Michael Jacox University of California, Santa Cruz Raphael Kudela , Christopher Edwards (UCSC)
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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
M. Jacox | Potential Improvement to Primary Productivity Estimates through Subsurface Chlorophyll and Irradiance Measurement | May 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
M. Jacox | Potential Improvement to Primary Productivity Estimates through Subsurface Chlorophyll and Irradiance Measurement | May 17, 2013
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
M. Jacox | Potential Improvement to Primary Productivity Estimates through Subsurface Chlorophyll and Irradiance Measurement | May 17, 2013
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
M. Jacox | Potential Improvement to Primary Productivity Estimates through Subsurface Chlorophyll and Irradiance Measurement | May 17, 2013
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
M. Jacox | Potential Improvement to Primary Productivity Estimates through Subsurface Chlorophyll and Irradiance Measurement | May 17, 2013
r2=0.33 r2=0.00
r2=0.21 r2=0.12
Goal: Create an Improved PP Model for the Southern CCS
M. Jacox | Potential Improvement to Primary Productivity Estimates through Subsurface Chlorophyll and Irradiance Measurement | May 17, 2013
Goal: Create an Improved PP Model for the Southern CCS
M. Jacox | Potential Improvement to Primary Productivity Estimates through Subsurface Chlorophyll and Irradiance Measurement | May 17, 2013
€
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
Goal: Create an Improved PP Model for the Southern CCS
Behrenfeld and Falkowski 1997
€
PoptB = f SST( )
M. Jacox | Potential Improvement to Primary Productivity Estimates through Subsurface Chlorophyll and Irradiance Measurement | May 17, 2013
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
M. Jacox | Potential Improvement to Primary Productivity Estimates through Subsurface Chlorophyll and Irradiance Measurement | May 17, 2013
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
M. Jacox | Potential Improvement to Primary Productivity Estimates through Subsurface Chlorophyll and Irradiance Measurement | May 17, 2013
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
M. Jacox | Potential Improvement to Primary Productivity Estimates through Subsurface Chlorophyll and Irradiance Measurement | May 17, 2013
Several CalCOFI Lines are Regularly Sampled by Spray Gliders
Monterey Bay
Pt. Arena
M. Jacox | Potential Improvement to Primary Productivity Estimates through Subsurface Chlorophyll and Irradiance Measurement | May 17, 2013
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
M. Jacox | Potential Improvement to Primary Productivity Estimates through Subsurface Chlorophyll and Irradiance Measurement | May 17, 2013
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
M. Jacox | Potential Improvement to Primary Productivity Estimates through Subsurface Chlorophyll and Irradiance Measurement | May 17, 2013
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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