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MAMA Malta meeting, 27-30 January 2004 Expert Meeting

MAMA Malta meeting, 27-30 January 2004 Expert Meeting. Towards Operational ecological models in coastal areas Marco.Zavatarelli@unibo.it. The pelagic physical-biological interactions in the ocean. 2. B. Nutrient limitation. light limitation. 1. A. C. 1. 2. New production.

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MAMA Malta meeting, 27-30 January 2004 Expert Meeting

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  1. MAMA Malta meeting, 27-30 January 2004 Expert Meeting Towards Operational ecological models in coastal areas Marco.Zavatarelli@unibo.it

  2. The pelagic physical-biological interactions in the ocean 2 B Nutrient limitation light limitation 1 A C 1 2 New production Regenerated production Stratification Mixing F Coastal Ecosystems Oceanic Ecosystems 3 3 5 D E Microbial food web Herbivorous food web Large phytoplankton Flagellates and bacteria 5 4 4 Legendre and Rassoulzadegan, 1995

  3. Ocean ecosystem dynamics strongly coupled with Ocean dynamics Factors limiting predictability: Data Predictability of the atmospheric forcing (coastal areas). Predictability of external inputs (River runoff and nutrient load) Model Open boundary condition (Limited area nested models) Definition of initial conditions for forecast simulations Initial adjustment problem for nested models. To overcome (or reduce) such problems, the forecasting System must encompass both the open and the coastal Ocean scales……

  4. The components of an interdisciplinary forecasting system

  5. Buoy stations Adricosm “in situ” Observing System Currently Running

  6. Adricosm remote Observing System SeaWifs AVHRR TOPEX ERS-2

  7. The coupled physica-ecological modelling system Need - Water column and sediment prognostic equations for Physical state variables Macro-scale: T, S, ρ, p, u, v, w(equation of motion equation of state equations for scalar properties conservation) Sub-grid scale: Kv, KH, Iz(turbulence closure equations radiative transfer equations) Air-sea fluxes:τw, Q, (E-P)(bulk formulae) Water sediment interactions: τb,(bulk formulae)

  8. The coupled physical-ecological modelling system Need - Water column and sediment prognostic equations for chemical state variables C, N, P, Si, (equation for non conservative scalar properties) biological state variables (Functional groups): Phytoplankton, bacteria, zooplankton etc Each organism can be described by a 4D vector Vj=[VC, VN,VP,VSi] Where the subscripts C, N, P, Si are the “chemical currencies” or concentrations of chemical consituents in each organism Basic Assumption:The dynamicsof the marine ecosystem can be expressed by the dynamics of the j-th element in each functional group V (biomass based model):

  9. The “Standard Organism” (Functional group approach) CO2 Nutr. Nutrient excretion Basal activity Stress respiration Organism (C:N:P)organism Predation Uptake Food components (C:N:P)food Predators (C:N:P)food Mortality Excretion Defaecation Detritus fractions

  10. Thus, the fundamental structure ofthe marine ecosystem • Model Is: • Physical environment description (macro and micro-scales) • Chemical currencies • Functional groups (Different species in a single group) • Closure hypothesis(or individual based modelling) for • Higher trophic levels. All components interacting in a deterministic way with bulk parameterizations

  11. The pelagic component of the MFSTEP Biogeochemical Fluxes Model

  12. The benthic component of the MFSTEP Biogeochemical Fluxes Model

  13. Mathematical formulation Where N are the number of the Biogeochemical interactions for Each functional group

  14. THE GENERAL STRUCTURE OF THE MODELS FORCING AND COUPLING w Particulate Inorganic Matter Qs Qb+Qe+Qh (E-P-R) Nutrient input PAR KH (x, y, z, t) T (x, y, z, t) A (x, y, z, t) S (x, y, z, t) Ecology Pelagic Model Circulation Model Transport Model u, v, w (x, y, z, t) Cp (x, y, z, t) Sedimentary and Water-Sediment diffusive processes Numerical Driver (Time Integration) Ecology Benthic Model

  15. Implementation towards operational use of ecological models • MFS strategy: • Implementation of 1D models in data rich areas to • validate/calibrate models and check the physical/ • biological coupling (MFSPP task accomplished) • Extend the implementation to 3D with climatological • forcing and nesting approach (MFSTEP task underway) • Explore the use of data assimilation schemes for • biogechemical state variables (MFSTEP task underway)

  16. 1D implementations: Validation Chlorophyll Phosphate Observed Seasonal Inorganic Suspended Matter Profiles(forcing functions in the light attenuation processes)

  17. 1D implementations: Validation under high frequency forcing Bacterial biomass: 48 h simulation with 6hr atmospheric forcing Observations Model

  18. ML Depth 1DImplementations physical/ecological interactions:the Sverdrup-like mechanism Critical Depth S1 Chl-a(Cd ave.) AA1 S3

  19. O Data + stdev Standard model Improved model 1DImplementationimproving biological processes Comparison with observedBacterial Carbon Production (BCP) rates BCP = -b*f(T)*B +(1-BGE)*U(substrate) BGE = 0.3 (standard) BGE = c – a*T(Rivkin and Legendre, 2001)

  20. 3D implementations: Nested approach based on MFSPP Circulation modelling OGCM Coupled Model The MFSTEP Coupled Models Domain Regional Coupled Models

  21. The Adriatic modelling system Based on the Princeton Ocean Model (POM) And the Modular Ocean Model (MOM) NASM (Northern Adriatic Shelf Model) POM Northern Adriatic only 1.5 km horizontal resolution 11 Sigma layers Nested with AIM AIM (Adriatic Intermediate Model) POM Whole Adriatic Sea. 5 km horizontal resolution, 21 sigma layers Nested with the Mediterranean Sea General Circulation Model Mediterranean Sea OGCM (MOM) 1/8° Horizontal resolution 31 levels

  22. Preliminary results forthe Adriatic Chlorophyll-a

  23. 10 days 20 days Surface DOC distribution mgC/m3 winter 30 days

  24. Testing data assimilation schemes: The Singular evolutive Kalman Filter (Triantafyllou et al.2003)

  25. Testing data assimilation schemes: The Singular evolutive Kalman Filter (Triantafyllou et al.2003)

  26. Testing data assimilation schemes: The Singular evolutive Kalman Filter (Triantafyllou et al.2003)

  27. CONCLUSIONS • Operational ecological modelling lags (naturally) behind • operational circulation modelling • The nested modelling approach can potentially face the problem of capturing and describing the many spatial and temporal scales manifested in marine ecosystem dynamics • Potential for predictions is apparent • Data assimilation schemes can be successfully used

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