Climate Variability and Phytoplankton Composition in the Pacific Ocean Rousseaux C.S., Gregg W.W. NASA Ocean Color Research Team Meeting, April 23- 25 2012, Seattle, WA Chlorophyll a, NASA Ocean Color El Niño La Niña
Jan 12, 2016
Climate Variability and Phytoplankton Composition in the
Pacific OceanRousseaux C.S., Gregg W.W.
NASA Ocean Color Research Team Meeting, April 23-25 2012, Seattle, WA
Chlorophyll a, NASA Ocean Color
El Niño
La Niña
1997-98 El Niño
Seabird abundance and anchoveta and sardine landings from Peru (Chavez et al. 2003)
When it comes to feeding fishes,
all phytoplankton are not equal…
Cyanobacteria
Diatoms
Coccolithophores
Chlorophytes
Chlo
rophyll
a
Are El Niño conditions unfavorable to all
phytoplankton groups or only some?
pCO2
pCO2
Nutrients PhytoplanktonDiatom
sDiatom
sChloro, Cocco,
CyanoChloro, Cocco,
Cyano
DIC
Fe, NO3, NH4
Fe, NO3, NH4 SiSi
Herbivores
N/C DetritusIron
Detritus
Silica Detritus
• Clouds• Precipitation water• Relative humidity
• Ozone
Wind Stress Wind speed
Mixing
Advection
Validation
Variable Global Difference % Correlation over Basins
Nitrate 18.9% 0.905 P<0.05Ammonia Not tested Not testedSilica 5.4% 0.952 P<0.05Dissolved Iron 45% 0.933 P<0.05Diatoms 15.5% 0.850 P<0.05Chlorophytes -16.2% 0.020 NSCyanobacteria 7.9% 0.970 P<0.05Coccolithophores -2.6% 0.700 P<0.05Total Chlorophyll vs In situ -17.1% 0.787 P<0.05 vs SeaWiFS -8.0% 0.618 P<0.05 vs Aqua 1.1% 0.469 NSHerbivores Not tested Not testedDetritus Not tested Not testedDiss. Inorganic Carbon 0.1% 0.972 P<0.05pCO2 0.0% 0.765 P<0.05Air-sea carbon flux 3.1% 0.741 P<0.05
Figure 2| Comparison of chlorophyll (mg m-3) from the assimilation model, the free-run model, and SeaWiFS. The assimilation and free-run chlorophyll distributions represent simulations for April 1, 2001. SeaWiFS data for the same day are shown for comparison, along with the monthly mean. Grey indicates land and coast, black indicates missing data, and white indicates sea ice.Bias Uncertainty N
SeaWiFS -1.3% 32.7% 2086
Free-run Model -1.4% 61.8% 4465
Assimilation Model 0.1% 33.4% 4465In situ data from Seabass and nodc
Data Assimilation
In ocean biology, Two Classes:Variational (e.g., adjoint, 4DVar)Sequential (e.g., Kalman Filter)
We use Sequential Methodologies,Conditional Relaxation Analysis MethodEnsemble Kalman Filter
Routinely assimilating SeaWiFS and Aqua Chlorophyll Data
(a)
(b)
(c)
(d)
Figure 3| Comparison of the free run, the multivariate and the univariate approach for chlorophyll and nutrients in the South Pacific Ocean. Time series of annual averages of (a) Chlorophyll, (b) Nitrate, (c) Silicate and (d) Iron. [Rousseaux & Gregg 2012]
(a)
(b)
(c)
MEI
Global Phytoplankton Relative Abundance
469 observations taken from figures in peer-reviewed papers; Available at GMAO Web site
How well does the NOBM compare to in situ data?
North Central Pacific
Equatorial Pacific South Pacific
Diatoms -3.50 (3) -0.87 (21) 25.58 (7)Chlorophytes -19.40 (2) -18.01 (17) -33.32 (7)Cyanobacteria 10.67 (24) -13.47 (20) 3.20 (2)Coccolithophores 1.99 (3) 36.77 (15) -2.11 (7)
Percentage difference between the NOBM and the in situ data. The number of observations used for the comparison is between parenthesis
Only >20% in 3 cases
How well does the NOBM compare to in situ data?
Intercomparison of model- and satellite-derived phytoplankton community composition
Hirata et al. 2011
Intercomparison of model- and satellite-derived phytoplankton community composition
North Central Pacific Equatorial Pacific South Pacific
Diatoms -3.06 (3) -8.00 (21) -7.00 (7)Chlorophytes -12.35 (2) -8.80 (17) -38.43 (7)Cyanobacteria -8.12 (24) 2.88 (20) 0.19 (2)Coccolithophores 8.31 (3) 17.68 (15) 2.53 (7)
Regions where the satellite-derived approach is closer to the in situ data than the NOBM
Percentage difference between Hirata’s method and the in situ data. The number of observations used for the comparison is between parenthesis
Percentage difference between Hirata’s method and the in situ data. The number of observations used for the comparison is between parenthesis
Only >20% in 1 cases
(a) North Central PacificMEI Diatoms Chlorophytes Cyanobacteria Coccolithophores
MEI 1.00Diatom -0.14 1.00
Chlorophytes -0.04 0.63* 1.00Cyanobacteria -0.05 0.59* 0.84* 1.00
Coccolithophores -0.03 0.64* 0.87* 0.95* 1.00
(a) Equatorial PacificMEI Diatoms Chlorophytes Cyanobacteria Coccolithophores
MEI 1.00Diatom -0.40* 1.00
Chlorophytes -0.47* 0.53* 1.00
Cyanobacteria -0.46* 0.58* 0.67* 1.00Coccolithophores -0.64* 0.63* 0.79* 0.82* 1.00
(a) South Pacific
MEI Diatoms Chlorophytes Cyanobacteria CoccolithophoresMEI 1.00Diatom -0.06 1.00Chlorophytes -0.01 0.12 1.00Cyanobacteria -0.02 0.13 0.85* 1.00
Coccolithophores 0.01 0.18* 0.88* 0.89* 1.00
1. Climate variability has most impact on the phytoplankton
community composition in the Equatorial Pacific
2. Large Shifts are observed both on temporal and spatial scale
3. These shifts have potential important consequences for the
carbon cycles and higher trophic levels
4. Different methods provide different views of the impact climate
variability has on the biology
Conclusion:
P<0.05
P<0.05 P<0.05
Supporting data and publications: Google gmao, click Research, thenOcean Biology Modeling (http://gmao.gsfc.nasa.gov/research/oceanbiology)
NS
Chlorophytes
0
10
20
30
40
50
60
70
80
N Atl
N Pac
NC Atl
NC Pac
N Ind
Eq Atl
Eq Pac
Eq In
dS A
tl
S Pac
S Ind
Antar
c
Pe
rce
nt o
f To
tal
Global mean difference model-data = -16.2%Correlation coefficient ( r ) = -0.022
Diatoms
0
20
40
60
80
100
120
N Atl
N Pac
NC Atl
NC Pac
N Ind
Eq Atl
Eq Pac
Eq In
dS A
tl
S Pac
S Ind
Antar
c
Pe
rce
nt o
f To
tal
Global mean difference model-data = 15.5%Correlation coefficient ( r ) = 0.847*
Cyanobacteria
0
10
20
30
40
50
60
70
80
90
N Atl
N Pac
NC Atl
NC Pac
N India
n
Eq Atl
Eq Pac
Eq In
dian
S Atl
S Pac
S India
n
Antar
c
Pe
rce
nt o
f To
tal
Global mean difference model-data = 7.9%Correlation coefficient ( r ) = 0.972*
Coccolithophores
0
10
20
30
40
50
60
N Atl
N Pac
NC Atl
NC Pac
N India
n
Eq Atl
Eq Pac
Eq In
dian
S Atl
S Pac
S India
n
Antar
c
Pe
rce
nt o
t To
tal
Global mean difference model-data = -2.6%Correlation coefficient ( r ) = 0.704*
Blue = NOBM; Green = Data
Gregg and Casey, 2007, Deep-Sea Research II