Analysis of Variability of the Black Sea Hydrophysical Fields in 1993 – 2012 Based on the Reanalysis Results

Dorofeev, Yu. G.; Sukhikh, L. I.

doi:10.22449/0233-7584-2016-1-33-48

Cited by 8 publications

(6 citation statements)

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“…The latter have low density and are observed as a strip around the basin. This distinguishing feature of the surface salinity distribution has been recorded and simulated previously [ Besiktepe et al ., ; Dorofeev and Sukhikh , ]. It appears that for this period our model underestimates the salinity of the strip, however, due to the lack of data, we could not evaluate the model error precisely.…”

Section: Model Validationmentioning

confidence: 88%

“…[]. Figure d is intended for comparison with the Figure in Dorofeev and Sukhikh []. The overall SSS distribution is similar: high salinity in the basin interior which is decreasing toward the coast.…”

Section: Model Validationmentioning

confidence: 99%

“…[], however neither sea surface salinity (SSS) nor water column salinity is presented and discussed. Numerous models developed in 21 st century are based on data assimilation [ Besiktepe et al ., ; Korotaev et al ., ; Cannaby et al ., ; Dorofeev et al ., ; Dorofeev and Sukhikh , ], and they required broadly available satellite data for sea surface temperature (SST) and altimetry to constrain model runs. Due to the lack of regular data for SSS, typically climatological data sets are used instead.…”

Section: Introductionmentioning

confidence: 99%

“…A key feature of all models is their need to use SSS time series to operate, while at the same time, Black Sea SSS observational data is scarce and inhomogeneous. Despite being limited, the existing observational evidence can be used to some extend for relaxation in hindcast simulations [ Korotaev et al ., ; Dorofeev et al ., ; Dorofeev and Sukhikh , ]. However, we crucially need a model capable to perform scenarios for the future as well.…”

Section: Introductionmentioning

confidence: 99%

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Black Sea thermohaline properties: Long‐term trends and variations

et al. 2017

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The current knowledge about spatial and temporal dynamics of the Black Sea's thermohaline structure is incomplete because of missing data and sparse distribution of existing measurements in space and time. This study presents 56 year continuous simulations of the Black Sea's hydrodynamics using the 3D General Estuarine Transport Model (GETM), without incorporating any relaxation toward climatological or observational data fields. This property of the model allows us to estimate independent temporal trends, in addition to resolving the spatial structure. The simulations suggest that the intermediate layer temperature is characterized by a weak positive trend (warming), whereas the surface temperature does not show a clear linear trend. Different salinity trends have been established at the surface (negative), upper (weaker negative) and main halocline (positive). Three distinct dynamic periods are identified (1960–1970, 1970–1995, 1995–2015), which exhibit pronounced changes in the Black Sea's thermohaline properties and basin circulation. Strengthening of the main cyclonic circulation, accompanied by intensification of the mesoscale anticyclonic eddy formation is found. Both events strongly affect the sea surface salinity but contribute in opposing directions. Specifically, strong composite large‐scale circulation leads to an increase in sea surface salinity, while enhanced formation of mesoscale anticyclones decreases it. Salinity evolution with time is thus the result of the competition of these two opposing yet interdependent processes.

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Section: Model Validationmentioning

confidence: 88%

Section: Model Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Black Sea thermohaline properties: Long‐term trends and variations

et al. 2017

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“…Использование методов ассимиляции данных измерений в гид-родинамических моделях океана при решении задач ретроспективного анализа обеспечивает реалистичное воспроизведение океанографических полей. В на-стоящее время к наиболее часто используемым методам относятся вариаци-онное усвоение данных наблюдений [1-3] и фильтр Калмана [4,5] в двух его разновидностях расчета матриц ковариаций ошибок прогноза: упрощенным методом [6][7][8] и по ансамблю реализаций [6,9,10]. Ансамблевый фильтр Калмана требует больших мощностей вычислительной техники и не всегда гарантирует получение оптимальных весовых коэффициентов [10].…”

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