Influence of the Arctic Oscillation and El Niño-Southern Oscillation (ENSO) on ice conditions in the Baltic Sea: The wavelet approach

Author(s):  
S. Jevrejeva ◽  
J. C. Moore ◽  
A. Grinsted
Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 538
Author(s):  
Jae-Seung Yoon ◽  
Il-Ung Chung ◽  
Ho-Jeong Shin ◽  
Kunmnyeong Jang ◽  
Maeng-Ki Kim ◽  
...  

In recent decades, extremely cold winters have occurred repeatedly throughout the Northern Hemisphere, including the Korean Peninsula (hereafter, Korea). Typically, cold winter temperatures in Korea can be linked to the strengthening of the Siberian High (SH). Although previous studies have investigated the typical relationship between the SH and winter temperatures in Korea, this study uniquely focused on a change in the relationship, which reflects the influence of the Arctic Oscillation (AO) and El Niño–Southern Oscillation (ENSO). A significant change in the 15-year moving correlation between the SH and the surface air temperature average in Korea (K-tas) was observed in January. The correlation changed from −0.80 during 1971–1990 to −0.16 during 1991–2010. The mean sea-level pressure pattern regressed with the temperature, and a singular value decomposition analysis that incorporated the temperature and pressure supports that the negative high correlation during 1971–1990 was largely affected by AO. This connection with AO is substantiated by empirical orthogonal function (EOF) analysis with an upper-level geopotential height at 300 hPa. In the second mode of the EOF, the temperature and pressure patterns were primarily affected by ENSO during 1991–2010. Consequently, the interdecadal change in correlation between K-tas and the SH in January can be attributed to the dominant effect of AO from 1971–1990 and of ENSO from 1991–2010. Our results suggest that the relative importance of these factors in terms of the January climate in Korea has changed on a multidecadal scale.


2018 ◽  
Vol 57 (3) ◽  
pp. 517-523 ◽  
Author(s):  
Christopher J. Goodman ◽  
Jennifer D. Small Griswold

AbstractA critical determinant of aircraft performance is density altitude, or the density given as a height above mean sea level, which is dependent on air temperature, pressure, and humidity. These meteorological variables change on various time scales (e.g., hourly, seasonal, and decadal) and are regionally impacted by large-scale climate variability as the result of phenomena such as El Niño–Southern Oscillation or the Arctic Oscillation. Here a statistical analysis is performed to determine the impacts of climate variability on seasonally averaged density altitude, a key metric used by pilots to determine aircraft performance and efficiency, as a function of El Niño–Southern Oscillation and the Arctic Oscillation using NCEP–NCAR reanalysis data and historical aviation meteorological records. Regressions show regional dependencies and impacts to density altitudes that vary as a function of season for both El Niño–Southern Oscillation and Arctic Oscillation cases. The results highlight the importance of understanding the regional nature of the impact of climate variability on density altitude and the potential impacts on aviation operations.


2015 ◽  
Vol 7 (1) ◽  
Author(s):  
G. V. Surkova ◽  
Victor S. Arkhipkin ◽  
Alexander V. Kislov

AbstractThe storm events in the Baltic Sea are examined in connection with the main weather patterns grouped into the circulation types (CTs), and their changes in present climate. A calendar of storms was derived from results of wave model SWAN (Simulating WAves Nearshore) experiments for 1948-2011. Based on this calendar, a catalogue of atmospheric sea level pressure (SLP) fields was prepared for CTs from the NCEP/NCAR dataset. SLP fields were then analyzed using a pattern recognition algorithm which employed empirical orthogonal decomposition and cluster analysis. For every CT we conducted an analysis of their seasonal and interannual changes, along with their role in storm event formation. An increase of the storm CTs’ frequency in the second part of the 20th century was shown to be in a close agreement with teleconnection circulation patterns such as the Arctic Oscillation, North Atlantic Oscillation and the Scandinavian blocking.


2013 ◽  
Vol 491 ◽  
pp. 111-124 ◽  
Author(s):  
M Lehtiniemi ◽  
E Gorokhova ◽  
S Bolte ◽  
H Haslob ◽  
B Huwer ◽  
...  

2014 ◽  
Vol 8 (6) ◽  
pp. 2409-2418 ◽  
Author(s):  
U. Löptien ◽  
L. Axell

Abstract. The Baltic Sea is a seasonally ice-covered marginal sea located in a densely populated area in northern Europe. Severe sea ice conditions have the potential to hinder the intense ship traffic considerably. Thus, sea ice fore- and nowcasts are regularly provided by the national weather services. Typically, the forecast comprises several ice properties that are distributed as prognostic variables, but their actual usefulness is difficult to measure, and the ship captains must determine their relative importance and relevance for optimal ship speed and safety ad hoc. The present study provides a more objective approach by comparing the ship speeds, obtained by the automatic identification system (AIS), with the respective forecasted ice conditions. We find that, despite an unavoidable random component, this information is useful to constrain and rate fore- and nowcasts. More precisely, 62–67% of ship speed variations can be explained by the forecasted ice properties when fitting a mixed-effect model. This statistical fit is based on a test region in the Bothnian Sea during the severe winter 2011 and employs 15 to 25 min averages of ship speed.


2014 ◽  
Vol 8 (4) ◽  
pp. 3811-3828
Author(s):  
U. Löptien ◽  
L. Axell

Abstract. The Baltic Sea is a seasonally ice covered marginal sea located in a densely populated area in northern Europe. Severe sea ice conditions have the potential to hinder the intense ship traffic considerably. Thus, sea ice fore- and nowcasts are regularly provided by the national weather services. Typically, several ice properties are allocated, but their actual usefulness is difficult to measure and the ship captains must determine their relative importance and relevance for optimal ship speed and safety ad hoc. The present study provides a more objective approach by comparing the ship speeds, obtained by the Automatic Identification System (AIS), with the respective forecasted ice conditions. We find that, despite an unavoidable random component, this information is useful to constrain and rate fore- and nowcasts. More precisely, 62–67% of ship speed variations can be explained by the forecasted ice properties when fitting a mixed effect model. This statistical fit is based on a test region in the Bothnian Bay during the severe winter 2011 and employes 15 to 25 min averages of ship speed.


2021 ◽  
Author(s):  
Fatemeh Najafzadeh ◽  
Nadezhda Kudryavtseva ◽  
Tarmo Soomere

Abstract Wave heights in the Baltic Sea in 1992–2015 have predominantly increased in the sea's western parts. The linear trends in the winter wave heights exhibit a prominent meridional pattern. Using the technique of Empirical Orthogonal Functions (EOF) applied to the multi-mission satellite altimetry data, we link a large part of this increase in the wave heights with the climatic indices of the Scandinavian mode, North Atlantic Oscillation, and Arctic Oscillation. The winter trends show a statistically significant negative correlation (correlation coefficient –0.47±0.19) with the Scandinavian pattern and a positive correlation with the North Atlantic Oscillation (0.31±0.22) and Arctic Oscillation (0.42±0.20). The meridional pattern is associated with more predominant north-westerly and westerly winds driven by the Scandinavian and North Atlantic Oscillation, respectively. All three climatic indices show a statistically significant time-variable correlation with Baltic Sea wave climate during the winter season. When the Scandinavian pattern's influence is strong, North Atlantic and Arctic Oscillations' effect is low and vice versa. The results are backed up by simulations using synthetic data that demonstrate that the percentage of variance retrieved using EOF analysis from the satellite-derived wave measurements is directly related to the percentage of noise in the data and the retrieved spatial patterns are insensitive to the level of noise.


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