scholarly journals North Atlantic warming and declining volume of arctic sea ice

2013 ◽  
Vol 7 (1) ◽  
pp. 245-265 ◽  
Author(s):  
V. A. Alexeev ◽  
V. V. Ivanov ◽  
R. Kwok ◽  
L. H. Smedsrud
Keyword(s):  
Sea Ice ◽  
Large Scale ◽  
Atlantic Water ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
First Year ◽  
Pacific Water ◽  
Svalbard Archipelago ◽  
Arctic Sea

Abstract. Long-term thinning of arctic sea ice over the last few decades has resulted in significant declines in the coverage of thick multi-year ice accompanied by a proportional increase in thinner first-year ice. This change is often attributed to changes in the arctic atmosphere, both in composition and large-scale circulation, and greater inflow of warmer Pacific water through the Bering Strait. The Atlantic Water (AW) entering the Arctic through Fram Strait has often been considered less important because of strong stratification in the Arctic Ocean and the deeper location of AW compared to Pacific water. In our combined examination of oceanographic measurements and satellite observations of ice concentration and thickness, we find evidence that AW has a direct impact on the thinning of arctic sea ice downstream of Svalbard Archipelago. The affected area extends as far as Severnaya Zemlya Archipelago. The imprints of AW appear as local minima in sea ice thickness; ice thickness is significantly less than that expected of first-year ice. Our lower-end conservative estimates indicate that the recent AW warming episode could have contributed up to 150–200 km3 of sea ice melt per year, which would constitute about 20% of the total 900 km3yr−1 negative trend in sea ice volume since 2004.

Sea ice representation in CMIP6 simulations with EC-Earth3-Veg

2020 ◽  
Author(s):  
Torben Koenigk ◽  
Evelien Dekker
Keyword(s):  
Sea Ice ◽  
Emission Scenario ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
First Year ◽  
Data Sets ◽  
Sea Ice Extent ◽  
Sea Ice Thickness ◽  
Arctic Sea

<p>In this study, we compare the sea ice in ensembles of historical and future simulations with EC-Earth3-Veg to the sea ice of the NSIDC and OSA-SAF satellite data sets. The EC-Earth3-Veg Arctic sea ice extent generally matches well to the observational data sets, and the trend over 1980-2014 is captured correctly. Interestingly, the summer Arctic sea ice area minimum occurs already in August in the model. Mainly east of Greenland, sea ice area is overestimated. In summer, Arctic sea ice is too thick compared to PIOMAS. In March, sea ice thickness is slightly overestimated in the Central Arctic but in the Bering and Kara Seas, the ice thickness is lower than in PIOMAS.</p><p>While the general picture of Arctic sea ice looks good, EC-Earth suffers from a warm bias in the Southern Ocean. This is also reflected by a substantial underestimation of sea ice area in the Antarctic.</p><p>Different ensemble members of the future scenario projections of sea ice show a large range of the date of first year with a minimum ice area below 1 million square kilometers in the Arctic. The year varies between 2024 and 2056. Interestingly, this range does not differ very much with the emission scenario and even under the low emission scenario SSP1-1.9 summer Arctic sea ice almost totally disappears.</p>

scholarly journals Variability and changes of Arctic sea ice draft distribution – submarine sonar measurements revisited

2011 ◽  
Vol 5 (4) ◽  
pp. 917-929 ◽  
Author(s):  
A. Oikkonen ◽  
J. Haapala
Keyword(s):  
Sea Ice ◽  
Seasonal Variability ◽  
Large Scale ◽  
Thickness Distribution ◽  
Quantitative Information ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
First Year ◽  
Pack Ice

Abstract. Changes in the mean sea ice thickness and concentration in the Arctic are well known. However, quantitative information about changes in the ice thickness distribution and the composition of the pack ice is lacking. In this paper we determine the ice draft distributions, mean and modal thicknesses, and their regional and seasonal variability in the Arctic for the time period 1975–2000. We compare characteristics of the Arctic pack ice for the years 1975–1987 and 1988–2000. These periods represent different large-scale atmospheric circulation modes and sea ice circulation patterns, most evident in clearly weaker Beaufort Gyre and stronger as well as westward shifted Transpolar Drift during the later period. The comparison of these two periods reveals that the peak of sea ice draft distributions has narrowed and shifted toward thinner ice, with reductions in both mean and modal ice draft. These noticeable changes are attributed to the loss of thick, mostly deformed ice. Springtime, loss of ice volume with draft greater than 5 m exceeds 35 % in all regions except the Nansen Basin, with as much as 45 % or more at the North Pole and in the Eastern Arctic. Autumn volume reduction, mostly of deformed ice, exceeds 40 % in the Canada Basin only, but is above 30 % also in the Beaufort and Chukchi Seas. During the later period, the volume of ice category consisting thin, mostly level first-year ice, is clearly larger than during the former period, especially in the spring. In the Beaufort Sea region, changes in the composition of ice cover have resulted in a shift of modal draft from level multiyear ice draft range to values of level first-year ice. The regional and seasonal variability of sea ice draft has decreased, since the thinning has been most pronounced in regions with the thickest pack ice (the Western Arctic), and during the spring (0.6–0.8 m per decade).

scholarly journals Arctic Sea Ice Retreat in 2007 Follows Thinning Trend

2009 ◽  
Vol 22 (1) ◽  
pp. 165-176 ◽  
Author(s):  
R. W. Lindsay ◽  
J. Zhang ◽  
A. Schweiger ◽  
M. Steele ◽  
H. Stern
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Ocean Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Extent ◽  
Pacific Side ◽  
Arctic Sea ◽  
The Arctic Ocean

Abstract The minimum of Arctic sea ice extent in the summer of 2007 was unprecedented in the historical record. A coupled ice–ocean model is used to determine the state of the ice and ocean over the past 29 yr to investigate the causes of this ice extent minimum within a historical perspective. It is found that even though the 2007 ice extent was strongly anomalous, the loss in total ice mass was not. Rather, the 2007 ice mass loss is largely consistent with a steady decrease in ice thickness that began in 1987. Since then, the simulated mean September ice thickness within the Arctic Ocean has declined from 3.7 to 2.6 m at a rate of −0.57 m decade−1. Both the area coverage of thin ice at the beginning of the melt season and the total volume of ice lost in the summer have been steadily increasing. The combined impact of these two trends caused a large reduction in the September mean ice concentration in the Arctic Ocean. This created conditions during the summer of 2007 that allowed persistent winds to push the remaining ice from the Pacific side to the Atlantic side of the basin and more than usual into the Greenland Sea. This exposed large areas of open water, resulting in the record ice extent anomaly.

scholarly journals Near Real Time Arctic sea ice thickness and volume from CryoSat-2

2016 ◽  
Author(s):  
R. L. Tilling ◽  
A. Ridout ◽  
A. Shepherd
Keyword(s):  
Sea Ice ◽  
Real Time ◽  
Arctic Sea Ice ◽  
Arctic Climate ◽  
The Arctic ◽  
Ice Thickness ◽  
Satellite Orbits ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Arctic Sea Ice Thickness

Abstract. Timely observations of sea ice thickness help us to understand Arctic climate, and can support maritime activities in the Polar Regions. Although it is possible to calculate Arctic sea ice thickness using measurements acquired by CryoSat-2, the latency of the final release dataset is typically one month, due to the time required to determine precise satellite orbits. We use a new fast delivery CryoSat-2 dataset based on preliminary orbits to compute Arctic sea ice thickness in near real time (NRT), and analyse this data for one sea ice growth season from October 2014 to April 2015. We show that this NRT sea ice thickness product is of comparable accuracy to that produced using the final release CryoSat-2 data, with an average thickness difference of 5 cm, demonstrating that the satellite orbit is not a critical factor in determining sea ice freeboard. In addition, the CryoSat-2 fast delivery product also provides measurements of Arctic sea ice thickness within three days of acquisition by the satellite, and a measurement is delivered, on average, within 10, 7 and 6 km of each location in the Arctic every 2, 14 and 28 days respectively. The CryoSat-2 NRT sea ice thickness dataset provides an additional constraint for seasonal predictions of Arctic climate change, and will allow industries such as tourism and transport to navigate the polar oceans with safety and care.

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

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

<p>As the Arctic’s sea ice extent continues to decline, remote sensing observations are becoming even more vital for the monitoring and understanding of this process.  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.</p>

Linkage of Arctic Sea Ice and Energy Transport

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

<p>The loss of Arctic sea ice as a consequence of global warming is changing the forcing of the atmospheric large-scale circulation.  Areas not covered with sea ice anymore may act as an additional heat source.  Associated changes in Rossby wave propagation can initiate tropospheric and stratospheric pathways of Arctic - Mid-latitude linkages.  These pathways have the potential to impact on the large-scale energy transport into the Arctic.  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 “ArctiC Amplification: Climate Relevant Atmospheric and SurfaCe Processes, and Feedback Mechanisms (AC)3” funded by the Deutsche Forschungsgemeinschaft.  Using the ERA interim and ERA5 reanalyses the meridional moist static energy transport during high ice and low ice periods is compared.  The investigation discriminates between contributions from planetary and synoptic scale.  Special emphasis is put on the seasonality of the modulations of the large-scale energy transport.</p>

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

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.

scholarly journals Estimating Arctic Sea Ice Thickness with CryoSat-2 Altimetry Data Using the Least Squares Adjustment Method

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7011
Author(s):  
Feng Xiao ◽  
Fei Li ◽  
Shengkai Zhang ◽  
Jiaxing Li ◽  
Tong Geng ◽  
...  
Keyword(s):  
Sea Ice ◽  
Least Squares ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Altimetry Data ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Least Squares Adjustment ◽  
Arctic Sea Ice Thickness

Satellite altimeters can be used to derive long-term and large-scale sea ice thickness changes. Sea ice thickness retrieval is based on measurements of freeboard, and the conversion of freeboard to thickness requires knowledge of the snow depth and snow, sea ice, and sea water densities. However, these parameters are difficult to be observed concurrently with altimeter measurements. The uncertainties in these parameters inevitably cause uncertainties in sea ice thickness estimations. This paper introduces a new method based on least squares adjustment (LSA) to estimate Arctic sea ice thickness with CryoSat-2 measurements. A model between the sea ice freeboard and thickness is established within a 5 km × 5 km grid, and the model coefficients and sea ice thickness are calculated using the LSA method. Based on the newly developed method, we are able to derive estimates of the Arctic sea ice thickness for 2010 through 2019 using CryoSat-2 altimetry data. Spatial and temporal variations of the Arctic sea ice thickness are analyzed, and comparisons between sea ice thickness estimates using the LSA method and three CryoSat-2 sea ice thickness products (Alfred Wegener Institute (AWI), Centre for Polar Observation and Modelling (CPOM), and NASA Goddard Space Flight Centre (GSFC)) are performed for the 2018–2019 Arctic sea ice growth season. The overall differences of sea ice thickness estimated in this study between AWI, CPOM, and GSFC are 0.025 ± 0.640 m, 0.143 ± 0.640 m, and −0.274 ± 0.628 m, respectively. Large differences between the LSA and three products tend to appear in areas covered with thin ice due to the limited accuracy of CryoSat-2 over thin ice. Spatiotemporally coincident Operation IceBridge (OIB) thickness values are also used for validation. Good agreement with a difference of 0.065 ± 0.187 m is found between our estimates and the OIB results.

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.

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