INTO THE BIGGREENY-BROWN

YONDER

INTO THE BIG, GREENY-BROWN YONDERChallenges

Modellingindividual to populationparameterisation of the mesoscale

Observationsbiologicalsynoptic

Analysishow should we describe patchiness?

PARAMETERISING INDIVIDUAL BEHAVIOUR

Prey tracking - cross diffusion?

Swarming

- use ideas from statistical physics?

Vertical migration - imposed population advection?

SWARMING

At the level of individual, what causes it?

social forcesproximity

arrayal forcesmatching speed and direction of neighbours

environmental effectschemical gradients hydrographycurrents

Flierl et al. (1999), J.Theor.Biol.

INTO THE BIG, GREENY-BROWN YONDERChallenges

Modellingindividual to populationparameterisation of the mesoscale

Observationsbiologicalsynoptic

Analysishow should we describe patchiness?

“The sea surface variability ofall properties shows a marked increase when the internalRossby radius of deformationis resolved. However, there areindications that tracers and processes…vary on yet smallerscales…”

“New production…increaseswith model resolution.”

INTO THE BIG, GREENY-BROWN YONDERChallenges

Modellingindividual to populationparameterisation of the mesoscale

Observationsbiologicalsynoptic

Analysishow should we describe patchiness?

LIMITATIONS ON BIOLOGICAL DATA

Very little spatial data, especially for…

zooplankton exacerbated by behaviour

rates (e.g. growth rate, grazing etc) vital for deriving functional forms governing modelled processes

community structure has the potential to significantly alter the

dynamics of a non-linear system

We have just enough data to show that all can displayconsiderable spatial structure.

Holligan, 1978

EMERGING SAMPLING TECHNIQUES

HPLC FRRF Hologrammetry Video sampling Multifrequency acoustics

Zoo

.

Rat

es

C.C

.

Also in situ nutrient sampling being developed.

All must still be used in conjunction with traditionaltechniques.

Prieur et al., 1993

HOLOCAM

Up to 100litresRate 0.1HzDepth 100mTarget speed<1m/s

P.Hobson (Brunel)R.Lampitt (SOC)

INTO THE BIG, GREENY-BROWN YONDERChallenges

Modellingindividual to populationparameterisation of the mesoscale

Observationsbiologicalsynoptic

Analysishow should we describe patchiness?

ACCOUNTING FOR LACK OF SYNOPTICITYOMEGA Project

J.T.Allen(SOC), M.Rixen (Liège)

3 techniques:

Geostrophic relocationUse estimate of velocity field to relocate observations to common time.Iterative procedure.

InterpolationInterpolate between two or more spatial surveys to create spatial coverage at a given time.

Spatio-temporal correlation methodAs the 2nd technique but weighted according time between observation and required common time.

Harvard Ocean Prediction SystemSrokosz et al., 1997; McGillicuddy et al., 1999;

Popova, 2001

Includes:- open ocean regional model- 6-cpt ecosystem model

- nitrate, phytoplankton, 2 zooplankton, detritus, ammonium

- data assimilation- temperature, salinity, nitrate, chlorophyll, 2 zooplankton, physical forcing

Resolution:- typically 2km for 200kmx200km domain

Can be used…- …to model data post-cruise- …to predict field evolution on cruise

-near real-time

Comparison with dataChl (mgC/m^3)

Phytoplankton*1.6 (mmolN/m^3)

Day 9

Day 15

Day 21

Day 24

AUTOSUB

INTO THE BIG, GREENY-BROWN YONDERChallenges

Modellingindividual to populationparameterisation of the mesoscale

Observationsbiologicalsynoptic

Analysishow should we describe patchiness?

Describing patchiness

The manner in which we describe a phenomenon affectsboth our understanding of it and the way in which we caninterrogate it.

Stating the obvious:The manner of description must suit the questionthat is to be asked.

Time to let spectra go.But what alternatives are there…

wavelets,3 point correlation functions,fractals…

Old theories of turbulence - big eddies begat little eddies - they do so the same everywhere

But observation contradicts this.Reality is intermittent.

True at all scales.

Patchiness theories need to berevised in light of intermittency.

Intermittent forcing?

Seuront et al.

(1999)

A QUICK GUIDE TO FRACTAL BEHAVIOUR

Structure function:<(S)q> = <|S(t+)-S(t)|q>

Scaling: <(S)q> = <(S)q>(/T)(q)

(q) is the scaling exponent

Monofractal: (q) is linearMultifractal: (q) is non-linear Universal multifractal: (q)=qH-[C1/(-1)](q-q)

=1+ (2)

Pascual et al., 1995

Seuront et al., 1999

Colour sensor on aircraftJune 2001700km transect5m resolution5 decades of data

“We expect that the connection between pattern andprocess for multifractal variability in the planktonwill develop along a similar path to spectral analysis.the initial uses of spectral analysis were purely descriptive. That use was followed by a connection of spectral analysis to phenomenological modelsand only later by a connection to mechanistic models.”

Pascual et al., 1995

Toroczkai et al., 1998

dSB/dt = -fSB + cvSB

Are distributions of biomass and production in theocean controlled by the geometrical structure of the flow?

How can the underlying geometrical structure befound?

- “full” velocity field required- at what temporal and spatial scales?

- how can it be found?