Michael Jacox University of California, Santa Cruz Raphael Kudela , Christopher Edwards (UCSC)

M. Jacox | Potential Improvement to Primary Productivity Estimates through Subsurface Chlorophyll and Irradiance Measurement | May 17, 2013

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

Michael JacoxUniversity of California, Santa Cruz

Raphael Kudela, Christopher Edwards (UCSC) Mati Kahru, Daniel Rudnick (UCSD)

45th International Liége ColloquiumMay 17, 2013


Shipboard:1985-2011CalCOFI Primary Productivity Casts~100 cruises>1500 stations

Satellite:Data starting in 1997SeaWiFS chlorophyllSeaWiFS/MODIS PARAVHRR Pathfinder SSTMatch-ups for 723 CalCOFI stations

Autonomous Profiling:Data starting in 2005Scripps Spray gliders CTD, FluorescenceRegular coverage of lines 80 and 90

Study Data: Primary Productivity and Ancillary Measurements


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

€

PP =1000 chl0

Globally:

The Roots of Satellite PP Models


Rank2

13131681182

18162

192

1367

101

201120

Friedrichs et al. 2009

Saba et al. 2011

€

PP =1000 chl0

28 Years Later, The Simplest Model is Often Among the Best


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

log

(mod

eled

pro

duct

ivity

) (m

g C

m-2 d

-1)

ESQRT VGPM

VGPM-KI MARRA

r2=0.55Bias=0.11

RMSD=0.25

r2=0.64Bias=0.20

RMSD=0.28

r2=0.62Bias=0.13

RMSD=0.25

r2=0.64Bias=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)

PP Model Performance for the CalCOFI dataset


r2=0.33 r2=0.00

r2=0.21 r2=0.12

Goal: Create an Improved PP Model for the Southern CCS




€

PP = 0.66125⋅ PoptB ⋅ dirr ⋅ chl0⋅PAR

PAR + 4.1⋅ zeuStart with VGPM:

€

PoptB = f chl0,dist( )

€

Popt,CALCB =

PP

0.66125⋅ dirr ⋅ chl0⋅PAR

PAR + 4.1⋅ zeu

Model r2 Bias RMSD

ESQRT 0.49 0.11 0.25

VGPM 0.59 0.20 0.29

VGPM-KI 0.57 0.12 0.24

MARRA 0.59 0.08 0.25

VGPM-SC 0.62 0.01 0.19

Model Statistics for 2005-2010


Behrenfeld and Falkowski 1997

€

PoptB = f SST( )


Summer 2000Surface chlorophyll well correlated with chl at depth

Fall 2002Surface chlorophyll poorly correlated with chl at depth

log(chlorophyll) (mg m-3)

D

epth

(m)

Model r2 Bias RMSD

ESQRT 0.41 0.16 0.23

VGPM 0.44 0.20 0.26

VGPM-KI 0.39 0.14 0.22

MARRA 0.45 0.07 0.21

Model r2 Bias RMSD

ESQRT 0.92 -0.06 0.13

VGPM 0.91 0.13 0.16

VGPM-KI 0.94 0.10 0.17

MARRA 0.90 -0.05 0.15

Dep

th (m

)

log(chlorophyll) (mg m-3)

Revised Goal: Understand What Limits Model Performance


N = 14 years, 56 quarterly cruises

r2 (N

PP

MO

DE

L, N

PP

IN S

ITU)

r2 (chl0, NPPIN SITU)r2 (

NP

PM

OD

EL,

NP

PIN

SIT

U)

r2 (PBOPT, MODEL, PB

OPT, CALC)

Revised Goal: Understand What Limits Model Performance

Model performance is strongly dependent on chl0 being representative of NPP

…but not on accurate estimation of the photosynthetic parameter


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

log

(mod

eled

pro

duct

ivity

) (m

g C

m-2 d

-1) SeaWiFS chlorophyll In situ surface chlorophyll

In situ chlorophyll profile In situ chlorophyll and light profiles

r2=0.59Bias=0.02

RMSD=0.21

r2=0.64Bias=0.03

RMSD=0.20

r2=0.74Bias=0.02

RMSD=0.17

r2=0.81Bias=0.02

RMSD=0.14

€

PP(z) = 2.9 ⋅dirr ⋅chl(z) ⋅PAR

PAR + 2.6

r2 (N

PP

MO

DE

L, N

PP

IN S

ITU)

r2 (chl0, NPPIN SITU)

Performance of a Simple Vertically Resolved Production Model

Jacox et al., submitted


Several CalCOFI Lines are Regularly Sampled by Spray Gliders

Monterey Bay

Pt. Arena


Los Angeles

San Diego

Jan 2009

Jul 2007

Chlorophyll (mg m-3)

Dep

thD

epth

Correct profile amplitude based on surface chlorophyll

Lavigne et al. (2012)

3*fluorescence

Converting Glider Fluorescence to Chlorophyll


Gliders Fluorescence Improves Productivity Estimates

Chlorophyll Data Light Data r2 RMSD

CalCOFI Surface SeaWiFS Surface 0.52 0.20

Glider Profile SeaWiFS Surface 0.59 0.19

CalCOFI Profile SeaWiFS Surface 0.59 0.18

CalCOFI Profile CalCOFI Profile 0.74 0.14

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

Potential for satellite alone

Potential for satellite/glider with fluorescence

Potential for satellite/glider with fluorescence and PAR

0-10 10-20 20-30 30-40 40-500

20

40

60

80

100

RMSD

Distance from Station (km)

% o

f Pot

entia

l Im

prov

emen

t

r2

0-10 10-20 20-30 30-40 40-50-20

0

20

40

60

80

100 RMSD

Time Difference (days)

% o

f Pot

entia

l Im

prov

emen

t r2


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.

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Michael Jacox University of California, Santa Cruz Raphael Kudela , Christopher Edwards (UCSC)

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