ESTIMATING OCEANIC ESTIMATING OCEANIC PRIMARY PRODUCTIVITY: PRIMARY PRODUCTIVITY:

Vincent S. Saba, Marjorie A.M. Friedrichs, Mary-Elena Carr, and Vincent S. Saba, Marjorie A.M. Friedrichs, Mary-Elena Carr, and the PPARR4 teamthe PPARR4 team

AN EVALUATION OF OCEAN COLOR AN EVALUATION OF OCEAN COLOR ALGORITHMS AND GENERAL CIRCULATION ALGORITHMS AND GENERAL CIRCULATION

MODELSMODELS

BackgroundBackground

Primary Productivity Algorithm Round Robin (PPARR):

- Evaluate algorithms that estimate primary productivity (PP).

- Ocean color (SAT) and biogeochemical circulation models (BOGCM).

- Benefits: Improve future PP and ecosystem models, global marine carbon fixation estimates, understand the variability of PP.

BackgroundBackground

Primary Productivity Algorithm Round Robin (PPARR):

- PPARR2: Campbell et al. 2002

- PPARR3a: Carr et al. 2006 - PPARR3b: Friedrichs et al. in press

- Fourth phase (PPARR4):

Compare model estimates of PP to in situ data at various marine ecosystems.

1. BATS (n = 197), 1988-2003 6. Arabian Sea (n = 42), 19952. NABE (n = 12), April-May 1989 7. HOT (n = 139), 1989-20053. NEA (n = 52), 1993-1998 8. Ross Sea (n = 164), 1996-20064. Black Sea (n = 43), 1992-1999 9. APFZ (n = 12), Dec. 19975. MED (n = 202), 1984-2007

1

23

45

6

7

8

9

AcknowledgementsFunding: NASA Ocean Biology and Biogeochemistry Program.

PPARR4 team: David Antoine, Robert Armstrong, Ichio Asanuma, Michael Behrenfeld, Val Bennington, Laurent Bopp, Erik Buitenhuis, Aurea Ciotti, Scott Doney, Mark Dowell, Stephanie Dutkiewicz, John Dunne, Watson Gregg, Nicolas Hoepffner, Takahiko Kameda, Ivan Lima, John Marra, Frédéric Mélin, Keith Moore, André Morel, Robert O’Malley, Jay O’Reilly, Michael Ondrusek, Michele Scardi, Tim Smyth, Shilin Tang, Jerry Tjiputra, Julia Uitz, Marcello Vichi, Kirk Waters, Toby Westberry, Andrew Yool.

Methods Methods Models: Estimate integrated PP to the 1% light-level

(mg C m-2 d-1). - 12 BOGCM models

Provided with date, location, day length.

- 23 SAT models Depth integrated or resolved.

Wavelength integrated or resolved.

Provided with in situ surface chlorophyll & SST, modeled PAR and MLD.Provided with SeaWiFS surface chlorophyll & PAR for all stations post-SeaWiFS.

Model Skill AnalysisModel Skill Analysis

Root mean square difference (RMSD).

Target diagrams.

Model misfit

Mea

n R

MSD

Model skill for each region

BATSNABE

NEA

Black SeaMED

Arabian SeaHOT

Ross Sea (in situ

)

Ross Sea (on deck)

APFZ

All Regions

Lower RMSD = Higher model skill

BATS, n = 197 (Mean Obs. PP = 528.68 (+/- 212) mg C m-2 d-1)

Eppley VGPM CbPM

SAT models BOGCMs

FollowsGregg

OPAL

NABE, n = 12 (Mean Obs. PP = 894.76 (+/- 336) mg C m-2 d-1)

SAT models BOGCMs

Northeast Atlantic, n = 52 (Mean Obs. PP = 534.86 (+/- 313) mg C m-2 d-1)

SAT models BOGCMs

HOT, n = 139 (Mean Obs. PP = 513.12 (+/- 152) mg C m-2 d-1)

SAT models BOGCMs

Ross Sea, n = 144 (Mean Obs. PP = 1177.57 (+/- 849) mg C m-2 d-1)

SAT models BOGCMs

SAT,DI,WI SAT,DR,WI SAT,DR,WR BOGCM

Over-estimates PP

Under-estimates PP

Over-estimates PP variability

Under-estimates PP variability

SAT,DI,WI SAT,DR,WI SAT,DR,WR BOGCM

SAT,DI,WI SAT,DR,WI SAT,DR,WR BOGCM

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

Ross Sea on deck

Bias

RMSDCP

SAT,DI,WI SAT,DR,WI SAT,DR,WR BOGCM

Mea

n R

MSD

BATS NEAMED

HOTRoss Sea

APFZ

All

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14

In situ chlorophyll, NCEP PARSeaWiFS chlorophyll, NCEP PARIn situ chlorophyll, SeaWiFS PARSeaWiFS chlorophyll, SeaWiFS PAR

R2 = 0.31

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0

R2 = 0.30

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0

SAT DR,WR Models SAT DR,WI Models

Log(in situ chlorophyll)

Mea

n SA

T m

odel

mis

fitAll SAT Models SAT DI,WI Models

All SAT Models, All Regions

R2 = 0.19

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0

R2 = 0.28

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0

Log(in situ chlorophyll)

Mea

n B

OG

CM

mis

fitAll BOCGMs, All Regions

R2 = 0.03

R2 = 0.75

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

In situ PP variability

In s

itu C

hlor

ophy

ll va

riabi

lity

NABE

APFZ

HOT

Arabian Sea

BATSBlack Sea

NEA

MED Ross Sea

Pelagic Coastal

R2 = 0.13

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

In situ PP variability

Mea

n SA

T m

odel

mis

fit

NABEAPFZ

HOTArabian Sea

Black SeaNEA

MED

Ross Sea

BATS

Pelagic Coastal

SAT Models

Mea

n B

OG

CM

mis

fitBOGCMs

Depth (m)

Mea

n SA

T m

odel

mis

fit

Log(

obs.

PP

)

BATS

Mea

n S

AT

mod

el m

isfit

-0.61Correlation

Mean SAT PP = No increase.

Fluor. chlor. = No increase.

HPLC chlor. = Increase.

Log(

obs.

PP

)

HOT

Mea

n S

AT

mod

el m

isfit

-0.82Correlation

R2 = 0.10

-1.5

-1.2

-0.9

-0.6

-0.3

0.0

0.3

1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

R2 = 0.09

2.0

2.2

2.4

2.6

2.8

3.0

3.2

3.4

1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Mean SAT PP = No increase.

Fluor. chlor. = No increase.

HPLC chlor. = Increase.

HPLC

Fluorometrichttp://hahana.soest.hawaii.edu/hot/

Summary - Models had highest skill in NABE, Arabian Sea, and APFZ; lowest skill in MED and Ross Sea.

- SAT models typically had higher skill than BOGCMs. - DR,WI - DR,WR

- SAT models performed better in BATS when SeaWiFS chlorophyll was used as opposed to in situ. Opposite was true at NEA and APFZ. SeaWiFS versus modeled PAR did not significantly affect skill.

- SAT models tended to underestimate PP at low chlorophyll values and overestimate PP at high chlorophyll values.

- Coastal regions: PP was typically overestimated. - Pelagic regions: PP was typically underestimated.

- In pelagic regions: as depth increased, PP was underestimated.

Summary - Increasing trend of PP at BATS and HOT was not captured by the models.

- For SAT models, this may be a function of the chlorophyll measurement (fluorometric vs. HPLC).

- Both HOT and BATS show an increase in HPLC measured chlorophyll but do not show an increase in fluorometric chlorophyll.

- Zooplankton biomass is also increasing at BATS and HOT (Steinberg et al. unpublished).

- Ocean color calibrated to HPLC rather than fluorometric ?

- Implications for studies that use PP models to assess the effect of climate change on marine carbon fixation.