Quantitative modelling of spatial variability in the north Atlantic spring phytoplankton bloom

(1)

University of Southampton Research Repository

ePrints Soton

copyright owners. A copy can be downloaded for personal non-commercial

research or study, without prior permission or charge. This thesis cannot be

reproduced or quoted extensively from without first obtaining permission in writing

from the copyright holder/s. The content must not be changed in any way or sold

commercially in any format or medium without the formal permission of the

copyright holders.

When referring to this work, full bibliographic details including the author, title,

awarding institution and date of the thesis must be given e.g.

AUTHOR (year of submission) "Full thesis title", University of Southampton, name

of the University School or Department, PhD Thesis, pagination

(2)

UNIVERSITY OF

SOUTHAMPTON

QUANTITATIVE

MODELLING

OF SPATIAL

VARIABILITY IN

THE

NORTH ATLANTIC SPRING

PHYTOPLANKTON

BLOOM

John

Christopher

Paul

Hemmings

A thesis submitted in candidature for the

degree

of

Doctor of

Philosophy

School

of Ocean and Earth Science

(3)

UNIVERSITY OF SOUTHAMPTON

ABSTRACT

FACULTY OF SCIENCE

OCEAN AND EARTH SCIENCE

Doctor of

Philosophy

QUANTITATIVE

MODELLING OF SPATIAL VARIABILITY IN THE NORTH

ATLANTIC SPRING PHYTOPLANKTON BLOOM

by

John

Christopher

Paul

Hemmings

The effects of

variability

in the

physical

environment on the

development

of the

spring

phytoplankton

bloom are

investigated

using

a

physically

forced model of the annual

plankton cycle

in theoceanmixed

layer.

The model is

optimised

to fit survey data from the easternNorth

Atlantic,

collectedover a1500x 1500 kmareabetween 39N and

54N,

from

April-June

1991,

establishing

the

feasibility

of

using

spatially

distributed

point-in-time

data

inmodel calibration.

Measurements made below the seasonal

pycnocline

show the existence of an

empirical

relationship

between

preformed

nitrate and

salinity

in this area,

allowing salinity-based

estimates of

pre-bloom

mixed

layer

nitrate concentration to be made. These estimates

provide

important

additional constraints for the model. The observed

spatio-temporal

patterns,

at scales between 36 km and 1500

km,

in

nutrients,

chlorophyll

and measures of

bloom

progression

derived from these data with referenceto

pre-bloom

nitrateare

discussed,

together

with the

corresponding

patterns

in seasonalstratification.

During

the

spring

bloom,

when

biogeochemical

concentrations _vary

rapidly

in response to

the

developing

stratification,

absence of data

defining

its

history

limits the value of

comparison

between

point-in-time

observations and model results. Predictions of variation

in stratification at the seasonal time-scale from

general

circulation models

(GCMs)

can be

used in

place

of observational data to force

ecosystem

models.

However,

the

degree

to

whichobservations are used to constrain the model solutions should allow for both model

errorin stratification and

misrepresentation

of the seasonal

development

ofstratification

by

the observations. The latter occurs due to

sampling

error associated with short-term

fluctuations.Itcanbecorrected for ifasuitable contemporary sea surface

temperature

data

setis availabletodefine the variation of mixed

layer

temperature

atthe seasonal time-scale.

Itis shown that the accuracy of the GCM

predictions

canbe

improved by

the

application

of

meteorology

specific

to the _year of observation. It is also shown that the

sensitivity

of

theecosystem model

predictions

to errorin the

physical

forcing

canbe reduced

by matching

model and observations

by

astratification measure, rather than

by

time,

when

comparing

fields. The _{survey data}showan

important

contribution to the stratification

arising

from the

'tilting'

action of vertical shear on

pre-existing

horizontal

buoyancy

gradients

in the winter¬

time mixed

layer.

This effectwas

severely

underestimated

by

the GCM. The

discrepancy

canbe accounted for

by

the absence of

density

fronts and mesoscale

dynamics

in the model.

Ecosystem

model results _suggestthat

spatial

variance in

Zooplankton grazing,

due to the

effect of differences in the

depth

and duration of winter-time

mixing

on the

over-wintering

success of

plankton populations,

is one ofthe

major

factors

controlling

the

spatial

and

(4)

CONTENTS

LIST OF FIGURES in

LIST OF TABLES xü

ACKNOWLEDGEMENTS

xiii

Chapter

1. INTRODUCTION 1

1.1 The Role of Plankton in the Global Climate

System

1

1.2 The North Atlantic

Spring

Bloom 4

1.3

Project

Overview 25

Chapter

2. THEVTVALDI 1991 SURVEY 27

2.1 The Vivaldi '91 Data Set 27

2.2 Overview ofData

Analysis

32

2.3 Seasonal Stratification 36

2.4 Contributionof

Buoyancy Transport

tothe Seasonal Stratification 48

2.5 Short-term Fluctuations in the Seasonal Stratification 64

2.6 Pre-Bloom

Mixed-Layer

NitrateConcentration 72

2.7 Nitrate Utilisation and

Phytoplankton

Standing

Stock 88

2.8

Summary

and Discussion of

Survey

Results 104

Chapters.

MODELLING THE 1991 SPRING BLOOM Ill

3.1 Overview of

Modelling Investigations

Ill

3.2 The Mixed

Layer

Ecosystem

Model , 114

3.3 Method:

Application

of the Modeltothe

Study

Area 124

3.4

Physical

Model Results 144

3.5

Ecosystem

Model Results 157

3.6

Spatial

Patternof Bloom

Development

185

(5)

Chapter

4. CONCLUSIONS 224

4.1

Comparing

Model Results With Point-in-time Observations 224

4.2 Main Conclusions 226

4.3

Implications

forFutureResearch 228

APPENDICES 230

Appendix

A. Calibration of

Chlorophyll

FluorescenceData 230

Appendix

B. TheExtentof Winter-time Mixed

Layer

Water 238

Appendix

C. Estimation of5 MeanSea Surface

Temperature

243

Appendix

D.

Physical

Model

Configuration

252

Appendix

E.

Ecosystem

Model Solutions 254

(6)

LIST OF FIGURES

Figure

2.1. The Vivaldi '91 survey area:RRS Charles Darwin CD58 andCD59 30

cruise tracks andCTDstation

positions,

overlaidonthermal satellite

image

showing

sea-surface

temperature

featuresat0848 GMTon26/05/91. Cloud is

maskedout.Thelowest

temperatures

areshowninblue and the

highest

inred.

Data

provided

by

S. Groom,

RAGU,

University

of

Plymouth.

Figure

2.2. Vertical sections of

density

relativeto theseasurface

(kg

m'3).

45

Isopycnals

overlaid

showing

oe

(kg

m"3).

Reference lines show

positions

of

daily

minimum

(solid)

and maximum

(dashed)

heatstorage.

Figure

2.3. Vertical sections of seasonal

density

anomaly

Ap

(kg

m"3).

46

Isopycnals

overlaid

showing

g6

(kg

m"3).

Figure

2.4. Surface seasonal

density anomaly

Ap(0)

and

temperature

at 150 m 47

7X150).

Figure

2.5. Vertical sections of seasonal

salinity anomaly

AS.

Isopycnals

59

overlaid

showing

ae

(kg

m'3).

Figure

salinity

S.

Isopycnals

overlaid

showing

ae

(kg

60

m-3).

Figure

2.7. &-S

profiles

for all Vivaldi stations. The differentsurvey

legs

are 61

indicated

by

colourasshown in the

key.

Isopycnals

are shown for reference and

the

heavy

black line indicates the V91 referencecurve

(Pollard

et

ed., 1996).

Figure

2.8.

(a)

300 kmmeansurface seasonal

density anomaly

Ap(0)

and 62

estimated

density

anomaly

duetoair-sea fluxes

only

(ALp)q(0).

The difference

is the estimated

buoyancy

transport component

(Ap)T(0).

(b)

The

percentage

contributionmade

by

transport

of

pre-existing

buoyancy

tothe observed

seasonal

density anomaly,

i.e.

(Ap)T(0)

/

Ap(0).

Figure

2.9. ADCPcurrent

velocity

at 100m

depth.

The

approximate position

63

of

major

fronts,

asidentified

by

Pollardetcd.

(1996),

arealso shown. Theseare

the PolarFront

(solid line)

and theweakerfront associated with the southern

(7)

Figure

2.10. Event-scaleSST

anomaly öT(0), showing

95%confidence 69 intervals.

Figure

2.11. 300 kmmeansurface seasonal

density anomaly Ap3o(O), showing

70

68% confidence intervals. Dotted line showsuncorrectedseasonal

density

anomaly Ap(0).

Figure

2.12. Distribution of event-scale

density anomaly

<5p(0) against

seasonal 71

density anomaly

Ap30(0).

Figure

2.13. N-S distribution for winter-time mixed

layer

water

(AOU

between 83

10and 35 mmol

m"3).

Figure

2.14. 6-S distribution for winter-time mixed

layer

water

(AOU

between 84

10 and 35 mmol

m'3).

Isopycnals

are shown for reference and the

heavy

black

line indicates theV91 referencecurve

(Pollard

et

al.,

1996).

Figure

2.15.

No-S

distribution for winter-time mixed

layer

water

(AOU

between 85

10 and35 mmol

m'3).

The model

prediction

is shown

by

thesolid line.

Figure

2.16.

(a)

Average

winter-time mixed

layer depth (m)

basedonLevitus' 86

(1982)

climatology, (b)

Winter-time mixed

layer

nitrateconcentration

(mmol

m"3)

estimated

by

the Glover andBrewermethod,

(c)

Winter-timemixed

layer

nitrateconcentration

(mmol

m'3)

estimated

by

the

salinity-based

method.

Figure

2.17.

(a) Average

winter-time mixed

layer depth

HatVivaldi station 87

positions,

(b)

AOU at

depth

H.

(c)

Deviation of3 winter-time nitrate estimates

from the estimate basedonmixed

layer

salinity

N0(S5.w).

The first estimate

iVH

is the nitrate at

depth //(as

inGlover and

Brewer,

1988),

the second is the

preformed

nitrate atthe same

depth

NoH

and the third is the model

preformed

nitrate basedonthe

salinity

atthis

depth

N0(S}\).

Figure

2.18. Surface nitrateNand estimated

pre-bloom

nitrate

No.

Reference 97

lines show

positions

of the PolarFront

(solid)

and the weaker front associated

with the southern branch of the NAC

(dashed).

Figure

chlorophyll

a

(mg

m"3).

Isopycnals

overlaid 98

showing

ae

(kg

m"3).

Chlorophyll

concentration is

represented

by

a

log

colour

(8)

Figure

2.20. Surface

phosphate,

silicate and nitrate. Reference lines show 99

positions

of the PolarFront

(solid)

and the weaker front associated with the

southern branch of theNAC

(dashed).

Figure

2.21. SeaSoartransect

legs

CD58AandB.Vertical sections of

(a)

100

density

relativetotheseasurface

(kg

m"3)

and

(b)

chlorophyll

a

(mg

m'3).

Isopycnals

overlaid

showing

ae

(kg

m"3).

Figure

2.22. Estimated surface

phytoplankton

concentration

P,

nitrate 101

utilisationANand

pre-bloom

nitrate

iV0.

positions

of the

Polar Front

(solid)

and the weaker front associated with the southern branch of

the NAC

(dashed).

Figure

2.23. Fractional nitrate utilisation

i?N

and fraction of nitrate utilisation 102

unaccounted for

by

standing

stock

RP.

positions

of the

Polar Front

(solid)

and the weaker front associated with the southern branch of

the NAC

(dashed).

Figure

2.24. Distribution of fractional nitrateutilisation

R^

against salinity

for 103

surfacewater

samples

taken

during

transects.

Figure

2.25.

(a)

Meanlatitude of

sampling,

(b)

300 kmmeansurface seasonal 109

density anomaly Ap30(0), showing

68% confidence

intervals,

and 300kmmean

uncorrected seasonal

density

anomaly Ap(0). Ap(0)

is

plotted

as anestimate of

Ap3o(O)

where the latter is unavailable,

(c)

300 kmmeanmixed

layer depth,

basedon a

density

difference criterion of 0.1

kg

m'3,

showing

rangefor each 300

kmtransect,

(d)

300 kmmeanestimated surface

phytoplankton

concentration

P,

nitrate utilisationANand

pre-bloom

nitrate

No.

Figure

2.26. Distribution of

(a)

nitrate utilisation

AN,

(b)

fractional nitrate 110

utilisation

i?N

and

(c)

fraction of nitrate utilisation unaccounted for

by

standing

stock

i?p,

against

seasonal

density anomaly 4p30(0).

Data

points

in_grey show

observations withnoevent-scale

anomaly

correction.

Figure

3.1. Schematic

diagram

of the mixed

layer ecosystem model, showing

123

nitrogen

compartments,

inter-compartmental

flows and mixed

layer imports

and

(9)

Figure

3.2. Model

monthly

meanmixed

layer

depth

(m)

for the

climatological

136

simulation.

Figure

3.3.

Climatological

bi-monthly

meanmixed

layer

depth

(m)

estimated 137

from

temperature

profiles

using

a1C difference criterion.

Maps

reproduced

from Lamb

(1984).

The box shows the

present

study

area.

Figure

3.4.

Climatological monthly

meanfractional cloudiness

(COADS).

138

Figure

3.5.

Salinity

fields for the sub-surface nitrate model:

(a)

observed surface 139

salinity, mapped

from300kmmeanvalues for each VivaldiSeaSoarrun,and

(b)

model

salinity

in mid-March for the

climatological

simulation.

Figure

3.6. Time series of5 meanheat

input

totheoceanfor Marchto

June,

140

integrated

from the

beginning

of the

heating

season.

Figure

3.7. Model

monthly

meanEkman

transport

(m2

s"1)

for the

climatological

141

simulation.

Figure

3.8. 1991

monthly

meanEkman

transport

(m2 s"1),

for

January

to

July,

142

basedonthe COADS 1991

objectively analysed

windstress vectors.

Figure

3.9. Model5 meantime series of surface seasonal

density anomaly,

151

from Marchto

June,

atVivaldi SeaSoarrunlocations. Results for the

climatological

and 1991 simulationsareshown. The data

points

show the

Vivaldi seasonal

density anomaly

estimate

Ap3o(O)

with 68% confidence

intervals.

Figure

3.10. Model5 meansurface seasonal

density anomaly

attime and 152

location of Vivaldi SeaSoarruns,

compared

with Vivaldi

Ap30(0)

estimate

showing

68% confidence intervals.

Figure

3.11. Time shift8/

required

for

matching

Vivaldi

Ap3o(O)

to seasonal 153

density

anomaly

for the 1991 simulation. 68% confidence intervalsare

shown,

basedon errorin the

Ap30(0)

estimate. The time shift determined

using

the

(10)

Figure

3.12. Model 5 meansurface seasonal

salinity anomaly

atVivaldi 154

SeaSoarrun

locations,

compared

with theobserved 300 kmmean

anomaly

atthe

surface and maximum

anomaly

overthe 0 -150m

depth

_range. Results are

shownforboth simulationsatthetimeof

sampling

tsandforthe 1991

simulationat_ts+6/.

Figure

3.13. Profiles of5 meanseasonal

density anomaly against

depth

for the 155

1991

simulation,

atVivaldi SeaSoarrun

locations,

atthe time of

sampling

ts and

at_ts+St. Observed 300 kmmean

profiles

of

Ap

and

(ALp)Q

areshown for

comparison

atlocations where stratification isestablished.

Figure

3.14.

(a)

5 meanmixed

layer

depth

for the 1991 simulationatVivaldi 156

SeaSoarrun

locations,

at_{time ts}+

S/,

compared

with observed 300 kmmean

value basedon a

density

difference from the surface of 0.1

kg

m"3.

Model 68%

confidence intervalsareshown basedon errorinthe

Ap30(0)

estimate,

(b)

Observed event-scale SST

anomaly

57(0)

with 95% confidenceintervals.

Figure

3.15. Model solutions for

nitrate,

from MarchtoJune

1991,

atVivaldi 173

SeaSoarrun

locations,

showing

observations used in the

parameter

optimisations.

Temporal

windows for

time-of-sampling

observationsreflect

68% confidence intervals for

Ap30(0)

estimate. Errorbars for

pre-bloom

nitrate

estimates also show 68% confidence intervals.

Figure

3.16. Model solutions for

chlorophyll

a, from MarchtoJune

1991,

at 174

Vivaldi SeaSoarrun

locations,

showing

observationsused in the

parameter

optimisations.

Temporal

windows for

time-of-sampling

observationsreflect

68% confidence intervals for

Ap30(0)

estimate.

Figure

3.17. Model solutionsatVivaldi SeaSoarrunlocations for

(a) pre-bloom

175

nitrate

No,

(b)

nitrate,

(c)

chlorophyll

aand model derived values for

(d)

nitrate

utilisation

AN,

(e)

fractional nitrate utilisation

i?N

and

(f)

fraction of nitrate

utilisation unaccounted for

by standing

stock

RP.

Observed300 kmmeanvalues

areshown for

comparison.

With the

exception

of

pre-bloom

nitrate,

the model

values arethoseatthe time of least misfit

(to

nitrate and

chlorophyll)

within the

observationwindows.

Figure

3.18. Model time series of

Rp,

the fraction of nitrate utilisation 177

unaccountedfor

by standing stock,

from MarchtoJune

1991,

atVivaldi

(11)

Figure

3.19.

(a)

Meanand

(b)

maximumnew

production (mmol

N

m"3

d"1)

for 178

each of the 1991 model solutionsoverthe

period during

which nitrate utilisation

isbetween 1 and6mmol

m"3. (c)

Model

pre-bloom

nitrate

(mmol

m"3).

Figure

3.20.

(a)

Annual areal

primary

production

and

(b)

new

production

(mol

179

N

m~2

y"1)

and

(c)

annual

/-ratio

for each of the

climatological

model solutions.

Figure

3.21.Annual

cycles

of areal

primary production

for each of the 180

climatological

model solutionsat5 intervals.

Figure

3.22. Annual

cycles

of arealnew

production

for each of the

climatological

181

model solutionsat5 intervals.

Figure

3.23. Annual

cycles

of/ratio

for each of the

climatological

model 182

solutionsat5 intervals.

Figure

3.24. Annual PON

export

for each of the

climatological

model solutions. 183

Figure

3.25. Annual

cycles

ofPON

export

for each of the

climatological

model 184

solutions at5 intervals.

Figure

3.26. Winter-time minima of

(a)

phytoplankton

and

(b)

197

logio(zooplankton)

concentrations

(mmol

N

m"3).

Figure

3.27. Time series of

phytoplankton

and

Zooplankton

concentrations 198

(mmol

N

m"3),

areal

Zooplankton

concentration

(mmol

N

m'2)

and mixed

layer

depth

from

January

toJune at5 intervals.

Figure

3.28. End-of-winter areal

Zooplankton

concentration

(mmol

N

m'2).

199

Figure

3.29. 10

day

interval time series from MarchtoJune,

(a)

Mixed

layer

200

depth

(m). (b)

Nitrate

(mmol

m3).

(c)

Phytoplankton (mmol

N

m'3).

(d)

Zooplankton (mmol

N

m'3).

(e)

10

day

meanareal

primary production (mmol

N

m"2

d"1).

(f)

10

day/-ratio,

(g)

10

day

meanPON

export

(mmol

N

m"2

d'1).

(12)

Figure

3.30. Time series of nitrate utilisation and

phytoplankton

from Marchto 202

June at5 intervals. The

pre-bloom

nitratelevel is shown for reference. Vertical

reference lines show 10

day

intervalsfromtime of initial stratification for

cross-reference with

Figure

3.33.

Figure

3.31. Time series of biomass

specific phytoplankton

fluxes,

from March 203

toJune,at 5 intervals. Vertical referencelines show 10

day

intervals from time

of initial stratification forcross-referencewith

Figure

3.33.

Figure

3.32. Nitrate utilisation for the

adjusted

winter nitraterun

compared

with 204

the standardrun. The

pre-bloom

nitratelevelis shown for reference.

Figure

3.33.

Spatial

_patternattime of initialstratificationabove 150 mand at10 205

day

intervals thereafter,

(a)

New

production

(mmol

N

m"3

d"1).

(b)

Phytoplankton (mmol

N

m"3).

(c)

Specific

new

production

(d"1).

(d)

Light

limited

specific growth

rate

(d"1).

(e)

Nitrate limitation factor,

(f)

Ammonium

inhibition factor.

Figure

phytoplankton, Zooplankton

and ammonium 206

concentrations and areal

Zooplankton

concentration from mid-Marchtothe end

of

May

at5 intervals. Vertical reference lines show 10

day

intervals from time

of initial stratification for cross-reference with

Figure

3.33.

Figure

nitrate,

from MarchtoJuneat5

intervals,

for the 220

1991 and

climatological

simulations and for the

climatological

simulation

matchedtothe 1991 simulation

by

seasonal

density anomaly.

Figure

phytoplankton,

from MarchtoJune at5

intervals,

221

for the 1991 and

climatological

simulations and for the

climatological

simulation

matchedtothe 1991 simulation

by

seasonal

density anomaly.

Figure

3.37. Time series of surface seasonal

density

anomaly,

from Marchto 222

Juneat5

intervals,

for the 1991 and

climatological

simulations.

Figure

3.38.

Temporal

maximum in

phytoplankton

concentration

(mmol

N

m"3)

223

as afunction of the winter-time nitrate maximum and the winter-time

(13)

Figure

A. 1. Estimated12hourmean

unquenched

fluorescence

yield

reciprocal

235

Rn

for

(a)

pumped

waterand

(b)

in situwater. The data

points

show the

yield

reciprocal

Rfor

night-time

observations

(black)

and

day-time

observations

(grey).

Unitsare

arbitrary

andnot

directly

comparable

between thetwo

figures

orbetweenthe 3

segments

delineated

by

the

heavy

verticallinesin

Figure (a).

These indicate the times ofinstrument

changes affecting

fluorescence

yield.

Figure

A.2. Variation of estimated reference

quenching

factor

0(7,1)

with PAR 236

I. Thecurves

represent

long-term

meanresponsesfor each cruise.

Figure

A.3. Estimated

quenching

factorataPARlevel of 100 W

m"2,

0(100,6).

237

Figure

B.I. Verticalsectionsof 300 kmmean

stability

for Vivalditransect

legs

241

8

m"1).

Figure

B.2. Vertical sections of 900 kmmeanAOU for Vivalditransect 242

legs

(mmol

m"3).

Figure

C.I. COADS SSTcoverage:

percentage

of the 25one

degree

bins 247

contributing

tothe5 meanSST

r30(0),

centredonVivaldi SeaSoarrunmid¬

points.

Figure

C.2.

Objectively

analysed

COADS

monthly

meanSST

(C)

for March 248

to June 1991.

Figure

C.3. Positional bias for COADS 5 meanSST with

respect

toVivaldi 249

SeaSoarrun

mid-points.

Figure

C.4. Time series of COADS 5 meanSST

r30(0),

from MarchtoJune 251

1991,

centredonVivaldi SeaSoarrun

mid-points. Temperature

scalesareoffset

for each locationto

give

the

temperature

difference relativetothe local March

value. The data

points

show themeans for each 10

day

bin with 95%

confidence intervals. Crosses show the Vivaldi 300 kmmeanSST

7(0).

Figure

E.I. Model 5 meantime series of mixed

layer

depth

atVivaldi SeaSoar 255

runlocations for the

climatological

and 1991 simulations.

Figure

E.2. Time seriesof CO ADS

objectively

analysed

fractional

cloudiness,

at 256

Vivaldi SeaSoarrun

locations,

from

climatological

and 1991 datasets.

Figure

E.3. Model solutions for

phytoplankton,

from Marchto June

1991,

at 257

(14)

Figure

Zooplankton,

from Marchto June

1991,

at 258

Vivaldi SeaSoarrunlocations.

Figure

detritus,

from MarchtoJune

1991,

atVivaldi 259

SeaSoarrunlocations.

Figure

ammonium,

from Marchto June

1991,

at 260

Vivaldi SeaSoarrunlocations.

Figure

primary production,

from MarchtoJune

1991,

261

atVivaldi SeaSoarrunlocations.

Figure

E.8. Model solutions

for^ratio,

from Marchto June

1991,

atVivaldi 262

(15)

LIST OF TABLES

Table2.1.

Navigation

data for 300 kmtransect

(SeaSoar run)

mid-points.

31

Table 3.1.

Ecosystem

model

parameters.

143

Table 3.2. Parametersetsforeachof the model solutions. 172

Table 3.3. Meanmisfit

Tobs/n

for each of themodelsolutions. 176

Table 3.4. Fraction of observed variance

explained

by

each of the model 176

solutions and

by

the surface seasonal

density anomaly.

Table 3.5. Maximum mixed

layer depths

atwhich the

specified plankton

188

concentrationscanbe maintainedat57.5N.

TableA. 1.

Quenching

function

parameters

with 68% confidence intervals. 233

Table A.2. Fluorescence calibration statistics. 234

Tabled. Event-scale

temperature

anomalies. 250

(16)

ACKNOWLEDGEMENTS

This workwas carriedout

part-time

under the

supervision

of Kelvin Richards and

Mike Fasham. I'd liketo thank both Kelvin and Mike formany

helpful

and

stimulating

discussions and

generally keeping

me onthe

right

track. In

particular,

I'd liketothank Mike

for his

guidance

and

understanding

in the combined role of linemanagerand research advisor

while Iworked in the Institute of

Oceanographic

Sciences

(later

part

of

Southampton

Oceanography

Centre)

and his continued

support

and

encouragement

thereafter.

Thanksarealso dueto_{everyone aboard the Charles Darwin}oncruises CD58 and

CD59 who worked

extremely

hardto make the Vivaldi '91 surveya

great

success.

My

thanks_go out

especially

to Stuart

Cunningham

and_everyoneelse involved in the mammoth

task of

working

up over700 hours of SeaSoar

data,

to Susan

Holley

for her workonthe

nutrient

data,

to

Morag

Stirling

for

keeping

me sanewhile

processing chlorophyll samples

at

sea onCD59 andtoTomAnderson for

holding

the

chlorophyll processing together

sowell

on CD58.

Iam

exceptionally grateful

toYanliJia for her

help

with

running

the MICOM

general

circulation model and Simon

Josey

for his

help

with

extracting

and

processing

COADS data. Also mythanks extendto anumber of other

people

who have

given

very

constructivecomments and adviceonthis work: Ian

Totterdell,

Tom

Anderson,

Peter

Challenor,

George

Nurser,

Mike

McCulloch,

Raymond

Pollard and_myexaminers Ric

Williams and Jonathan

Sharpies.

I'd

finally

liketothank all_myfriends and

family

for their

patience

(Trog

deservesa

special

mention

here)

andmostof all Sue for

showing

methe_way,

making

it

possible

forme

(17)

Chapter

1

INTRODUCTION

1.1

The Role

of

Plankton in the

Global Climate

System

In the last decadeor so

oceanographers

have takenon a new

challenge:

to

develop

a

realistic model of theoceanfor the purposes of

understanding

its role in Earth's climate

system

(Mooney 1992).

Itis

recognised

that

anthropogenic

activities,

principally

fossil fuel

burning

and land-use

changes,

have ledto adramatic increase in

atmospheric

concentrations

of

radiatively

active gases,with the

potential

tocause

significant

changes

in climate

through

a

warming

of the

planet's

surfaceatthe

global

scale

(Intergovernmental

PanelonClimate

Change,

1990 &

1996).

These _gasesareoften referredtoas

greenhouse

_gasesbecause

they

act

by reducing

theamountof heat

escaping

through

the

atmosphere.

While

anthropogenically

induced

changes

in climateare

currently

difficultto measurein the

presenceof natural

variability,

the 1996IPCC

report

concludes that "the balance of evidence

suggests

that there isadiscernible human influenceon

global

climate".

The most

important

of these

anthropogenic

greenhouse

_gasesis carbon

dioxide,

the

atmospheric

concentration of which has increased froma

pre-industrial

level of about 280 ppmv to_{358 ppmv} in 1994

(IPCC,

1996).

Only

about half the

anthropogenic input

remains

in the

atmosphere.

The remaindermustbeaccounted for

by

increases in

uptake by

theocean

and

by

terrestrial

ecosystems.

Anenormous amount of research effort is

currently

directed

at

improving

our

understanding

of the roles of these

sub-systems

in the

global

carbon

cycle.

Moore

(1992)

discusses the ocean's role in the overall

system.

Carbon

uptake by

the

oceansis

essentially

driven

by

two_processes, which

together

draw down carbon_away from

the surfacewaters and maintainavertical

gradient

of dissolved

inorganic

carbon

(DIC)

in the

(18)

-1-1.1 TheRole

of

Plankton intheGlobalClimate

System

DICtotheocean

depths

as aresult of

deep

waterformationat

high

latitudes. The other

process isthevertical carbonfluxdueto

biological

activity,

or

biological

pump,

by

whicha

proportion

of the carbonfixed

by primary

production

in the

euphotic

zone is

eventually

remineralised in

deep

wateror inthe sediments

(Longhurst

and

Harrison,

1989).

Estimates

from

modelling

studies

(Bacastow

and

Maier-Raimer, 1990;

Shaffer, 1993)

suggestthat

about three

quarters

of the difference in DIC between the surface and

deep

waters is

attributabletothe

biological

pump.

Itis

important

tounderstand the different processes involved in the carbon

cycle,

in

orderto

predict

how sensitive

they might

betoincreases in carbon dioxide and what

feedbacks

might

result. These could be

positive, increasing atmospheric

concentrations and

speeding

upclimate

change,

or

negative,

decreasing

atmospheric

concentrations and

slowing

it down. At

present

wedonotknow whether there has beenany

significant

change

in the

biological

pump as aresult ofhumanactivities.Incontrast toterrestrial

ecosystems,

adirect

carbon fertilisation effect is considered

unlikely

because of the abundance of DIC

throughout

the ocean,

although

it has been shown that

primary production

maybe carbon limited in

certain

phytoplankton species

whichare

only

abletotake_upcarbon in the form of

dissolved carbon dioxide_gas

(Riebesell

et

al., 1993).

Indirect effects

resulting

from climate

changes

induced

by

the increase in carbon dioxide and other

greenhouse

gasescould be of

much

greater

importance

(Williamson, 1992).

Such feedback mechanismsarenot

yet

well

understood and climate modellersare

only

just beginning

to

tentatively

include them in their

models

(Klepper

et

al., 1994,

Sarmiento and

LeQuere,

1996;

Woods and

Barkmann, 1993).

Finally, although

mostof the research into the role of

plankton

in the

planet's

climate

system, is focusedonthe carbon

cycle,

this isnotthe

only

source of feedback

by

which

changes

in

plankton

ecology

could affect climate. Another

potentially important

feedback

mechanismwhich hasbeen identifiedconcernsthe role of the

biogenic

gas

dimethyl

sulphide

(DMS)

incloud formation. Charlsonet

al.,

(1987)

hypothesised

that

DMS,

which is

produced

by

mostmarine

phytoplankton species,

is the mainsourceof cloud condensation

(19)

1.1 The Role

of

PlanktonintheGlobal Climate

System

planet's

surface

depending

oncloud

height,

thickness and radiative

properties

(IPCC, 1996).

Phytoplankton

also affect the vertical distribution of solar

heating

of thewatercolumn

by

virtue of their influenceonthe inherent

optical

properties

of thewater

(Kirk, 1988).

Absorption

and

scattering

of

light by

phytoplankton pigments

increases the

efficiency

of

heating

nearthe surface. This increaseswatercolumn

stability,

with

implications

for the

exchange

of

heat,

nutrients and

biogenic

particles

between the surface

layer

and theocean

interior

(Sathyendranath

et

al.,

1991).

At

high

and

temperate latitudes,

plankton

ecology

and the associated action of the

biological

pumpand other climate

system

processesaredominated

by

seasonal

variability.

If the action of these processes istobe

properly represented

in climate models it is

thereforenecessary for the modelsto

resolve,

oratleast

parameterise,

variability

atthe

seasonal time-scale. Further

understanding

of this

variability

and the

consequent

changes

in

property

fluxes is

required

before

they

canbe simulated withauseful

degree

of confidence.

Oneof the mostobvious

expressions

of seasonal

variability

inthe oceanic

pelagic

ecosystem

is the annual increase in

primary

production

occurring

in the

spring.

Nowhere is this

expression

more dramatic than in the North

Atlantic,

where it is

invariably

associated witha

large

accumulation of

phytoplankton

standing

stock

(Yoder

et

ah,

1993).

This

phenomenon

is referredtoasthe

spring phytoplankton

bloom and its simulation with referenceto

(20)

Chapter

1. INTRODUCTION

1.2

The

North Atlantic

Spring

Bloom

In,

1889VictorHensenset outonthe German Plankton

Expedition

to testhis

hypothesis

that

plankton,

which he wasto describe as "dies blut des meeres"

("this

blood of

the

sea"),

was

evenly

distributed

throughout

theoceaninspaceand time and that oceanic

production

could therefore be estimated

by

asuitable

sampling

programme

(Mills, 1989).

It

wasafter this

expedition,

from which hewasforcedto conclude that his

objective

was

unfeasible,

that the

concept

of the

bloomwas first introduced

by

the

Kiel School of

Oceanography.

Now,

a

century later,

we areableto

directly

observe the

annual

phenomenon

of the

spring

bloom from satellitesas adramatic

change

inoceancolour

acrossthe whole of the

temperate

North Atlantic

(Esaias

et

ah,

1986;

U.S.

JGOFS, 1989;

McClainet

al,

1998).

The basic

dynamics

of the

spring

bloomaredescribed in Section 1.2.1 and the

various factors

causing spatial variability

in the bloomatscales fromtens tothousands of

kmarediscussed in Section 1.2.2. Observations andrecent

modelling

studies of the

spring

bloom in the North Atlantic areoutlined

briefly

inSection 1.2.3 and Section 1.2.4

respectively.

1.2.1 Bloom

Dynamics

The

major

factors

affecting phytoplankton growth

ratesin theocean are

light,

nutrients and

temperature.

The effect of

temperature

on

phytoplankton growth

has been

reviewed

by Eppley (1972),

who shows thatwe should_expect

temperature

to setan_upper

limitonthe

growth

rate of

phytoplankton.

However,

undermostconditions in the ocean,

growth

is either

light

ornutrient limited and it is difficultto obtain evidence fora

temperature

effect from environmental observations. Accumulation of

phytoplankton

biomass under favourable conditions for

growth

is controlled

mainly

by consumption

or

(21)

Spring

Bloom

transport

or

passive

sinking

canalsocause

major

losses,

as indicated

by

the appearance of

large

amountsof

phytodetritus

ontheocean floorsomeweeks after theoccurrenceof

phytoplankton

bloomsatthe surface

(Billet

et

al., 1983). Sinking

ratesfor

phytoplankton

vary

greatly

between

species

and thismay be an

important

factor in succession.

Many large

diatom

species

for

example

are

adapted

toturbulent conditions and

rely

onturbulent

mixing

to

keep

them in thesurface

layer (Harris, 1986).

Inorder fora

phytoplankton

bloomto occur

primary production

mustexceedtheselosses.

Inthe

temperate

and

polar

zones of the North Atlantic where

spring

bloomsoccur,

nutrients areabundant in the

euphotic

zoneinwinter and

light

is the

limiting

factor for

phytoplankton growth.

Mostof the

primary

production

in the oceantakes

place

in the

turbulent surface

boundary

layer,

often referredtoasthe surface mixed

layer,

or

simply

the

mixed

layer.

Theaverageamountof

light

availableto

phytoplankton

cells in the mixed

layer

is reduced in winter dueto acombination of

deeper

vertical

mixing

and reduced

day

length

and solar elevation. Itis

widely accepted

that the dominant factor

contributing

tothe

initiation of the bloom is the

shoaling

of the mixed

layer

dueto stabilisation of the water

column in

spring,

caused

by

aseasonal increase in heat

input

totheocean,combined witha

reduction in the

input

of turbulent kinetic _energy from the windstress. This

theory

has been

formalised intermsofacritical

depth

for the mixed

layer

atwhich bloomscanoccur,a

concept

first introduced

by

Gran and Braarud

(1935)

and

developed

as a

quantitative

model

of bloom initiation

by

Sverdrup

(1953).

Sverdrup's critical-depth

model is based around the idea ofa

compensation

depth

at

which biomass

gain

dueto

photosynthesis

is

balanced,

over a24hour

period,

by

loss dueto

respiration. Phytoplankton

cells inan

actively

mixing

surface

layer experience

rapidly

fluctuating

light

conditionsand,when the mixed

layer

is

deeper

than the

compensation

depth,

abloomcan occur

only

if theaverage

primary production

in the mixed

layer

exceeds

respiration.

The critical

depth

is definedasthe mixed

layer depth

for which theaverage

light

intensity,

over24

hours,

equals

the

light intensity

atthe

compensation depth.

It isassumed

(22)

1.2 TheNorthAtlantic

Spring

Bloom

by

Smetacek and Passow

(1990),

theterm

respiration

referstototal

community

respiration,

whereas it has often been

interpreted

as

phytoplankton respiration

only.

Smetacekand Passow arguethattheterm

respiration

is,

in

fact,

inappropriate

because

community

respiration

does not

fully

accountfor

phytoplankton

losses: notall

phytoplankton

consumed is

subsequently respired

on a24hour time-scale and sedimentation

plays

a

major

role in

removing

biomass fromthemixed

layer. They

also

point

outthatthere isnoevidence

to

suggest

that either

phytoplankton

respiration

or

grazing

_pressure isconstant with

depth,

making

practical

use of the model difficult. The idea ofawell-mixed

layer

is alsoan

idealisation,

as

acknowledged by Sverdrup (1953).

In

reality,

the time-scales of turbulence

maynot be

sufficiently

short,

compared

with time-scales of

phytoplankton growth,

to

redistribute biomass

evenly throughout

the surface

layer.

Despite

the

problems

associated with the critical

depth

model,

it is

conceptually

useful and has

recently

been extended

by

Plattetal.

(1991a).

These authors introduceda

morerealistic non-linear

photosynthesis-irradiance

curveanda

daily

irradiance

cycle.

They

also extended the

approach

todetermineacharacteristic time scale for

development

ofa

bloom,

whichmustbe short

compared

with the time intervalbetweenstorm events

causing

mixed

layer

deepening

in order forabloomtooccur.

Global satellite

imagery

shows

that,

while

blooms do occurin

other

temperate

regions,

the bloom in the North Atlantic isamuchmoredramaticevent

(Yoder

et

ah, 1993).

Ithas been shownto

deplete

surface nitrateovermuch of the

region,

causing

aseasonal transition between

eutrophic

and

oligotrophic

regimes (Strass

and

Woods,

1988;

Weekset

al.,

1993b).

Once nitrate is

depleted'in

the mixed

layer

a

deep

chlorophyll

maximum tendstobecome establishedas

significant

new

primary

production

is confinedto

the seasonal

pycnocline.

New

production

is definedasthat in which assimilated

nitrogen

is

obtained from

nitrate,

as

opposed

to

regenerated

production

in which ammonium is used

(Dugdale

and

Goering, 1967).

The

depth

of the

chlorophyll

maximum_appearstoincrease

during

the summer asthe nutrientsatthe

top

of the

pycnocline

are used_up

(Strass

and

(23)

Spring

Bloom

Incontrastwith the situationin theNorth

Atlantic,

seasonal increases in

primary

production

atmid- and

high-

latitudesinthe North Pacific and the Southern Oceanarenot

matched

by

a

large

accumulation of

phytoplankton

stock and nitrate isnot

depleted.

This

has ledtothe classification of theseas

high-nutrient low-chlorophyll (HNLC)

regions,

in

contrast totheNorth Atlantic which is describedas alow-nutrient

high-chlorophyll

(LNHC)

region.

While

light

limitation has been

put

forwardas a

possible explanation

for the

absenceofa

spring

bloom in the SouthernOcean

(Mitchell

et

al.,

1991;

Nelson and

Smith,

1991),

itcannot

explain

the differences between the North Atlantic and the North Pacific

where summer-time mixed

layer

depths

and cloudcoverare_verysimilar

(Fasham, 1995).

This indicates that the

spring shoaling

of the mixed

layer

isnot

always

asufficient condition

for bloom initiation.

One of the main

hypotheses

put

forwardto

explain

the absence ofabloom and

maintenance of

high

nitrate concentrations in the HNLCareas,is the

potential

limitation of

phytoplankton

growth

ratesdue to low concentrations of iron

(Martin

and

Fitzwater,

1988).

This micronutrient is

important

in nitrate

uptake,

because of its role in nitrate

reduction,

and in

photosynthesis,

because of its role in

chlorophyll synthesis

and electron

transport.

In

apparent

contradictionto this

hypothesis,

biomass

specific growth

rates in the

HNLC subarctic

region

of the Pacific donot_appeartobe low. In

fact,

measurements

by

Welshmeyer

etal.

(1993)

suggest

that

they

are_amongthe

highest

for_{any open-ocean}

environment.

However,

experiments

in the same

region

(Boyd

et

al., 1996)

have showna

rapid

accumulation of

phytoplankton

biomass

following

the addition of iron. These results

canbe reconciled in the

light

ofevidence

presented

by Boyd

etal.

(1996)

which indicatesa

size

dependence

in iron limitation of

phytoplankton

growth. Initially,

small

(<5 m)

celled

autotrophs

were dominant. Growthrateswere

high

butrates of biomass accumulationwere

low,

suggesting

stronggrazercontrol. The blooms observed after

adding

ironweredominated

by large

diatoms,

formerly

low in

abundance,

with

growth

rates increasedtoneartheir

(24)

1.2 TheNorth Atlantic

Spring

Bloom

smaller

phytoplankton.

The blooms occurred

despite

a

high

abundance of micro-and

meso-zooplankton

grazers,

probably

dueto the

development

of cells whichweretoo

large

for

grazing

ata

significant

rate

by

the

microzooplankton.

ecology

which

might

cause amismatch between

production

and

consumption

in the

Atlantic,

butnotinthe Pacific. Differences in

grazing

rates

(Frost

1987,1991,1993;

Milleret

al, 1991)

and differences in the size ofover¬

wintering Zooplankton

populations (Evans

and

Parslow, 1985;

Fasham

1995)

have both

been

invoked,

the latter

potentially arising

as aconsequenceof much

deeper

winter-time

mixing

in the North Atlantic than elsewhere.

1.2.2

Spatial Variability

The mechanisms which

generate

spatial

pattern

in

plankton

abundancecanbe

classifiedas

vectorial, co-active,

reproductive,

social and stochastic

(Hutchinson, 1953;

Haury

et

al.,

1978). Haury

etal. considered the stochastic mechanismas afactor inherent

within the other four mechanisms rather thanas a

separate

process in itsown

right.

The

vectorial classcovers all

passive

transport

of_organismsas aresult of the

physical dynamics

of the ocean. Co-active_processesinclude

grazing,

predator-prey

interactions,

parasitism

and

competition.

Each of these mechanisms has the

potential

to

generate pattern

acting

in

isolation,

buttheactual

patterns

of

variability

in the marine environmentarethe

consequence ofa

complex

interaction betweenallfour. Because

plankton

and the nutrients

onwhich

they depend

tendto move

passively

with the water,

physical

variability

is

(25)

Spring

Bloom

and behavioural responsesto_copewith

variability

in the

physical

environmentata_{range of}

frequencies,

from annual

cycles

tothe

rapid

fluctuation in

light

conditions

experienced by

phytoplankton

inthe mixed

layer. Aspects

of this

variability

canbe seen asresourceswhich

are

exploited

more orless

effectively by

different

species, potentially

determining

the outcomeof

competition

(Harris, 1980).

With

regard

tothe

spatial

scales of

variability,

Denmanand Powell

(1984)

suggested

as a

generalisation

thatthe

important

physical

processes with

respect

toa

particular biological

_process areasubset of those

having

similar

time scales. The

spatial

scales of these

physical

processes then determine the

spatial

scales

of the

biological

response.

Basin-scale

variability

in the characteristics of the North Atlantic

spring

is best

summarised with referencetothe

biogeochemical

domains defined

by

Longhurst

(1995).

The

areain which

spring

bloomsoccuris divided between the westerlies domaintothe south of

the Polar

Front,

which

separates

the

sub-polar

and

sub-tropical

gyres,and the

polar

domain

tothe north. Within the

polar

domain,

seasonal ice meltcausesthe

development

ofa

brackish

layer

which hasa

stabilising

effectonthewatercolumn. The influence of melt

waterextendssouthwardsfrom the ice

edge

aslenses of low

salinity

waterare

transported

around the sub-arctic gyre. The enhanced

stability

canleadtobloomsas

early

asthose much

further

south,

despite

lower levels of solar irradianceatthe seasurface. Inthis

domain,

nutrient concentrationsare

high,

duetoacombination of

deep

winter

mixing (Glover

and

Brewer,

1988)

and Ekman

upwelling

(McClain

and

Firestone,

1993). Primary

production

is

mainly light

limited and showsa summermaximum. In the westerlies

domain,

the

important

physical

characteristicsare

seasonality

in heat flux and wind stress, whichcause a

deepening

of the mixed

layer

in

winter,

entraining

nutrients and

setting

upthe conditions forabloom

when the

layer

shoals

again

in

spring.

In this domain thereare

typically

spring

andautumn

blooms withanutrient limited

period

of low

production during

thesummer.

Winter-time mixed

layer

nutrient concentration estimates

(Glover

and

Brewer,

1988)

show the existence of

strong

latitudinal

gradients

in

major

nutrients

(nitrate,

phosphate

and

(26)

Spring

Bloom

mixed

layer

depth

caused

by

weather

patterns

and the

gyre-scale

circulation,

in

particular

the GulfStream and its downstream

expression

tothe north-eastas the North Atlantic

Drift,

arethemostobvious factors

contributing

to

variability

of the

spring

bloomat

sub-basin scales.

Biological

and chemical concentrations in theocean are

highly

variableatmuch

smaller scales too. Watsonetdl.

(1991)

have shown that

spatial

variability

atscales less

than 100 km could bea

significant

sourceoferrorin

estimating global

carbon fluxes from

existing

data. Satellite

images

ofocean colour show that

patchiness

in

phytoplankton

abundanceonscales oftens tohundreds of km is

ubiquitous

and

clearly

relatedtofeatures in

the turbulent flow

(Gower

et

al, 1980).

This_rangeof scales_spansthe

peak

in the kinetic

energy

spectrum

of theoceanassociated with

geostrophic

turbulence,

which exceeds that

associated with theocean_gyres

by

afactor of

nearly

100

(Woods, 1980).

The

physical

dynamics

atthis

scale,

often referredtoasthe ocean

mesoscale,

is dominated

by eddy

features

which,

toafirst

approximation,

exist in

geostrophic

balance: astatein which the

water motion is such that the Coriolis force dueto_{the Earth's rotation balances pressure}

gradients

in the fluid and flow is

along

iso-lines of

geopotential.

Mesoscale eddies have

widely varying properties

(Allen

et

al., 1991).

Diameters from less than 10 kmtoaround

200 km have been recorded. Maximum flowratescansometimes bemorethan

lms"1,

although speeds

in the order of 10 cm

s"1

are more

typical. They

occurin all areasof the

ocean

although

there is

great

regional

variability

in the

intensity

of

eddy activity,

witha

maximum in

regions

of

permanent

fronts andwestern

boundary

currents

(Robinson, 1983).

The main mechanisms for

eddy generation

arebaroclinic

instability,

where the

eddy

kinetic_energyis derived from available

potential

_energyassociated with inclined

density

surfaces,

and

barotropic instability

resulting

from shear in themeanflow

field,

where

eddy

kineticenergyis derived frommeanflow kineticenergy. Strassetal.

(1992)

presented

evidence foraseasonal increase in mesoscale

variability

in the North Atlantic Drift

region,

which

they

attributedtoenhanced conditions for baroclinic

instability

as a_{consequence of}

(27)

Spring

Bloom

the seasurface canbe another

important

sourceof

eddy activity,

particularly

in mid-ocean

regions

away from

major

fronts. Eddiescanalso

spin

_upas a_consequenceof subduction of

waterbodies as

they

areadvected downwards

along sloping isopycnals (Woods, 1985).

This occurswhen the stratification of the subductedwater

parcel

is different from the

surrounding

water. The water

parcel

will tendto stretchor_compress in the

vertical,

under

gravity,

such that the stratification

anomaly

is decreased.

During

this process, knownas

geostrophic

adjustment,

the

change

in

spacing

between

isopycnals

is

accompanied

by

a

change

in relative

vorticity

(i.e.

the vertical

component

of the

vorticity

relativeto the

rotating

earth)

such that the

potential

vorticity

of thewater

parcel

is conserved

(Rossby,

1940; Ertel,

1942). Cyclonic

eddies canbe formed

by

subduction of

strongly

stratifiedwaterfrom the

upper

pycnocline,

asthe enhanced stratification

gives

thewatera

high

potential

vorticity.

However,

eddies

generated by

subductionare more

commonly

anticyclonic,

having

a coreof

low

potential

vorticity

waterformed

by mixing

in the surface

boundary layer

(Allen

et

ah,

1991).

It is chaotic interaction between eddies which leadstothe existence ofa

geostrophic

turbulence field in theocean. Some

aspects

of this fieldcanhe understood with referenceto

two-dimensional turbulence

theory (Kraichnan, 1967,1971;

Kraichnan and

Montgomery,

1980)

which

predicts

an

enstrophy

cascade

by

which turbulence transfers

enstrophy

(vorticity variance)

froma source

region

inwavenumber spaceto

higher

wavenumbersover

aninertial_range.The

application

of two-dimensional turbulence

theory

to

geostrophic

turbulence,

inwhich stratification effects and the Coriolis forcemustbe

considered,

is

discussed

by

Rhines

(1979),

who

argued

thataninertial_rangewould exist fox

potential

enstrophy

(potential vorticity variance)

in mid-oceanareas_awayfrom

major

fronts. This

inertial_rangeextends from thesource

region

in wave-numberspacearound 100

km,

to

length

scales of around 1 m atwhich

potential enstrophy

is

destroyed

by

_processeswhichcause

overturning

of

density

surfaces

(Woods, 1980).

The characteristics of

spatial patchiness

which

might

be

expected

from the

(28)

/. 2 TheNorth Atlantic

Spring

Bloom

investigated by

Bennettand Denman

(1985).

Their work

suggests

that non-conservative

biological

processescannotintroduce

patterns

which

persist

against

a

background

of

mesoscale turbulence and that

phytoplankton

behaves

largely

as a

passive

traceroverthe

inertial_range.

Analysis

of the

relationship

betweenoceancolour

images

and

corresponding

images

of sea-surface

temperature

provides

supporting

evidence for this

theory (Denman

and

Abbot, 1988;

1994).

However,

atscales above the inertial _range,Bennett and Denman

(1989)

showed

using mixing length

theory,

in which turbulence is

parameterised

by

an

eddy

diffusion

coefficient,

that interaction between annual

plankton cycles

and mesoscale

turbulence

might

causethe

development

of

patchiness

onscales of several hundred km.

Aswellas

affecting

the

spatial

distribution of

phytoplankton by

means of

horizontal

transport,

mesoscale

variability

also affects

phytoplankton growth

rates. It does

this

by introducing

vertical velocities in the flow field and

influencing

the

depth

of

boundary

layer

mixing.

Both of these effectshave

implications

for

light

availability

in the mixed

layer

and the

exchange

of nutrients and

phytoplankton

biomass between this surface

layer

and the

oceaninterior. Patchiness in mixed

layer

depth

canbe caused

by

patterns

of shear

instability

atthe base of the mixed

layer resulting

from interaction between

geostrophic

turbulence and

the wind-driven flow

(Klein

and

Hua,

1988).

Such interaction alsocausesEkman

upwelling

and

downwelling,

as a_consequence of horizontal

divergence

in the

boundary

layer

flow

(Walstad

and

Robinson,

1993).

These vertical

velocities,

of the order oftensofcmper

day,

areof

ecological importance

in theirown

right

aswellas

generating significant spatial

patchiness

in mixed

layer depth

ontime scales of weeksto

months,

by modifying

the

entrainment

velocity

atthe base of the mixed

layer.

The

ecological

effects of this

patchiness

have been

investigated

ina

modelling study by

Smithetal.

(1996).

Vertical Ekman velocities

arealso caused

by

variationsinthe wind-stress

patterns

themselves and

upwelling

generated

in this waycanbean

important

source of nutrients in mid-ocean

oligotrophic

regions

(Falkowski

et

al.,

1991),

as wellas

producing

eddies

by distorting

the subsurface

stratification.

Away

fromthemid-ocean

regions,

vertical advection ofasimilar

magnitude

is

(29)

Spring

Bloom

density

fronts. The

upwelling

in the interior of

anti-cyclonic

eddies of this

type

canlikewise

provide

an

important supply

of nutrientstothe

euphotic

zone

(Nelson

et

al.,

1989).

The vertical velocities associated with the processes described aboveare

relatively

weak

compared

with those whichoccur aseddies interact.

Straining

of

potential vorticity

patterns

in the horizontal deformation field associated with

geostrophic

turbulencecauses

frontsto

develop

and

intensify. Vigorous

mesoscale

frontogenesis

occurs

throughout

the

ocean,

producing

transient mesoscale

jets

of upto 100 kmin

length

ontime scales ofafew

days

(Woods,

1988).

These features

typically

persist

for several weeks and often

develop

instabilities with

wavelengths

of 10-100 km. Vertical velocities associated with the smaller

scale

ageostrophic dynamics

of the fronts between eddiesare

greater

than those in the

eddy

interiors

by

typically

two orders of

magnitude.

Modelling

studies

(e.g.

Onken, 1992;

Wang,

1993;

Zhang

and

Nurser,

1995)

imply

vertical velocities of 10-100m

d'1

and these are

supported

by

calculationsbasedon_{survey data}from frontal

regions

which indicate similar

values

(Pollard

and

Regier,

1990;

1992).

Vertical intrusions of

chlorophyll,

often

extending

to

depths

ofmorethan 200m,have been attributedto

downwelling

atfronts

(Fasham

et

al.,

1985;

Pollard and

Regier,

1992;

Zhang

and

Nurser,

1995).

Injection

of nutrientsas aresult of

upwelling

atmesoscale fronts has been

put

forward

by

Strass

(1992)

asthe

probable

cause

of observed

patchiness

in the

deep chlorophyll

maximumatscales of around 20 km.

Modelling

studies

support

this

interpretation, suggesting

that vertical nutrient fluxes

associated with frontal

dynamics

are

likely

tobe of much

greater

significance

than the fluxes

associated with the

upwelling

in

eddy

interiors

(Kishi,

1994;

McGillicuddy

et

al., 1995b;

McGillicuddy

and

Robinson,

1997).

While the vertical velocities associated with frontal

dynamics

contributeto

exchange

between the mixed

layer

and the

interior,

ageostrophic

vertical shear in the horizontal flow

tendstoenhance

stability,

leading

toareduction in the

spatially

averaged

mixed

layer

depth

in the

region

of thefront

(Nurser

and

Zhang,

in

revision).

This has

implications

for

light

availability

andcan

potentially

leadto a

major

increase in

primary production

ina

eutrophic

(30)

Spring

Bloom

Finally,

in

discussing

the

ecological significance

of

eddies,

Angel

and Fasham

(1983)

makean

important

distinction between advective eddies with closed circulations

(often

referredtoas

rings),

whichare formedas

loops split

off from

meandering

currents, and

features suchas

wind-generated

eddies whichare

propagated

across oceanbasins in the form

of

Rossby

waves

(Gill, 1982).

The lattercausetransient

changes

tothewater

through

which

they

passon atime scale of weeks to months.Incontrast, closed eddies

typically persist

for much

longer (months

to

years)

andcan

transport

the enclosedwater

body

into areas

where the

physical

and/or

biological

environment is rather different from thesource

region,

causing

changes

in its

ecology.

Tranteretal.

(1980),

for

example,

observed

high productivity

ina warm-core

ring

shed from the Australian current, which

they

attributedtothe

transport

of thecorewaterintoa

region

where the

atmosphere

was

colder,

causing deeper mixing

and

the

import

of

previously

unavailable nutrients.

Angel

and Fasham

(1983)

suggest

diffusive

colonisation of the

eddy by populations

from the

surrounding

water as another

potentially

important ecological

process.

1.2.3 Observations

Satellite

imagery

of the

spring

bloom has been obtained

by

the Coastal Zone Colour

Scanner

(CZCS)

from 1979-1986

(Esaias

et

al., 1986)

andmore

recently by

SeaWiFs,

launched inAutumn 1997

(McClain

et

ah,

1998).

These

images

provide

the

only

basin scale

coverage of theeventandarevital for

extrapolating

carbon flux estimatestothe scales

required

for

global analysis.

However,

they give

insufficient information for

modelling

the

processesinvolved in the bloom's

development

in ordertoderive these estimates. Little

progresscould be made without additional data fromin situ surveys. The available

observational data

relating

tothe North Atlantic

spring

bloom is reviewed

briefly

in this

section.

Seasonal and interannual

variability

in

plankton populations

in the North Atlantic

(31)

Spring

Bloom

voluntary observing

ships

established in the 1940s

(Colebrook,

1982).

Some

early

observations aimed

specifically

at

investigating

the

relationships

between

biogeochemical

and

physical

variability

overthe annual

cycle

weremade

during

the 1970satOcean Weather

Station

India,

located in the Iceland Basinat

approximately

60N 20W

(Williams

and

Robinson,

1973;

Williams

1988).

The first

study

of the

impact

of mesoscale eddieson

biogeochemical properties

during

the North Atlantic

spring

bloomwas an

investigation

ofa

cyclonic

cold-core

eddy

in

May

1985

(Mittelstaedt,

1987;

Lochte and

Pfannkuche,

1987;

Beckmannet

al.,

1987).

The

eddy

was afeature'cutoff fromameander of the PolarFront

and,

incommonwith

of Gulf Stream

rings,

the nutrient

chemistry

and

biological populations

observed

during

this _survey showed marked differences between

the

eddy

interior and the

surrounding

water.

With the aim of

investigating

the

relationships

between the

development

of the

phytoplankton

bloom and seasonal stratificationon a

larger

scale,

aseries of latitudinal transectswerecarriedoutbetween theAzores and Greenland in the mid

1980s,

atdifferent

times

during

spring

andsummer

(Strass

and

Woods, 1988,1991).

The Kiel "SeaRover"

system

wasusedtoobtain

high

resolution vertical

hydrographic

sections,

including

chlorophyll

afluorescenceas a measureof

phytoplankton

biomass. These _surveys included two

repeat

transects in late

April

1985. The first of

these,

heading north,

showedan

early

bloom between about47N and the end of the data recordat50.5N. The second transect,

heading south,

showed that the

bloom,

though patchy,

wasevident almost

everywhere

south of about 51N. The

chlorophyll

distribution showed somecorrelation with

spatial

patchiness

in seasonal

stratification,

supporting

the

hypothesis

of bloom initiation as a responsetothe

shoaling

ofthe mixed

layer.

Atransectcarriedoutin

June/July

1986

appeared

toshow three different

phases

of the bloom. North of the Polar Frontat52N

therewaslittle

sign

ofabloom and nitratewasstill

fairly

high

(about

60% of winter-time

nitrate

estimates).

Abloomwasobservedtothe south of the PolarFrontasfar southas

46N,

buttothe south of this nitrate was

depleted

atthe surface anda

deep

chlorophyll

(32)

Spring

Bloom

maximum,

typical

ofan

oligotrophic regime,

extending

all the_wayfrom the Azorestothe

PolarFront.

Amuchmore

comprehensive

surveyof the

spring

bloom,

onthe 20 W

meridian,

wasundertaken

by

the international

community

in 1989as

part

of the Joint Global Ocean

Flux

Study (JGOFS) project.

Asmaller_surveyof the bloom in the westernNorth Atlantic

wasalso carriedoutin the same_yearfor

comparison

(Harrison

et

al.,

1993)

and

together

these surveys

comprised

the JGOFS North Atlantic Bloom

Experiment (NABE,

Ducklow

and

Harris,

1993).

Theeastern_surveyincluded meridionaltransectsbetween about 42N

and 60N at

approximately monthly

intervals from

April

to

August (Weeks

et

al., 1993b)

and intensive studiesat47N

(Lochte

et

al,

1993)

and 59N

(Weeks

et

al.,

1993a)

spanning

the entire bloom

period.

Some additional observationsweremadeat

18N,

33N,

40N and

72N.

NABE observations

provided

further _supportfor the link between bloom initiation

and the

development

of seasonal stratification. Evidence ofa

b