scholarly journals The Value of Sustained Ocean Observations for Sea Ice Predictions in the Barents Sea

2019 ◽  
Vol 32 (20) ◽  
pp. 7017-7035 ◽  
Author(s):  
Mitchell Bushuk ◽  
Xiaosong Yang ◽  
Michael Winton ◽  
Rym Msadek ◽  
Matthew Harrison ◽  
...  
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Initial Conditions ◽  
Heat Budget ◽  
Heat Content ◽  
Arctic Sea Ice ◽  
Heat Fluxes ◽  
Seasonal Forecasts ◽  
Surface Heat Fluxes ◽  
The Barents Sea

ABSTRACT Dynamical prediction systems have shown potential to meet the emerging need for seasonal forecasts of regional Arctic sea ice. Observationally constrained initial conditions are a key source of skill for these predictions, but the direct influence of different observation types on prediction skill has not yet been systematically investigated. In this work, we perform a hierarchy of observing system experiments with a coupled global data assimilation and prediction system to assess the value of different classes of oceanic and atmospheric observations for seasonal sea ice predictions in the Barents Sea. We find notable skill improvements due to the inclusion of both sea surface temperature (SST) satellite observations and subsurface conductivity–temperature–depth (CTD) measurements. The SST data are found to provide the crucial source of interannual variability, whereas the CTD data primarily provide climatological and trend improvements. Analysis of the Barents Sea ocean heat budget suggests that ocean heat content anomalies in this region are driven by surface heat fluxes on seasonal time scales.

Quantifying the Influence of Atlantic Heat on Barents Sea Ice Variability and Retreat*

2012 ◽  
Vol 25 (13) ◽  
pp. 4736-4743 ◽  
Author(s):  
M. Årthun ◽  
T. Eldevik ◽  
L. H. Smedsrud ◽  
Ø. Skagseth ◽  
R. B. Ingvaldsen
Keyword(s):  
Sea Ice ◽  
Heat Transport ◽  
Barents Sea ◽  
Heat Budget ◽  
Heat Content ◽  
Ocean Model ◽  
Sea Ice Extent ◽  
The Barents Sea ◽  
Ice Retreat ◽  
Sea Ice Retreat

Abstract The recent Arctic winter sea ice retreat is most pronounced in the Barents Sea. Using available observations of the Atlantic inflow to the Barents Sea and results from a regional ice–ocean model the authors assess and quantify the role of inflowing heat anomalies on sea ice variability. The interannual variability and longer-term decrease in sea ice area reflect the variability of the Atlantic inflow, both in observations and model simulations. During the last decade (1998–2008) the reduction in annual (July–June) sea ice area was 218 × 103 km2, or close to 50%. This reduction has occurred concurrent with an increase in observed Atlantic heat transport due to both strengthening and warming of the inflow. Modeled interannual variations in sea ice area between 1948 and 2007 are associated with anomalous heat transport (r = −0.63) with a 70 × 103 km2 decrease per 10 TW input of heat. Based on the simulated ocean heat budget it is found that the heat transport into the western Barents Sea sets the boundary of the ice-free Atlantic domain and, hence, the sea ice extent. The regional heat content and heat loss to the atmosphere scale with the area of open ocean as a consequence. Recent sea ice loss is thus largely caused by an increasing “Atlantification” of the Barents Sea.

On the Accuracy of the Simple Ocean Data Assimilation Analysis for Estimating Heat Budgets of the Near-Surface Arabian Sea and Bay of Bengal*

2005 ◽  
Vol 35 (3) ◽  
pp. 395-400 ◽  
Author(s):  
S S C. Shenoi ◽  
D. Shankar ◽  
S. R. Shetye
Keyword(s):  
Data Assimilation ◽  
Bay Of Bengal ◽  
Arabian Sea ◽  
Heat Budget ◽  
Heat Content ◽  
Heat Fluxes ◽  
Simple Ocean Data Assimilation ◽  
Near Surface ◽  
Surface Heat Fluxes ◽  
Ocean Data Assimilation

Abstract The accuracy of data from the Simple Ocean Data Assimilation (SODA) model for estimating the heat budget of the upper ocean is tested in the Arabian Sea and the Bay of Bengal. SODA is able to reproduce the changes in heat content when they are forced more by the winds, as in wind-forced mixing, upwelling, and advection, but not when they are forced exclusively by surface heat fluxes, as in the warming before the summer monsoon.

scholarly journals Arctic sea ice signatures: L-Band brightness temperature sensitivity comparison using two radiation transfer models

2016 ◽  
Author(s):  
Friedrich Richter ◽  
Matthias Drusch ◽  
Lars Kaleschke ◽  
Nina Maaß ◽  
Xiangshan Tian-Kunze ◽  
...  
Keyword(s):  
Sea Ice ◽  
Brightness Temperature ◽  
Initial Conditions ◽  
Winter Season ◽  
Arctic Sea Ice ◽  
Heat Fluxes ◽  
Brightness Temperatures ◽  
Ice Coverage ◽  
L Band ◽  
Transfer Models

Abstract. Sea ice is a crucial component for short-, medium- and long term numerical weather predictions. Most importantly changes of sea ice coverage and areas covered by thin sea ice have a large impact on heat fluxes between the ocean and the atmosphere. L-Band brightness temperatures from ESA's first Earth Explorer SMOS (Soil Moisture and Ocean Salinity) have been proven to be a valuable tool to estimate mean thin sea ice thicknesses. Potentially, these measurements can be assimilated in forecasting systems to constrain the ice analysis leading to more accurate initial conditions and subsequently more accurate forecasts. As a first step, we use two different radiative transfer models as forward operators to generate top of atmosphere brightness temperatures based on ORAP5 model output for the 2012/2013 winter season. The simulations are then compared against actual SMOS measurements. The results indicate that both models are able to capture the general variability of measured brightness temperatures over sea ice. We identify one model to be favorable for brightness temperature assimilation purposes in the ORAP5 setup. The simulated brightness temperatures are dominated by sea ice coverage and thickness changes most pronounced in the marginal ice zone where new sea ice is formed. There we observe largest differences of more than 20 Kelvin over sea ice between simulated and observed brightness temperatures. We conclude that the assimilation of SMOS brightness temperatures yield high potential for forecasting models to correct for uncertainties in sea ice thicknesses of less than 0.5 meter and caution that uncertainties in sea ice fractional coverage may induce large errors.

scholarly journals A mechanism of spring Barents Sea ice effect on the extreme summer droughts in northeastern China

2021 ◽  
Author(s):  
YiBo Du ◽  
Jie Zhang ◽  
Siwen Zhao ◽  
Zhiheng Chen
Keyword(s):  
Sea Ice ◽  
North Atlantic ◽  
Barents Sea ◽  
Kara Sea ◽  
Northeastern China ◽  
Arctic Sea Ice ◽  
Extreme Drought ◽  
Negative Phase ◽  
Vapor Flux ◽  
The Barents Sea

Abstract The frequency of extreme drought events in northeastern China (NEC) has increased since the 2000s, and such a decadal anomalous trend may lead to significant stress on agriculture and economic development. The correlation between Arctic sea ice loss in spring and extreme summer droughts over NEC was investigated. The results show that the loss of sea ice over the Barents Sea in spring is associated with extreme droughts and positive height anomalies over NEC in summer. The physical processes include two pathways. First, Arctic ice loss from the Barents Sea to the Kara Sea results in reducing baroclinicity over the ice loss region but increasing baroclinicity over the ice melting region, which is favorable to the wave ridge over northern Europe and negative-phase Summer North Atlantic Oscillation (SNAO). One wave train originates from negative-phase SNAO over North Atlantic–Europe and spreads to central Europe, central Asia, and NEC. Second, another wave motion flux originates from the Barents–Kara Sea propagating eastward, and then disperses southward to NEC. Both wave trains lead to anomalous anticyclonic circulation and westward subtropical high, which favors descending motion and less water vapor flux, thereby contributing to extreme drought.

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.

Development of the temperature and salinity structure of the upper ocean over two months in an area 150x150 km

1983 ◽  
Vol 308 (1503) ◽  
pp. 311-325 ◽  
Keyword(s):  
Heat Budget ◽  
Salt Content ◽  
Heat Content ◽  
Time History ◽  
Heat Fluxes ◽  
Spatial Average ◽  
Survey Area ◽  
Surface Heat Fluxes ◽  
History Of ◽  
Salinity Structure

During the two months of the JASIN experiment a regular conductivity-temperaturedepth station pattern was worked by Hr. Neth. Ms. Tydeman , giving a total of eight surveys of temperature and salinity structure in an area 100 km x 150 km. The data are averaged to obtain hourly and spatial mean profiles. It is shown that this averaging conserves heat and salt content. The spatial mean profiles are compared with heat budget computations, and it is found that, on average, surface heat fluxes dominate the time history of the oceanic heat content on the scale of the whole survey area. On smaller scales advection due to mesoscale structures dominates, masking any differences in heating in different water masses. Comparison with results from a onedimensional mixed-layer model indicates that this type of model satisfactorily simulates the development of the temperature profile as a spatial average for the survey area.

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.

Linear predictability of Barents Sea ice cover: effects of coupling and resolution

2020 ◽  
Author(s):  
Chuncheng Guo ◽  
Aleksi Nummelin
Keyword(s):  
Sea Ice ◽  
Response Function ◽  
Barents Sea ◽  
Heat Content ◽  
Coupled System ◽  
Ice Cover ◽  
Statistical Prediction ◽  
Lead Times ◽  
The Barents Sea ◽  
Small Set

<p>Wintertime Barents Sea ice cover has been strongly linked to heat transport through the Barents Sea opening and Barents Sea heat content. Previous studies have shown predictability at seasonal timescales with short lead times. However, studies that have used statistical prediction have focused on a small set of predictors in the vicinity of the Barents Sea. Here we will extend the analysis further south following the path of the Norwegian Atlantic Current and show that monthly predictability with lead times up to 1-2 years can be achieved in CMIP6 models using Climate Response Function (CRF's). We further examine the effects of model resolution and coupling in the predictability and compare the results to CRF derived from observations. Our results suggest that higher resolution generally leads to stronger predictability and the fully coupled system provides the most realistic response function. The ocean provides a narrow range of lead times corresponding to an advective timescale, while coupling to the atmosphere broadens the lead times that are important for prediction. Finally, we show that even the upstream sea surface temperatures provide relatively high predictability of the Barents Sea ice cover both in the models and in the observations.</p>

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.

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