Observed Atmospheric Coupling between Barents Sea Ice and the Warm-Arctic Cold-Siberian Anomaly Pattern

2016 ◽  
Vol 29 (2) ◽  
pp. 495-511 ◽  
Author(s):  
Svetlana A. Sorokina ◽  
Camille Li ◽  
Justin J. Wettstein ◽  
Nils Gunnar Kvamstø
Keyword(s):  
Sea Ice ◽  
Large Scale ◽  
Barents Sea ◽  
Winter Season ◽  
Surface Air Temperature ◽  
Turbulent Heat Flux ◽  
Turbulent Heat ◽  
Atmospheric Variability ◽  
Atmospheric Flow ◽  
Anomaly Pattern

Abstract The decline in Barents Sea ice has been implicated in forcing the “warm-Arctic cold-Siberian” (WACS) anomaly pattern via enhanced turbulent heat flux (THF). This study investigates interannual variability in winter [December–February (DJF)] Barents Sea THF and its relationship to Barents Sea ice and the large-scale atmospheric flow. ERA-Interim and observational data from 1979/80 to 2011/12 are used. The leading pattern (EOF1: 33%) of winter Barents Sea THF variability is relatively weakly correlated (r = 0.30) with Barents Sea ice and appears to be driven primarily by atmospheric variability. The sea ice–related THF variability manifests itself as EOF2 (20%, r = 0.60). THF EOF2 is robust over the entire winter season, but its link to the WACS pattern is not. However, the WACS pattern emerges consistently as the second EOF (20%) of Eurasian surface air temperature (SAT) variability in all winter months. When Eurasia is cold, there are indeed weak reductions in Barents Sea ice, but the associated THF anomalies are on average negative, which is inconsistent with the proposed direct atmospheric response to sea ice variability. Lead–lag correlation analyses on shorter time scales support this conclusion and indicate that atmospheric variability plays an important role in driving observed variability in Barents Sea THF and ice cover, as well as the WACS pattern.

scholarly journals The Winter Atmospheric Response to Sea Ice Anomalies in the Barents Sea

2014 ◽  
Vol 27 (2) ◽  
pp. 914-924 ◽  
Author(s):  
Jessica Liptak ◽  
Courtenay Strong
Keyword(s):  
Sea Ice ◽  
Large Scale ◽  
Barents Sea ◽  
Turbulent Heat Flux ◽  
Turbulent Heat ◽  
Atmospheric Response ◽  
Sea Ice Concentration ◽  
The North ◽  
The Barents Sea ◽  
The North Atlantic Oscillation

Abstract The atmospheric response to sea ice anomalies over the Barents Sea during winter was determined by boundary forcing the Community Atmosphere Model (CAM) with daily varying high and low sea ice concentration (SIC) anomalies that decreased realistically from December to February. The high- and low-SIC anomalies produced localized opposite-signed responses of surface turbulent heat flux and wind stress that decreased in magnitude and extent as winter progressed. Responses of sea level pressure (SLP) and 500-mb height evolved from localized, opposite-signed features into remarkably similar large-scale patterns resembling the negative phase of the North Atlantic Oscillation (NAO). Hilbert empirical orthogonal function (HEOF) analysis of the composite high-SIC and low-SIC SLP responses uncovered how they differed. The hemispheric pattern in the leading HEOF was similar for the high-SIC and low-SIC responses, but the high-SIC response cycled through the pattern once per winter, whereas the low-SIC response cycled through the pattern twice per winter. The second HEOF differed markedly between the responses, with the high-SIC response featuring zonally oriented Atlantic and Pacific wave features and the low-SIC response featuring a meridionally oriented Atlantic dipole pattern.

Relative contributions of internal atmospheric variability and surface processes to the interannual variations in wintertime Arctic surface air temperatures

2021 ◽  
pp. 1-48
Author(s):  
Fengmin Wu ◽  
Wenkai Li ◽  
Peng Zhang ◽  
Wei Li
Keyword(s):  
Sea Ice ◽  
Large Scale ◽  
Surface Air Temperature ◽  
Surface Processes ◽  
The Arctic ◽  
Interannual Variations ◽  
Atmospheric Variability ◽  
Near Surface ◽  
Atmospheric Circulations ◽  
Downward Longwave Radiation

AbstractSuperimposed on a warming trend, Arctic winter surface air temperature (SAT) exhibits substantial interannual variability, whose underlying mechanisms are unclear, especially regarding the role of sea-ice variations and atmospheric processes. Here, atmospheric reanalysis data and idealized atmospheric model simulations are used to reveal the mechanisms by which sea-ice variations and atmospheric anomalous conditions affect interannual variations in wintertime Arctic SAT. Results show that near-surface interannual warming in the Arctic is accompanied by comparable warming throughout large parts of the Arctic troposphere and large-scale anomalous atmospheric circulation patterns. Within the Arctic, changes in large-scale atmospheric circulations due to internal atmospheric variability explain a substantial fraction of interannual variation in SAT and tropospheric temperatures, which lead to an increase in moisture and downward longwave radiation, with the rest likely coming from sea ice-related and other surface processes. Arctic winter sea-ice loss allows the ocean to release more heat and moisture, which enhances Arctic warming; however, this effect on SAT is confined to the ice-retreat area and has a limited influence on large-scale atmospheric circulations.

scholarly journals Mechanism of Seasonal Arctic Sea Ice Evolution and Arctic Amplification

2016 ◽  
Author(s):  
Kwang-Yul Kim ◽  
Benjamin D. Hamlington ◽  
Hanna Na ◽  
Jinju Kim
Keyword(s):  
Heat Flux ◽  
Sea Ice ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Longwave Radiation ◽  
The Arctic ◽  
Turbulent Heat ◽  
Arctic Amplification ◽  
Ice Melting ◽  
Downward Longwave Radiation

Abstract. Sea ice melting is proposed as a primary reason for the Artic amplification, although physical mechanism of the Arctic amplification and its connection with sea ice melting is still in debate. In the present study, monthly ERA-interim reanalysis data are analyzed via cyclostationary empirical orthogonal function analysis to understand the seasonal mechanism of sea ice melting in the Arctic Ocean and the Arctic amplification. While sea ice melting is widespread over much of the perimeter of the Arctic Ocean in summer, sea ice remains to be thin in winter only in the Barents-Kara Seas. Excessive turbulent heat flux through the sea surface exposed to air due to sea ice melting warms the atmospheric column. Warmer air increases the downward longwave radiation and subsequently surface air temperature, which facilitates sea surface remains to be ice free. A 1 % reduction in sea ice concentration in winter leads to ~ 0.76 W m−2 increase in upward heat flux, ~ 0.07 K increase in 850 hPa air temperature, ~ 0.97 W m−2 increase in downward longwave radiation, and ~ 0.26 K increase in surface air temperature. This positive feedback mechanism is not clearly observed in the Laptev, East Siberian, Chukchi, and Beaufort Seas, since sea ice refreezes in late fall (November) before excessive turbulent heat flux is available for warming the atmospheric column in winter. A detailed seasonal heat budget is presented in order to understand specific differences between the Barents-Kara Seas and Laptev, East Siberian, Chukchi, and Beaufort Seas.

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

2015 ◽  
Vol 28 (14) ◽  
pp. 5477-5509 ◽  
Author(s):  
Mitchell Bushuk ◽  
Dimitrios Giannakis ◽  
Andrew J. Majda
Keyword(s):  
Sea Ice ◽  
Large Scale ◽  
Climate Model ◽  
Surface Air Temperature ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Arctic Sea ◽  
Spring Sea Ice ◽  
Phase Relationships

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.

The Role of Moist Intrusions in Winter Arctic Warming and Sea Ice Decline

2016 ◽  
Vol 29 (12) ◽  
pp. 4473-4485 ◽  
Author(s):  
Cian Woods ◽  
Rodrigo Caballero
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Surface Air Temperature ◽  
The Arctic ◽  
Positive Trend ◽  
Sea Ice Concentration ◽  
Local Conditions ◽  
The Barents Sea ◽  
Strong Inversion ◽  
Ice Concentration

Abstract This paper examines the trajectories followed by intense intrusions of moist air into the Arctic polar region during autumn and winter and their impact on local temperature and sea ice concentration. It is found that the vertical structure of the warming associated with moist intrusions is bottom amplified, corresponding to a transition of local conditions from a “cold clear” state with a strong inversion to a “warm opaque” state with a weaker inversion. In the marginal sea ice zone of the Barents Sea, the passage of an intrusion also causes a retreat of the ice margin, which persists for many days after the intrusion has passed. The authors find that there is a positive trend in the number of intrusion events crossing 70°N during December and January that can explain roughly 45% of the surface air temperature and 30% of the sea ice concentration trends observed in the Barents Sea during the past two decades.

scholarly journals Estimation of Degree of Sea Ice Ridging Based on Dual-Polarized C-band SAR Data

2017 ◽  
Author(s):  
Alexandru Gegiuc ◽  
Markku Similä ◽  
Juha Karvonen ◽  
Mikko Lensu ◽  
Marko Mäkynen ◽  
...  
Keyword(s):  
Sea Ice ◽  
Baltic Sea ◽  
Large Scale ◽  
Winter Season ◽  
Texture Features ◽  
Route Optimization ◽  
Sea Ice Concentration ◽  
Essential Information ◽  
Ice Concentration ◽  
Dual Polarized

Abstract. For navigation in Baltic Sea ice during winter season, parameters such as ice edge, ice concentration, ice thickness, ice drift and degree of ridging are usually reported daily in the manually prepared Ice Charts, which provide icebreakers essential information for route optimization and fuel calculations. However, manual ice charting requires long analysis times and detailed analysis is not possible for large scale maps (e.g. Arctic Ocean). Here, we propose a method for automatic estimation of degree of ridging density in the Baltic Sea region, based on RADARSAT-2 C-band dual-polarized (HH/HV channels) SAR texture features and the sea ice concentration information extracted from the Finnish Ice Charts. The SAR images were first segmented and then several texture features were extracted for each
 segment. Using the Random Forest classification, we classified them into four classes of ridging intensity and compared them to the reference data extracted from the digitized Ice Charts. The overall agreement between the ice chart based degree of ice ridging (DIR) and the automated results varied monthly, being 83 %, 63 % and 81 % in January, February and March 2013, respectively. The correspondence between the degree of ice riding of the manual Ice Charts and the actual ridge density was good when this issue was studied based on an extensive field campaign data in March 2011.

scholarly journals Classification of sea ice types in Sentinel-1 synthetic aperture radar images

2020 ◽  
Vol 14 (8) ◽  
pp. 2629-2645
Author(s):  
Jeong-Won Park ◽  
Anton Andreevich Korosov ◽  
Mohamed Babiker ◽  
Joong-Sun Won ◽  
Morten Wergeland Hansen ◽  
...  
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Winter Season ◽  
Texture Features ◽  
Open Water ◽  
First Year ◽  
Validation Data ◽  
Manual Work ◽  
Time Service ◽  
Radar Images

Abstract. A new Sentinel-1 image-based sea ice classification algorithm using a machine-learning-based model trained in a semi-automated manner is proposed to support daily ice charting. Previous studies mostly rely on manual work in selecting training and validation data. We show that the readily available ice charts from the operational ice services can reduce the amount of manual work in preparation of large amounts of training/testing data. Furthermore, they can feed highly reliable data to the trainer by indirectly exploiting the best ability of the sea ice experts working at the operational ice services. The proposed scheme has two phases: training and operational. Both phases start from the removal of thermal, scalloping, and textural noise from Sentinel-1 data and calculation of grey level co-occurrence matrix and Haralick texture features in a sliding window. In the training phase, the weekly ice charts are reprojected into the SAR image geometry. A random forest classifier is trained with the texture features on input and labels from the rasterized ice charts on output. Then, the trained classifier is directly applied to the texture features from Sentinel-1 images operationally. Test results from the two datasets spanning winter (January–March) and summer (June–August) seasons acquired over the Fram Strait and the Barents Sea showed that the classifier is capable of retrieving three generalized cover types (open water, mixed first-year ice, old ice) with overall accuracies of 87 % and 67 % in winter and summer seasons, respectively. For the summer season, the classifier failed in distinguishing mixed first-year ice from old ice with accuracy of only 12 %; however, it performed rather like an ice–water discriminator with high accuracy of 98 % as the misclassification between the mixed first-year ice and old ice was between them. The accuracy for five cover types (open water, new ice, young ice, first-year ice, old ice) in the winter season was 60 %. The errors are attributed both to incorrect manual classification on the ice charts and to the semi-automated algorithm. Finally, we demonstrate the potential for near-real-time service of the ice map using daily mosaicked Sentinel-1 images.

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.

scholarly journals Interactions between Antarctic sea ice and large-scale atmospheric modes in CMIP5 models

2016 ◽  
Author(s):  
Serena Schroeter ◽  
Will Hobbs ◽  
Nathaniel L. Bindoff
Keyword(s):  
Sea Ice ◽  
Large Scale ◽  
Coupled Model ◽  
Southern Annular Mode ◽  
Cmip5 Models ◽  
Atmospheric Variability ◽  
Annular Mode ◽  
Antarctic Sea Ice ◽  
Ice Retreat ◽  
Sea Ice Retreat

Abstract. The response of Antarctic sea ice to large-scale patterns of atmospheric variability varies according to sea ice sector and season. In this study, interannual atmosphere-sea ice interactions were explored using observation-based data and compared with simulated interactions by models in the Coupled Model Intercomparison Project Phase 5. Simulated relationships between atmospheric variability and sea ice variability generally reproduced the observed relationships, though more closely during the season of sea ice advance than the season of sea ice retreat. Atmospheric influence on sea ice is known to be strongest during its advance, with the ocean emerging as a dominant driver of sea ice retreat; therefore, while it appears that models are able to capture the dominance of the atmosphere during advance, simulations of ocean-atmosphere-sea ice interactions during retreat require further investigation. A large proportion of model ensemble members overestimated the relative importance of the Southern Annular Mode compared with other modes on high southern latitude climate, while the influence of tropical forcing was underestimated. This result emerged particularly strongly during the season of sea ice retreat. The amplified zonal patterns of the Southern Annular Mode in many models and its exaggerated influence on sea ice overwhelm the comparatively underestimated meridional influence, suggesting that simulated sea ice variability would become more zonally symmetric as a result. Across the seasons of sea ice advance and retreat, 3 of the 5 sectors did not reveal a strong relationship with a pattern of large-scale atmospheric variability in one or both seasons, indicating that sea ice in these sectors may be influenced more strongly by atmospheric variability unexplained by the major atmospheric modes, or by heat exchange in the ocean.

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