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Black Sea Dynamics Joanna Staneva Click to edit Master text styles - - PowerPoint PPT Presentation

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Black Sea Dynamics Joanna Staneva Click to edit Master text styles Second level Third level in cooperation w ith: Fourth level E. Stanev Fifth level D. Dietrich, M. Bow m an, K. Buesseler, H. Livingston, J. Murrey, J.

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Joanna Staneva

in cooperation w ith:

  • E. Stanev
  • D. Dietrich, M. Bow m an, K. Buesseler, H. Livingston, J. Murrey,
  • J. Bullister, V. Kourafallou ( USA)
  • C. Lancelot, J.-M. Beckers, M. Gregoire ( Belgium ) , T. Oguz ( Turkey) ,
  • K. Schrum ( Norw ay) , K. Tsiaras ( Greece)
  • A. Stips ( JRC) , M. Vichi, ( I taly)
  • V. Roussenov, E. Peneva ( Bulgaria) .... and others ...

International Workshop for Numerical Ocean Modeling and Prediction Taipei, Taiwan, 23-25 April, 2008.

Black Sea Dynamics

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OUTLINE

  • Introduction

– The Black Sea – Models used

  • Results

– Simulation of processes – Circulation – Water mass formation

  • Coupled physical-biogeochemical modelling
  • Conclusions
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The Black Sea is an estuarine basin

The total freshwater supply of 3 x 102 km3/year is large compared to the basin volume (~ 5.4 x 105 km3) Unique water properties are due to small exchange in the straits.

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The Black Sea

Ocean models Atmospheric model

is where most kinds of numerical models based on primitive equations have been used to simulate circulation

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Ocean models

Ocean regimes and vertical coordinates

MOM DieCAST HOPS POP NCOM1 POM GHER GETM, ROMS, TOMS, NCOM2, OPA, z+ σ POLCOMS MICOM HYCOM (z+ σ+ ρ) NLOM HIM OPYC

H

∞

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Eastern Gyre Western Gyre

Bospho rus Sakaryia Synop Kizilirmak Batumi Caucasus Crimea Sevastopol

  • 10
  • 8
  • 6
  • 4
  • 2

4 6 8 10 12 2 (cm) Kaliakr a Danub e

Staneva et al. (2001, JMS)

Black Sea DieCAST (5 nm horizontal resolution), low friction model

Mechanisms of Black Sea circulation

Seasonal amplitudes: ~ 10 cm Amplitudes of interannual variations: ~ 5 cm.

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7 Deflection of Rim Current and entrainment of coastal water into the open sea The transition between summer and winter circulation is controlled by baroclinic eddies.

Specific questions answered by numerical models

The large seasonal stratification cycle above a relatively shallow and strong pycnocline shapes the seasonal cycle of potential vorticity. Staneva et al. (2001, JMS)

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DieCAST Results

Sea level Anomaly T/ P data: basin Rossby waves

Staneva et al. (2001, JMS)

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CIW formation in the Black Sea

  • General characteristics
  • Mixing basin
  • Fresh water

flux~300km3yr-1

  • Two-layer exchange in

straits

  • Limited vertical

exchange-the necessary condition of CIW mass formation

  • Open questions
  • Regions of formation
  • Rates of formation
  • Temporal and spatial

patterns

  • Transport of CIW
  • No winter data
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10 Below σt = 15.5 stratification is entirely dependent on salinity

Stanev et al. (2003, JMR)

Black Sea MOM (5 nm resolution)

The vertical circulation (~ 105m 3/ s) is much weaker than horizontal circulation (~ 5 x 106m 3/ s) and comparable with the amount of water entrained by the Mediterranean plume

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44.5N, 31E 42.5N, 32E

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Decadal changes (Black Sea MOM, 5 nm resolution)

Model versus hydrographic survey data (no assimilation)

Stanev et al. (2003, JMR)

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Coastal-Open ocean exchange: Black Sea MOM (5 nm resolution)

Convective heat flux: QCF= ro Cp (Ta

  • Tb

)/ Δt w conv= QCF / (ro Cp T)

Lee et al. (2002)

Replenishment time of CIL ~ 5 years

Water mass formation controlled by dynamics (topography)

)

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EBS-MOM-3 km Sea Surface Temperature

1991 1993

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Temperature Vertical Sections (1991)

EBS-MOM-3 km

Zonal Section –February Meridional Section –February Zonal Section –August Meridional Section –August

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Temperature (EBS-MOM-3 km)

A Time vs depth in box B B Time vs depth in box A

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Temperature (EBS-MOM-3 km)

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The Black Sea provides optimal possibilities, using easily manageable models and

  • bservational data to address a wide spectrum
  • f processes observed in the ocean.

It is a useful test region for developing models, which can then be applied to larger scales.

To conclude this part

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  • Models: ERSEM, NPZD, BI OGEN
  • w ith different levels of com plexity

and different resolution

Biogeochem ical m odelling Biogeochem ical m odelling Determ ine Determ ine: : process

process and and m echanism s m echanism s

  • f
  • f m ass tranfer from

m ass tranfer from land to land to ocean

  • cean

Validation: Validation: m odel sim ulations

m odel sim ulations versus survey and satellite data survey and satellite data

... ... to

to address the functioning address the functioning of

  • f the

the ecosystem ecosystem and and its response its response to to clim ate clim ate variabiliy variabiliy and hum an and hum an forcing forcing

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WHY A MODEL CAN DO BETTER THAN CORRELATIONS BETWEEN EVENTS?

  • The synergy between the different human forcing cannot

be assessed from simple correlations between ecological

  • bservations and historical correlations.
  • Mechanistic models, which describe the kinetics between

biological and chemical compartments as a function of meteorological and human forcings provide a powerful tool which encompasses this complexity.

  • The ecological model ERSEM is established in order to

assess the response of the north-western Black Sea ecosystem to human-induced changes and predict its future evolution.

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ERSEM

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Pelagic model Benthos model

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  • Atmospheric forcing – high frequency

atmospheric analyses data from ECMWF

  • River discharge – daily data taken from A.

Cociasu

  • Open BC – Black Sea MOM
  • Initialization:

Physical sub-model – MOM output Biogeochemical sub-model – ERSEM,

  • bservational data

OFF-LINE COUPLING BETWEEN MOM AND ERSEM

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Seasonal evolution of the vertical profiles of T and S

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  • Reg. 4

Model-data comparissons in 2003

D 1 2 3 4

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Biogeochemical modelling (Satellite v/s Model Data)

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Vertical profiles - 2003

Spring

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Scenario simulations

top-Danube discharge; bottom-model results. Black line 1984 (pre-eutrophication period); red line- 1993 (high eutrophication)

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Time evolution of some of the ecosystem variables

Black line 1984; red line 1993.

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Scenaria: Evolution of the vertical profiles

copepods [mg.C m –3 ] 80s 90s diatoms [mg.Chla. m –3 ]

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SCENARIO - SIMULATIONS

Diatoms (mg Cla /m3)

  • 0.5

0.5 1.5 2.5 3.5 Sc0 Sc1 Sc2 Sc3 Sc4 Sc5 Annual

Copepods (mg C /m

3)

5 10 15 20 Sc0 Sc1 Sc2 Sc3 Sc4 Sc5 Annual

Flagellates (mg Cla /m

3)

0.5 1 1.5 2 2.5 Sc0 Sc1 Sc2 Sc3 Sc4 Sc5 Annual

Microzooplankton (mg C /m

3)

5 10 15 20 Sc0 Sc1 Sc2 Sc3 Sc4 Sc5 Annual

Bacteria (mg C /m

3)

5 10 15 20 Sc0 Sc1 Sc2 Sc3 Sc4 Sc5 Annual

Oxygen saturation (%)

20 40 60 80 100 120 Sc0 Sc1 Sc2 Sc3 Sc4 Sc5 Annual

PO4 (mmol /m

3)

0.5 1 1.5 2 2.5 3 3.5 Sc0 Sc1 Sc2 Sc3 Sc4 Sc5 Annual

NO3 (mmol /m3)

5 10 15 20 25 Sc0 Sc1 Sc2 Sc3 Sc4 Sc5 Annual

SiO (mmol/m

3)

5 10 15 20 25 30 Sc0 Sc1 Sc2 Sc3 Sc4 Sc5 Annual

Sc1 “Business usual scenario” Sc4 “Green scenario” Sc2 “High production scenario” Sc5 “Policy scenario” Sc3 “Best available technique scenario”

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SCENARIO - INDICATORS

10 20 30 40 50 60 70 Sc0 Sc1 Sc2 Sc3 Sc4 Sc5 N-P ratio 20 40 60 80 100 120 140 Sc0 Sc1 Sc2 Sc3 Sc4 Sc5 DO sat%

50 100 150 200 250 300 350 400 Sc0 Sc1 Sc2 Sc3 Sc4 Sc5 Phy-Zoo ratio 200 400 600 800 1000 1200 Sc0 Sc1 Sc2 Sc3 Sc4 Sc5 Benthos (g/m2)

Indicators ranges

N/P P/Z O2 Benthic biomass

Bad state <10 and > 25 <100 50-75% 1.5-200 g.m-2 Moderate state 10-14and18-25 10-100 75-90% 200-850 g.m-2 Good state 15 – 17 >10 > 90% 850 - 6500g.m-2

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SUMMARY

  • The sequence of ecological events that took place

in the north-western Black Sea can be reproduced by the coupled model.

  • The model addresses the study of an ecosystem

submitted to changes both of its structure and functioning.

  • The

model predictions show that the euthrophication-related problem is a question of changing of the nutrient balance, not only qualitatively, but also quantitatively.

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SUMMARY

  • The model is calibrated for the Black Sea

conditions using hierarchy of observational data

  • It allows testing of different scenarios, regarding

the structure of the ecosystem as a function of meteorological and human forcing.

  • Thus, the model predictions will be of interest for

both scientists and policy makers and can be used for management purposes.

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Conclusions

  • 1. Motivating results
  • 2. Further increase of multidisciplinary studies as

well as synergy between observations and models is needed

  • 3. Rapid development in the field of ecosystem

modelling, climate variability, coastal

  • perational oceanography is expected in the

years to come (e.g. a number of international & national programmes, ECOOP, SESAME, EU- FP7 )

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