The Early Winter Sea Ice Variability under the Recent Arctic Climate Shift

2014 ◽  
Vol 27 (13) ◽  
pp. 5092-5110 ◽  
Author(s):  
Xiao-Yi Yang ◽  
Xiaojun Yuan
Keyword(s):  
Sea Ice ◽  
Phase Relationship ◽  
Stationary Wave ◽  
The Arctic ◽  
Late Winter ◽  
Rapid Decline ◽  
Early Winter ◽  
Flux Divergence ◽  
Sea Ice Extent ◽  
Barents And Kara Seas

This study reveals that sea ice in the Barents and Kara Seas plays a crucial role in establishing a new Arctic coupled climate system. The early winter sea ice before 1998 shows double dipole patterns over the Arctic peripheral seas. This pattern, referred to as the early winter quadrupole pattern, exhibits the anticlockwise sequential sea ice anomalies propagation from the Greenland Sea to the Barents–Kara Seas and to the Bering Sea from October to December. This early winter in-phase ice variability contrasts to the out-of-phase relationship in late winter. The mean temperature advection and stationary wave heat flux divergence associated with the atmospheric zonal wave-2 pattern are responsible for the early winter in-phase pattern. Since the end of the last century, the early winter quadrupole pattern has broken down because of the rapid decline of sea ice extent in the Barents–Kara Seas. This remarkable ice retreat modifies the local ocean–atmosphere heat exchange, forcing an anomalous low air pressure over the Barents–Kara Seas. The subsequent collapse of the atmospheric zonal wave-2 pattern is likely responsible for the breakdown of the early winter sea ice quadrupole pattern after 1998. Therefore, the sea ice anomalies in the Barents–Kara Seas play a key role in establishing new atmosphere–sea ice coupled relationships in the warming Arctic.

scholarly journals A Regional Seasonal Forecast Model of Arctic Minimum Sea Ice Extent: Reflected Solar Radiation vs. Late Winter Coastal Divergence

2021 ◽  
pp. 1
Author(s):  
Rachel Kim ◽  
Bruno Tremblay ◽  
Charles Brunette ◽  
Robert Newton
Keyword(s):  
Solar Radiation ◽  
Sea Ice ◽  
Open Water ◽  
Forecast Model ◽  
Ice Cover ◽  
The Arctic ◽  
Late Winter ◽  
Coupled Models ◽  
Sea Ice Extent ◽  
Predictive Skill

AbstractThinning sea ice cover in the Arctic is associated with larger interannual variability in the minimum Sea Ice Extent (SIE). The current generation of forced or fully coupled models, however, have difficulty predicting SIE anomalies from the long-term trend, highlighting the need to better identify the mechanisms involved in the seasonal evolution of sea ice cover. One such mechanism is Coastal Divergence (CD), a proxy for ice thickness anomalies based on late winter ice motion, quantified using Lagrangian ice tracking. CD gains predictive skill through the positive feedback of surface albedo anomalies, mirrored in Reflected Solar Radiation (RSR), during melt season. Exploring the dynamic and thermodynamic contributions to minimum SIE predictability, RSR, initial SIE (iSIE) and CD are compared as predictors using a regional seasonal sea ice forecast model for July 1, June 1 and May 1 forecast dates for all Arctic peripheral seas. The predictive skill of June RSR anomalies mainly originates from open water fraction at the surface, i.e. June iSIE and June RSR have equal predictive skill for most seas. The finding is supported by the surprising positive correlation found between June Melt Pond Fraction (MPF) and June RSR in all peripheral seas: MPF anomalies indicate presence of ice or open water that is key to creating minimum SIE anomalies. This contradicts models that show correlation between melt onset, MPF and the minimum SIE. A hindcast model shows that for a May 1 forecast, CD anomalies have better predictive skill than RSR anomalies for most peripheral seas.

scholarly journals Seasonal Cumulative Effect of Ural Blocking Episodes on the Frequent Cold events in China during the Early Winter of 2020/21

2022 ◽  
Author(s):  
Yao Yao ◽  
Wenqi Zhang ◽  
Dehai Luo ◽  
Linhao Zhong ◽  
Lin Pei
Keyword(s):  
Sea Ice ◽  
Cumulative Effect ◽  
Cumulative Effects ◽  
The Arctic ◽  
Early Winter ◽  
Sea Ice Extent ◽  
Cold Event ◽  
Cold Events ◽  
Extreme Cold ◽  
The Impact

AbstractStarting in mid-November, China was hit by several cold events during the early winter of 2020/21. The lowest temperature observed at Beijing station on 7 January reached −19.6°C. In this paper, we show that the outbreak of the record-breaking extreme cold event can be attributed to a huge merging Ural blocking (UB) ridge over the Eurasian region. The sea-ice cover in the Kara and East Siberia Seas (KESS) in autumn was at its lowest value since 1979, which could have served as a precursor signal. Further analysis shows that several successive UB episodes occurred from 1 September 2020 to 10 January 2021. The persistent UB that occurred in late September/early October 2020 may have made an important contribution to the October historical minimum of sea ice in the KESS region. Our results also show that, after each UB episode in winter, significant upward propagation of wave activity occurred around 60°E, which resulted in weakening the stratospheric vortex. Meanwhile, each UB episode also caused a significant reduction in sea-ice extent in KESS and a significant weakening of the westerly jet in mid-high-latitude Eurasia. Results suggest that the Arctic vortex, which is supposed to enhance seasonally, became weaker and more unstable than the climatic mean under the seasonal cumulative effects of UB episodes, KESS warming, and long-lasting negative-phase North Atlantic Oscillation (NAO-). Those seasonal cumulative effects, combined with the impact of La Niña winter, led to the frequent occurrence of extreme cold events.

scholarly journals Dynamic Preconditioning of the Minimum September Sea-Ice Extent

2016 ◽  
Vol 29 (16) ◽  
pp. 5879-5891 ◽  
Author(s):  
James Williams ◽  
Bruno Tremblay ◽  
Robert Newton ◽  
Richard Allard
Keyword(s):  
Sea Ice ◽  
Linear Trend ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Late Winter ◽  
Fram Strait ◽  
Sea Ice Extent ◽  
Dynamical Mechanism ◽  
Arctic Oscillation Index ◽  
Ice Edge

Abstract There has been an increased interest in seasonal forecasting of the Arctic sea ice extent in recent years, in particular the minimum sea ice extent. Here, a dynamical mechanism, based on winter preconditioning, is found to explain a significant fraction of the variance in the anomaly of the September sea ice extent from the long-term linear trend. To this end, a Lagrangian trajectory model is used to backtrack the September sea ice edge to any time during the previous winter and quantify the amount of sea ice advection away from the Eurasian and Alaskan coastlines as well as the Fram Strait sea ice export. The late-winter anomalous sea ice drift away from the coastline is highly correlated with the following September sea ice extent minimum . It is found that the winter mean Fram Strait sea ice export anomaly is also correlated with the minimum sea ice extent the following summer . To develop a hindcast model of the September sea ice extent—which does not depend on a priori knowledge of the minimum sea ice extent—a synthetic ice edge initialized at the beginning of the melt season (1 June) is backtracked. It is found that using a multivariate regression model of the September sea ice extent anomaly based on ice export from the peripheral Arctic seas and Fram Strait ice export as predictors reduces the error by 38%. A hindcast model based on the mean December–April Arctic Oscillation index alone reduces the error by 24%.

Assessing trends in Arctic sea-ice distribution in the Barents and Kara seas using the Kosmos–Okean satellite series

Polar Record ◽  
1995 ◽  
Vol 31 (177) ◽  
pp. 129-134 ◽  
Author(s):  
Gennady I. Belchansky ◽  
Ilia N. Mordvintsev ◽  
Gregory K. Ovchinnikov ◽  
David C. Douglas
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Satellite Image ◽  
Kara Sea ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Published Data ◽  
Sea Ice Extent ◽  
The Barents Sea ◽  
Barents And Kara Seas

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 investigated for the Barents and western Kara seas and adjacent parts of the Arctic Ocean during 1984–1993. Four definitions of ice extent were examined, based on thresholds of ice concentration: >90%, >70%, >40%, and >10% (El, E2, E3, and E4, respectively). Trends were studied using ice maps produced by the Russian Hydro-Meteorological Service, Kosmos and Okean satellite imagery, and data extracted from published literature. During 1984–1993, an increasing trend in the extent of minimum sea-ice cover was observed in the Barents, Kara, and combined Barents–Kara seas, for all ice-extent definitions. Root-mean-square differences between hydro-meteorological ice maps and satellite-image ice classifications for coincident areas and dates were 15.5%, 19.3%, 18.8%, and 11.5%, for ice extensions El–E4, respectively. The differences were subjected to Monte Carlo analyses to construct confidence intervals for the 10-year ice-map trends. With probability p = 0.8, the average 10-year increase in the minimum monthly mean sea-ice extent (followed in brackets by the average increase in the absolute annual minimum ice extent) was 12–46% [26–96%], 31–71% [55–140%], 30–69% [26–94%], and 48–94% [35–108%] in the Barents Sea; 20–60% [32–120%], 10–45% [20–92%], 2–36% [13–78%], and 10–47% [8–69%] in the Kara Sea; and 9–43% [26–59%], 9–41% [30–63%], 8–41% [22–52%] and 15–51% [21–51%] in the combined Barents–Kara seas, for ice concentrations El–E4, respectively. Including published data from 1966–1983, the trend in minimum monthly mean sea-ice extent for the combined 28-year period showed an average reduction of 8% in the Barents Sea and a 55% reduction in the western Kara Sea; ice extent at the end of August showed an average reduction of 33% in the Barents Sea.

scholarly journals Sea Ice Concentration and Sea Ice Extent Mapping with L-Band Microwave Radiometry and GNSS-R Data from the FFSCat Mission Using Neural Networks

2021 ◽  
Vol 13 (6) ◽  
pp. 1139
Author(s):  
David Llaveria ◽  
Juan Francesc Munoz-Martin ◽  
Christoph Herbert ◽  
Miriam Pablos ◽  
Hyuk Park ◽  
...  
Keyword(s):  
Sea Ice ◽  
Microwave Radiometer ◽  
Cost Effective ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Neural Network Approach ◽  
Ice Concentration ◽  
L Band ◽  
Moderate Cost

CubeSat-based Earth Observation missions have emerged in recent times, achieving scientifically valuable data at a moderate cost. FSSCat is a two 6U CubeSats mission, winner of the ESA S3 challenge and overall winner of the 2017 Copernicus Masters Competition, that was launched in September 2020. The first satellite, 3Cat-5/A, carries the FMPL-2 instrument, an L-band microwave radiometer and a GNSS-Reflectometer. This work presents a neural network approach for retrieving sea ice concentration and sea ice extent maps on the Arctic and the Antarctic oceans using FMPL-2 data. The results from the first months of operations are presented and analyzed, and the quality of the retrieved maps is assessed by comparing them with other existing sea ice concentration maps. As compared to OSI SAF products, the overall accuracy for the sea ice extent maps is greater than 97% using MWR data, and up to 99% when using combined GNSS-R and MWR data. In the case of Sea ice concentration, the absolute errors are lower than 5%, with MWR and lower than 3% combining it with the GNSS-R. The total extent area computed using this methodology is close, with 2.5% difference, to those computed by other well consolidated algorithms, such as OSI SAF or NSIDC. The approach presented for estimating sea ice extent and concentration maps is a cost-effective alternative, and using a constellation of CubeSats, it can be further improved.

scholarly journals Influence of Northern Hemispheric Winter Warming on the Pacific Storm Track

2021 ◽  
Author(s):  
Hyung-Ju Park ◽  
Kwang-Yul Kim
Keyword(s):  
Heat Flux ◽  
Global Warming ◽  
Energy Conversion ◽  
Storm Track ◽  
The Arctic ◽  
Late Winter ◽  
Turbulent Heat ◽  
Early Winter ◽  
Eddy Heat Flux ◽  
Northern Hemispheric

AbstractEffect of global warming on the sub-seasonal variability of the Northern Hemispheric winter (NDJFM) Pacific storm-track (PST) activity has been investigated. Previous studies showed that the winter-averaged PST has shifted northward and intensified, which was explained in terms of energy exchange with the mean field. Effect of global warming exhibits spatio-temporal heterogeneity with predominance over the Arctic region and in the winter season. Therefore, seasonal averaging may hide important features on sub-seasonal scales. In this study, distinct sub-seasonal response in storm track activities to winter Northern Hemispheric warming is analyzed applying cyclostationary empirical orthogonal function analysis to ERA5 data. The key findings are as follows. Change in the PST is not uniform throughout the winter; the PST shifts northward in early winter (NDJ) and intensifies in late winter (FM). In early winter, the combined effect of weakened baroclinic process to the south of the climatological PST and weakened barotropic damping to the north is responsible for the northward shift. In late winter, both processes contribute to the amplification of the PST. Further, change in baroclinic energy conversion is quantitatively dominated by eddy heat flux, whereas axial tilting of eddies is primarily responsible for change in barotropic energy conversion. A close relationship between anomalous eddy heat flux and anomalous boundary heating, which is largely determined by surface turbulent heat flux, is also demonstrated.

scholarly journals Observations and Simulations of Meteorological Conditions over Arctic Thick Sea Ice in Late Winter during the Transarktika 2019 Expedition

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 174
Author(s):  
Günther Heinemann ◽  
Sascha Willmes ◽  
Lukas Schefczyk ◽  
Alexander Makshtas ◽  
Vasilii Kustov ◽  
...  
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Open Water ◽  
The Arctic ◽  
Late Winter ◽  
Good Representation ◽  
Hourly Temperature ◽  
Ice Model

The parameterization of ocean/sea-ice/atmosphere interaction processes is a challenge for regional climate models (RCMs) of the Arctic, particularly for wintertime conditions, when small fractions of thin ice or open water cause strong modifications of the boundary layer. Thus, the treatment of sea ice and sub-grid flux parameterizations in RCMs is of crucial importance. However, verification data sets over sea ice for wintertime conditions are rare. In the present paper, data of the ship-based experiment Transarktika 2019 during the end of the Arctic winter for thick one-year ice conditions are presented. The data are used for the verification of the regional climate model COSMO-CLM (CCLM). In addition, Moderate Resolution Imaging Spectroradiometer (MODIS) data are used for the comparison of ice surface temperature (IST) simulations of the CCLM sea ice model. CCLM is used in a forecast mode (nested in ERA5) for the Norwegian and Barents Seas with 5 km resolution and is run with different configurations of the sea ice model and sub-grid flux parameterizations. The use of a new set of parameterizations yields improved results for the comparisons with in-situ data. Comparisons with MODIS IST allow for a verification over large areas and show also a good performance of CCLM. The comparison with twice-daily radiosonde ascents during Transarktika 2019, hourly microwave water vapor measurements of first 5 km in the atmosphere and hourly temperature profiler data show a very good representation of the temperature, humidity and wind structure of the whole troposphere for CCLM.

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 Sea-ice induced growth decline in Arctic shrubs

2017 ◽  
Vol 13 (8) ◽  
pp. 20170122 ◽  
Author(s):  
Mads Forchhammer
Keyword(s):  
Sea Ice ◽  
Ring Width ◽  
Plant Productivity ◽  
Climatic Conditions ◽  
Arctic Sea Ice ◽  
Annual Growth ◽  
The Arctic ◽  
Growth Time ◽  
Sea Ice Extent ◽  
Negative Effect

Measures of increased tundra plant productivity have been associated with the accelerating retreat of the Arctic sea-ice. Emerging studies document opposite effects, advocating for a more complex relationship between the shrinking sea-ice and terrestrial plant productivity. I introduce an autoregressive plant growth model integrating effects of biological and climatic conditions for analysing individual ring-width growth time series. Using 128 specimens of Salix arctica , S. glauca and Betula nana sampled across Greenland to Svalbard, an overall negative effect of the retreating June sea-ice extent was found on the annual growth. The negative effect of the retreating June sea-ice was observed for younger individuals with large annual growth allocations and with little or no trade-off between previous and current year's growth.

scholarly journals Stationary Wave Interference and Its Relation to Tropical Convection and Arctic Warming

2016 ◽  
Vol 29 (4) ◽  
pp. 1369-1389 ◽  
Author(s):  
Michael Goss ◽  
Steven B. Feldstein ◽  
Sukyoung Lee
Keyword(s):  
Sea Ice ◽  
Stationary Wave ◽  
Warm Pool ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Boreal Winter ◽  
Destructive Interference ◽  
Constructive Interference ◽  
Sea Ice Concentration ◽  
Stratospheric Polar Vortex

Abstract The interference between transient eddies and climatological stationary eddies in the Northern Hemisphere is investigated. The amplitude and sign of the interference is represented by the stationary wave index (SWI), which is calculated by projecting the daily 300-hPa streamfunction anomaly field onto the 300-hPa climatological stationary wave. ERA-Interim data for the years 1979 to 2013 are used. The amplitude of the interference peaks during boreal winter. The evolution of outgoing longwave radiation, Arctic temperature, 300-hPa streamfunction, 10-hPa zonal wind, Arctic sea ice concentration, and the Arctic Oscillation (AO) index are examined for days of large SWI values during the winter. Constructive interference during winter tends to occur about one week after enhanced warm pool convection and is followed by an increase in Arctic surface air temperature along with a reduction of sea ice in the Barents and Kara Seas. The warming of the Arctic does occur without prior warm pool convection, but it is enhanced and prolonged when constructive interference occurs in concert with enhanced warm pool convection. This is followed two weeks later by a weakening of the stratospheric polar vortex and a decline of the AO. All of these associations are reversed in the case of destructive interference. Potential climate change implications are briefly discussed.

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