Remote sensing of Trichodesmium blooms (and their relation to atmospheric dust)

Toby WestberryPh.D. defenseJuly 26, 2005

Committee members:Mark BrzezinskiNatalie MahowaldNorm NelsonDave Siegel (chair)

Goals of my dissertation

1. Develop technique for discriminating Trichodesmium blooms from satellite

2. Map bloom distributions in space and time using SeaWiFS ocean color data

3. Assess contribution to total oceanic N2 fixation

4. Investigate linkage between presence of blooms of N2 fixers (Tricho) and dust flux to the surface ocean

What is Trichodesmium?(Greek, trichoma = hair, desmus = bonded)

- colonial, filamentous, non-heterocystous, photosynthetic, diazotrophic cyanobacteria

cell(5-15 m)

trichomecolonies(2-5 mm)

Photos: B . Bergman

Why do we care? (1)

- represents a pathway for nutrients to enter ocean ecosystem (i.e., atmospheric N2 converted to NH4

+)

- inputs via Trichodesmium are biogeochemically significant (Michaels et al., 1996; Capone et al., 1997; Gruber and Sarmiento, 1997; Capone et al., 2005)

- might play a role in climate change (i.e., McElroy, 1983; Falkowski, 1997; Broecker and Henderson, 1998)

- Large-scale distributions of Trichodesmium are not well known

Why do we care? (2)• Global N budget (after Codispoti et al., 2001) (e.g., Michaels et al., 1996; Capone et al., 1997; Karl et al., 1997; Gruber and Sarmiento, 1997) Process Annual Flux

(Tg N yr-1)Sources

Pelagic N2 fixation 110 ± 40

Benthic N2 fixation 15 ± 10

River input (DN + PON) 76 ± 10

Atmospheric dep. 86 ± 5

Sinks

Organic N export 1

Benthic Denitrification 300

Water column Denitrification

150

Sedimentation 25 ± 10

N2O loss 6

Total = 287

Total = 482

Can account for about ½ the geochemically inferred flux

Deutsch et al.2001

Gruber & Sarmiento.1997 (adjusted)

1.

3.

2.

4.

Trichodesmium blooms (1)

100 km

Trichodesmium blooms (2)

- Blooms are episodic/ephemeral - role of blooms is largely unconstrained

- We have no appreciation for:

- extent of blooms (time/space)

- contribution to global N budget

- causes of blooms- why?, where?, when?, how

often?

So what regulates rates of N2 fixation?(and possibly bloom formation)

- unfortunately, many things.... (see Karl et al., 2002 for review)

- O2

- Energy, ATP- Temperature- Nitrogen (both amount and speciation)- Phosphorous - trace nutrients (Fe, Mb, Zn, Cu, …)

- essential for nitrogenase (N2 fixing enzyme)

- Raven (1988) estimated 100x requirement

- might be more like 2-8x requirement (Sañudo-Wilhelmy et al., 2001; Berman – Frank et al., 2001)

What do we know about Fe in ocean??

- concentrations are VERY low (< 1 nM) in most of surface ocean

Due to:- speciation- particle scavenging- efficient uptake- ligand binding?- source limited

-supply to surface open ocean is through upwelling or atmospheric deposition of mineral dust

“ ... might turn the skies milkier and leave a light coating of reddish-brown dust on your car, the result of a small amount of iron content. It also could make the sunrise and sunset spectacular...”, - Florida Sun-Sentinel, 7/22/05

Dust plumes seen from space

Fe supply to ocean (1)...it adds 1.2 – 5.7 x 1011 mol Fe yr -1 to surface oceanDuce and Tindale (1991), Tegen & Fung (1995), Mahowald et al. (1999)

Mahowald et al., 1999

Fe supply to ocean (2)...accounts for >70% of the Fe demand in much of the ocean(Fung et al., 2000; Moore et al., 2002)

Moore et al., 2002

Large scale hypothesesChanges in oceanic N2 fixation due to dust inputs account for directly alter efficiency of ‘biological pump’ and consequently, pCO2 changes on glacial/intercglacial timescales

(ala McElroy, 1983, Falkowski, 1997; Broecker and Henderson, 1998)

Chapter 1. Bio-optical modeling of Trichodesmium

Goal: Develop a method for estimating a Tricho index from routinely measured optical quantities (i.e., ocean color data)

Why should it work? There are several unique optical aspects that distinguish Tricho from other phytoplankton

Trichodesmium optical properties

1. Phycoerythrin absorption & fluorescence (Subramaniam et al., 1999; Lewis et al., 1988; Shimura and Fujita, 1975)

2. Gas vacuoles (Borstad et al. 1992; Walsby, 1991; Margalef, 1965)

3. >> CDOM (Subramaniam et al. 1999; Gilbert and Bronk, 1994)

Wavelength (nm)Wavelength (nm)

bb

* ()

[

m2 m

g C

hl-1

]

a* (

)

[m

2 m

g C

hl-1

]

Trichodesmium in red

Ahn et al. (1992)Bricaud et al. (1998)

“Color” of Trichodesmium

- optical properties will be manifested in the remote sensing reflectance spectrum, Rrs()

Tricho

“blue water”

“green water”

Wavelength (nm)

Rrs

()

[sr-

1]

Trichodesmium - Rrs() dataset

1. Trichodesmium biomass [col m-3 OR trichomes L-1]

2. Rrs(0-,) – spectral remote sensing reflectance

N=130, (1994-present)

AMTN2 Biocomplexity

BATS

Data Distribution

min max mean medianTricho 0 11071* 887 170Total Chl 0.01 2.68 0.21 0.16

* literature bloom values range from 105 - 108

trichomes L-1 mg Chl m-3 Wavelength (nm)

Rrs

(sr-

1)

Tricho-specific reflectance model (1)

- Modified UCSB Ocean Color Model (Garver & Siegel, 1997; Maritorena et al., 2002)- Add new parameter inputs and outputs

Tricho IOPmodelRrs()

Products(Chl, aCDM & Tricho)

Parameters(aph

*(), S, atri*(), bbtri

*(), ...)

2

1

( ) ( ) ( )( )

( ) ( ) ( ) ( ) ( ) ( ) ( )

i

w p trichoi

i w p tricho w ph cdm tricho

bb bb bbRrs g

bb bb bb a a a a

Tricho-specific reflectance model (1)

Step 1. From radiative transfer (i.e., Gordon, 1988)

2

1

( )( )

( ) ( )

i

ii

bbRrs g

bb a

Step 2. Write a() and bb() as their components,

*( ) ( )h pp hChl aa

0 0( )( ) exp[ ( )]cdc mdm Saa

red = unknown

cyan = measured or modeled

0 = 443 nm

0.766 550( ) 0.416 0.002 0.02 0.5 0.25logbp Chl Chb l

*( ( )) trichotricho trichoChlbb bb

*( ( )) trichotricho trichoC ahla

0.766 *

*0. *760

6

550( ) 0.416 0.002 0.02 0.5 0.25log

( )550

( ) 0.416 0.002 0.02 0.5 0.

( )

( ) ( )25log e p[) x(

tbw tricho

trich

i

b o cdm

richo

tricho pw h

Chl Chl Chl

Chl Chl

bb

bb C a S

b

R

hl Chl a

rs g

b

*

2

10( )] ( )

i

itritric o choha Chl

Tricho-specific reflectance model (2)

Step 4. Substitute and solve

Step 3. Parameterize each component

Bloom Identification - Tricho-specific reflectance model (Westberry et al., 2005)

- Predicts bloom presence/absence (threshold = 3200 trichomes L-1)

in situ model development (•)92 % bloom correct84% non-bloom correct satellite model validation (x)76% bloom correct71% non-bloom correct

False positives

False negative

Chapter 2. Interpreting patterns of Trichodesmium bloom

distributionsApply to SeaWiFS imagery:

- 8-day composites

- 45S – 45N

- ‘standard’ SeaWiFS processing

- 0.25 degree resolution

- 09/1997 – 12/2003

- bathymetric mask (<100 m)

- atmospheric contamination mask

Frequency of Occurrence

- 6-year mean (Sep 1997-Dec 2003)

Summary of bloom distribution

- 30% of ocean NEVER has a Trichodesmium bloom

- 70% of ocean sees blooms <5% of time

- 90% of ocean sees blooms <10% of time

blooms are RARE!

Frequency of occurrence (seasonal)

- widespread, infrequent blooms throughout low latitudes

- large seasonal response in N. Indian Ocean

- semi-frequent blooms in equatorial Pacific (10°S, 120°W)

Global N2 Fixation [mmol/m2/yr]

~130 TgN/yr(40o S-65o N)

From C. Deutsch

N2 fixation from blooms

2

2

2

N fix rate AREAL RATE x #PIXELS x PIXELAREA

μmol N μmol N mx pixels x

year m day pixel

1500 mol N m-2 d-1 (after Capone et al., in press GBC)

** compared to total oceanic N2 fixation ~ 100 Tg N yr-1

Global N2 fix = 8.5 ± 1.2 Tg N yr-1

Dust deposition

Warm SST

Trichodesmiumbloom

“echo” phytoplanktonblooms

(Lenes et al., 2001; Coles et al., 2004)

time

Forcing of blooms (1)

aggregation

growth

Remineralizationof new DON

Low wind +Shallow MLD

Annual mean fields

1. Spatial means within region

2. Bin obs. seasonally (MAM, JJA, SON, DJF)

3. Calculate percent change between bloom and non- bloom conditions

For example,100tricho non tricho

non tricho

SST SST xSST

Regional property differences (1)

Regional property differences (2)

% change from non-Tricho bloom conditions as a function of season

SST(C)

5% 2%

0% 8%

WIND(m s-1)

-14% -15%

-4% -1%

MLD(m)

-50% -57%

-41% -35%

DUST(g m-2 day-1)

-18% 57%

-8% -22%

- ex. from Caribbean/ Gulf of Mexico (10N-30N x 95W-70W)

- red indicates change in expected direction (i.e., >SST, <Wind, <MLD, >Dust)

SpringSummer

Fall Winter

Cross-correlation analyses

- Cxy quantifies linear correlation between two time series X & Y where Y(t) = ƒ(X(t+))

- red indicates significant lead/lag relationship in “expected”

direction- is called the “lag”

Region Chl a SST Dust MLD Wind

Arabian Sea -2 (0.43) 3 (0.37) -14 (0.53) -27 (0.47) 4 (-0.44)

S. Indian Ocean 0 (0.75) -14 (0.37) 0 (-0.33) -15 (-0.63) -16 (-0.52)

E. Tropical Atlantic 3 (0.36) -8 (0.24) -4 (0.25) 3 (0.24) 0 (0.34)

Carribbean and Gulf of Mexico 0 (0.56) 20 (0.46) 18 (0.45) 22 (-0.53) 16 (-0.38)

S. Eq. Pacific 0 (0.44) 0 (0.42) 2 (-0.26) -2 (-0.36) 46 (0.17)

Probability distributions

- look at PDF in all observations and just those with blooms

- conditional probability = PDF(V)tricho / PDF(V)

2-D Probability distributions

- color is obs. without Tricho

- contours are obs. with Tricho

express data density as % of Nobs

4-D Probability distributions

- each “dimension” represents a parameter space

- joint probability for given SST, wind, MLD, dust dep.

= p(SST, wind, MLD, dust dep.)

- can do same for observations WITH blooms

tricho = p(SST, wind,MLD,dust dep.)

- Now, calculate 4-D conditional probability distribution

p(tricho | SST | wind | MLD | dust dep.)= tricho /

Conditional Probability distributions

- make LUT which can be interpolated for any combination

of SST, wind speed, MLD, and dust dep. rate

- use seasonal mean values in each parameter

Conclusions (1)• We can predict presence/absence of

Trichodesmium blooms using current ocean color data

• Trichodesmium blooms are infrequent, widespread phenomena with few areas of persistent blooms (N. Indian Ocean, Eq. Pacific)

• Contribution to global oceanic N2 fixation is small (~10 Tg N yr-1)

• We can diagnose preferred conditions for Trichodesmium blooms (i.e., < Wind Speed, <MLD, > Dust dep.)

Conclusions (2)• Blooms are rare when:

MLD>100mWind speed > 10 m s-1

SST ~ <23.5C ?Dust dep. = ???

- in fact, “ideal” set of conditions are: MLD = 20mwind = 4 m s-1, SST = 26.8CDust dep. = 0.16 g m-2 d-1

Conclusions (3)• Cross-correlation analyses shows some

significant lead/lag relationships between bloom frequency and extent and environmental parameters

• Conditional probability distributions allow us to make probability maps of bloom occurrence that correspond to retrieved bloom patterns fairly well

• Differences in the two approaches are suggestive of limitation by other factors, i.e. NO3 or PO4