scholarly journals Interdecadal Variation of Winter Cold Surge Path in East Asia and Its Relationship with Arctic Sea Ice

2020 ◽  
Vol 33 (11) ◽  
pp. 4907-4925 ◽  
Author(s):  
Xiaoye Yang ◽  
Gang Zeng ◽  
Guwei Zhang ◽  
Zhongxian Li
Keyword(s):  
Sea Ice ◽  
East Asia ◽  
Barents Sea ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Probability Density Distribution ◽  
Cold Surge ◽  
Cold Air ◽  
Synoptic Features ◽  
Winter Cold

AbstractThe paths of winter cold surge (CS) events in East Asia (EA) from 1979 to 2017 are tracked by the Flexible Particle (FLEXPART) model using ERA-Interim daily datasets, and the probability density distribution of the paths is calculated by the kernel density estimation (KDE) method. The results showed that the paths of CSs are significantly correlated with the intensity of the CSs, which shows an interdecadal transition from weak to strong around 1995. CS paths can be classified into two types, namely, the western path type and the northern path type, which were more likely to occur before and after 1995, respectively. Before 1995, the cold air mainly originated from Europe and moved from west to east, and the synoptic features were associated with the zonal wave train. After 1995, cold air accumulated over western Siberia and then invaded EA along the northern path, and the synoptic features were mainly associated with the blocking structure. The geopotential height (GPH) anomalies over the Arctic were abnormally strong. This paper further analyzes the relationship between CSs and winter sea ice concentration (SIC) in the Arctic. The results show that the intensity of CSs is negatively correlated with the Barents SIC (BSIC). When the BSIC declines, the upward wave flux over the Barents Sea is enhanced and expanded to the midlatitude region. GPH anomalies over the Arctic are positive and form a negative AO-like pattern, which is conducive to the formation of the northern path CS. Furthermore, the observed results are supported by numerical experiments with the NCAR Community Atmosphere Model, version 5.3 (CAM5.3).

Patterns of Asian Winter Climate Variability and Links to Arctic Sea Ice

2015 ◽  
Vol 28 (17) ◽  
pp. 6841-6858 ◽  
Author(s):  
Bingyi Wu ◽  
Jingzhi Su ◽  
Rosanne D’Arrigo
Keyword(s):  
Sea Ice ◽  
East Asia ◽  
Barents Sea ◽  
Winter Precipitation ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Atmospheric Variability ◽  
Winter Climate ◽  
Arctic Sea ◽  
Arctic Sea Ice Loss

Abstract This paper describes two dominant patterns of Asian winter climate variability: the Siberian high (SH) pattern and the Asia–Arctic (AA) pattern. The former depicts atmospheric variability closely associated with the intensity of the Siberian high, and the latter characterizes the teleconnection pattern of atmospheric variability between Asia and the Arctic, which is distinct from the Arctic Oscillation (AO). The AA pattern plays more important roles in regulating winter precipitation and the 850-hPa meridional wind component over East Asia than the SH pattern, which controls surface air temperature variability over East Asia. In the Arctic Ocean and its marginal seas, sea ice loss in both autumn and winter could bring the positive phase of the SH pattern or cause the negative phase of the AA pattern. The latter corresponds to a weakened East Asian winter monsoon (EAWM) and enhanced winter precipitation in the midlatitudes of the Asian continent and East Asia. For the SH pattern, sea ice loss in the prior autumn emerges in the Siberian marginal seas, and winter loss mainly occurs in the Barents Sea, Labrador Sea, and Davis Strait. For the AA pattern, sea ice loss in the prior autumn is observed in the Barents–Kara Seas, the western Laptev Sea, and the Beaufort Sea, and winter loss only occurs in some areas of the Barents Sea, the Labrador Sea, and Davis Strait. Simulation experiments with observed sea ice forcing also support that Arctic sea ice loss may favor frequent occurrence of the negative phase of the AA pattern. The results also imply that the relationship between Arctic sea ice loss and winter atmospheric variability over East Asia is unstable, which is a challenge for predicting the EAWM based on Arctic sea ice loss.

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 Effects of sea ice cover on satellite-detected primary production in the Arctic Ocean

2016 ◽  
Vol 12 (11) ◽  
pp. 20160223 ◽  
Author(s):  
Mati Kahru ◽  
Zhongping Lee ◽  
B. Greg Mitchell ◽  
Cynthia D. Nevison
Keyword(s):  
Sea Ice ◽  
Primary Production ◽  
Arctic Ocean ◽  
Barents Sea ◽  
Net Primary Production ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Primary Input ◽  
High Productivity ◽  
The Arctic Ocean

The influence of decreasing Arctic sea ice on net primary production (NPP) in the Arctic Ocean has been considered in multiple publications but is not well constrained owing to the potentially large errors in satellite algorithms. In particular, the Arctic Ocean is rich in coloured dissolved organic matter (CDOM) that interferes in the detection of chlorophyll a concentration of the standard algorithm, which is the primary input to NPP models. We used the quasi-analytic algorithm (Lee et al . 2002 Appl. Opti. 41 , 5755−5772. ( doi:10.1364/AO.41.005755 )) that separates absorption by phytoplankton from absorption by CDOM and detrital matter. We merged satellite data from multiple satellite sensors and created a 19 year time series (1997–2015) of NPP. During this period, both the estimated annual total and the summer monthly maximum pan-Arctic NPP increased by about 47%. Positive monthly anomalies in NPP are highly correlated with positive anomalies in open water area during the summer months. Following the earlier ice retreat, the start of the high-productivity season has become earlier, e.g. at a mean rate of −3.0 d yr −1 in the northern Barents Sea, and the length of the high-productivity period has increased from 15 days in 1998 to 62 days in 2015. While in some areas, the termination of the productive season has been extended, owing to delayed ice formation, the termination has also become earlier in other areas, likely owing to limited nutrients.

scholarly journals Model-data comparison and data assimilation of mid-Holocene Arctic sea-ice concentration

2013 ◽  
Vol 9 (6) ◽  
pp. 6515-6549 ◽  
Author(s):  
F. Klein ◽  
H. Goosse ◽  
A. Mairesse ◽  
A. de Vernal
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
Atmospheric Circulation ◽  
Barents Sea ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Model Data ◽  
Sea Ice Concentration ◽  
Arctic Sea ◽  
Ice Concentration

Abstract. The consistency between a new quantitative reconstruction of Arctic sea-ice concentration based on dinocyst assemblages and the results of climate models has been investigated for the mid-Holocene. The comparison shows that the simulated sea-ice changes are weaker and spatially more homogeneous than the recorded ones. Furthermore, although the model-data agreement is relatively good in some regions such as the Labrador Sea, the skill of the models at local scale is low. The response of the models follows mainly the increase in summer insolation at large scale. This is modulated by changes in atmospheric circulation leading to differences between regions in the models that are albeit smaller than in the reconstruction. Performing simulations with data assimilation using the model LOVECLIM amplifies those regional differences, mainly through a reduction of the southward winds in the Barents Sea and an increase in the westerly winds in the Canadian Basin of the Arctic. This leads to an increase in the ice concentration in the Barents and Chukchi Seas and a better agreement with the reconstructions. This underlines the potential role of atmospheric circulation to explain the reconstructed changes during the Holocene.

scholarly journals Arctic sea ice anomalies during the MOSAiC winter 2019/20

2021 ◽  
Author(s):  
Klaus Dethloff ◽  
Wieslaw Maslowski ◽  
Stefan Hendricks ◽  
Younjoo Lee ◽  
Helge F. Goessling ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Barents Sea ◽  
Arctic Sea Ice ◽  
Heat Fluxes ◽  
The Arctic ◽  
Turbulent Heat ◽  
Central Arctic Ocean ◽  
Sea Ice Thickness ◽  
The Arctic Oscillation

Abstract. As the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) project went into effect during the winter of 2019/2020, the Arctic Oscillation (AO) has experienced some of the largest shifts from a highly negative index in November 2019 to an extremely positive index during January-February-March (JFM) 2020. Here we analyse the sea ice thickness (SIT) distribution based on CryoSat-2/SMOS satellite data augmented with results from the hindcast simulation by the fully coupled Regional Arctic System Model (RASM) for the time period from November 2019 through March 2020. A notable result of the positive AO phase during JFM 2020 were large SIT anomalies, up to 1.3 m, which emerged in the Barents-Sea (BS), along the northeastern Canadian coast and in parts of the central Arctic Ocean. These anomalies appear to be driven by nonlinear interactions between thermodynamic and dynamic processes. In particular, in the Barents- and Kara Seas (BKS) they are a result of an enhanced ice growth connected with the colder temperature anomalies and the consequence of intensified atmospheric-driven sea ice transport and deformations (i.e. divergence and shear) in this area. Low-pressure anomalies, which developed over the Eastern Arctic during JFM 2020, increased northerly winds from the cold Arctic Ocean to the BS and accelerated the southward drift of the MOSAiC ice floe. The satellite-derived and model-simulated sea ice velocity anomalies, which compared well during JFM 2020, indicate a strong acceleration of the Transpolar Drift relative to the mean for the past decade, with intensified speeds up to 6 km/day. As a consequence, sea ice transport and deformations driven by atmospheric wind forcing accounted for bulk of SIT anomalies, especially in January and February 2020. The unusual AO shift and the related sea ice anomalies during the MOSAiC winter 2019/20 are within the range of simulated states in the forecast ensemble. RASM intra-annual ensemble forecast simulations, forced with different atmospheric boundary conditions from November 1, 2019 through April 30, 2020, show a pronounced internally generated variability in the sea ice volume. A comparison of the respective SIT distribution and turbulent heat fluxes during the positive AO phase in JFM 2020 and the negative AO phase in JFM 2010 further corroborates the conclusion, that winter sea ice conditions of the Arctic Ocean can be significantly altered by AO variability.

Atmospheric and oceanic drivers of regional Arctic winter sea-ice variability in present and future climates

2021 ◽  
Author(s):  
Jakob Dörr ◽  
Marius Årthun ◽  
Tor Eldevik ◽  
Erica Madonna
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Heat Transport ◽  
Barents Sea ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ocean Heat Transport ◽  
The Pacific ◽  
Arctic Sea ◽  
The Arctic Ocean

<p>The recent retreat of Arctic sea ice area is overlaid by strong internal variability on all timescales. In winter, sea ice retreat and variability are currently dominated by the Barents Sea, primarily driven by variable ocean heat transport from the Atlantic. Climate models from the latest intercomparison project CMIP6 project that the future loss of winter Arctic sea ice spreads throughout the Arctic Ocean and, hence, that other regions of the Arctic Ocean will see increased sea-ice variability. It is, however, not known how the influence of ocean heat transport will change, and to what extent and in which regions other drivers, such as atmospheric circulation or river runoff into the Arctic Ocean, will become important. Using a combination of observations and simulations from the Community Earth System Model Large Ensemble (CESM-LE), we analyze and contrast the present and future regional drivers of the variability of the winter Arctic sea ice cover. We find that for the recent past, both observations and CESM-LE show that sea ice variability in the Atlantic and Pacific sector of the Arctic Ocean is influenced by ocean heat transport through the Barents Sea and Bering Strait, respectively. The two dominant modes of large-scale atmospheric variability – the Arctic Oscillation and the Pacific North American pattern – are only weakly related to recent regional sea ice variability. However, atmospheric circulation anomalies associated with regional sea ice variability show distinct patterns for the Atlantic and Pacific sectors consistent with heat and humidity transport from lower latitudes. In the future, under a high emission scenario, CESM-LE projects a gradual expansion of the footprint of the Pacific and Atlantic inflows, covering the whole Arctic Ocean by 2050-2079. This study highlights the combined importance of future Atlantification and Pacification of the Arctic Ocean and improves our understanding of internal climate variability which essential in order to predict future sea ice changes under anthropogenic warming.   </p><p> </p>

Autumn Arctic predictors and predictions for winter marine cold air outbreaks over the Barents Sea

2020 ◽  
Author(s):  
Iuliia Polkova ◽  
Hilla Afargan-Gerstman ◽  
Daniela Domeisen ◽  
Martin King ◽  
Paolo Ruggieri ◽  
...  
Keyword(s):  
Sea Ice ◽  
Large Scale ◽  
Cross Correlation ◽  
Barents Sea ◽  
Arctic Sea Ice ◽  
Prediction Skill ◽  
Cold Air ◽  
Cross Correlation Analysis ◽  
Arctic Sea ◽  
High Prediction

<p>The air temperature over Arctic sea ice can fall strongly below 0°C, while for adjacent areas of open water, sea surface temperature remains close to freezing. This creates a strong temperature gradient across the sea ice edge. Transports of cold air masses from the sea ice toward open ocean water, known as marine cold air outbreaks (MCAOs), modify vertical stability of the atmospheric column and thus can create conditions favorable for the formation of hazardous maritime cyclones (polar lows), which pose risks to marine and coastal infrastructure and society. For marine management, MCAO predictions would be highly beneficial. Previous studies analyze the genesis of MCAOs, while predictability and large-scale drivers of MCAOs remain poorly understood. </p><p><br>We investigate (i) the ability of the Earth System Model from the Max-Planck Institute for Meteorology (MPI-ESM) to predict MCAOs at a seasonal timescale and (ii) options to improve predictability of MCAOs through their large-scale drivers. To identify MCAO preconditions, we utilize the atmospheric reanalysis ERA-Interim using lagged cross-correlation analysis, composite analysis, and causal effect network (CEN).</p><p><br>Our results show that the MPI-ESM has high prediction skill for MCAOs over the Barents Sea (BS), Greenland-Iceland-Norwegian Seas and the Labrador Sea for about 2-2.5 weeks ahead starting from the November and February initial conditions. This holds for the prediction skill analyzed from daily model output. For MCAO properties such as extreme MCAO values occurring during a month, or the frequency of MCAO events per month, we find high prediction skill for up to a month ahead. Whereas the lagged cross-correlation analysis indicates a relationship between September and October atmospheric circulation and sea ice conditions with November BS-MCAOs, the CEN identifies the causal link only from the Arctic sea ice cover.</p>

Impact of wave-induced sea ice fragmentation on sea ice dynamics in the MIZ

2020 ◽  
Author(s):  
Guillaume Boutin ◽  
Timothy Williams ◽  
Pierre Rampal ◽  
Einar Olason ◽  
Camille Lique
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Extent ◽  
Ice Dynamics ◽  
Sea Ice Dynamics ◽  
Wave Induced ◽  
The Impact

<p>The decrease in Arctic sea ice extent is associated with an increase of the area where sea ice and open ocean interact, commonly referred to as the Marginal Ice Zone (MIZ). In this area, sea ice is particularly exposed to waves that can penetrate over tens to hundreds of kilometres into the ice cover. Waves are known to play a major role in the fragmentation of sea ice in the MIZ, and the interactions between wave-induced sea ice fragmentation and lateral melting have received particular attention in recent years. The impact of this fragmentation on sea ice dynamics, however, remains mostly unknown, although it is thought that fragmented sea ice experiences less resistance to deformation than pack ice. In this presentation, we will introduce a new coupled framework involving the spectral wave model WAVEWATCH III and the sea ice model neXtSIM, which includes a Maxwell-Elasto Brittle rheology. We use this coupled modelling system to investigate the potential impact of wave-induced sea ice fragmentation on sea ice dynamics. Focusing on the Barents Sea, we find that the decrease of the internal stress of sea ice resulting from its fragmentation by waves results in a more dynamical MIZ, in particular in areas where sea ice is compact. Sea ice drift is enhanced for both on-ice and off-ice wind conditions. Our results stress the importance of considering wave–sea-ice interactions for forecast applications. They also suggest that waves likely modulate the area of sea ice that is advected away from the pack by ocean (sub-)mesoscale eddies near the ice edge, potentially contributing to the observed past, current and future sea ice cover decline in the Arctic. </p>

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