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.

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 Observed Feedback between Winter Sea Ice and the North Atlantic Oscillation

2009 ◽  
Vol 22 (22) ◽  
pp. 6021-6032 ◽  
Author(s):  
Courtenay Strong ◽  
Gudrun Magnusdottir ◽  
Hal Stern
Keyword(s):  
Sea Ice ◽  
North Atlantic Oscillation ◽  
North Atlantic ◽  
Barents Sea ◽  
Synthetic Data ◽  
Sea Ice Concentration ◽  
Local Maxima ◽  
The North ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

Abstract Feedback between the North Atlantic Oscillation (NAO) and winter sea ice variability is detected and quantified using approximately 30 years of observations, a vector autoregressive model (VAR), and testable definitions of Granger causality and feedback. Sea ice variability is quantified based on the leading empirical orthogonal function of sea ice concentration over the North Atlantic [the Greenland Sea ice dipole (GSD)], which, in its positive polarity, has anomalously high sea ice concentrations in the Labrador Sea region to the southwest of Greenland and low sea ice concentrations in the Barents Sea region to the northeast of Greenland. In weekly data for December through April, the VAR indicates that NAO index (N) anomalies cause like-signed anomalies of the standardized GSD index (G), and that G anomalies in turn cause oppositely signed anomalies of N. This negative feedback process operates explicitly on lags of up to four weeks in the VAR but can generate more persistent effects because of the autocorrelation of G. Synthetic data are generated with the VAR to quantify the effects of feedback following realistic local maxima of N and G, and also for sustained high values of G. Feedback can change the expected value of evolving system variables by as much as a half a standard deviation, and the relevance of these results to intraseasonal and interannual NAO and sea ice variability is discussed.

scholarly journals Satellite-observed sea ice area flux through Baffin Bay: 1988–2015

2018 ◽  
Author(s):  
Haibo Bi ◽  
Yunhe Wang ◽  
Xiuli Xu ◽  
Yu Liang ◽  
Jue Huang ◽  
...  
Keyword(s):  
Sea Ice ◽  
Vital Role ◽  
Positive Trend ◽  
Sea Ice Concentration ◽  
Baffin Bay ◽  
The North ◽  
Time Period ◽  
Sea Ice Drift ◽  
The North Atlantic Oscillation ◽  
Increasing Trends

Abstract. Sea ice export through Baffin Bay plays a vital role in modulating the meridional overturning process in the downstream Labrador Sea. In this study, satellite-derived sea ice products are explored to obtain the sea ice flux (SIF) through three passages (referred to as A, B, and C for the north, middle, and south passages, respectively) of Baffin Bay. Over the period 1988–2015, the average annual (October–September) sea ice area export is 555 × 103 km2, 642 × 103 km2, and 551 × 103 km2 through passages A, B, and C, respectively. These amounts are less than that observed through the Fram Strait (FS, 707 × 103 km2). Clear increasing trends in annual sea ice export on the order of 53.1 × 103 km2/de and 43.2 × 103 km2/de are identified at passages A and B, respectively. The trend at the south passage (C), however, is slightly negative (−13.3 × 103 km2/de). The positive trends in annual SIF at A and B are primarily attributable to the increase during winter months, which is triggered by the accelerated sea ice motion (SIM) and partly compensated by the reduced sea ice concentration (SIC). During the summer months, the sea ice export through each Baffin Bay passage usually presents a negative trend, primarily because of the decline in SIM and it is further enhanced by a dramatic decrease in SIC. A significant positive trend in the net SIF (i.e. net ice inflow) is found for between the passages A (or B) and C at 54.5 (or 64.2) × 103 km2/de. Therefore, Baffin Bay may have presented a greater convergence of ice. Overall, the connection between Baffin Bay sea ice export and the North Atlantic Oscillation (NAO) is tenuous, although the correlation is sensitive to variations in the selected time period. In contrast, the association with the cross-gate sea level pressure difference (SLPD) is robust in Baffin Bay (R = 0.69–0.71 depending on the passages), but relatively weaker compared with that in the FS (R = 0.74). Baffin Bay is bounded by Baffin Island to the west and Greenland to the east, thus, sea ice drift is not converted to the free state observed in the FS.

scholarly journals Impacts of large-scale atmospheric circulation changes due to winter sea-ice retreat on Black Carbon transport and deposition to the Arctic

2016 ◽  
Author(s):  
Luca Pozzoli ◽  
Srdan Dobricic ◽  
Simone Russo ◽  
Elisabetta Vignati
Keyword(s):  
Sea Ice ◽  
Atmospheric Circulation ◽  
North Atlantic ◽  
Large Scale ◽  
Southern Oscillation ◽  
The Arctic ◽  
The North ◽  
Ice Retreat ◽  
The North Atlantic Oscillation ◽  
Sea Ice Retreat

Abstract. Winter warming and sea ice retreat observed in the Arctic in the last decades determine changes of large scale atmospheric circulation pattern that may impact as well the transport of black carbon (BC) to the Arctic and its deposition on the sea ice, with possible feedbacks on the regional and global climate forcing. In this study we developed and applied a new statistical algorithm, based on the Maximum Likelihood Estimate approach, to determine how the changes of three large scale weather patterns (the North Atlantic Oscillation, the Scandinavian Blocking, and the El Nino-Southern Oscillation), associated with winter increasing temperatures and sea ice retreat in the Arctic, impact the transport of BC to the Arctic and its deposition. We found that the three atmospheric patterns together determine a decreasing winter deposition trend of BC between 1980 and 2015 in the Eastern Arctic while they increase BC deposition in the Western Arctic. The increasing trend is mainly due to the more frequent occurrences of stable high pressure systems (atmospheric blocking) near Scandinavia favouring the transport in the lower troposphere of BC from Europe and North Atlantic directly into to the Arctic. The North Atlantic Oscillation has a smaller impact on BC deposition in the Arctic, but determines an increasing BC atmospheric load over the entire Arctic Ocean with increasing BC concentrations in the upper troposphere. The El Nino-Southern Oscillation does not influence significantly the transport and deposition of BC to the Arctic. The results show that changes in atmospheric circulation due to polar atmospheric warming and reduced winter sea ice significantly impacted BC transport and deposition. The anthropogenic emission reductions applied in the last decades were, therefore, crucial to counterbalance the most likely trend of increasing BC pollution in the Arctic.

scholarly journals The link between the Barents Sea and ENSO events simulated by NEMO model

Ocean Science ◽  
2012 ◽  
Vol 8 (6) ◽  
pp. 971-982 ◽  
Author(s):  
V. N. Stepanov ◽  
H. Zuo ◽  
K. Haines
Keyword(s):  
Sea Ice ◽  
North Atlantic ◽  
Barents Sea ◽  
Southern Oscillation ◽  
Similar Response ◽  
Enso Events ◽  
Ice Volume ◽  
The North ◽  
The Barents Sea ◽  
The North Atlantic

Abstract. An analysis of observational data in the Barents Sea along a meridian at 33°30' E between 70°30' and 72°30' N has reported a negative correlation between El Niño/La Niña Southern Oscillation (ENSO) events and water temperature in the top 200 m: the temperature drops about 0.5 °C during warm ENSO events while during cold ENSO events the top 200 m layer of the Barents Sea is warmer. Results from 1 and 1/4-degree global NEMO models show a similar response for the whole Barents Sea. During the strong warm ENSO event in 1997–1998 an anomalous anticyclonic atmospheric circulation over the Barents Sea enhances heat loses, as well as substantially influencing the Barents Sea inflow from the North Atlantic, via changes in ocean currents. Under normal conditions along the Scandinavian peninsula there is a warm current entering the Barents Sea from the North Atlantic, however after the 1997–1998 event this current is weakened. During 1997–1998 the model annual mean temperature in the Barents Sea is decreased by about 0.8 °C, also resulting in a higher sea ice volume. In contrast during the cold ENSO events in 1999–2000 and 2007–2008, the model shows a lower sea ice volume, and higher annual mean temperatures in the upper layer of the Barents Sea of about 0.7 °C. An analysis of model data shows that the strength of the Atlantic inflow in the Barents Sea is the main cause of heat content variability, and is forced by changing pressure and winds in the North Atlantic. However, surface heat-exchange with the atmosphere provides the means by which the Barents sea heat budget relaxes to normal in the subsequent year after the ENSO events.

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 Predictability of the Barents Sea Ice in Early Winter: Remote Effects of Oceanic and Atmospheric Thermal Conditions from the North Atlantic

2014 ◽  
Vol 27 (23) ◽  
pp. 8884-8901 ◽  
Author(s):  
Takuya Nakanowatari ◽  
Kazutoshi Sato ◽  
Jun Inoue
Keyword(s):  
Sea Ice ◽  
North Atlantic ◽  
Barents Sea ◽  
Temperature Anomaly ◽  
Early Winter ◽  
Subsurface Temperature ◽  
The North ◽  
The Barents Sea ◽  
Subsurface Temperature Anomaly ◽  
The North Atlantic

Abstract Predictability of sea ice concentrations (SICs) in the Barents Sea in early winter (November–December) is studied using canonical correlation analysis with atmospheric and ocean anomalies from the NCEP Climate Forecast System Reanalysis (CFSR) data. It is found that the highest prediction skill for a single-predictor model is obtained from the 13-month lead subsurface temperature at 200-m depth (T200) and the in-phase meridional surface wind (Vsfc). T200 skillfully predicts SIC variability in 35% of the Barents Sea, mainly in the eastern side. The T200 for negative sea ice anomalies exhibits warm anomalies in the subsurface ocean temperature downstream of the Norwegian Atlantic Slope Current (NwASC) on a decadal time scale. The diagnostic analysis of NCEP CFSR data suggests that the subsurface temperature anomaly stored below the thermocline during summer reemerges in late autumn by atmospheric cooling and affects the sea ice. The subsurface temperature anomaly of the NwASC is advected from the North Atlantic subpolar gyre over ~3 years. Also, Vsfc skillfully predicts SIC variability in 32% of the Barents Sea, mainly in the western side. The Vsfc for the negative sea ice anomalies exhibits southerly wind anomalies; Vsfc is related to the large-scale atmospheric circulation patterns from the subtropical North Atlantic to the Eurasian continent. This study suggests that both atmospheric and oceanic remote effects have a potential impact on the forecasting accuracy of SIC.

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 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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