Arctic Sea Ice Reemergence: The Role of Large-Scale Oceanic and Atmospheric Variability*

Abstract Arctic sea ice reemergence is a phenomenon in which spring sea ice anomalies are positively correlated with fall anomalies, despite a loss of correlation over the intervening summer months. This work employs a novel data analysis algorithm for high-dimensional multivariate datasets, coupled nonlinear Laplacian spectral analysis (NLSA), to investigate the regional and temporal aspects of this reemergence phenomenon. Coupled NLSA modes of variability of sea ice concentration (SIC), sea surface temperature (SST), and sea level pressure (SLP) are studied in the Arctic sector of a comprehensive climate model and in observations. It is found that low-dimensional families of NLSA modes are able to efficiently reproduce the prominent lagged correlation features of the raw sea ice data. In both the model and observations, these families provide an SST–sea ice reemergence mechanism, in which melt season (spring) sea ice anomalies are imprinted as SST anomalies and stored over the summer months, allowing for sea ice anomalies of the same sign to reappear in the growth season (fall). The ice anomalies of each family exhibit clear phase relationships between the Barents–Kara Seas, the Labrador Sea, and the Bering Sea, three regions that compose the majority of Arctic sea ice variability. These regional phase relationships in sea ice have a natural explanation via the SLP patterns of each family, which closely resemble the Arctic Oscillation and the Arctic dipole anomaly. These SLP patterns, along with their associated geostrophic winds and surface air temperature advection, provide a large-scale teleconnection between different regions of sea ice variability. Moreover, the SLP patterns suggest another plausible ice reemergence mechanism, via their winter-to-winter regime persistence.

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Gridded Arctic sea ice concentration reconstruction for the first half of the 20th century based on different proxy data

10.5194/egusphere-egu21-3106 ◽

2021 ◽

Author(s):

Vladimir Semenov ◽

Tatiana Matveeva

Keyword(s):

Sea Ice ◽

20Th Century ◽

Climate Model ◽

Surface Air Temperature ◽

Arctic Sea Ice ◽

The Arctic ◽

Sea Ice Concentration ◽

The North ◽

Arctic Sea ◽

Ice Concentration

Global warming in the recent decades has been accompanied by a rapid recline of the Arctic sea ice area most pronounced in summer (10% per decade). To understand the relative contribution of external forcing and natural variability to the modern and future sea ice area changes, it is necessary to evaluate a range of long-term variations of the Arctic sea ice area in the period before a significant increase in anthropogenic emissions of greenhouse gases into the atmosphere. Available observational data on the spatiotemporal dynamics of Arctic sea ice until 1950s are characterized by significant gaps and uncertainties. In the recent years, there have appeared several reconstructions of the early 20th century Arctic sea ice area that filled the gaps by analogue methods or utilized combined empirical data and climate model&#8217;s output. All of them resulted in a stronger that earlier believed negative sea ice area anomaly in the 1940s concurrent with the early 20th century warming (ETCW) peak. In this study, we reconstruct the monthly average gridded sea ice concentration (SIC) in the first half of the 20th century using the relationship between the spatiotemporal features of SIC variability, surface air temperature over the Northern Hemisphere extratropical continents, sea surface temperature in the North Atlantic and North Pacific, and sea level pressure. In agreement with a few previous results, our reconstructed data also show a significant negative anomaly of the Arctic sea ice area in the middle of the 20th century, however with some 15% to 30% stronger amplitude, about 1.5 million km2 in September and 0.7 million km2 in March. The reconstruction demonstrates a good agreement with regional Arctic sea ice area data when available and suggests that ETWC in the Arctic has been accompanied by a concurrent sea ice area decline of a magnitude that have been exceeded only in the beginning of the 21st century.

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Mechanisms for and Predictability of a Drastic Reduction in the Arctic Sea Ice: APPOSITE Data with Climate Model MIROC

Journal of Climate ◽

10.1175/jcli-d-18-0195.1 ◽

2019 ◽

Vol 32 (5) ◽

pp. 1361-1380 ◽

Cited By ~ 3

Author(s):

J. Ono ◽

H. Tatebe ◽

Y. Komuro

Keyword(s):

Sea Ice ◽

Climate Model ◽

Arctic Sea Ice ◽

The Arctic ◽

Ensemble Prediction ◽

Forecast Errors ◽

Drastic Reduction ◽

Sea Ice Concentration ◽

Sea Ice Extent ◽

Arctic Sea

Abstract The mechanisms for and predictability of a drastic reduction in the Arctic sea ice extent (SIE) are investigated using the Model for Interdisciplinary Research on Climate (MIROC) version 5.2. Here, a control (CTRL) with forcing fixed at year 2000 levels and perfect-model ensemble prediction (PRED) experiments are conducted. In CTRL, three (model years 51, 56, and 57) drastic SIE reductions occur during a 200-yr-long integration. In year 56, the sea ice moves offshore in association with a positive phase of the summer Arctic dipole anomaly (ADA) index and melts due to heat input through the increased open water area, and the SIE drastically decreases. This provides the preconditioning for the lowest SIE in year 57 when the Arctic Ocean interior is in a warm state and the spring sea ice volume has a large negative anomaly due to drastic ice reduction in the previous year. Although the ADA is one of the key mechanisms behind sea ice reduction, it does not always cause a drastic reduction. Our analysis suggests that wind direction favoring offshore ice motion is a more important factor for drastic ice reduction events. In years experiencing drastic ice reduction events, the September SIE can be skillfully predicted in PRED started from July, but not from April. This is because the forecast errors for the July sea level pressure and those for the sea ice concentration and sea ice thickness along the ice edge are large in PRED started from April.

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Arctic sea ice volume budget decomposition satellite product for the CryoSat-2 (2010-2020) period

10.5194/egusphere-egu21-12079 ◽

2021 ◽

Author(s):

Harry Heorton ◽

Michel Tsamados ◽

Paul Holland ◽

Jack Landy

Keyword(s):

Sea Ice ◽

Climate Model ◽

Volume Growth ◽

Arctic Sea Ice ◽

First Year ◽

Sea Ice Concentration ◽

Marginal Seas ◽

Ice Volume ◽

Arctic Sea ◽

Ice Concentration

We combine satellite-derived observations of sea ice concentration, drift, and thickness to provide the first observational decomposition of the dynamic (advection/divergence) and thermodynamic (melt/growth) drivers of wintertime Arctic sea ice volume change. Ten winter growth seasons are analyzed over the CryoSat-2 period between October 2010 and April 2020. Sensitivity to several observational products is performed to provide an estimated uncertainty of the budget calculations. The total thermodynamic ice volume growth and dynamic ice losses are calculated with marked seasonal, inter-annual and regional variations. Ice growth is fastest during Autumn, in the Marginal Seas and over first year ice.&#160;Our budget decomposition methodology can help diagnose the processes confounding climate model predictions of sea ice. We make our product and code available to the community in monthly pan-Arctic netcdft files for the entire October 2010 to April 2020 period.

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Cryospheric Impacts of Soviet River Diversion Schemes

Annals of Glaciology ◽

10.3189/1984aog5-1-61-68 ◽

1984 ◽

Vol 5 ◽

pp. 61-68 ◽

Cited By ~ 4

Author(s):

T. Holt ◽

P. M. Kelly ◽

B. S. G. Cherry

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

River Discharge ◽

Large Scale ◽

Arctic Sea Ice ◽

The Arctic ◽

Sea Ice Concentration ◽

Ice Concentration ◽

The Arctic Ocean ◽

The Impact

Soviet plans to divert water from rivers flowing into the Arctic Ocean have led to research into the impact of a reduction in discharge on Arctic sea ice. We consider the mechanisms by which discharge reductions might affect sea-ice cover and then test various hypotheses related to these mechanisms. We find several large areas over which sea-ice concentration correlates significantly with variations in river discharge, supporting two particular hypotheses. The first hypothesis concerns the area where the initial impacts are likely to which is the Kara Sea. Reduced riverflow is associated occur, with decreased sea-ice concentration in October, at the time of ice formation. This is believed to be the result of decreased freshening of the surface layer. The second hypothesis concerns possible effects on the large-scale current system of the Arctic Ocean and, in particular, on the inflow of Atlantic and Pacific water. These effects occur as a result of changes in the strength of northward-flowing gradient currents associated with variations in river discharge. Although it is still not certain that substantial transfers of riverflow will take place, it is concluded that the possibility of significant cryospheric effects and, hence, large-scale climate impact should not be neglected.

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Atmospheric teleconnection processes linking winter air stagnation and haze extremes in China with regional Arctic sea ice decline

10.5194/acp-2019-1023 ◽

2019 ◽

Author(s):

Yufei Zou ◽

Yuhang Wang ◽

Zuowei Xie ◽

Hailong Wang ◽

Philip J. Rasch

Keyword(s):

Sea Ice ◽

Climate Model ◽

Teleconnection Pattern ◽

Arctic Sea Ice ◽

The Arctic ◽

Transient Eddy ◽

Regional Ventilation ◽

Atmospheric Teleconnection ◽

Upper Troposphere ◽

Arctic Sea

Abstract. Recent studies suggested significant impacts of boreal cryosphere changes on wintertime air stagnation and haze pollution extremes in China. However, the underlying mechanism of such a teleconnection relationship remains unclear. Here we used the Whole Atmosphere Community Climate Model (WACCM) to investigate dynamic processes leading to atmospheric circulation and air stagnation responses to Arctic sea ice changes. We conducted four climate sensitivity experiments by perturbing sea ice concentrations (SIC) and corresponding sea surface temperature (SST) in autumn and early winter over the whole Arctic and three sub-regions in the climate model. The results indicate different responses in the general circulation and regional ventilation to the region-specific Arctic changes, with the largest increase of both the probability (by 120 %) and the intensity (by 32 %) of air stagnation extreme events being found in the experiment driven by SIC and SST changes over the Pacific sector of the Arctic (the East Siberian and Chukchi Seas). The increased air stagnation extreme events are mainly driven by an amplified hemispheric-scale atmospheric teleconnection pattern that resembles the negative phase of the Eurasian (EU) pattern. Dynamical diagnostics suggest that convergence of transient eddy forcing in the vicinity of Scandinavia in winter is largely responsible for the amplification of the teleconnection pattern. Transient eddy vorticity fluxes dominate the transient eddy forcing and produce a barotropic anticyclonic anomaly near Scandinavia and wave-train propagation across Eurasia to the downstream regions in East Asia. The piecewise potential vorticity inversion analysis reveals that this long-range atmospheric teleconnection of the Arctic origin takes place primarily in the middle and upper troposphere. The anomalous ridge over East Asia in the middle and upper troposphere worsens regional ventilation conditions by weakening monsoon northwesterlies and enhancing temperature inversion near the surface, leading to more and stronger air stagnation and pollution extremes over eastern China in winter. Ensemble projections based on the state-of-the-art climate models in the Coupled Model Intercomparison Project Phase 6 (CMIP6) corroborate this teleconnection relationship between high-latitude environmental changes and middle-latitude weather extremes, though the tendency and magnitude vary considerably among each participating model.

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Mechanisms causing reduced Arctic sea ice loss in a coupled climate model

The Cryosphere Discussions ◽

10.5194/tcd-6-2653-2012 ◽

2012 ◽

Vol 6 (4) ◽

pp. 2653-2687 ◽

Cited By ~ 1

Author(s):

A. E. West ◽

A. B. Keen ◽

H. T. Hewitt

Keyword(s):

Sea Ice ◽

Climate Model ◽

Historical Record ◽

Arctic Sea Ice ◽

The Arctic ◽

Slight Reduction ◽

Coupled Climate Model ◽

Arctic Sea ◽

Ensembles Project ◽

Arctic Sea Ice Loss

Abstract. The fully-coupled climate model HadGEM1 produces one of the most accurate simulations of the historical record of Arctic sea ice seen in the IPCC AR4 multi-model ensemble. In this study, we examine projections of sea ice decline out to 2030, produced by two ensembles of HadGEM1 with natural and anthropogenic forcings included. These ensembles project a significant slowing of the rate of ice loss to occur after 2010, with some integrations even simulating a small increase in ice area. We use an energy budget of the Arctic to examine the causes of this slowdown. A negative feedback effect by which rapid reductions in ice thickness north of Greenland reduce ice export is found to play a major role. A slight reduction in ocean-to-ice heat flux in the relevant period, caused by changes in the MOC and subpolar gyre in some integrations, is also found to play a part. Finally, we assess the likelihood of a slowdown occurring in the real world due to these causes.

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Large-scale Arctic sea ice motion from Sentinel-1 and the RADARSAT Constellation Mission

10.5194/egusphere-egu21-514 ◽

2021 ◽

Author(s):

Stephen Howell ◽

Mike Brady ◽

Alexander Komarov

Keyword(s):

Sea Ice ◽

Spatial Resolution ◽

Temporal Resolution ◽

Large Scale ◽

Arctic Sea Ice ◽

The Arctic ◽

High Temporal Resolution ◽

Sar Images ◽

Sea Ice Extent ◽

Arctic Sea

As the Arctic&#8217;s sea ice extent continues to decline, remote sensing observations are becoming even more vital for the monitoring and understanding of this process.&#160; Recently, the sea ice community has entered a new era of synthetic aperture radar (SAR) satellites operating at C-band with the launch of Sentinel-1A in 2014, Sentinel-1B in 2016 and the RADARSAT Constellation Mission (RCM) in 2019. These missions represent a collection of 5 spaceborne SAR sensors that together can routinely cover Arctic sea ice with a high spatial resolution (20-90 m) but also with a high temporal resolution (1-7 days) typically associated with passive microwave sensors. Here, we used ~28,000 SAR image pairs from Sentinel-1AB together with ~15,000 SAR images pairs from RCM to generate high spatiotemporal large-scale sea ice motion products across the pan-Arctic domain for 2020. The combined Sentinel-1AB and RCM sea ice motion product provides almost complete 7-day coverage over the entire pan-Arctic domain that also includes the pole-hole. Compared to the National Snow and Ice Data Center (NSIDC) Polar Pathfinder and Ocean and Sea Ice-Satellite Application Facility (OSI-SAF) sea ice motion products, ice speed was found to be faster with the Senintel-1AB and RCM product which is attributed to the higher spatial resolution of SAR imagery. More sea ice motion vectors were detected from the Sentinel-1AB and RCM product in during the summer months and within the narrow channels and inlets compared to the NSIDC Polar Pathfinder and OSI-SAF sea ice motion products. Overall, our results demonstrate that sea ice geophysical variables across the pan-Arctic domain can now be retrieved from multi-sensor SAR images at both high spatial and temporal resolution.

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Linkage of Arctic Sea Ice and Energy Transport

10.5194/egusphere-egu21-2057 ◽

2021 ◽

Author(s):

Ines Höschel ◽

Dörthe Handorf ◽

Christoph Jacobi ◽

Johannes Quaas

Keyword(s):

Sea Ice ◽

Large Scale ◽

Energy Transport ◽

Arctic Sea Ice ◽

The Arctic ◽

Moist Static Energy ◽

Large Scale Circulation ◽

Arctic Sea ◽

Rossby Wave Propagation ◽

Static Energy

The loss of Arctic sea ice as a consequence of global warming is changing the forcing of the atmospheric large-scale circulation. &#160;Areas not covered with sea ice anymore may act as an additional heat source. &#160;Associated changes in Rossby wave propagation can initiate tropospheric and stratospheric pathways of Arctic - Mid-latitude linkages.&#160; These pathways have the potential to impact on the large-scale energy transport into the Arctic.&#160; On the other hand, studies show that the large-scale circulation contributes to Arctic warming by poleward transport of moist static energy. This presentation shows results from research within the Transregional Collaborative Research Center &#8220;ArctiC Amplification: Climate Relevant Atmospheric and SurfaCe Processes, and Feedback Mechanisms (AC)3&#8221; funded by the Deutsche Forschungsgemeinschaft.&#160; Using the ERA interim and ERA5 reanalyses the meridional moist static energy transport during high ice and low ice periods is compared. &#160;The investigation discriminates between contributions from planetary and synoptic scale.&#160; Special emphasis is put on the seasonality of the modulations of the large-scale energy transport.

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Variability of Arctic Sea Ice Based on Quantile Regression and the Teleconnection with Large-Scale Climate Patterns

Journal of Climate ◽

10.1175/jcli-d-19-0375.1 ◽

2020 ◽

Vol 33 (10) ◽

pp. 4009-4025

Author(s):

Shuyu Zhang ◽

Thian Yew Gan ◽

Andrew B. G. Bush

Keyword(s):

Sea Ice ◽

Quantile Regression ◽

Large Scale ◽

Southern Oscillation ◽

Arctic Sea Ice ◽

The Arctic ◽

Climate Indices ◽

The Pacific ◽

Arctic Sea ◽

Climate Patterns

AbstractUnder global warming, Arctic sea ice has declined significantly in recent decades, with years of extremely low sea ice occurring more frequently. Recent studies suggest that teleconnections with large-scale climate patterns could induce the observed extreme sea ice loss. In this study, a probabilistic analysis of Arctic sea ice was conducted using quantile regression analysis with covariates, including time and climate indices. From temporal trends at quantile levels from 0.01 to 0.99, Arctic sea ice shows statistically significant decreases over all quantile levels, although of different magnitudes at different quantiles. At the representative extreme quantile levels of the 5th and 95th percentiles, the Arctic Oscillation (AO), the North Atlantic Oscillation (NAO), and the Pacific–North American pattern (PNA) have more significant influence on Arctic sea ice than El Niño–Southern Oscillation (ENSO), the Pacific decadal oscillation (PDO), and the Atlantic multidecadal oscillation (AMO). Positive AO as well as positive NAO contribute to low winter sea ice, and a positive PNA contributes to low summer Arctic sea ice. If, in addition to these conditions, there is concurrently positive AMO and PDO, the sea ice decrease is amplified. Teleconnections between Arctic sea ice and the climate patterns were demonstrated through a composite analysis of the climate variables. The anomalously strong anticyclonic circulation during the years of positive AO, NAO, and PNA promotes more sea ice export through Fram Strait, resulting in excessive sea ice loss. The probabilistic analyses of the teleconnections between the Arctic sea ice and climate patterns confirm the crucial role that the climate patterns and their combinations play in overall sea ice reduction, but particularly for the low and high quantiles of sea ice concentration.

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