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.

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

2012 ◽  
Vol 9 (3) ◽  
pp. 2121-2151
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 anticyclonic atmospheric circulation is settled over the Barents Sea instead of a usual cyclonic circulation. This change enhances heat loses in the Barents Sea, 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 Barents Sea inflow is the main source for the variability of Barents Sea heat content, and is forced by changing pressure and winds in the North Atlantic. However, surface heat-exchange with atmosphere can also play a dominant role in the Barents Sea annual heat balance, especially for the subsequent year after ENSO events.

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.

scholarly journals Seasonal forecast of sea ice extent in the Barents sea

2019 ◽  
Vol 65 (1) ◽  
pp. 5-14
Author(s):  
N. I. Glok ◽  
G. V. Alekseev ◽  
A. E. Vyazilova
Keyword(s):  
Sea Ice ◽  
North Atlantic ◽  
Barents Sea ◽  
Sea Ice Extent ◽  
Annual Variability ◽  
The North ◽  
The Barents Sea ◽  
The North Atlantic ◽  
Main Component ◽  
Sst Anomalies

Earlier, the authors established a close relationship between the temperature of water coming from the North Atlantic and the sea ice extent (SIE) in the Barents Sea, which accounts for up to 75 % of the inter-annual variability of the monthly SIE from January to June. In turn, temperature variations of the incoming Atlantic water are affected from anomalies of sea surface temperature (SST) in the low latitudes of the North Atlantic. These dependences served as the basis for the development of a forecast method. The empirical orthogonal functions decomposition of the SIE set from January to June for 1979–2014 was used. The main component of decomposition reflects 83 % of the inter-annual variability of SIE from January to June. Regression model of forecast is based on the relation of the main component with SST anomalies taking into account the delay. Comparison of prognostic and actual values of the climatic component for each of the 6 months showed the correctness of forecasts with a lead time of 27 to 32 months is 83 %, and for the prediction of the initial values of SIE 79 %. Appealing to the second predictor — SST anomalies in the Norwegian Sea allowed to improve the quality of the forecast of the observed values of SIE. At the same time, the forecast advance time was reduced to 9–14 months.

scholarly journals Seasonal and Interannual Variability of the Barents Sea Temperature

2019 ◽  
Vol 25 ◽  
pp. 1-13
Author(s):  
Ilya V. Serykh ◽  
Andrey G. Kostianoy
Keyword(s):  
Interannual Variability ◽  
North Atlantic ◽  
Barents Sea ◽  
Southern Oscillation ◽  
North Atlantic Current ◽  
Sea Temperature ◽  
The North ◽  
The Barents Sea ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

Analysis of the monthly average temperature data of the Barents Sea at various depths for the period 1948-2016 showed its growth, which accelerated significantly since the mid-1980s. Against the background of this growth, interannual variability was found over periods of 2 to 7 years and about 10 years. It is shown that periods of this variability can be associated, respectively, with El Nino - Southern Oscillation and the North Atlantic Oscillation. It has been hypothesized that the Global Atmospheric Oscillation may be the synchronizing mechanism of the interannual variability of the tropics of the Pacific Ocean, the North Atlantic and the Barents Sea. Interdecadal changes with a period of about 15 years were also found, which are most likely related to surface temperature anomalies carried by the North Atlantic Current.

Understanding cyclone variability in the Barents Sea

2020 ◽  
Author(s):  
Erica Madonna ◽  
Gabriel Hes ◽  
Clio Michel ◽  
Camille Li ◽  
Peter Yu Feng Siew
Keyword(s):  
Climate Variability ◽  
Sea Ice ◽  
North Atlantic ◽  
Barents Sea ◽  
The Arctic ◽  
High Latitudes ◽  
The North ◽  
The Barents Sea ◽  
The North Atlantic ◽  
Cyclone Frequency

<p>Extratropical cyclones are a key player for the global energy budget as they transport a large amount of moisture and heat from mid- to high-latitudes. One of the main corridors for cyclones entering the Arctic from the North Atlantic is the Barents Sea, a region that has experienced the largest decrease in winter sea ice during the past decades. On the one hand, some studies showed that moisture transported by cyclones to the Arctic can lead to drastic temperature increases and sea ice melt. On the other hand, it has been suggested that the location of the sea ice edge can influence the tracks of cyclones. Therefore, it is crucial to understand what controls cyclone tracks through the Barents Sea into the Arctic to explain and potentially predict climate variability at high latitudes.</p><p>To address this question, we track cyclones from 1979 to 2018 in the ERA-Interim data set, characterizing and quantifying them depending on their genesis location and path. The focus is on cyclones entering the Barents Sea from the North Atlantic as they carry the most moisture into the Arctic. Despite a clear declining trend in sea ice in the Barents Sea, our results show neither significant changes in cyclone frequency nor in their tracks. However, we find that the large-scale flow and in particular the presence or absence of blocking in the Barents Sea influence the cyclone frequency in this region, providing a potential mechanism that controls high latitude climate variability.</p>

scholarly journals Connection between Sea Surface Anomalies and Atmospheric Quasi-Stationary Waves

2020 ◽  
Vol 33 (1) ◽  
pp. 201-212
Author(s):  
G. Wolf ◽  
A. Czaja ◽  
D. J. Brayshaw ◽  
N. P. Klingaman
Keyword(s):  
Sea Ice ◽  
North Atlantic ◽  
Southern Oscillation ◽  
Late Summer ◽  
Ice Cover ◽  
Sea Surface ◽  
Late Winter ◽  
Driving Mechanism ◽  
The North ◽  
The North Atlantic

AbstractLarge-scale, quasi-stationary atmospheric waves (QSWs) are known to be strongly connected with extreme events and general weather conditions. Yet, despite their importance, there is still a lack of understanding about what drives variability in QSW. This study is a step toward this goal, and it identifies three statistically significant connections between QSWs and sea surface anomalies (temperature and ice cover) by applying a maximum covariance analysis technique to reanalysis data (1979–2015). The two most dominant connections are linked to El Niño–Southern Oscillation and the North Atlantic Oscillation. They confirm the expected relationship between QSWs and anomalous surface conditions in the tropical Pacific and the North Atlantic, but they cannot be used to infer a driving mechanism or predictability from the sea surface temperature or the sea ice cover to the QSW. The third connection, in contrast, occurs between late winter to early spring Atlantic sea ice concentrations and anomalous QSW patterns in the following late summer to early autumn. This new finding offers a pathway for possible long-term predictability of late summer QSW occurrence.

The influence of the North Atlantic on climate variations in the Barents Sea and their predictability

2016 ◽  
Vol 41 (8) ◽  
pp. 544-558 ◽  
Author(s):  
G. V. Alekseev ◽  
N. I. Glok ◽  
A. V. Smirnov ◽  
A. E. Vyazilova
Keyword(s):  
North Atlantic ◽  
Barents Sea ◽  
Climate Variations ◽  
The North ◽  
The Barents Sea ◽  
The North Atlantic

scholarly journals Multi-model assessment of the late-winter tropospheric response to El Niño and La Niña

2021 ◽  
Author(s):  
Bianca Mezzina ◽  
Javier García-Serrano ◽  
Ileana Bladé ◽  
Froila M. Palmeiro ◽  
Lauriane Batté ◽  
...  
Keyword(s):  
Rossby Wave ◽  
North Atlantic ◽  
Large Scale ◽  
Southern Oscillation ◽  
Late Winter ◽  
Model Assessment ◽  
Wave Source ◽  
Enso Events ◽  
The North ◽  
The North Atlantic

<p>El Niño-Southern Oscillation (ENSO) is known to affect the Northern Hemisphere tropospheric circulation in late-winter (January–March), but whether El Niño and La Niña lead to symmetric impacts and with the same underlying dynamics remains unclear, particularly in the North Atlantic. Three state-of-the-art atmospheric models forced by symmetric anomalous sea surface temperature (SST) patterns, mimicking strong ENSO events, are used to robustly diagnose symmetries and asymmetries in the extra-tropical ENSO response. Asymmetries arise in the sea-level pressure (SLP) response over the North Pacific and North Atlantic, as the response to La Niña tends to be weaker and shifted westward with respect to that of El Niño. The difference in amplitude can be traced back to the distinct energy available for the two ENSO phases associated with the non-linear diabatic heating response to the total SST field. The longitudinal shift is embedded into the large-scale Rossby wave train triggered from the tropical Pacific, as its anomalies in the upper troposphere show a similar westward displacement in La Niña compared to El Niño. To fully explain this shift, the response in tropical convection and the related anomalous upper-level divergence have to be considered together with the climatological vorticity gradient of the subtropical jet, i.e. diagnosing the tropical Rossby wave source. In the North Atlantic, the ENSO-forced SLP signal is a well-known dipole between middle and high latitudes, different from the North Atlantic Oscillation, whose asymmetry is not indicative of distinct mechanisms driving the teleconnection for El Niño and La Niña.</p><p>The multi-model assessment, with 50 members for each experiment, contributes to the ERA4CS-funded MEDSCOPE project and includes: EC-EARTH/IFS (L91, 0.01hPa), CNRM/ARPEGE (L91, 0.01hPa), CMCC/CAM (L46, 0.3hPa).</p>

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.

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