Co-variability of North Atlantic Oscillation and Maximum Sea Ice Extent in the Baltic Sea in CMIP5 Climate Models

2015 ◽  
Author(s):  
Teija Seitola ◽  
Peter Ukkonen ◽  
Alexey Karpechko ◽  
Kimmo Ruosteenoja ◽  
Hilppa Gregow
2006 ◽  
Vol 45 (7) ◽  
pp. 982-994 ◽  
Author(s):  
Matthias Drusch

Abstract Sea ice concentration plays a fundamental role in the exchange of water and energy between the ocean and the atmosphere. Global real-time datasets of sea ice concentration are based on satellite observations, which do not necessarily resolve small-scale patterns or coastal features. In this study, the global National Centers for Environmental Prediction (NCEP) 0.5° sea ice concentration dataset is compared with a regional high-resolution analysis for the Baltic Sea produced 2 times per week by the Swedish Meteorological and Hydrological Institute (SMHI). In general, the NCEP dataset exhibits less spatial and temporal variability during the winter of 2003/04. Because of the coarse resolution of the NCEP dataset, ice extent is generally larger than in the SMHI analysis. Mean sea ice concentrations derived from both datasets are in reasonable agreement during the ice-growing and ice-melting periods in January and April, respectively. For February and March, during which the sea ice extent is largest, mean sea ice concentrations are lower in the NCEP dataset relative to the SMHI product. Ten-day weather forecasts based on the NCEP sea ice concentrations and the SMHI dataset have been performed, and they were compared on the local, regional, and continental scales. Turbulent surface fluxes have been analyzed based on 24-h forecasts. The differences in sea ice extent during the ice-growing period in January cause mean differences of up to 30 W m−2 for sensible heat flux and 20 W m−2 for latent heat flux in parts of the Gulf of Bothnia and the Gulf of Finland. The comparison between spatially aggregated fluxes yields differences of up to 36 and 20 W m−2 for sensible and latent heat flux, respectively. The differences in turbulent fluxes result in different planetary boundary height and structure. Even the forecast cloud cover changes by up to 40% locally.


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.


2017 ◽  
Vol 10 (8) ◽  
pp. 3105-3123 ◽  
Author(s):  
Per Pemberton ◽  
Ulrike Löptien ◽  
Robinson Hordoir ◽  
Anders Höglund ◽  
Semjon Schimanke ◽  
...  

Abstract. The Baltic Sea is a seasonally ice-covered marginal sea in northern Europe with intense wintertime ship traffic and a sensitive ecosystem. Understanding and modeling the evolution of the sea-ice pack is important for climate effect studies and forecasting purposes. Here we present and evaluate the sea-ice component of a new NEMO–LIM3.6-based ocean–sea-ice setup for the North Sea and Baltic Sea region (NEMO-Nordic). The setup includes a new depth-based fast-ice parametrization for the Baltic Sea. The evaluation focuses on long-term statistics, from a 45-year long hindcast, although short-term daily performance is also briefly evaluated. We show that NEMO-Nordic is well suited for simulating the mean sea-ice extent, concentration, and thickness as compared to the best available observational data set. The variability of the annual maximum Baltic Sea ice extent is well in line with the observations, but the 1961–2006 trend is underestimated. Capturing the correct ice thickness distribution is more challenging. Based on the simulated ice thickness distribution we estimate the undeformed and deformed ice thickness and concentration in the Baltic Sea, which compares reasonably well with observations.


2017 ◽  
Author(s):  
Per Pemberton ◽  
Ulrike Löptien ◽  
Robinson Hordoir ◽  
Anders Höglund ◽  
Semjon Schimanke ◽  
...  

Abstract. The Baltic Sea is a seasonally ice covered marginal sea in northern Europe with intense wintertime ship traffic and a sensitive ecosystem. Understanding and modeling the evolution of the sea-ice pack is important for climate effect studies and forecasting purposes. Here we present and evaluate the sea-ice component of a new NEMO–LIM3.6 based ocean–sea ice setup for the North Sea and Baltic Sea region. The setup includes a new depth-based fast ice parametrization for the Baltic Sea. The evaluation focuses on long-term statistics, from a 45-year long hindcast, although short-term daily performance is also briefly evaluated. Different sea-ice metrics such as sea-ice extent, concentration and thickness are compared to the best available observational dataset to identify model biases. Overall the model agrees well with the observations in terms of the long-term mean sea-ice extent and thickness. The variability of the annual maximum Baltic Sea ice extent is well in line with the observations but the 1961–2006 trend is underestimated. Based on the simulated ice thickness distribution we estimate the undeformed and deformed ice thickness and concentration in the Baltic Sea, which compares reasonably well with observations. We conclude that the new North Sea/Baltic Sea ocean–sea ice setup is well suited for further climate studies and sea ice forecasts.


2021 ◽  
Vol 7 (31) ◽  
pp. eabg4893
Author(s):  
Peter Yu Feng Siew ◽  
Camille Li ◽  
Mingfang Ting ◽  
Stefan P. Sobolowski ◽  
Yutian Wu ◽  
...  

Arctic sea ice extent in autumn is significantly correlated with the winter North Atlantic Oscillation (NAO) in the satellite era. However, questions about the robustness and reproducibility of the relationship persist. Here, we show that climate models are able to simulate periods of strong ice-NAO correlation, albeit rarely. Furthermore, we show that the winter circulation signals during these periods are consistent with observations and not driven by sea ice. We do so by interrogating a multimodel ensemble for the specific time scale of interest, thereby illuminating the dynamics that produce large spread in the ice-NAO relationship. Our results support the importance of internal variability over sea ice but go further in showing that the mechanism behind strong ice-NAO correlations, when they occur, is similar in longer observational records and models. Rather than sea ice, circulation anomalies over the Urals emerge as a decisive precursor to the winter NAO signal.


Ocean Science ◽  
2012 ◽  
Vol 8 (4) ◽  
pp. 473-483 ◽  
Author(s):  
J. Karvonen

Abstract. An algorithm for computing ice drift from pairs of synthetic aperture radar (SAR) images covering a common area has been developed at FMI. The algorithm has been developed based on the C-band SAR data over the Baltic Sea. It is based on phase correlation in two scales (coarse and fine) with some additional constraints. The algorithm has been running operationally in the Baltic Sea from the beginning of 2011, using Radarsat-1 ScanSAR wide mode and Envisat ASAR wide swath mode data. The resulting ice drift fields are publicly available as part of the MyOcean EC project. The SAR-based ice drift vectors have been compared to the drift vectors from drifter buoys in the Baltic Sea during the first operational season, and also these validation results are shown in this paper. Also some navigationally useful sea ice quantities, which can be derived from ice drift vector fields, are presented.


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