# Presentation: 602886

## ESTIMATING OCEANIC PRIMARY PRODUCTIVITY:  

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

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

## Background

**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.**

## Background

**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.**

**Notes:**
Campbell – 12 models, 9 ecosystems but only 89 stations       Carr – 31 models using SAT and modeled data.  Friedrichs – 30 models using in situ data only in the eq. pacific nearly 1000 stations.  Here we have 9 regions with nearly 900 stations.

## 1. BATS (n = 197), 1988-2003	6. Arabian Sea (n = 42), 1995			

**1. BATS (*****n***** = 197), 1988-2003****	****6. Arabian Sea (*****n***** = 42), 1995****	****	****	**
**2. NABE (*****n***** = 12), April-May 1989****	****7. HOT (*****n***** = 139), 1989-2005**
**3. NEA (*****n***** = 52), 1993-1998****	****	****8. Ross Sea (*****n***** = 164), 1996-2006**
**4. Black Sea (*****n***** = 43), 1992-1999****	****9. APFZ (*****n***** = 12), Dec. 1997**
**5. MED (*****n***** = 202), 1984-2007**

**1**

**2**

**3**

**4**

**5**

**6**

**7**

**8**

**9**

**Notes:**
Mention that the Ross Sea is both on-deck and in situ measured PP.  JGOFS stations are in situ, rest are on deck.

## Acknowledgements

**Funding:  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 

**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.**

**Notes:**
Also mention that some models acquired additional paramters: i.e. CbPM use additional SeaWiFS parameters such as backscatter and absorption coefficients.  Explain the use of level-3 9km weekly composites and took a 3x3 pixel (27x27 km) average with station as center point.  Also mention perturbation analysis.

## Model Skill Analysis

**Root mean square difference (*****RMSD*****).**

**Target diagrams.**

**Model misfit**

## Mean RMSD

**Mean RMSD**

**Model skill for each region**

**BATS**

**NABE**

**NEA**

**Black Sea**

**MED**

**Arabian Sea**

**HOT**

**Ross Sea ****(*****in situ*****)**

**Ross Sea (****on deck)**

**APFZ**

**All Regions**

***Lower RMSD = Higher model skill***

## SAT models

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

**SAT models**

**BOGCMs**

## SAT models

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

**SAT models**

**BOGCMs**

## SAT models

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

**SAT models**

**BOGCMs**

## SAT models

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

**SAT models**

**BOGCMs**

## SAT models

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

**SAT models**

**BOGCMs**

## SAT,DI,WI

**SAT,DI,WI**

**SAT,DR,WI**

**SAT,DR,WR**

**BOGCM**

## SAT,DI,WI

**SAT,DI,WI**

**SAT,DR,WI**

**SAT,DR,WR**

**BOGCM**

## SAT,DI,WI

**SAT,DI,WI**

**SAT,DR,WI**

**SAT,DR,WR**

**BOGCM**

## SAT,DI,WI

**SAT,DI,WI**

**SAT,DR,WI**

**SAT,DR,WR**

**BOGCM**

## Mean RMSD

**BATS**

**NEA**

**MED**

**HOT**

**Ross Sea**

**APFZ**

**All**

## Log(in situ chlorophyll)

-  SAT DR,WR Models

-  SAT DR,WI Models

**Log(*****in situ *****chlorophyll)**

**Mean SAT model misfit**

- All SAT Models

-  SAT DI,WI Models

**All SAT Models, All Regions**

## Log(in situ chlorophyll)

**Mean BOGCM misfit**

**All BOCGMs, All Regions**

**R****2 ****= 0.03**

## Pelagic

***In situ *****PP variability**

***In situ***** Chlorophyll variability**

**NABE**

**APFZ**

**HOT**

**Arabian Sea**

**BATS**

**Black Sea**

**NEA**

**MED**

**Ross Sea**

**Pelagic**

**Coastal**

**Notes:**
NABE and APFZ would be shifted right if we had longer time-series.  Tropical regions BATS, HOT, and Arabian should be low in PP variability.

## SAT Models

***In situ *****PP variability**

**Mean SAT model misfit**

**NABE**

**APFZ**

**HOT**

**Arabian Sea**

**Black Sea**

**NEA**

**MED**

**Ross Sea**

**BATS**

**Pelagic**

**Coastal**

**SAT Models**

## BOGCMs

**Mean BOGCM misfit**

**BOGCMs**

## Depth (m)

**Depth (m)**

**Mean SAT model misfit**

**Notes:**
In pelagic regions, as depth increases SAT models underestimate PP.  In the Black Sea, as depth increases SAT models overestimate PP.  No clear pattern in other coastal areas.

## BATS

**BATS**

- Mean SAT model misfit

- -0.61

- Correlation

**Mean SAT PP = No increase.**

**Fluor. chlor.  = No increase.**

**HPLC chlor. = Increase.**

**Notes:**
Mean SAT PP shows no increasing trend !  This likely because we provided models with Turner chlor. (flor.) as opposed to HPLC.  HPLC chlor. at BATS is increasing with PP but Turner is not !  Should SeaWiFS algorithms be calibrated to HPLC chlor. as opposed to Turner.

## HOT

**HOT**

- Mean SAT model misfit

- -0.82

- Correlation

**Mean SAT PP = No increase.**

**Fluor. chlor.  = No increase.**

**HPLC chlor. = Increase.**

## HPLC

**HPLC**

**Fluorometric**

- http://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.**

**Notes:**
Campbell showed increased PP error with increasing chl. Error. Consistent with Carr et al. 2006 showing model divergence at extreme values of chlorophyll.  Consistent with PPARR3b in Eq. Pacific.

## 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.**

**Notes:**
Campbell showed increased PP error with increasing chl. Error. Consistent with Carr et al. 2006 showing model divergence at extreme values of chlorophyll.  Consistent with PPARR3b in Eq. Pacific.