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

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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

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Global mean difference model-data = -16.2%Correlation coefficient ( r ) = -0.022

Diatoms

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20

40

60

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100

120

N Atl

N Pac

NC Atl

NC Pac

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Eq Atl

Eq Pac

Eq In

dS A

tl

S Pac

S Ind

Antar

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Pe

rce

nt o

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Global mean difference model-data = 15.5%Correlation coefficient ( r ) = 0.847*

Cyanobacteria

0

10

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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

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Global mean difference model-data = 7.9%Correlation coefficient ( r ) = 0.972*

Coccolithophores

0

10

20

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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