MAMA Malta meeting, January 2004 Expert Meeting Towards Operational ecological models in coastal areas

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

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……

The components of an interdisciplinary forecasting system

Buoy stations Adricosm “in situ” Observing System Currently Running

Adricosm remote Observing System SeaWifs AVHRR TOPEX ERS-2

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: K v, K H, I z (turbulence closure equations radiative transfer equations) Air-sea fluxes: τ w, Q, (E-P) (bulk formulae) Water sediment interactions: τ b, (bulk formulae)

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=[V C, V N,V P,V Si ] 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):

Organism (C:N:P)organism CO 2 Basal activity Stress respiration Food components (C:N:P) food Uptake Predation Predators (C:N:P) food Detritus fractions Mortality Excretion Defaecation Nutr. Nutrient excretion The “Standard Organism” (Functional group approach)

Thus, the fundamental structure ofthe marine ecosystem Model Is: 1.Physical environment description (macro and micro-scales) 2.Chemical currencies 3.Functional groups (Different species in a single group) 4.Closure hypothesis(or individual based modelling) for Higher trophic levels. All components interacting in a deterministic way with bulk parameterizations

The pelagic component of the MFSTEP Biogeochemical Fluxes Model

The benthic component of the MFSTEP Biogeochemical Fluxes Model

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

Ecology Pelagic Model Ecology Benthic Model Circulation Model T (x, y, z, t) S (x, y, z, t) K H (x, y, z, t) A (x, y, z, t) u, v, w (x, y, z, t) Nutrient input Particulate Inorganic Matter QsQs Q b +Q e +Q h ww (E-P-R) PAR Sedimentary and Water-Sediment diffusive processes THE GENERAL STRUCTURE OF THE MODELS FORCING AND COUPLING Transport Model C p (x, y, z, t) Numerical Driver (Time Integration)

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)

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

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

S1 AA1S3 Critical Depth ML Depth Chl-a (C d ave.) 1DImplementations physical/ecological interactions: the Sverdrup-like mechanism

O Data + stdev Standard model Improved model Comparison with observed Bacterial 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) 1DImplementationi mproving biological processes

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

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

Preliminary results forthe Adriatic Chlorophyll-a

Surface DOC distribution mgC/m 3 winter 10 days 20 days 30 days

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

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

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

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