can form, such as ice wedges, or massive lenses...
Assessing trends in Arctic sea-ice distribution in the Barents and Kara seas using the Kosmos–Okean satellite series
Assessing trends in Arctic sea-ice distribution in the Barents and Kara seas using the Kosmos–Okean satellite series
AbstractTrends in the annual minimum sea-ice extent, determined by three criteria (absolute annual minimum, minimum monthly mean, and the extent at the end of August), were investi...
The sea ice in Young Sound: Implications for carbon cycling
The sea ice in Young Sound: Implications for carbon cycling
Most of the year, Young Sound is covered by c. 160 cm thick sea ice overlain by a 20-100 cm thick snow cover. During the last 50 years the sea-ice-free period has varied between 63...
Modelling the present-day imbalance of the Antarctic Ice Sheet
Modelling the present-day imbalance of the Antarctic Ice Sheet
Recent human-driven climate change has very likely caused more frequent heatwaves, extreme weather events, and rising global sea levels. When it comes to rising sea levels, two pri...
Seasonal Arctic sea ice predictability and prediction
Seasonal Arctic sea ice predictability and prediction
Arctic sea ice plays a central role in the Earth’s climate. Changes in the sea ice on seasonal-to-interannual timescales impact ecosystems, populations and a growing number of stak...
A new HPLC-MS method for fatty acid detection in sea ice
A new HPLC-MS method for fatty acid detection in sea ice
The presence of marine-sourced fatty acids1,2,3, in Antarctic ice cores has been linked to changes in sea ice conditions2,3. It has been proposed that the phytoplankton within and ...
Modelling very recent ice ages on Mars with the Planetary Climate Model
Modelling very recent ice ages on Mars with the Planetary Climate Model
Protected by centimeters of dry sediments, a planetary-scale mantle of relatively pure water ice covers the entire mid and high latitudes of Mars. Its presence down has been shown ...
Sea-ice ridges - an understudied yet key component of the Arctic sea-ice system
Sea-ice ridges - an understudied yet key component of the Arctic sea-ice system
Sea-ice ridges (or more precisely, deformed ice) constitute a large fraction of the Arctic ice pack, however, estimates range broadly from 30 to 70%. Yet, we know disproportionally...

