The Hawaii Ocean Time-series (HOT): ( )Highlights and perspectives from two

decades of ocean observations

M A T T H E W C H U R C HU N I V E R S I T Y O F H A W A I I

O C B S C O P I N G W O R K S H O PS E P T E M B E R 2 0 1 0

A Dedicated HOT Team

NSFNSFNSFNSF

What’s HOT?

Program objectives:

Quantify time-dependent variability in key physical, biogeochemical and ecological properties and processesbiogeochemical, and ecological properties and processes

Define relationships between plankton community structure and biogeochemical dynamicsstructure and biogeochemical dynamics

Quantify physical and biological processes controlling i b k f i d ioceanic carbon uptake, transformation, and sequestration

The Hawaii Ocean Ti i (HOT)Time-series (HOT)

Near monthly cruises to

Station ALOHA since

October 1988

Deep ocean (~4800 m)

observatory

Shipboard and remote Shipboard and remote

measurements of ocean

biogeochemistry, physics,

and plankton ecology

4-day cruises, intensive

sampling to 1000 m

Time, water, and change

The value of HOT observations continues to increase with timecontinues to increase with time.

HOT provides some of the best and only records of

ALOHA

and only records of biogeochemical and physical variability in the open ocean waters of the Pacific across multiple time scales: episodic, seasonal, interannual, and multi-decadal.

Knowledge gained from HOT furthers our understanding of global-scale ocean change.g g

Ongoing projects supported by HOT: If you build it they will comeIf you build it, they will come…

Ocean Carbon System Variability, NSF; A. Dickson (P.I.), 1988-present WHOI Hawaii Ocean Time Series Station (WHOTS), NOAA-NSF; R. Weller WHOI Hawaii Ocean Time Series Station (WHOTS), NOAA NSF; R. Weller

(P.I.), 2004-present CFC and SF6 Water Mass Tracers, NOAA; J. Bullister (P.I.), 2004-present Microbial Oceanography: Genomes to Biomes – Summer training course,

NSF-Agouron Institute-Gordon and Betty Moore Foundation; D. Karl (P.I.), 2006-presentpresent

Marine Microbiology Initiative, Gordon and Betty Moore Foundation; D. Karl (P.I.), 2005-present

Center for Microbial Oceanography: Research and Education (C-MORE) NSF; D Karl (P I ) 2006-present(C-MORE), NSF; D. Karl (P.I.), 2006 present

Si Cycling and Dynamics, NSF; M. Brzezinski (P.I.), 2007-present ALOHA Cabled Observatory, NSF; B. Howe (P.I.), 2007-present Diazotrophy in a High CO2 World, NSF; M. Church (P.I.),2009-present Profiling Floats for Ocean Biogeochemistry NSF-NOPP; K Johnson 2007- Profiling Floats for Ocean Biogeochemistry, NSF NOPP; K. Johnson, 2007

present Subsurface Moored Profiler, NSF; M. Alford (P.I.), 2009-present Taxon-specific Variability of Organic Matter Production and

Remineralization, NSF; A. White (P.I.), 2009-present Primary Productivity as a Function of Absorption, Pigment-based

Phytoplankton Diversity, and Particle Size Distributions, NASA; A. White (P.I.), 2009-present

From the Predictable to the Unexpected:Highlights on 3 interlinked themes in ocean biogeochemistry

Ocean carbon cycling

Plankton productivity and the importance of community structure

New production and export

HOT carbon reservoirs and fluxes

Carbon reservoirs: C b t t DIC t t l lk li it H CO Carbonate system: DIC, total alkalinity, pH, pCO2

Coulometry (DIC), potentiometric titration (total alkalinity), spectrophotometric (pH), shipboard (KM) pCO2 equilibrator (SOEST), and moored pCO2 sensor (C. Sabine-PMEL)

Total organic carbon (TOC) High temperature combustion

Particulate carbon (POC and PIC) High temperature combustion (POC); acidification/ IR detection (PIC)

Carbon fluxes: Biological carbon production (POC and DOC)

14C-bicarbonate assimilation (primary production), changes in carbon and oxygen

Particulate carbon export Sediment trap particle collections

Air trend: +1.70 atm yr-1

Rising COCO2,

Falling pHOcean trend:+1.92 atm yr-1

pH trend -0.0018 yr-1

Dore et al. (2009)

T l Temporal variability in mixed layer

2020y

inorganic carbon

DIC

ol

C L

-1)

1980

2000

Interannual variations in

DIC

D(

m1940

1960

35 5DIC concentrations

closely coincident with

Salinity

35.0

35.5

coincident with changes to

upper ocean salinity.

Y88 90 92 94 96 98 00 02 04 06 08 10

y

34.5

yYear

Dore et al. (2003)

Interannual Interannual variability

in inorganic b lcarbon pools

Annual accumulation (0-150 m) of nDIC ~ 0.1 mol C m-2 yr-1

Interannual variations in the E-P balance and E P balance and mixing important controls on carbon inventories

Winn et al. 1994, Dore et al. 2003, 2009

ATMOSPHERECO

Processes influencing carbon cycling in the upper ocean at Station ALOHA

CO2

Gas exchange E-P balance

Net community MIXED DIC

Organic

g

productionLAYER

Mixed layer

DIC Carbon(DOC, POC)

Mixed layer boundary

Diffusion

Entrainment

Diffusion

THERMOCLINE

DIC

Particle exportAdapted from Keeling et al. (2004)

PhotosynthesisWhat is the biological contribution to ocean carbon flux at ALOHA?

+ O + O2, - CO2

RespirationRespiration

-O2 , + CO2

PAR ( l t -2 -1) 14

The upper ocean habitat

PAR (mol quanta m-2 s-1)100 101 102 103

0 0

14C-PP (mol C L-1 d-1)0.0 0.1 0.2 0.3 0.4 0.5 0.6

14C-PP

50 MLD 50

14C-PPPAR

epth

(m)

100 1001%

De

150 150 0.1%Chl a

N+N

200

0 1 2 3 4 5 0 0 0 2 0 4 0 6 0 8

200

Nitrate + Nitrite (mol N L-1)0 1 2 3 4 5

Chlorophyll a (g L-1)

0.0 0.2 0.4 0.6 0.8

Light is an important

h bi

5 m

habitat control

l

125 m

0-75 mSeasonal

changes in light appear to drive ppproductivity in both the near surface waters

125 msurface waters

and deep chlorophyll

imaximum

08)

vina

et a

l. (2

00

-2) 30

35Boyce et al. (2010)Po

lov

HOT Chl a increasing 0.22 mg m-2 yr-1

Chl

a (m

g m

-

10152025 observations

indicate both chlorophyll and

primary

14C-PP (80

100

C

510 primary

production are increasing. Prominent i

14C-PP increasing 0.33 mmol C m-2 yr-10-125 m

(mm

ol C m

-2

20

40

60increases occurring below the depth of

Year89 91 93 95 97 99 01 03 05 07 09

d-1)

20 depth of satellite

detection.Corno et al. (2007), Saba et al. (2010), Luo et al.

Material transfer through the food web

y w

eigh

t

2.5

3.0

plan

kton

dry

(g m

-2)

1.5

2.0

Mes

ozoo

p

0.5

1.0

Year94 95 97 99 01 03 05 07 09

0.0

Zooplankton dry weight (0-150 m) increasing at 37 mg m-2 per year

Sheridan and Landry (2004)

Measurements and models of primary production at Station ALOHAp

Carbon production 14C-bicarbonate assimilation (daylight

incubation period ~12 hours) DIC, TOC, and PC variability

Oxygen production In vitro O2 bottle incubations

I it O d i lid fl t In situ O2 dynamics: gliders, floats, moorings, and ships

18O2 production from H218O

16O, 17O, 18O Triple O2 isotopes

Bio-optics and satellites Bio optical approaches Bio-optical approaches Satellite remote sensing

In vitro O GPP, NCP, and RIn vitro O2dynamics at

Station ALOHA

, ,(mol O2 L

-1 d-1)

-0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.00

4 years of measurements (2001, 2005-2007)

N t C it pth

(m)

25

50

75

GPPR

Net Community Production:-4.2 to -7.4 mol C m-2 yr-1

Gross Primary

Dep 100

125

150

NCP

Gross Primary Production: 11.9-14.0 mol C m-2 yr-1

Respiration: 17-21 mol , NC

Pm

-2 d

-1)

0

50

100

p 7C m-2 yr-1

Conclusion: Net heterotrophy due to

GPP

, R,

(mm

ol O

2

-100

-50

0

incubation conditions and/or under sampling

e.g. Williams et al. (2004)

Day of year100 200 300

Mixed layer O2 is in equilibrium with the atmosphereatmosphere

Rate of subsurface O2Rate of subsurface O2accumulation provides information on NCP

In situ OIn situ O2dynamics at

Station ALOHAALOHA

6 f i i

75 m

>6+ years of in situ measurements (2003-05, 2008-present) p )

Net Community Production: 1.1-1.7 mol C m-2 yr-1mol C m yr

Riser and Johnson (2008)

Net Community Production at Station ALOHA

MethodRate

mol C m-2 yr-1Period of

measurements ReferencesMixed Layer O A b d

1.4 - 3.7 (± 1.0) 1992–2008 Emerson et al. (1997); Hamme and Emerson O2 + Ar budgets Hamme and Emerson (2006); Juanek and Quay (2005); Quay et al. (2010)

DIC + DI13C budgets 2.7 - 2.8 (± 1.4) 1988–2002 Quay and Stutsman (2003); Keeling et al (2004)Keeling et al. (2004)

Mooring O2 4.1 (± 1.8) 2005 Emerson et al. (2008)

Sub-mixed layer float profiles

1.1 - 1.7 (±0.2) 2003-2010 Riser and Johnson (2008)p

Sub-mixed layer glider surveys

0.9 (± 0.1) 2005 Nicholson et al. (2008)

Sediment traps 0.9 (± 0.3) 1989–2009 HOT core data

In vitro O2incubations

-6.1 (± 4.6) 2001, 2005-2007 Williams et al. (2004)

NCP appears constrained to ~2 fold variabilityNCP appears constrained to ~2-fold variabilityGPP estimated ~20-fold greater than NCP

What controls variability innutrient supply supporting

N2 fixation

net community production?

Organic matterNH4

+

2

NH4+Organic

matter

OO NO2-NO3

-

NO3- NH4

+NO2-

Organic matter

603 5

Annual cycle of productivity and export

150

im. p

rod.

C m

-2 d

-1)

40

50C

arbon e(m

mol C

m2.5

3.0

3.5150 m14

C-p

ri(m

mol

20

30

exportm

-2 d-1)

1.5

2.0

0-150 m

port

d-1

)

400

500

Sil( m

o80

1004000 m 4000 m

Car

bon

Exp

( m

ol C

m-2

200

300

ica Exportol Si m

-2 d-1)

40

60

Jan

Fe

b

Mar

A

pr

May

Ju

n

Jul

Aug

Se

p

Oct

N

ov

Dec

Ja

n

(

100

Jan

Fe

b

Mar

A

pr

May

Ju

n

Jul

Aug

Se

p

Oct

N

ov

Dec

Ja

n

)

20

Month

A

MonthA

Plankton community structure plays a key role in carbon flux to the deep sea

Controls on nitrogen supply t th to the upper ocean

Physical: Mixing, upwelling, diffusion, advection

NO3- supported new production

Bi l i lBiological: N2 fixation (N2 → NH3)

N supported new production N2 supported new production

Physical supply of nutrients: Mixing-2

) 30 00-100 m

mol

N m

-

152025 M

LD (

255075

0 00

N+N

(mm

05

10

(m)100

125150

Year89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

0 150

Winter mixing “increases” upper ocean nitrate concentrations

Mesoscale variability at Station ALOHA

202475

24.6 kg m-3

SSHa (0

10

ratu

re (o C

)

22

pth

(m)

100

125

150(cm)

-20

-10

Tem

per

18

20

140 m

Dep 150

175

200 Y = 2.2 + 136, R2 = 0.47

SSHa (cm)1/05 1/06 1/07

SSHa (cm)-20 -10 0 10 20

,

Biological supply of nutrients: 1 ) 200

N2 fixation

xatio

n

N m

-2 d

-1

100

150

200

N2

fix

( m

ol N

0

50

100

Month

Jan

FebMar AprMayJu

neJu

lyAugSep

tOctNovDec

0

Month

Rates of N2 fixation are variable, but generallyRates of N2 fixation are variable, but generally increase in the spring and summer

) 28ALOHA

erat

ure

(oC

222426ALOHA

Tem

pe161820

July 19-26, 2005

01 03 05 07 09 11 01 03 05 2005 2006

ALOHA

C. Sabine, PMEL

July 2005April 2005January 2005

J l 20050

July 2005

(m)

50

“typical profile”

Dep

th (

100

150 p150

200

Chloropigment (g L-1)

0.0 0.2 0.4 0.6 0.8 1.0

Fong et al. (2007)

C)

28Whole Seawater

Time series measurements of near-surface

N erat

ure

(o C

24

26

28

ocean N2fixation at

Station ALOHA -15 -10 -5 0 5 10 15 20

Tem

p

20

22

Episodic increases in N2

fixation by re (o C

)26

28

<10m Seawater

fixation by diazotrophs >10

m appear associated with em

pera

tur

22

24

26

mesoscale (anticyclonic)

eddies. SSHa (cm)-15 -10 -5 0 5 10 15 20

Te 20

0.20.512510nmol N L-1 d-1Church et al. (2009)

N2 fixation based new production

NO3- based new

production

1

2

3

1

NO3-, PO4

3-, Si, CO2

1

22

NO3-, PO4

3-, Si, CO2

Both appear to support new production, but for different reasons

HOT insights

Biological and physical processes interact to control time-variability in ocean carbon inventories and fluxes.

The complexity of ocean ecosystem change demands The complexity of ocean ecosystem change demands interdisciplinary studies.

i di d d b HOT measurements indicate we need to study carbon cycle processes at a range of scales, from decadal to seasonal to episodic.

The value of HOT continues to increase with time.

Facing Future

The complexity of ecosystem dynamics, even in this “stable” ecosystem, demands sustained observations.

The shipboard time series program continues to enrich The shipboard time series program continues to enrich our understanding of the NPSG, and help direct application of remote and autonomous sensing technologiestechnologies.

These multi-decadal time series programs are some of our best (and only) barometers of ocean ecosystem change.