remote sensing of trichodesmium blooms (and their relation to atmospheric dust) toby westberry ph.d....
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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