estimating oceanic primary productivity:
DESCRIPTION
ESTIMATING OCEANIC PRIMARY PRODUCTIVITY: . AN EVALUATION OF OCEAN COLOR ALGORITHMS AND GENERAL CIRCULATION MODELS. Vincent S. Saba, Marjorie A.M. Friedrichs, Mary-Elena Carr, and the PPARR4 team. Background. Primary Productivity Algorithm Round Robin (PPARR): - PowerPoint PPT PresentationTRANSCRIPT
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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
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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.
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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.
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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
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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.
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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.
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Model Skill AnalysisModel Skill Analysis
Root mean square difference (RMSD).
Target diagrams.
Model misfit
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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
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BATS, n = 197 (Mean Obs. PP = 528.68 (+/- 212) mg C m-2 d-1)
Eppley VGPM CbPM
SAT models BOGCMs
FollowsGregg
OPAL
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NABE, n = 12 (Mean Obs. PP = 894.76 (+/- 336) mg C m-2 d-1)
SAT models BOGCMs
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Northeast Atlantic, n = 52 (Mean Obs. PP = 534.86 (+/- 313) mg C m-2 d-1)
SAT models BOGCMs
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HOT, n = 139 (Mean Obs. PP = 513.12 (+/- 152) mg C m-2 d-1)
SAT models BOGCMs
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Ross Sea, n = 144 (Mean Obs. PP = 1177.57 (+/- 849) mg C m-2 d-1)
SAT models BOGCMs
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SAT,DI,WI SAT,DR,WI SAT,DR,WR BOGCM
Over-estimates PP
Under-estimates PP
Over-estimates PP variability
Under-estimates PP variability
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SAT,DI,WI SAT,DR,WI SAT,DR,WR BOGCM
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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
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SAT,DI,WI SAT,DR,WI SAT,DR,WR BOGCM
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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
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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
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Log(in situ chlorophyll)
Mea
n B
OG
CM
mis
fitAll BOCGMs, All Regions
R2 = 0.03
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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
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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
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Mea
n B
OG
CM
mis
fitBOGCMs
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Depth (m)
Mea
n SA
T m
odel
mis
fit
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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.
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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.
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HPLC
Fluorometrichttp://hahana.soest.hawaii.edu/hot/
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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.
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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.