scholarly journals Imprint of Arctic sea ice cover in North-Greenland ice cores

2019 ◽  
Author(s):  
Damiano Della Lunga ◽  
Hörhold Maria ◽  
Birthe Twarloh ◽  
Behrens Melanie ◽  
Dallmayr Remi ◽  
...  
Keyword(s):  
Sea Ice ◽  
Ice Cores ◽  
Open Water ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
Blowing Snow ◽  
Sea Ice Extent ◽  
Arctic Sea ◽  
Frost Flowers ◽  
North Greenland

Abstract. Sea ice is a key component of the climate system, since it modifies the surface albedo, the radiation balance, as well as the exchange of heat, moisture and gases between the ocean and the overlying atmosphere. Hence, the reconstruction of sea ice cover before the instrumental era and the industrial times is crucial to understand the evolution of Arctic climate in the last millennium and better predict its future evolution. However, identifying relevant paleo proxies in climate archives related to sea ice cover is not straightforward. Ice cores from polar regions offer great potential to provide high-resolution records of Arctic sea ice variability from chemical impurities such as Bromine species, which were recently proposed as indicators of sea ice extent, although their variability might be modulated by regional influences. We here use Bromine and Bromine enrichment of two ice cores form North Greenland (B17 & B26) and investigate its potential as proxy to reconstruct sea ice extent over the period 1363–1993 AD. Across the instrumental period, a good correlation is observed with the Baffin Bay and the Greenland Sea for B26 and B17 respectively, with both record showing minima corresponding to known Artic warming events such as the 1420 AD (for B17) and 1920–1940 (Early century warming, B17 & B26), together with a strong decline starting in the late 19th century. We simultaneously derived a chemical classification of sea ice-related contributors of ionic species (i.e. blowing snow, frost flowers, open water) utilizing the depletion of SO42− compare to Ca2+, K+ and Mg2+ characterizing sea ice brines and blowing snow as well the excess of Br− and Cl−, characterizing frost flowers, to elucidate the evolution of the different sources. In both B17 and B26 records we observe a strong contribution of blowing snow in the earliest part of the datasets, gradually declining in recent years in favour of open water sources.

scholarly journals Supplementary material to "Imprint of Arctic sea ice cover in North-Greenland ice cores"

2019 ◽  
Author(s):  
Damiano Della Lunga ◽  
Hörhold Maria ◽  
Birthe Twarloh ◽  
Behrens Melanie ◽  
Dallmayr Remi ◽  
...  
Keyword(s):  
Sea Ice ◽  
Ice Cores ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
Arctic Sea ◽  
Supplementary Material ◽  
North Greenland

scholarly journals Late-Twentieth-Century Simulation of Arctic Sea Ice and Ocean Properties in the CCSM4

2012 ◽  
Vol 25 (5) ◽  
pp. 1431-1452 ◽  
Author(s):  
Alexandra Jahn ◽  
Kara Sterling ◽  
Marika M. Holland ◽  
Jennifer E. Kay ◽  
James A. Maslanik ◽  
...  
Keyword(s):  
Twentieth Century ◽  
Sea Ice ◽  
Internal Variability ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Motion Field ◽  
Sea Ice Extent ◽  
Arctic Sea ◽  
Ensemble Mean

To establish how well the new Community Climate System Model, version 4 (CCSM4) simulates the properties of the Arctic sea ice and ocean, results from six CCSM4 twentieth-century ensemble simulations are compared here with the available data. It is found that the CCSM4 simulations capture most of the important climatological features of the Arctic sea ice and ocean state well, among them the sea ice thickness distribution, fraction of multiyear sea ice, and sea ice edge. The strongest bias exists in the simulated spring-to-fall sea ice motion field, the location of the Beaufort Gyre, and the temperature of the deep Arctic Ocean (below 250 m), which are caused by deficiencies in the simulation of the Arctic sea level pressure field and the lack of deep-water formation on the Arctic shelves. The observed decrease in the sea ice extent and the multiyear ice cover is well captured by the CCSM4. It is important to note, however, that the temporal evolution of the simulated Arctic sea ice cover over the satellite era is strongly influenced by internal variability. For example, while one ensemble member shows an even larger decrease in the sea ice extent over 1981–2005 than that observed, two ensemble members show no statistically significant trend over the same period. It is therefore important to compare the observed sea ice extent trend not just with the ensemble mean or a multimodel ensemble mean, but also with individual ensemble members, because of the strong imprint of internal variability on these relatively short trends.

scholarly journals Early start of 20th-century Arctic sea-ice decline recorded in Svalbard coralline algae

Geology ◽  
2019 ◽  
Vol 47 (10) ◽  
pp. 963-967 ◽  
Author(s):  
Steffen Hetzinger ◽  
Jochen Halfar ◽  
Zoltán Zajacz ◽  
Max Wisshak
Keyword(s):  
Sea Ice ◽  
20Th Century ◽  
Global Climate ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
Coralline Algae ◽  
Sea Ice Extent ◽  
Early 19Th Century ◽  
Arctic Sea ◽  
Annual Resolution

Abstract The fast decline of Arctic sea ice is a leading indicator of ongoing global climate change and is receiving substantial public and scientific attention. Projections suggest that Arctic summer sea ice may virtually disappear within the course of the next 50 or even 30 yr with rapid Arctic warming. However, limited observational records and lack of annual-resolution marine sea-ice proxies hamper the assessment of long-term changes in sea ice, leading to large uncertainties in predictions of its future evolution under global warming. Here, we use long-lived encrusting coralline algae that strongly depend on light availability as a new in situ proxy to reconstruct past variability in the duration of seasonal sea-ice cover. Our data represent the northernmost annual-resolution marine sea-ice reconstruction to date, extending to the early 19th century off Svalbard. Algal records show that the decreasing trend in sea-ice cover in the high Arctic had already started at the beginning of the 20th century, earlier than previously reported from sea-ice reconstructions based on terrestrial archives. Our data further suggest that, although sea-ice extent varies on multidecadal time scales, the lowest sea-ice values within the past 200 yr occurred at the end of the 20th century.

scholarly journals Holocene variability in sea ice and primary productivity in the northeastern Baffin Bay

arktos ◽  
2020 ◽  
Vol 6 (1-3) ◽  
pp. 55-73 ◽  
Author(s):  
Jeetendra Saini ◽  
Ruediger Stein ◽  
Kirsten Fahl ◽  
Jens Weiser ◽  
Dierk Hebbeln ◽  
...  
Keyword(s):  
Sea Ice ◽  
Ocean Circulation ◽  
Water Discharge ◽  
Greenland Ice Sheet ◽  
Open Water ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
Baffin Bay ◽  
Arctic Sea ◽  
Ice Edge

AbstractArctic sea ice is a critical component of the climate system, known to influence ocean circulation, earth’s albedo, and ocean–atmosphere heat and gas exchange. Current developments in the use of IP25 (a sea ice proxy with 25 carbon atoms only synthesized by Arctic sea ice diatoms) have proven it to be a suitable proxy for paleo-sea ice reconstructions over hundreds of thousands to even millions of years. In the NE Baffin Bay, off NW Greenland, Melville Bugt is a climate-sensitive region characterized by strong seasonal sea ice variability and strong melt-water discharge from the Greenland Ice Sheet (GIS). Here, we present a centennial-scale resolution Holocene sea ice record, based on IP25 and open-water phytoplankton biomarkers (brassicasterol, dinosterol and HBI III) using core GeoB19927-3 (73° 35.26′ N, 58° 05.66′ W). Seasonal to ice-edge conditions near the core site are documented for most of the Holocene period with some significant variability. In the lower-most part, a cold interval characterized by extensive sea ice cover and very low local productivity is succeeded by an interval (~ 9.4–8.5 ka BP) with reduced sea ice cover, enhanced GIS spring melting, and strong influence of the West Greenland Current (WGC). From ~ 8.5 until ~ 7.8 ka BP, a cooling event is recorded by ice algae and phytoplankton biomarkers. They indicate an extended sea ice cover, possibly related to the opening of Nares Strait, which may have led to an increased influx of Polar Water into NE-Baffin Bay. The interval between ~ 7.8 and ~ 3.0 ka BP is characterized by generally reduced sea ice cover with millennial-scale variability of the (late winter/early spring) ice-edge limit, increased open-water conditions (polynya type), and a dominant WGC carrying warm waters at least as far as the Melville Bugt area. During the last ~ 3.0 ka BP, our biomarker records do not reflect the late Holocene ‘Neoglacial cooling’ observed elsewhere in the Northern Hemisphere, possibly due to the persistent influence of the WGC and interactions with the adjacent fjords. Peaks in HBI III at about ~ 2.1 and ~ 1.3 ka BP, interpreted as persistent ice-edge situations, might correlate with the Roman Warm Period (RWP) and Medieval Climate Anomaly (MCA), respectively, in-phase with the North Atlantic Oscillation (NAO) mode. When integrated with marine and terrestrial records from other circum-Baffin Bay areas (Disko Bay, the Canadian Arctic, the Labrador Sea), the Melville Bugt biomarker records point to close ties with high Arctic and Northern Hemispheric climate conditions, driven by solar and oceanic circulation forcings.

scholarly journals Seasonal changes in Arctic sea-ice morphology

2001 ◽  
Vol 33 ◽  
pp. 171-176 ◽  
Author(s):  
Donald K. Perovich ◽  
Jacqueline A. Richter-Menge ◽  
Walter B. Tucker
Keyword(s):  
Sea Ice ◽  
Heat Budget ◽  
Open Water ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Surface ◽  
Arctic Sea ◽  
Dynamic Forcing ◽  
Break Up

AbstractThe morphology of the Arctic sea-ice cover undergoes large changes over an annual cycle. These changes have a significant impact on the heat budget of the ice cover, primarily by affecting the distribution of the solar radiation absorbed in the ice-ocean system. In spring, the ice is snow-covered and ridges are the prominent features. The pack consists of large angular floes, with a small amount of open water contained primarily in linear leads. By the end of summer the ice cover has undergone a major transformation. The snow cover is gone, many of the ridges have been reduced to hummocks and the ice surface is mottled with melt ponds. One surface characteristic that changes little during the summer is the appearance of the bare ice, which remains white despite significant melting. The large floes have broken into a mosaic of smaller, rounded floes surrounded by a lace of open water. Interestingly, this break-up occurs during summer when the dynamic forcing and the internal ice stress are small During the Surface Heat Budget of the Arctic Ocean (SHEBA) field experiment we had an opportunity to observe the break-up process both on a small scale from the ice surface, and on a larger scale via aerial photographs. Floe break-up resulted in large part from thermal deterioration of the ice. The large floes of spring are riddled with cracks and leads that formed and froze during fall, winter and spring. These features melt open during summer, weakening the ice so that modest dynamic forcing can break apart the large floes into many fragments. Associated with this break-up is an increase in the number of floes, a decrease in the size of floes, an increase in floe perimeter and an increase in the area of open water.

scholarly journals How does sea ice influence δ<sup>18</sup>O of Arctic precipitation?

2016 ◽  
Author(s):  
Anne-Katrine Faber ◽  
Bo Møllesøe Vinther ◽  
Jesper Sjolte ◽  
Rasmus Anker Pedersen
Keyword(s):  
Sea Ice ◽  
Ice Cover ◽  
Research Community ◽  
Arctic Sea Ice ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Atmospheric Research ◽  
Community Atmosphere Model ◽  
Arctic Sea ◽  
Ice Concentration

Abstract. This study investigates how variations in Arctic sea ice cover influence δ18O of presentday Arctic precipitation. This is done using the model isoCAM3, an isotope-equipped version of the National Center for Atmospheric Research Community Atmosphere Model version 3. Four sensitivity experiments and one control simulation are performed with prescribed SSTs and sea ice. Each of 5 the four experiments simulates the atmospheric and isotopic response to Arctic oceanic conditions for selected years after the beginning of the satellite era in 1979. Results show that δ18O of precipitation is sensitive to local changes of sea ice concentration. Reduced sea ice extent yields more enriched isotope values while increased sea ice extent yields more depleted isotope values. The configuration of the sea ice cover is essential for the spatial distribution 10 of the simulated changes in δ18O. The experiments of this study show no changes of δ18O for central Greenland. However, this does not exclude that simulations based on other sea ice configurations might yield changes in Greenland δ18O.

Seasonal Forecasts of the Pan-Arctic Sea Ice Extent Using a GCM-Based Seasonal Prediction System

2013 ◽  
Vol 26 (16) ◽  
pp. 6092-6104 ◽  
Author(s):  
Matthieu Chevallier ◽  
David Salas y Mélia ◽  
Aurore Voldoire ◽  
Michel Déqué ◽  
Gilles Garric
Keyword(s):  
Sea Ice ◽  
General Circulation ◽  
Global Climate Model ◽  
Circulation Model ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Seasonal Forecasts ◽  
Sea Ice Extent ◽  
Arctic Sea

Abstract An ocean–sea ice model reconstruction spanning the period 1990–2009 is used to initialize ensemble seasonal forecasts with the Centre National de Recherches Météorologiques Coupled Global Climate Model version 5.1 (CNRM-CM5.1) coupled atmosphere–ocean general circulation model. The aim of this study is to assess the skill of fully initialized September and March pan-Arctic sea ice forecasts in terms of climatology and interannual anomalies. The predictions are initialized using “full field initialization” of each component of the system. In spite of a drift due to radiative biases in the coupled model during the melt season, the full initialization of the sea ice cover on 1 May leads to skillful forecasts of the September sea ice extent (SIE) anomalies. The skill of the prediction is also significantly high when considering anomalies of the SIE relative to the long-term linear trend. It confirms that the anomaly of spring sea ice cover in itself plays a role in preconditioning a September SIE anomaly. The skill of predictions for March SIE initialized on 1 November is also encouraging, and it can be partly attributed to persistent features of the fall sea ice cover. The present study gives insight into the current ability of state-of-the-art coupled climate systems to perform operational seasonal forecasts of the Arctic sea ice cover up to 5 months in advance.

scholarly journals The Arctic sea ice cover of 2016: a year of record-low highs and higher-than-expected lows

2018 ◽  
Vol 12 (2) ◽  
pp. 433-452 ◽  
Author(s):  
Alek A. Petty ◽  
Julienne C. Stroeve ◽  
Paul R. Holland ◽  
Linette N. Boisvert ◽  
Angela C. Bliss ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
North Atlantic ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Extent ◽  
Atlantic Sector ◽  
Arctic Sea ◽  
The Arctic Ocean

Abstract. The Arctic sea ice cover of 2016 was highly noteworthy, as it featured record low monthly sea ice extents at the start of the year but a summer (September) extent that was higher than expected by most seasonal forecasts. Here we explore the 2016 Arctic sea ice state in terms of its monthly sea ice cover, placing this in the context of the sea ice conditions observed since 2000. We demonstrate the sensitivity of monthly Arctic sea ice extent and area estimates, in terms of their magnitude and annual rankings, to the ice concentration input data (using two widely used datasets) and to the averaging methodology used to convert concentration to extent (daily or monthly extent calculations). We use estimates of sea ice area over sea ice extent to analyse the relative “compactness” of the Arctic sea ice cover, highlighting anomalously low compactness in the summer of 2016 which contributed to the higher-than-expected September ice extent. Two cyclones that entered the Arctic Ocean during August appear to have driven this low-concentration/compactness ice cover but were not sufficient to cause more widespread melt-out and a new record-low September ice extent. We use concentration budgets to explore the regions and processes (thermodynamics/dynamics) contributing to the monthly 2016 extent/area estimates highlighting, amongst other things, rapid ice intensification across the central eastern Arctic through September. Two different products show significant early melt onset across the Arctic Ocean in 2016, including record-early melt onset in the North Atlantic sector of the Arctic. Our results also show record-late 2016 freeze-up in the central Arctic, North Atlantic and the Alaskan Arctic sector in particular, associated with strong sea surface temperature anomalies that appeared shortly after the 2016 minimum (October onwards). We explore the implications of this low summer ice compactness for seasonal forecasting, suggesting that sea ice area could be a more reliable metric to forecast in this more seasonal, “New Arctic”, sea ice regime.

scholarly journals Seasonal cycle and long-term trend of solar energy fluxes through Arctic sea ice

2014 ◽  
Vol 8 (6) ◽  
pp. 2219-2233 ◽  
Author(s):  
S. Arndt ◽  
M. Nicolaus
Keyword(s):  
Sea Ice ◽  
Open Water ◽  
Ice Cover ◽  
Reanalysis Data ◽  
Arctic Sea Ice ◽  
Light Transmittance ◽  
First Year ◽  
Sea Ice Concentration ◽  
Solar Heat ◽  
Arctic Sea

Abstract. Arctic sea ice has not only decreased in volume during the last decades, but has also changed in its physical properties towards a thinner and more seasonal ice cover. These changes strongly impact the energy budget, and might affect the ice-associated ecosystems. In this study, we quantify solar shortwave fluxes through sea ice for the entire Arctic during all seasons. To focus on sea-ice-related processes, we exclude fluxes through open water, scaling linearly with sea ice concentration. We present a new parameterization of light transmittance through sea ice for all seasons as a function of variable sea ice properties. The maximum monthly mean solar heat flux under the ice of 30 × 105 Jm−2 occurs in June, enough heat to melt 0.3 m of sea ice. Furthermore, our results suggest that 96% of the annual solar heat input through sea ice occurs during only a 4-month period from May to August. Applying the new parameterization to remote sensing and reanalysis data from 1979 to 2011, we find an increase in transmitted light of 1.5% yr−1 for all regions. This corresponds to an increase in potential sea ice bottom melt of 63% over the 33-year study period. Sensitivity studies reveal that the results depend strongly on the timing of melt onset and the correct classification of ice types. Assuming 2 weeks earlier melt onset, the annual transmitted solar radiation to the upper ocean increases by 20%. Continuing the observed transition from a mixed multi-year/first-year sea ice cover to a seasonal ice cover results in an increase in light transmittance by an additional 18%.

scholarly journals Benchmarking of Data-Driven Causality Discovery Approaches in the Interactions of Arctic Sea Ice and Atmosphere

2021 ◽  
Vol 4 ◽  
Author(s):  
Yiyi Huang ◽  
Matthäus Kleindessner ◽  
Alexey Munishkin ◽  
Debvrat Varshney ◽  
Pei Guo ◽  
...  
Keyword(s):  
Sea Ice ◽  
Time Series Data ◽  
Open Water ◽  
Meridional Wind ◽  
Arctic Sea Ice ◽  
Data Driven ◽  
The Arctic ◽  
Series Data ◽  
Sea Ice Extent ◽  
Arctic Sea

The Arctic sea ice has retreated rapidly in the past few decades, which is believed to be driven by various dynamic and thermodynamic processes in the atmosphere. The newly open water resulted from sea ice decline in turn exerts large influence on the atmosphere. Therefore, this study aims to investigate the causality between multiple atmospheric processes and sea ice variations using three distinct data-driven causality approaches that have been proposed recently: Temporal Causality Discovery Framework Non-combinatorial Optimization via Trace Exponential and Augmented lagrangian for Structure learning (NOTEARS) and Directed Acyclic Graph-Graph Neural Networks (DAG-GNN). We apply these three algorithms to 39 years of historical time-series data sets, which include 11 atmospheric variables from ERA-5 reanalysis product and passive microwave satellite retrieved sea ice extent. By comparing the causality graph results of these approaches with what we summarized from the literature, it shows that the static graphs produced by NOTEARS and DAG-GNN are relatively reasonable. The results from NOTEARS indicate that relative humidity and precipitation dominate sea ice changes among all variables, while the results from DAG-GNN suggest that the horizontal and meridional wind are more important for driving sea ice variations. However, both approaches produce some unrealistic cause-effect relationships. Additionally, these three methods cannot well detect the delayed impact of one variable on another in the Arctic. It also turns out that the results are rather sensitive to the choice of hyperparameters of the three methods. As a pioneer study, this work paves the way to disentangle the complex causal relationships in the Earth system, by taking the advantage of cutting-edge Artificial Intelligence technologies.

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