MAMAMalta 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

B

C

D

Stratification Mixing

lightlimitatio

n

New production

Regenerated production

Oceanic Ecosystems

Coastal

Ecosystems

Flagellatesand bacteria

Large phytoplankton

Microbial food web

Herbivorous food web

3

2

5

1

4

1 2

3

45

A

F

E

Legendre and Rassoulzadegan, 1995

Nutrient limitatio

n

Ocean ecosystem dynamics strongly coupled with Ocean dynamics

Factors limiting predictability:

DataPredictability of the atmospheric forcing (coastal areas).Predictability of external inputs (River runoff and nutrient load)

ModelOpen boundary condition (Limited area nested models)Definition of initial conditions for forecast simulationsInitial adjustment problem for nested models.

To overcome (or reduce) such problems, the forecasting System must encompass both the open and the coastalOcean scales……

The components of an interdisciplinary forecasting system

Buoy stations

Adricosm“in situ”ObservingSystemCurrentlyRunning

Adricosm remoteObserving System

SeaWifs

AVHRR

TOPEX

ERS-2

The coupled physica-ecological modelling system

Need - Water column and sediment prognostic equations for

Physical state variablesMacro-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)

The coupled physical-ecological modelling system

Need - Water column and sediment prognostic equations for

chemical state variablesC, N, P, Si, (equation for non conservative scalar properties)

biological state variables (Functional groups): Phytoplankton, bacteria, zooplankton etcEach organism can be described by a 4D vectorVj=[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):

Organism (C:N:P)organism

CO2

Basal activityStress respiration

Food components(C:N:P)food

Uptake Predation

Predators(C:N:P)food

Detritus fractions

MortalityExcretion Defaecation

Nutr.Nutrientexcretion

The “Standard Organism” (Functional group approach)

Thus, the fundamental structure ofthe marine ecosystemModel Is:

1. Physical environment description (macro and micro-scales)2. Chemical currencies3. Functional groups (Different species in a single group)4. Closure hypothesis(or individual based modelling) for Higher trophic levels.

All components interacting in a deterministicway with bulk parameterizations

The pelagic component of the MFSTEP Biogeochemical Fluxes Model

                                                                                                                                                                                    

The benthic component of the MFSTEP Biogeochemical Fluxes Model

Mathematical formulation

biolphys

j

tV

tV

t

V

jHHHj

Vj

SjHH

phys

j VKz

VK

zz

VwwVU

t

V

jk

N

1kbiol

j Ft

V

Where N are the number of theBiogeochemical interactions forEach functional group

EcologyPelagic Model

EcologyBenthic Model

CirculationModel

T (x, y, z, t)

S (x, y, z, t)

KH (x, y, z, t)

A (x, y, z, t)

u, v, w (x, y, z, t)

Nutrient inputParticulate

Inorganic Matter Qs Qb+Qe+Qhw (E-P-R)

PAR

Sedimentary andWater-Sediment

diffusive processes

THE GENERAL STRUCTURE OF THE MODELS FORCING AND COUPLING

TransportModel

Cp (x, y, z, t)

NumericalDriver

(Time Integration)

bio

p

t

C

phys

p

t

C

t

Cp

t

Cb

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 forcingBacterial biomass: 48 h simulation with 6hr atmospheric forcing

Observations Model

S1

AA1 S3

Critical Depth

ML Depth

Chl-a(Cd ave.)

1DImplementations physical/ecological interactions:the Sverdrup-like mechanism

O Data + stdev Standard model Improved model

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)

1DImplementationimproving biological processes

3D implementations: Nested approach based on MFSPPCirculation modelling

OGCMCoupled Model

RegionalCoupled Models

The MFSTEP Coupled Models Domain

The MFSTEP Coupled Models Domain

The Adriatic modelling systemBased on the Princeton Ocean Model (POM)

And the Modular Ocean Model (MOM)

AIM (Adriatic Intermediate Model)POMWhole Adriatic Sea.5 km horizontal resolution, 21 sigma layersNested with the Mediterranean SeaGeneral Circulation Model

NASM (Northern Adriatic Shelf Model)POMNorthern Adriatic only1.5 km horizontal resolution11 Sigma layersNested with AIM

Mediterranean Sea OGCM (MOM)1/8° Horizontal resolution31 levels

Preliminary results forthe Adriatic

Chlorophyll-a

Surface DOC distribution

mgC/m3

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