Sea surface wind and Sea ice in the Barents Sea using microwave sensing data from Meteor-M N1 and GCOM-W1 satellites in January–March 2013

2016 ◽  
Vol 52 (9) ◽  
pp. 1041-1050 ◽  
Author(s):  
L. M. Mitnik ◽  
M. L. Mitnik ◽  
G. M. Chernyavsky ◽  
I. V. Cherny ◽  
A. V. Vykochko ◽  
...  
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Surface Wind ◽  
Sea Surface ◽  
Sensing Data ◽  
Sea Surface Wind ◽  
The Barents Sea

Palynological evidence of sea-surface conditions in the Barents Sea off northeast Svalbard during the postglacial period

2020 ◽  
pp. 1-15
Author(s):  
Camille Brice ◽  
Anne de Vernal ◽  
Elena Ivanova ◽  
Simon van Bellen ◽  
Nicolas Van Nieuwenhove
Keyword(s):  
Sea Ice ◽  
Surface Waters ◽  
Barents Sea ◽  
Ice Cover ◽  
Atlantic Water ◽  
Transitional Period ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Conditions ◽  
The Barents Sea

Abstract Postglacial changes in sea-surface conditions, including sea-ice cover, summer temperature, salinity, and productivity were reconstructed from the analyses of dinocyst assemblages in core S2528 collected in the northwestern Barents Sea. The results show glaciomarine-type conditions until about 11,300 ± 300 cal yr BP and limited influence of Atlantic water at the surface into the Barents Sea possibly due to the proximity of the Svalbard-Barents Sea ice sheet. This was followed by a transitional period generally characterized by cold conditions with dense sea-ice cover and low-salinity pulses likely related to episodic freshwater or meltwater discharge, which lasted until 8700 ± 700 cal yr BP. The onset of “interglacial” conditions in surface waters was marked by a major change in dinocyst assemblages, from dominant heterotrophic to dominant phototrophic taxa. Until 4100 ± 150 cal yr BP, however, sea-surface conditions remained cold, while sea-surface salinity and sea-ice cover recorded large amplitude variations. By ~4000 cal yr BP optimum sea-surface temperature of up to 4°C in summer and maximum salinity of ~34 psu suggest enhanced influence of Atlantic water, and productivity reached up to 150 gC/m2/yr. After 2200 ± 1300 cal yr BP, a distinct cooling trend accompanied by sea-ice spreading characterized surface waters. Hence, during the Holocene, with exception of an interval spanning about 4000 to 2000 cal yr BP, the northern Barents Sea experienced harsh environments, relatively low productivity, and unstable conditions probably unsuitable for human settlements.

Climatic trends of sea surface temperature and sea ice concentration in the Barents Sea

2021 ◽  
Author(s):  
Bayoumy Mohamed ◽  
Frank Nilsen ◽  
Ragnheid Skogseth
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Seasonal Cycle ◽  
Barents Sea ◽  
Warming Trend ◽  
Sea Surface ◽  
Sea Ice Concentration ◽  
The Barents Sea ◽  
Ice Concentration

<p>Sea ice loss in the Arctic region is an important indicator for climate change. Especially in the Barents Sea, which is expected to be free of ice by the mid of this century (Onarheim et al., 2018). Here, we analyze 38 years (1982-2019) of daily gridded sea surface temperature (SST) and sea ice concentration (SIC) from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) project. These data sets have been used to investigate the seasonal cycle and linear trends of SST and SIC, and their spatial distribution in the Barents Sea. From the SST seasonal cycle analysis, we have found that most of the years that have temperatures above the climatic mean (1982-2019) were recorded after 2000. This confirms the warm transition that has taken place in the Barents Sea over the last two decades. The year 2016 was the warmest year in both winter and summer during the study period.   </p><p>Results from the linear trend analysis reveal an overall statistically significant warming trend for the whole Barents Sea of about 0.33±0.03 °C/decade, associated with a sea ice reduction rate of about -4.9±0.6 %/decade. However, the SST trend show a high spatial variability over the Barents Sea. The highest SST trend was found over the eastern part of the Barents Sea and south of Svalbard (Storfjordrenna Trough), while the Northern Barents Sea shows less distinct and non-significant trends. The largest negative trend of sea ice was observed between Novaya Zemlya and Franz Josef Land. Over the last two decades (2000-2019), the data show an amplified warming trend in the Barents Sea where the SST warming trend has increased dramatically (0.46±0.09 °C/decade) and the SIC is here decreasing with rate of about -6.4±1.5 %/decade.  Considering the current development of SST, if this trend persists, the Barents Sea annual mean SST will rise by around 1.4 °C by the end of 2050, which will have a drastic impact on the loss of sea ice in the Barents Sea.   </p><p> </p><p>Keywords: Sea surface temperature; Sea ice concentration; Trend analysis; Barents Sea</p>

scholarly journals Seasonal Prediction from Arctic Sea Surface Temperatures: Opportunities and Pitfalls

2018 ◽  
Vol 31 (20) ◽  
pp. 8197-8210 ◽  
Author(s):  
Erik W. Kolstad ◽  
Marius Årthun
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Arctic Sea Ice ◽  
Sea Surface ◽  
Sea Ice Extent ◽  
Norwegian Sea ◽  
The Barents Sea ◽  
Arctic Sea ◽  
Lagged Correlation ◽  
Sst Anomalies

Arctic sea ice extent and sea surface temperature (SST) anomalies have been shown to be skillful predictors of weather anomalies in the midlatitudes on the seasonal time scale. In particular, below-normal sea ice extent in the Barents Sea in fall has sometimes preceded cold winters in parts of Eurasia. Here we explore the potential for predicting seasonal surface air temperature (SAT) anomalies in Europe from seasonal SST anomalies in the Nordic seas throughout the year. First, we show that fall SST anomalies not just in the Barents Sea but also in the Norwegian Sea have the potential to predict wintertime SAT anomalies in Europe. Norwegian Sea SST anomalies in spring are also significant predictors of European SAT anomalies in summer. Second, we demonstrate that the potential for prediction is sensitive to trends in the data. In particular, the lagged correlation between Norwegian Sea SST anomalies in spring and European SAT anomalies in summer is considerably higher for raw data than linearly detrended data, largely due to warming SST and SAT trends in recent decades. Third, we show that the potential for prediction has not been stationary in time. One key result is that, according to two twentieth-century reanalyses, the strength of the negative lagged correlation between Barents Sea SST anomalies in fall and European SAT anomalies in winter after 1979 is unprecedented since 1900.

scholarly journals A Model-Based Decomposition of the Sea Ice–Atmosphere Feedback over the Barents Sea during Winter

2014 ◽  
Vol 27 (7) ◽  
pp. 2533-2544 ◽  
Author(s):  
Jessica Liptak ◽  
Courtenay Strong
Keyword(s):  
Sea Ice ◽  
Positive Feedback ◽  
Barents Sea ◽  
Initial Conditions ◽  
Surface Wind ◽  
Atmospheric Temperature ◽  
Modeling Framework ◽  
Sea Ice Concentration ◽  
Positive Feedbacks ◽  
The Barents Sea

Abstract The feedback between Barents Sea ice and the winter atmosphere was studied in a modeling framework by decomposing it into two sequential boundary forcing experiments. The Community Ice Code (CICE) model was initialized with anomalously high sea ice concentration (SIC) over the Barents Sea and forced with an atmosphere produced by positive SIC anomalies, and CICE was initialized with low Barents Sea SIC and forced with an atmosphere produced by negative SIC anomalies. Corresponding control runs were produced by exposing the same SIC initial conditions to climatological atmospheres, and the monthly mean sea ice response showed a positive feedback over the Barents Sea for both experiments: the atmosphere produced by positive SIC anomalies increased SIC over the Barents Sea during the winter, and the atmosphere produced by negative SIC anomalies decreased SIC. These positive feedbacks were driven primarily by thermodynamic forcing from surface longwave flux anomalies and were weakened somewhat by atmospheric temperature advection. Dynamical effects also opposed the positive feedback, with enhanced surface wind stress divergence over the Barents Sea in the high-SIC case and enhanced convergence in the low-SIC case.

scholarly journals Retrieval of Sea Surface Wind Speed from Spaceborne SAR over the Arctic Marginal Ice Zone with a Neural Network

2020 ◽  
Author(s):  
Xiao-ming Li ◽  
Tingting Qin ◽  
Ke Wu
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Sea Ice ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Sea Surface ◽  
The Arctic ◽  
Sea Surface Wind Speed ◽  
Sea Surface Wind ◽  
The Neural Network

In this paper, we presented a method of retrieving sea surface wind speed from Sentinel-1 synthetic aperture radar (SAR) horizontal-horizontal (HH) polarization data in extra-wide mode, which have been extensively acquired over the Arctic for sea ice monitoring. In contrast to the conventional algorithm, i.e., using a geophysical model function (GMF) to retrieve sea surface wind by spaceborne SAR, we introduced an alternative method based on physical model guided neural network. Parameters of SAR normalized radar cross section, incidence angle, and wind direction are used as the inputs of the backward propagation (BP) neural network, and the output is the sea surface wind speed. The network is developed based on more than 11,000 HH-polarized EW images acquired in the marginal ice zone (MIZ) of the Arctic and their collocations with scatterometer measurements. Verification of the neural network based on the testing dataset yields a bias of 0.23 m/s and a root mean square error (RMSE) of 1.25 m/s compared to the scatterometer wind speed. Further comparison of the SAR retrieved sea surface wind speed with independent buoy measurements shows a bias and RMSE of 0.12 m/s and 1.42 m/s, respectively. We also analyzed the uncertainty of retrieval when the wind direction data of a reanalysis model are used as inputs to the neural network. By combining the detected sea ice cover information based on the EW data, one can expect to derive simultaneously sea ice and marine-meteorological parameters by spaceborne SAR in a high spatial resolution in the Arctic.

Associations among temperature, sea ice and phytoplankton bloom dynamics in the Barents Sea

2020 ◽  
Vol 635 ◽  
pp. 25-36 ◽  
Author(s):  
K Dong ◽  
ØK Kvile ◽  
NC Stenseth ◽  
LC Stige
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Barents Sea ◽  
Phytoplankton Bloom ◽  
Sea Surface ◽  
The Barents Sea ◽  
Ice Retreat ◽  
Sea Ice Retreat ◽  
Time Substitution

Variations in physical conditions caused by climate change are likely to have large influences on marine organisms, including phytoplankton. Here, we investigated associations between satellite-derived chlorophyll a data from the Barents Sea and 2 key abiotic factors: sea surface temperature and sea-ice concentration. Specifically, we investigated how climate variability, through the measured physical factors, associated with phytoplankton phenology between 1998 and 2014. Associations between sea surface temperature and phytoplankton bloom dynamics differed depending on the area. The spring phytoplankton bloom occurred earlier and had higher magnitude in warm compared to cold years in the northern part of the Barents Sea, but there was no significant association in the southern part. In seasonally ice-covered regions, the association between the timing of the sea-ice retreat and the phytoplankton peak was nonlinear: sea-ice retreat time before mid-May was not associated with bloom timing, whereas the phytoplankton bloom occurred before or immediately following the ice retreat when the ice retreated after mid-May. Although drivers that are relatively constant across years, such as insolation, probably influenced the spatial gradient in chlorophyll, a space-for-time substitution captured the predicted effects of sea-ice retreat on the timing and magnitude of the phytoplankton bloom quite well.

scholarly journals Does Arctic warming reduce preservation of organic matter in Barents Sea sediments?

2020 ◽  
Vol 378 (2181) ◽  
pp. 20190364 ◽  
Author(s):  
Johan C. Faust ◽  
Mark A. Stevenson ◽  
Geoffrey D. Abbott ◽  
Jochen Knies ◽  
Allyson Tessin ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Sea Ice ◽  
Surface Sediments ◽  
Barents Sea ◽  
Ice Cover ◽  
Atlantic Water ◽  
Sea Surface ◽  
Arctic Warming ◽  
Reactive Iron ◽  
The Barents Sea

Over the last few decades, the Barents Sea experienced substantial warming, an expansion of relatively warm Atlantic water and a reduction in sea ice cover. This environmental change forces the entire Barents Sea ecosystem to adapt and restructure and therefore changes in pelagic–benthic coupling, organic matter sedimentation and long-term carbon sequestration are expected. Here we combine new and existing organic and inorganic geochemical surface sediment data from the western Barents Sea and show a clear link between the modern ecosystem structure, sea ice cover and the organic carbon and CaCO 3 contents in Barents Sea surface sediments. Furthermore, we discuss the sources of total and reactive iron phases and evaluate the spatial distribution of organic carbon bound to reactive iron. Consistent with a recent global estimate we find that on average 21.0 ± 8.3 per cent of the total organic carbon is associated to reactive iron (fOC-Fe R ) in Barents Sea surface sediments. The spatial distribution of fOC-Fe R , however, seems to be unrelated to sea ice cover, Atlantic water inflow or proximity to land. Future Arctic warming might, therefore, neither increase nor decrease the burial rates of iron-associated organic carbon. However, our results also imply that ongoing sea ice reduction and the associated alteration of vertical carbon fluxes might cause accompanied shifts in the Barents Sea surface sedimentary organic carbon content, which might result in overall reduced carbon sequestration in the future. This article is part of the theme issue ‘The changing Arctic Ocean: consequences for biological communities, biogeochemical processes and ecosystem functioning’.

scholarly journals Satellite ice extent, sea surface temperature, and atmospheric methane trends in the Barents and Kara Seas

2018 ◽  
Author(s):  
Ira Leifer ◽  
F. Robert Chen ◽  
Thomas McClimans ◽  
Frank Muller Karger ◽  
Leonid Yurganov
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Barents Sea ◽  
Mixed Layer Depth ◽  
Kara Sea ◽  
Sea Surface ◽  
The South ◽  
The Barents Sea ◽  
Barents And Kara Seas

Abstract. Over a decade (2003–2015) of satellite data of sea-ice extent, sea surface temperature (SST), and methane (CH4) concentrations in lower troposphere over 10 focus areas within the Barents and Kara Seas (BKS) were analyzed for anomalies and trends relative to the Barents Sea. Large positive CH4 anomalies were discovered around Franz Josef Land (FJL) and offshore west Novaya Zemlya in early fall. Far smaller CH4 enhancement was found around Svalbard, downstream and north of known seabed seepage. SST increased in all focus areas at rates from 0.0018 to 0.15 °C yr−1, CH4 growth spanned 3.06 to 3.49 ppb yr−1. The strongest SST increase was observed each year in the southeast Barents Sea in June due to strengthening of the warm Murman Current (MC), and in the south Kara Sea in September. The southeast Barents Sea, the south Kara Sea and coastal areas around FJL exhibited the strongest CH4 growth over the observation period. Likely sources are CH4 seepage from subsea permafrost and hydrate thawing and the petroleum reservoirs underlying the central and east Barents Sea and the Kara Sea. The spatial pattern was poorly related to seabed depth. However, the increase in CH4 emissions over time may be explained by a process of shoaling of strengthening warm ocean currents that would also advect the CH4 to areas where seasonal deepening of the surface ocean mixed layer depth leads to ventilation of these water masses. Continued strengthening of the MC will further increase heat transfer to the BKS, with the Barents Sea ice-free in ~ 15 years. We thus expect marine CH4 flux to the atmosphere from this region to continue increasing.

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