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Potential Improvements to Primary Productivity Estimates through Subsurface Chlorophyll and Light Measurement. Michael Jacox University of California, Santa Cruz Raphael Kudela , Christopher Edwards (UCSC) Mati Kahru , Daniel Rudnick (UCSD). 45 th International Liége Colloquium

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slide1

Potential Improvements to Primary Productivity Estimates through Subsurface Chlorophyll and Light Measurement

Michael Jacox

University of California, Santa Cruz

Raphael Kudela, Christopher Edwards (UCSC)

MatiKahru, Daniel Rudnick (UCSD)

45th International Liége Colloquium

May 17, 2013

slide2

Study Data: Primary Productivity and Ancillary Measurements

Shipboard:

1985-2011

CalCOFIPrimary Productivity Casts

~100 cruises

>1500 stations

Satellite:

Data starting in 1997

SeaWiFS chlorophyll

SeaWiFS/MODIS PAR

AVHRR Pathfinder SST

Match-ups for 723 CalCOFI stations

Autonomous Profiling:

Data starting in 2005

Scripps Spray gliders

CTD, Fluorescence

Regular coverage of lines 80 and 90

slide3

The Roots of Satellite PP Models

Globally:

SC Bight:

22 cruises, ~270 stations (1974-1983)

Correlated NPP with surface environmental variables

Most of the variability explained is due to variability in surface chlorophyll

Some explained by temperature and day length, which may reflect seasonality

Shortcoming:

All information on vertical structure is lost

slide5

PP Model Performance for the CalCOFI dataset

VGPM

ESQRT

log (modeledproductivity) (mg C m-2 d-1)

r2=0.55

Bias=0.11

RMSD=0.25

r2=0.64

Bias=0.20

RMSD=0.28

MARRA

VGPM-KI

r2=0.62

Bias=0.13

RMSD=0.25

r2=0.64

Bias=0.08

RMSD=0.24

ESQRT: Eppley square root model (Eppley et al. 1985)

VGPM: Vertically Generalized Production Model (Behrenfeld and Falkowski 1997)

VGPM-KI: VGPM variant with two phytoplankton size classes (Kameda and Ishizaka 2005)

MARRA: Vertically resolved model based on chl-specific absorption (Marra et al. 2003)

log (in situ productivity) (mg C m-2 d-1)

slide8

Start with VGPM:

Goal: Create an Improved PP Model for the Southern CCS

Model Statistics for 2005-2010

Behrenfeld and Falkowski 1997

slide9

Revised Goal: Understand What Limits Model Performance

Fall 2002

Surface chlorophyll poorly correlated with chl at depth

Summer 2000

Surface chlorophyll well correlated with chl at depth

Depth (m)

Depth (m)

log(chlorophyll) (mg m-3)

log(chlorophyll) (mg m-3)

slide10

Revised Goal: Understand What Limits Model Performance

r2 (NPPMODEL, NPPIN SITU)

r2 (NPPMODEL, NPPIN SITU)

Model performance is strongly dependent on chl0 being representative of NPP

…but not on accurate estimation of the photosynthetic parameter

r2 (chl0, NPPIN SITU)

r2 (PBOPT,MODEL, PBOPT,CALC)

N = 14 years, 56 quarterly cruises

slide11

Performance of a Simple Vertically Resolved Production Model

r2 (NPPMODEL, NPPIN SITU)

In situ surface chlorophyll

SeaWiFS chlorophyll

log (modeledproductivity) (mg C m-2 d-1)

r2=0.59

Bias=0.02

RMSD=0.21

r2=0.64

Bias=0.03

RMSD=0.20

r2 (chl0, NPPINSITU)

In situ chlorophyll

and light profiles

In situ chlorophyll profile

r2=0.74

Bias=0.02

RMSD=0.17

r2=0.81

Bias=0.02

RMSD=0.14

log (in situ productivity) (mg C m-2 d-1)

Jacox et al., submitted

slide13

Los Angeles

Jul 2007

San Diego

Jan 2009

Correct profile amplitude based on surface chlorophyll

Lavigne et al. (2012)

Converting Glider Fluorescence to Chlorophyll

Depth

Depth

Chlorophyll (mg m-3)

3*fluorescence

gliders fluorescence improves p roductivity estimates
Gliders Fluorescence Improves Productivity Estimates

Potential for satellite alone

r2

Potential for satellite/glider with fluorescence

Potential for satellite/glider with fluorescence and PAR

Data forCalCOFI/glider match-ups within 10km and 10 days (N=39)

conclusions
Conclusions

Satellite model performance in the SCCS is largely determined by correlations between surface chlorophyll and NPP

Knowledge of in situ vertical chlorophyll and light profiles raises model performance well above the variability between existing models

The combination of surface satellite data and subsurface profiler data is a powerful one and a growing database of autonomous profiler data can now be used to refine PP estimates

“In view of these prospects and challenges we urge our colleagues to examine their own data on primary production and chlorophyll. There is much yet to be done.”

-Eppley et al. 1985, J. Plankton Res.