207 research outputs found
Seasonal and Inter-Annual Variability of the Phytoplankton Dynamics in the Black Sea Inner Basin
No full textWe explore the patterns of Black Sea phytoplankton growth as driven by the thermohaline structure and circulation system and the freshwater nutrient loads. Seasonal and inter-annual variability of the phytoplankton blooms is examined using hydrodynamic simulations that resolve mesoscale eddies and online coupled bio-geochemical model. This study suggests that the bloom seasonality is homogeneous across geographic locations of the Black Sea inner basin, with the strongest bloom occurring in winter (February–March), followed by weaker bloom in spring (April–May), summer deep biomass maximum (DBM) (June–September) and a final bloom in autumn (October–November). The winter phytoplankton bloom relies on vertical mixing of nitrate from the intermediate layers, where nitrate is abundant. The winter bloom is highly dependent on the strength of the cold intermediate layers (CIL), while spring/summer blooms take advantage of the CIL weakness. The maximum phytoplankton transport across the North Western Shelf (NWS) break occurs in September, prior to the basin interior autumn bloom. Bloom initiation in early autumn is associated with the spreading of NWS waters, which in turn is caused by an increase in mesoscale eddy activity in late summer months. In summary, the intrusion of low salinity and nitrate-rich water into the basin interior triggers erosion of the thermocline, resulting in vertical nitrate uplifting. The seasonal phytoplankton succession is strongly influenced by the recent CIL disintegration and amplification of the Black Sea circulation, which may alter the natural Black Sea nitrate dynamics, with subsequent effects on phytoplankton and in turn on all marine life
Scenario simulations of the changing Black Sea ecosystem (SIMSEA)
No full text<p>The regional Black Sea ecosystem model (BSEM) has been applied for the first time for biogeochemical simulations of the Black Sea ecosystem (Oguz, T., H. W. Ducklow, J. E. Purcell, and P. Malanotte-Rizzoli (2001), Modeling the response of topdown control exerted by gelatinous carnivores on the Black Sea pelagic food web. J. Geophys. Res., 106, 4543–4564). The BSEM model is able to describe the Black Sea specific features as demonstrated by the analysis presented herein. One of the key modification of the existing models is the introduction of two new components - the carnivore predators <em>Mnemiopsis</em> and <em>Noctiluca shunt.</em> Originally they began to exist in the lower trophic Black sea food web since the 80s. They feed on zooplankton and are responsible for the reduction of zooplankton standing stock that represents an ecological concern. Detailed description of the BSEM can be found in Miladinova S., A. Stips, E. Garcia-Gorriz, D. Macias Moy (2016c), Modelling Toolbox 2: The Black Sea ecosystem model, EUR 28372 EN, doi:10.2788/677808.</p>
<p>The model is coupled to the General Estuarine Transport Model (GETM). It is forced with fluxes, obtained from realistic meteorological conditions and tuned for the Black Sea ecosystem in particular. The main advantage of the GETM-BSEM model set-up for the Black Sea is the possibility to study: (i) the long-term evolution of the Black Sea ecosystem; (ii) the effect of nutrient load and regional weather on the biogeochemical structure.</p>
<p>Data sets consists of monthly mean values of total phytoplankton (large + small) and nitrate concentrations on a horizontal grid of 423x172 data points (2 longitude minutes x 2 latitude minutes) and 70 vertical levels. Simulations are performed for the period from 1960 to 2014 (55 years). The 3D monthly mean vertical coordinate (m), phytoplankton (mmol N/m**3) and nitrate (mmol N/m**3) are stored in NetCDF format.</p>
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Modelling the carbonate system to adequately quantify ocean acidification
No full textGiven specific CO2 emission scenarios, predictions of future ocean carbonate chemistry are relatively certain at the global scale. However future regional ocean acidification and ocean carbonate chemistry are less well understood. A major challenge is assessing the risk of ocean acidification on marine food webs, ecosystems and ocean biogeochemistry.
Due to a range of natural physical and biological processes, riverine inputs, boundary conditions and runoff, the natural variability of dissolved CO2 in sea water is relatively high in regional seas. Some species, calcifying or not, have the capacity to adapt to such conditions, others do not. Establishing the biological impacts of ocean acidification is difficult due to a range of physiological and ecological trade-offs. Including the carbonate system in such complicated regions is a challenge, and significant development will be required to adequately model this in regional seas
Turbulence and Zooplankton Production: Insight from PROVESS.
No full textAbstract not availableJRC.H - Institute for environment and sustainability (Ispra
Dissipation Measurement: Theory
No full textThe dissipation rate of turbulent kinetic energy is a key parameter to quantify the level of turbulence and the resulting mixing processes in natural waters. Based on viscous dissipation rate measurements, vertical diffusion coefficients and flux rates as well as friction velocities can be determined.This chapter describes the theory of the dissipation measurements.JRC.H.3 - Global environement monitorin
A Comparison of Parameterized, Simulated and Measured Turbulent Mixing in the Gulf of Finland, the Baltic Sea
No full textThree time series of shear microstructure measurements (duration 13, 24 and 14 h respectively) have been performed in 3 different wind forcing regimes as well as in 3 different background density stratification and current velocity shear situations at the entrance to the Gulf of Finland, in July 1998. Vertical shear of current velocity was enhanced by near-inertial waves during the first (A1) and the third (A3) time series, the background stratification weakened continuously from A1 toward A3. We compared eddy diffusivities based on the Richardson number parameterization and eddy diffusivities simulated using the two equation k-e turbulence closure (General Ocean Turbulence Model, GOTM) with the �measured� ones. For two out of the three time series the eddy diffusivities calculated via the Richardson number parameterization and via the k-e model simulation agreed well with the experimental data. However, summing up the discrepancy of all three time series both methods resulted in a remarkable and consistent bias against the measured eddy diffusivity. On the contrary, the calculations with a new parameterization scheme, which considers the internal wave kinetic energy fitted well for all three time series. Similarly the modified k-e simulations which considered the internal wave energy level matched better the measured profiles.JRC.DDG.H.3 - Global environement monitorin
Black Sea thermohaline properties: Long-term trends and variations
No full text<p>3D velocity (m/s), temperature (°C) and salinity fields for the Black Sea are simulated by the use of the General Estuarine Ocean Model (GETM) and General Ocean Turbulence Model (GOTM). Data sets consists of monthly mean value on a horizontal grid of 423x172 data points (2 longitude minutes x 2 latitude minutes) and 70 vertical levels. Simulations are performed for the period from 1960 to 2015 (56 years). The 3D monthly mean vertical coordinate, temperature and salinity are stored in monthly NetCDF data files available for download.</p>
<p>Detailed description: The 3D hydrodynamic model comprises of 3D GETM and 1D GOTM initialized on high resolution 2 x 2 min latitude–longitude horizontal grid. The model bathymetry grid is produced from ETOPO1 global bathymetric grid with horizontal resolution of 1 min. Linear programming procedure was applied to smooth slightly the bathymetry. The maximum depth of the model domain is 2200 m with a 70 levels general vertical grid which is compressed towards the surface. A detailed description of the GETM equations can be found in Stips et al. [2004].One way to minimize dissipation and dispersion is to use a numerical method which satisfies the Total Variation Diminishing (TVD) property. Flux-limiter methods satisfy the TVD property and switch between a second-order approximation when the field is smooth and a first-order approximation when it is near a discontinuity. Flux-limiter methods have been applied to the different numerical approaches so that oscillations present in the numerical solution can be minimized. The second-order monotone scheme with the Superbee limiter is used herein [Burchard and Bolding 2002]. The meteorological forcing from the European Centre for Medium Range Weather Forecast (ECMWF) available from http://www.ecmwf.int, has been applied, namely, ERA-40 project (1958-2001) and ERA-Interim project (1979-2015). Two model runs have been chosen: Run1 with ERA-40 (1958-1979) followed by forcing with ERA-Interim (1980-2015) and Run2 forced with ERA-Interim (1979-2015), in order to study the effect of the starting year and forcing data and to identify possible artificial trends due to computational and forcing uncertainties. Freshwater input has been evaluated using the values from the Global Runoff Data Centre (GRDC, http://www.bafg.de/GRDC) runoff. Being an estuarine basin, the Black sea is very sensitive to variations in the fresh water input. The resulting buoyancy flow induced by the river runoff is essential for establishing the basin circulation. Comparison between runoff data sets from different data centres has revealed similar climatological mean annual cycles for all rivers considered herein [Miladinova-Marinova et al., 2016]. The mode has been forced the GRDC data because it contains long term daily records of the Danube River. Water exchange in the Bosphorus and Kerch Straits is simulated as a river flow that contains surface outflow/inflow and bottom inflow/outflow. Assuming the long term steady state water and salt budgets in the Black Sea, the monthly averaged volume fluxes have been estimated [Miladinova-Marinova et al., 2016] and used further as a forcing condition. The model is initialized by means of temperature and salinity 3D fields coming from the project MEDAR/MEDATLAS II (http://www.ifremer.fr/medar). The MEDAR data set for the Black Sea reflects the main features known from observations – the strong halocline at 70-150 m, the CIL at approximately 25-70 m and the doming of the isohalines due to the cyclonic Rim current. The detailed model setup and an extended validation is presented in Miladinova-Marinova et al. [2016]. Burchard, H., and K. Bolding (2002), Getm: A general estuarine transport model. Scientific documentation, Joint Research Centre Ispra Tech. Rep. EUR 20253 EN, Eur. Comm; Stips, A., K. Bolding, T. Pohlmann, and H. Burchard (2004), Simulating the temporal and spatial dynamics of the North Sea using the new model GETM (General Estuarine Transport Model), Ocean Dynam., 54, 266-283; Miladinova-Marinova S., A. Stips, E. Garcia-Gorriz, D. Macias Moy (2016), Black Sea ecosystem model: setup and validation, EUR 27786, doi: 10.2788/601495</p>
<p>Simulation zip files from Run1, BLACK_SEA_HYDRO1_YEAR_MONTH.zip, and simulation zip files from Run2, BLACK_SEA_HYDRO2_YEAR_MONTH.zip, are stored herein.</p>
Regime Shifts and Trends in the Baltic Sea area: a statistical approach
No full textDuring the late 1980s air and sea surface temperature started to increase in the Baltic Sea area. Sea ice broke up earlier and overall ice coverage declined. This resulted in a longer growing season and in increases in phytoplankton biomass as well as changes in the zooplankton and fish communities. These changes are often considered to represent a regime shift in the ecology of the central Baltic Sea, which according to some authors could be caused by a related sign change in the North Atlantic Oscillation (NAO).
The aim of this investigation is to inspect relevant physical and ecosystem variables for trends and structural breakpoints in the concerned time series. We use sound statistical methods that include confidence tests at the 5% error probability level.
For this purpose we investigated a broad range of physical variables including air temperature, wind speed, sea surface temperature, ice cover, precipitation, oxygen as well as ecosystem variables such as phytoplankton biomass, zooplankton and several fish species from the Baltic Sea region.
Most of these time series do exhibit a statistical significant linear trend. But tests for structural breakpoints in these time series reveal only for some investigated variables the existence of a breakpoint in the 70-80ties of the last century. In contradiction to the seemingly well established “regime shift” hypothesis in the Baltic Sea no clear breakpoint can be identified in many physical variables and also not in most ecosystem variables including fish. Finally also the proposed reason for the supposed ecological regime shift in the Baltic Sea, the change of the NAO sign at around 1987, is not statistically significant.
Therefore we conclude that the Baltic Sea regime shift does remain a hypothesis and most physical and ecosystem time series data from the Baltic Sea region are statistically best described by a linear trend.JRC.H.1 - Water Resource
Yet Another Assessment of Climate Change in the Baltic Sea Area: Breakpoints in Climate Time Serie
No full textThe aim of the present study is to assess changes in the Baltic Sea climate based on different available meteorological data sources (ERA40 and ERA-INTERIM) and various published Baltic Sea climate indices. This regional assessment will be presented in relation to global climate change and assessments available from the literature.
The climate of the Baltic Sea which is located between 50N and 70N is mainly influenced by the competition of westerly humid air flow and easterly continental type air masses and is therefore highly variable. We are investigating air temperature, wind speed, cloud cover, solar radiation and precipitation. Comparisons to climate indices of general relevance as the Baltic ice cover will be conducted.
Using regression analysis we could confirm the following basic trends, increase in air temperature, increase in precipitation, increase in cloudiness. The increase in air temperature in the Baltic Sea area (0.02K/year) is much more rapid then the warming trend for the global air temperature (0.005K/year). The increase in cloudiness has resulted in an effective reduction of incoming solar radiation therefore the accelerated warming is not a result of increased solar radiation, but likely due to an increased net long wave radiation input. Further it has to be mentioned that not all available data sets confirmed the trend in cloudiness, ERA40 data show a nonsignificant decrease instead. No clear trend in the wind velocities could be detected, but wind velocities from ERA40 reanalysis project show an insignificant increase in wind speeds.
Results from model runs with the GETM model (General Estuarine Transport Model, http://getm.eu) show sea surface warming consistent with the increase in heat flux forcing and with satellite observations. The warmer sea surface without an adequate warming in the deeper parts results in a much stronger vertical density stratification and consequently to reduced vertical mixing. A more thorough inspection of the available regional and global data provides some reasonable doubt concerning the application of least square regression analysis to the available time series. Indeed it can be shown by a test based on the F statistics that most of the analyzed time series cannot be considered as stationary and therefore drawing simple regression lines trough these datasets is statistically incorrect. Testing for structural breakpoints in these time series reveals for many investigated parameters and also for many tested climate indices the existence of such breakpoints in the 70-80ties of the last century. Therefore it has to be concluded that the simple trend estimation for many climate parameters is statistically incorrect. Instead for statistical investigations it has to be assumed that there exist either 2 different climate states with either 2 different means or alternatively with 2 different trends which have to be estimated separately.JRC.DDG.H.3 - Global environement monitorin
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