scholarly journals Evidence for extreme export of Arctic sea ice leading the abrupt onset of the Little Ice Age

2020 ◽  
Vol 6 (38) ◽  
pp. eaba4320
Author(s):  
Martin W. Miles ◽  
Camilla S. Andresen ◽  
Christian V. Dylmer
Keyword(s):  
Sea Ice ◽  
Little Ice Age ◽  
Abrupt Onset ◽  
Arctic Sea Ice ◽  
Ice Age ◽  
The Arctic ◽  
Ocean Stratification ◽  
Proxy Records ◽  
Arctic Sea ◽  
Robust Evidence

Arctic sea ice affects climate on seasonal to decadal time scales, and models suggest that sea ice is essential for longer anomalies such as the Little Ice Age. However, empirical evidence is fragmentary. Here, we reconstruct sea ice exported from the Arctic Ocean over the past 1400 years, using a spatial network of proxy records. We find robust evidence for extreme export of sea ice commencing abruptly around 1300 CE and terminating in the late 1300s. The exceptional magnitude and duration of this “Great Sea-Ice Anomaly” was previously unknown. The pulse of ice along East Greenland resulted in downstream increases in polar waters and ocean stratification, culminating ~1400 CE and sustained during subsequent centuries. While consistent with external forcing theories, the onset and development are notably similar to modeled spontaneous abrupt cooling enhanced by sea-ice feedbacks. These results provide evidence that marked climate changes may not require an external trigger.

scholarly journals Multidecadal Arctic sea ice thickness and volume derived from ice age

2020 ◽  
Vol 14 (4) ◽  
pp. 1325-1345 ◽  
Author(s):  
Yinghui Liu ◽  
Jeffrey R. Key ◽  
Xuanji Wang ◽  
Mark Tschudi
Keyword(s):  
Sea Ice ◽  
Global Climate ◽  
Arctic Sea Ice ◽  
Ice Age ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Ice Volume ◽  
Arctic Sea ◽  
Arctic Sea Ice Thickness

Abstract. Sea ice is a key component of the Arctic climate system, and has impacts on global climate. Ice concentration, thickness, and volume are among the most important Arctic sea ice parameters. This study presents a new record of Arctic sea ice thickness and volume from 1984 to 2018 based on an existing satellite-derived ice age product. The relationship between ice age and ice thickness is first established for every month based on collocated ice age and ice thickness from submarine sonar data (1984–2000) and ICESat (2003–2008) and an empirical ice growth model. Based on this relationship, ice thickness is derived for the entire time period from the weekly ice age product, and the Arctic monthly sea ice volume is then calculated. The ice-age-based thickness and volume show good agreement in terms of bias and root-mean-square error with submarine, ICESat, and CryoSat-2 ice thickness, as well as ICESat and CryoSat-2 ice volume, in February–March and October–November. More detailed comparisons with independent data from Envisat for 2003 to 2010 and CryoSat-2 from CPOM, AWI, and NASA GSFC (Goddard Space Flight Center) for 2011 to 2018 show low bias in ice-age-based thickness. The ratios of the ice volume uncertainties to the mean range from 21 % to 29 %. Analysis of the derived data shows that the ice-age-based sea ice volume exhibits a decreasing trend of −411 km3 yr−1 from 1984 to 2018, stronger than the trends from other datasets. Of the factors affecting the sea ice volume trends, changes in sea ice thickness contribute more than changes in sea ice area, with a contribution of at least 80 % from changes in sea ice thickness from November to May and nearly 50 % in August and September, while less than 30 % is from changes in sea ice area in all months.

scholarly journals Impact of Volcanic Eruptions on Decadal to Centennial Fluctuations of Arctic Sea Ice Extent during the Last Millennium and on Initiation of the Little Ice Age

2018 ◽  
Vol 31 (6) ◽  
pp. 2145-2167 ◽  
Author(s):  
Joanna Slawinska ◽  
Alan Robock
Keyword(s):  
Sea Ice ◽  
Little Ice Age ◽  
Volcanic Eruptions ◽  
Arctic Sea Ice ◽  
Ice Age ◽  
Last Millennium ◽  
Sea Ice Extent ◽  
Decadal Scale ◽  
Arctic Sea ◽  
Arctic Sea Ice Extent

This study evaluates different hypotheses of the origin of the Little Ice Age, focusing on the long-term response of Arctic sea ice and oceanic circulation to solar and volcanic perturbations. The authors analyze the Last Millennium Ensemble of climate model simulations carried out with the Community Earth System Model at the National Center for Atmospheric Research. The authors examine the duration and strength of volcanic perturbations, and the effects of initial and boundary conditions, such as the phase of the Atlantic multidecadal oscillation. They evaluate the impacts of these factors on decadal-to-multicentennial perturbations of the cryospheric, oceanic, and atmospheric components of the climate system. The authors show that, at least in the Last Millennium Ensemble, volcanic eruptions are followed by a decadal-scale positive response of the Atlantic multidecadal overturning circulation, followed by a centennial-scale enhancement of the Northern Hemispheric sea ice extent. It is hypothesized that a few mechanisms, not just one, may have to play a role in consistently explaining such a simulated climate response at both decadal and centennial time scales. The authors argue that large volcanic forcing is necessary to explain the origin and duration of Little Ice Age–like perturbations in the Last Millennium Ensemble. Other forcings might play a role as well. In particular, prolonged fluctuations in solar irradiance associated with solar minima potentially amplify the enhancement of the magnitude of volcanically triggered anomalies of Arctic sea ice extent.

scholarly journals Multidecadal Arctic sea ice thickness and volume derived from ice age

2019 ◽  
Author(s):  
Yinghui Liu ◽  
Jeffrey R. Key ◽  
Xuanji Wang ◽  
Mark Tschudi
Keyword(s):  
Sea Ice ◽  
Global Climate ◽  
Arctic Sea Ice ◽  
Ice Age ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Ice Volume ◽  
Arctic Sea ◽  
Arctic Sea Ice Thickness

Abstract. Arctic sea ice is a key component of the Arctic climate system, which in turn impacts global climate. Ice concentration, thickness, and volume are among the most important Arctic sea ice parameters. This study presents a new record of Arctic sea ice thickness and volume from 1984 to 2018 based on an existing satellite-derived ice age product. The relationship between ice age and ice thickness is first established for every month based on collocated ice age and ice thickness from submarine sonar data (1984–2000), the Ice, Cloud, and land Elevation Satellite (ICESat, 2003–2008), and an empirical ice growth model. Based on this relationship, ice thickness is derived for the entire time period from the weekly ice age product, and the Arctic monthly sea ice volume is then calculated. The ice age-based thickness and volume show good agreement in terms of bias and root mean square error with submarine, ICESat, and CryoSat-2 ice thickness, as well as ICESat and CryoSat-2 ice volume, in February/March and October/November. Sea ice volume exhibits a decreasing trend of −411 km3/year from 1984 to 2018, stronger than the trends from other datasets. Of the factors affecting volume, changes in sea ice thickness from November to May contribute at least 80 %, decreasing to around 50 % in August and September. Changes in sea ice area contribute less than 30 % in all months.

Prévoir les variations saisonnières de la glace de mer arctique et leurs impacts sur le climat

2020 ◽  
pp. 024
Author(s):  
Rym Msadek ◽  
Gilles Garric ◽  
Sara Fleury ◽  
Florent Garnier ◽  
Lauriane Batté ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Region ◽  
Arctic Sea Ice ◽  
Climate Impacts ◽  
The Arctic ◽  
Recent Effort ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Ice Conditions ◽  
Upper Level

L'Arctique est la région du globe qui s'est réchauffée le plus vite au cours des trente dernières années, avec une augmentation de la température de surface environ deux fois plus rapide que pour la moyenne globale. Le déclin de la banquise arctique observé depuis le début de l'ère satellitaire et attribué principalement à l'augmentation de la concentration des gaz à effet de serre aurait joué un rôle important dans cette amplification des températures au pôle. Cette fonte importante des glaces arctiques, qui devrait s'accélérer dans les décennies à venir, pourrait modifier les vents en haute altitude et potentiellement avoir un impact sur le climat des moyennes latitudes. L'étendue de la banquise arctique varie considérablement d'une saison à l'autre, d'une année à l'autre, d'une décennie à l'autre. Améliorer notre capacité à prévoir ces variations nécessite de comprendre, observer et modéliser les interactions entre la banquise et les autres composantes du système Terre, telles que l'océan, l'atmosphère ou la biosphère, à différentes échelles de temps. La réalisation de prévisions saisonnières de la banquise arctique est très récente comparée aux prévisions du temps ou aux prévisions saisonnières de paramètres météorologiques (température, précipitation). Les résultats ayant émergé au cours des dix dernières années mettent en évidence l'importance des observations de l'épaisseur de la glace de mer pour prévoir l'évolution de la banquise estivale plusieurs mois à l'avance. Surface temperatures over the Arctic region have been increasing twice as fast as global mean temperatures, a phenomenon known as arctic amplification. One main contributor to this polar warming is the large decline of Arctic sea ice observed since the beginning of satellite observations, which has been attributed to the increase of greenhouse gases. The acceleration of Arctic sea ice loss that is projected for the coming decades could modify the upper level atmospheric circulation yielding climate impacts up to the mid-latitudes. There is considerable variability in the spatial extent of ice cover on seasonal, interannual and decadal time scales. Better understanding, observing and modelling the interactions between sea ice and the other components of the climate system is key for improved predictions of Arctic sea ice in the future. Running operational-like seasonal predictions of Arctic sea ice is a quite recent effort compared to weather predictions or seasonal predictions of atmospheric fields like temperature or precipitation. Recent results stress the importance of sea ice thickness observations to improve seasonal predictions of Arctic sea ice conditions during summer.

scholarly journals Retrieval of Snow Depth over Arctic Sea Ice Using a Deep Neural Network

2019 ◽  
Vol 11 (23) ◽  
pp. 2864 ◽  
Author(s):  
Jiping Liu ◽  
Yuanyuan Zhang ◽  
Xiao Cheng ◽  
Yongyun Hu
Keyword(s):  
Neural Network ◽  
Sea Ice ◽  
Snow Depth ◽  
Deep Neural Network ◽  
Arctic Basin ◽  
Temporal Variations ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Thickness ◽  
Arctic Sea

The accurate knowledge of spatial and temporal variations of snow depth over sea ice in the Arctic basin is important for understanding the Arctic energy budget and retrieving sea ice thickness from satellite altimetry. In this study, we develop and validate a new method for retrieving snow depth over Arctic sea ice from brightness temperatures at different frequencies measured by passive microwave radiometers. We construct an ensemble-based deep neural network and use snow depth measured by sea ice mass balance buoys to train the network. First, the accuracy of the retrieved snow depth is validated with observations. The results show the derived snow depth is in good agreement with the observations, in terms of correlation, bias, root mean square error, and probability distribution. Our ensemble-based deep neural network can be used to extend the snow depth retrieval from first-year sea ice (FYI) to multi-year sea ice (MYI), as well as during the melting period. Second, the consistency and discrepancy of snow depth in the Arctic basin between our retrieval using the ensemble-based deep neural network and two other available retrievals using the empirical regression are examined. The results suggest that our snow depth retrieval outperforms these data sets.

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.

scholarly journals Polar cod in jeopardy under the retreating Arctic sea ice

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Mats Brockstedt Olsen Huserbråten ◽  
Elena Eriksen ◽  
Harald Gjøsæter ◽  
Frode Vikebø
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Keystone Species ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
Biophysical Model ◽  
The Arctic ◽  
Polar Cod ◽  
Arctic Sea ◽  
Shelf Sea

Abstract The Arctic amplification of global warming is causing the Arctic-Atlantic ice edge to retreat at unprecedented rates. Here we show how variability and change in sea ice cover in the Barents Sea, the largest shelf sea of the Arctic, affect the population dynamics of a keystone species of the ice-associated food web, the polar cod (Boreogadus saida). The data-driven biophysical model of polar cod early life stages assembled here predicts a strong mechanistic link between survival and variation in ice cover and temperature, suggesting imminent recruitment collapse should the observed ice-reduction and heating continue. Backtracking of drifting eggs and larvae from observations also demonstrates a northward retreat of one of two clearly defined spawning assemblages, possibly in response to warming. With annual to decadal ice-predictions under development the mechanistic physical-biological links presented here represent a powerful tool for making long-term predictions for the propagation of polar cod stocks.

scholarly journals On the Link between Arctic Sea Ice Decline and the Freshwater Content of the Beaufort Gyre: Insights from a Simple Process Model

2014 ◽  
Vol 27 (21) ◽  
pp. 8170-8184 ◽  
Author(s):  
Peter E. D. Davis ◽  
Camille Lique ◽  
Helen L. Johnson
Keyword(s):  
Sea Ice ◽  
Momentum Transfer ◽  
Momentum Flux ◽  
Process Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Simple Process ◽  
Beaufort Gyre ◽  
Arctic Sea ◽  
The Arctic Ocean

Abstract Recent satellite and hydrographic observations have shown that the rate of freshwater accumulation in the Beaufort Gyre of the Arctic Ocean has accelerated over the past decade. This acceleration has coincided with the dramatic decline observed in Arctic sea ice cover, which is expected to modify the efficiency of momentum transfer into the upper ocean. Here, a simple process model is used to investigate the dynamical response of the Beaufort Gyre to the changing efficiency of momentum transfer, and its link with the enhanced accumulation of freshwater. A linear relationship is found between the annual mean momentum flux and the amount of freshwater accumulated in the Beaufort Gyre. In the model, both the response time scale and the total quantity of freshwater accumulated are determined by a balance between Ekman pumping and an eddy-induced volume flux toward the boundary, highlighting the importance of eddies in the adjustment of the Arctic Ocean to a change in forcing. When the seasonal cycle in the efficiency of momentum transfer is modified (but the annual mean momentum flux is held constant), it has no effect on the accumulation of freshwater, although it does impact the timing and amplitude of the annual cycle in Beaufort Gyre freshwater content. This suggests that the decline in Arctic sea ice cover may have an impact on the magnitude and seasonality of the freshwater export into the North Atlantic.

Modeling and assessing impact of artificially enhancing the Arctic Sea Ice Albedo

2019 ◽  
Author(s):  
Subarna Bhattacharyya ◽  
Detelina Ivanova ◽  
Velimir Mlaker ◽  
Leslie Field
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Arctic Sea

scholarly journals Deep Learning Convolutional Neural Network Applying for the Arctic Acoustic Tomography Current Inversion Accuracy Improvement

2021 ◽  
Vol 9 (7) ◽  
pp. 755
Author(s):  
Kangkang Jin ◽  
Jian Xu ◽  
Zichen Wang ◽  
Can Lu ◽  
Long Fan ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sea Ice ◽  
Convolutional Neural Network ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Small Scale ◽  
Acoustic Tomography ◽  
Arctic Sea ◽  
Current Inversion

Warm current has a strong impact on the melting of sea ice, so clarifying the current features plays a very important role in the Arctic sea ice coverage forecasting study field. Currently, Arctic acoustic tomography is the only feasible method for the large-range current measurement under the Arctic sea ice. Furthermore, affected by the high latitudes Coriolis force, small-scale variability greatly affects the accuracy of Arctic acoustic tomography. However, small-scale variability could not be measured by empirical parameters and resolved by Regularized Least Squares (RLS) in the inverse problem of Arctic acoustic tomography. In this paper, the convolutional neural network (CNN) is proposed to enhance the prediction accuracy in the Arctic, and especially, Gaussian noise is added to reflect the disturbance of the Arctic environment. First, we use the finite element method to build the background ocean model. Then, the deep learning CNN method constructs the non-linear mapping relationship between the acoustic data and the corresponding flow velocity. Finally, the simulation result shows that the deep learning convolutional neural network method being applied to Arctic acoustic tomography could achieve 45.87% accurate improvement than the common RLS method in the current inversion.

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