scholarly journals Energy exchange processes in the marginal ice zone of the Barents Sea, Arctic Ocean, during spring 1999

2003 ◽  
Vol 49 (166) ◽  
pp. 415-419 ◽  
Author(s):  
Boris V. Ivanov ◽  
Sebastian Gerland ◽  
Jan-Gunnar Winther ◽  
Harvey Goodwin
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Energy Exchange ◽  
Barents Sea ◽  
Lower Boundary ◽  
Turbulent Heat ◽  
The Barents Sea ◽  
Exchange Processes ◽  
Marginal Ice Zone ◽  
Ice Conditions

AbstractWe present some new results describing energy exchange processes of drifting sea ice in the marginal ice zone (MIZ) in the Barents Sea, Arctic Ocean. All measurements and observations of meteorological parameters and ice conditions were taken on board the Norwegian research vessel Lance from 3 to 22 May 1999. Components of surface heat balance were measured and correlated with ice conditions and synoptic observations. These results can be used in atmospheric boundary layer modelling as lower boundary conditions. A relationship was found between modelled turbulent heat fluxes and observed sea-ice concentrations.

scholarly journals Observations of surface momentum exchange over the marginal-ice-zone and recommendations for its parameterization

2015 ◽  
Vol 15 (19) ◽  
pp. 26609-26660 ◽  
Author(s):  
A. D. Elvidge ◽  
I. A. Renfrew ◽  
A. I. Weiss ◽  
I. M. Brooks ◽  
T. A. Lachlan-Cope ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Sea Ice ◽  
Barents Sea ◽  
The Arctic ◽  
Momentum Exchange ◽  
The Barents Sea ◽  
Aircraft Observations ◽  
Marginal Ice Zone ◽  
Ice Conditions ◽  
Ice Fraction

Abstract. Comprehensive aircraft observations are used to characterise surface roughness over the Arctic marginal ice zone (MIZ) and consequently make recommendations for the parameterization of surface momentum exchange in the MIZ. These observations were gathered in the Barents Sea and Fram Strait from two aircraft as part of the Aerosol–Cloud Coupling And Climate Interactions in the Arctic (ACCACIA) project. They represent a doubling of the total number of such aircraft observations currently available over the Arctic MIZ. The eddy covariance method is used to derive estimates of the 10 m neutral drag coefficient (CDN10) from turbulent wind velocity measurements, and a novel method using albedo and surface temperature is employed to derive ice fraction. Peak surface roughness is found at ice fractions in the range 0.6 to 0.8 (with a mean interquartile range in CDN10 of 1.25 to 2.85 × 10−3). CDN10 as a function of ice fraction is found to be well approximated by the negatively skewed distribution provided by a leading parameterization scheme (Lüpkes et al., 2012) tailored for sea ice drag over the MIZ in which the two constituent components of drag – skin and form drag – are separately quantified. Current parameterization schemes used in the weather and climate models are compared with our results and the majority are found to be physically unjustified and unrepresentative. The Lüpkes et al. (2012) scheme is recommended in a computationally simple form, with adjusted parameter settings. A good agreement is found to hold for subsets of the data from different locations despite differences in sea ice conditions. Ice conditions in the Barents Sea, characterised by small, unconsolidated ice floes, are found to be associated with higher CDN10 values – especially at the higher ice fractions – than those of Fram Strait, where typically larger, smoother floes are observed. Consequently, the important influence of sea ice morphology and floe size on surface roughness is recognised, and improvement in the representation of this in parameterization schemes is suggested for future study.

scholarly journals Observations of surface momentum exchange over the marginal ice zone and recommendations for its parametrisation

2016 ◽  
Vol 16 (3) ◽  
pp. 1545-1563 ◽  
Author(s):  
A. D. Elvidge ◽  
I. A. Renfrew ◽  
A. I. Weiss ◽  
I. M. Brooks ◽  
T. A. Lachlan-Cope ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Sea Ice ◽  
Barents Sea ◽  
The Arctic ◽  
Momentum Exchange ◽  
The Barents Sea ◽  
Aircraft Observations ◽  
Marginal Ice Zone ◽  
Ice Conditions ◽  
Ice Fraction

Abstract. Comprehensive aircraft observations are used to characterise surface roughness over the Arctic marginal ice zone (MIZ) and consequently make recommendations for the parametrisation of surface momentum exchange in the MIZ. These observations were gathered in the Barents Sea and Fram Strait from two aircraft as part of the Aerosol–Cloud Coupling And Climate Interactions in the Arctic (ACCACIA) project. They represent a doubling of the total number of such aircraft observations currently available over the Arctic MIZ. The eddy covariance method is used to derive estimates of the 10 m neutral drag coefficient (CDN10) from turbulent wind velocity measurements, and a novel method using albedo and surface temperature is employed to derive ice fraction. Peak surface roughness is found at ice fractions in the range 0.6 to 0.8 (with a mean interquartile range in CDN10 of 1.25 to 2.85  ×  10−3). CDN10 as a function of ice fraction is found to be well approximated by the negatively skewed distribution provided by a leading parametrisation scheme (Lüpkes et al., 2012) tailored for sea-ice drag over the MIZ in which the two constituent components of drag – skin and form drag – are separately quantified. Current parametrisation schemes used in the weather and climate models are compared with our results and the majority are found to be physically unjustified and unrepresentative. The Lüpkes et al. (2012) scheme is recommended in a computationally simple form, with adjusted parameter settings. A good agreement holds for subsets of the data from different locations, despite differences in sea-ice conditions. Ice conditions in the Barents Sea, characterised by small, unconsolidated ice floes, are found to be associated with higher CDN10 values – especially at the higher ice fractions – than those of Fram Strait, where typically larger, smoother floes are observed. Consequently, the important influence of sea-ice morphology and floe size on surface roughness is recognised, and improvement in the representation of this in parametrisation schemes is suggested for future study.

Sea Ice Thickness Forecast Performance in the Barents Sea

2020 ◽  
Author(s):  
Laura Hume-Wright ◽  
Emma Fiedler ◽  
Nicolas Fournier ◽  
Joana Mendes ◽  
Ed Blockley ◽  
...  
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Forecast Model ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Forecast Performance ◽  
In Situ Data ◽  
The Barents Sea ◽  
Ice Conditions

Abstract The presence of sea ice has a major impact on the safety, operability and efficiency of Arctic operations and navigation. While satellite-based sea ice charting is routinely used for tactical ice management, the marine sector does not yet make use of existing operational sea ice thickness forecasting. However, data products are now freely available from the Copernicus Marine Environment Monitoring Service (CMEMS). Arctic asset managers and vessels’ crews are generally not aware of such products, or these have so far suffered from insufficient accuracy, verification, resolution and adequate format, in order to be well integrated within their existing decision-making processes and systems. The objective of the EU H2020 project “Safe maritime operations under extreme conditions: The Arctic case” (SEDNA) is to improve the safety and efficiency of Arctic navigation. This paper presents a component focusing on the validation of an adaption of the 7-day sea ice thickness forecast from the UK Met Office Forecast Ocean Assimilation Model (FOAM). The experimental forecast model assimilates the CryoSat-2 satellite’s ice freeboard daily data. Forecast skill is evaluated against unique in-situ data from five moorings deployed between 2015 and 2018 by the Barents Sea Metocean and Ice Network (BASMIN) Joint Industry Project. The study shows that the existing FOAM forecasts produce adequate results in the Barents Sea. However, while studies have shown the assimilation of CryoSat-2 data is effective for thick sea ice conditions, this did not improve forecasts for the thinner sea ice conditions of the Barents Sea.

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.

Transformation of Atlantic Water in the Barents Sea in winter: overview of “Transarctika-2019” cruise results

2020 ◽  
Author(s):  
Vladimir Ivanov ◽  
Ivan Frolov ◽  
Kirill Filchuk
Keyword(s):  
Characteristic Feature ◽  
Sea Ice ◽  
Arctic Ocean ◽  
Water Column ◽  
Barents Sea ◽  
Atlantic Water ◽  
The Arctic ◽  
The Barents Sea ◽  
Open Sea ◽  
The Arctic Ocean

<p>In the recent few years the topic of accelerated sea ice loss, and related changes in the vertical structure of water masses in the East-Atlantic sector of the Arctic Ocean, including the Barents Sea and the western part of the Nansen Basin, has been in the foci of multiple studies. This region even earned the name the “Arctic warming hotspot”, due to the extreme retreat of sea ice and clear signs of change in the vertical hydrographic structure from the Arctic type to the sub-Arctic one. A gradual increase in temperature and salinity in this area has been observed since the mid-2000s. This trend is hypothetically associated with a general decrease in the volume of sea ice in the Arctic Ocean, which leads to a decrease of ice import in the Barents Sea, salinization, weakening of density stratification, intensification of vertical mixing and an increase of heat and salt fluxes from the deep to the upper mixed layer. The result of such changes is a further reduction of sea ice, i.e. implementation of positive feedback, which is conventionally refereed as the “atlantification. Due to the fact that the Barents Sea is a relatively shallow basin, the process of atlantification might develop here much faster than in the deep Nansen Basin. Thus, theoretically, the hydrographic regime in the northern part of the Barents Sea may rapidly transform to a “Nordic Seas – wise”, a characteristic feature of which is the year-round absence of the ice cover with debatable consequences for the climate and ecosystem of the region and adjacent land areas. Due to the obvious reasons, historical observations in the Barents Sea mostly cover the summer season. Here we present a rare oceanographic data, collected during the late winter - early spring in 2019. Measurements were occupied at four sequential oceanographic surveys from the boundary between the Norwegian Sea and the Barents Sea – the so called Barents Sea opening to the boundary between the Barents Sea and the Kara Sea. Completed hydrological sections allowed us to estimate the contribution of the winter processes in the Atlantic Water transformation at the end of the winter season. Characteristic feature of the observed transformation is the homogenization of the near-to-bottom part of the water column with remaining stratification in the upper part. A probable explanation of such changes is the dominance of shelf convection and cascading of dense water over the open sea convection. In this case, complete homogenization of the water column does not occur, since convection in the open sea is impeded by salinity and density stratification, which is maintained by melting of the imported sea ice in the relatively warm water. The study was supported by RFBR grant # 18-05-60083.</p>

Benthic Cycling of Phosphate in the changing Arctic Ocean

2021 ◽  
Author(s):  
Constance Lefebvre ◽  
Felipe S. Freitas ◽  
Katharine Hendry ◽  
Sandra Arndt
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Primary Productivity ◽  
Barents Sea ◽  
Atlantic Water ◽  
Iron Cycling ◽  
Reactive Iron ◽  
The Barents Sea ◽  
P Cycling ◽  
Model Approach

<p>The Arctic Ocean is currently experiencing rapid oceanographic shifts and significant sea-ice loss as a result of regional atmospheric and oceanic warming. The Barents Sea is a notable example of these phenomena, having seen a near 40% decline of its April sea-ice extent since 1979, and a progressive northward expansion of Atlantic Water (i.e., Atlantification). Such changes affect primary productivity and nutrient cycling in ways that remain poorly understood. Longer ice-free periods and the inflow of warmer Atlantic Water are expected to lead to extended bloom seasons on short, near-future timescales and therefore increase nutrient uptake in upper water layers. The benthic recycling of nutrients is believed to play an important part in replenishing nutrient inventories in overlying waters thus maintaining high primary productivity over the continuously expanding growth season. Therefore, it is crucial to increase our understanding of nutrient dynamic controls in changing oceans to make more accurate predictions and decipher the complex feedbacks involved in these evolving environments. However, most efforts to constrain and quantify nutrient fluxes so far have been directed at silicon, nitrogen or iron. This study aims to provide specific insight into phosphorus (P) cycling through its response to OM fluctuations and coupling with iron cycling. An integrated data-model approach was used to investigate the dynamics of P cycling at the sediment-water interface across five locations along the 30°E meridian that were drilled in the framework of the ChAOS project in the Barents Sea. The model approach allowed to explore the sensitivity of P cycling to plausible ranges of reactive iron and OM inputs. Greater inputs of reactive iron were found to decrease benthic phosphate fluxes (J<sub>PO4</sub>) whereas greater inputs of OM increased phosphate return to the water column. The quality of these inputs is equally significant: J<sub>PO4 </sub>decreased when iron hydroxides were made more reactive and increased with more reactive OM. Our findings indicate that variation in climatically sensitive processes, such as burial of terrestrial sediments and iron cycling, could represent powerful feedbacks on J<sub>PO4</sub> through adsorption/desorption mechanisms. Results also reveal significant oceanographic controls on J<sub>PO4</sub>, suggesting Atlantification of the Barents Sea will play into future phosphate availability.</p>

A biomarker-based reconstruction of sea ice conditions for the Barents Sea in recent centuries

The Holocene ◽  
2010 ◽  
Vol 20 (4) ◽  
pp. 637-643 ◽  
Author(s):  
Lindsay L. Vare ◽  
Guillaume Massé ◽  
Simon T. Belt
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
The Barents Sea ◽  
Ice Conditions

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.

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