Characterizing upper ocean CDOM dynamics using Characterizing upper ocean CDOM dynamics using integrated laboratory, satellite and global field dataintegrated laboratory, satellite and global field data

Chantal M. Swan, David A. Siegel, Norman B. Nelson, Tihomir S. Kostadinov, Craig A. Carlson

University of California Santa Barbara

Characterizing upper ocean CDOM dynamics using Characterizing upper ocean CDOM dynamics using integrated laboratory, satellite and global field dataintegrated laboratory, satellite and global field data

Chantal M. Swan, David A. Siegel, Norman B. Nelson, Tihomir S. Kostadinov, Craig A. Carlson

University of California Santa Barbara

Ocean Color Research Team MeetingOcean Color Research Team MeetingNew Orleans, LANew Orleans, LA

May 12, 2010May 12, 2010

• CDOM (mCDOM (m-1-1) = ) = light-absorbing DOM (light-absorbing DOM (≤≤0.2µm)0.2µm)

• Open-ocean CDOM Open-ocean CDOM <<<< DOM DOM• Does not covary with Chl or DOCDoes not covary with Chl or DOC on annual time scaleson annual time scales

• Destroyed by sunlight (photolysis) in surface oceanDestroyed by sunlight (photolysis) in surface ocean• Net produced through microbial remin. of DOC & POCNet produced through microbial remin. of DOC & POC• These processes modulated by transportThese processes modulated by transport

• Dominates non-water UV absorption in ocean (up to 90%) Dominates non-water UV absorption in ocean (up to 90%)

• CDOM causes measurable bias in satellite Chl estimates CDOM causes measurable bias in satellite Chl estimates [S[Siegel et al. 2005]iegel et al. 2005]

CDOM in the Open Ocean

Absorption coefficient (m-1)at 325 nm

CDOM Spectrum

CDOM in the Open OceanUCSB Global CDOM Survey (2003 – present)

Cruise transects of U.S. CO2/CLIVAR Repeat Hydrography Program

7-yr mean (1997 – 2005) colored dissolved and detrital materials (“CDM” m-1, 443 nm) estimated from GSM algorithm [Siegel et al. 2002] using SeaWiFS

P2

I9N

I6S I8S

A20

P16

A16

A22

P18

P6

Measuring CDOM in the Open Ocean

• 0.2-μm filtered water samples collected from niskins

• 1.93 m liquid waveguide spectrophotometer = detection of low CDOM

• Refractive index correction for salinity of samples

• Spectral Slope (S) estimation:

aCDOM(λ) = aCDOM(λo) e – S (λ – λo)

CDOM Spectra

acdom325 [1/m]P16

r2 = 0.81,n = 1522

AOU vs. CDOM(z > 100m)

150°WCDOM in the PacificCDOM in the Pacific[[Swan et al., DSR-I, 2009Swan et al., DSR-I, 2009]]

• Pacific basin characterized by weak ventilation and strong meridional gradient in CDOM and biogeochemical properties

SALINITY [psu]

NPIW

AABW

CDW

AAIW

CDOM in the North AtlanticCDOM in the North Atlantic[[Nelson et al., DSR-I, 2007Nelson et al., DSR-I, 2007]]

A22 (66°W)

• Low variability of CDOM in deep waters• Rapidly advecting NADW = dominant process for CDOM distribution in N. Atlantic• Strong mode water signal (STMW) = photobleached surface waters entrained

NADW

STMW

DeepCaribbean

DeepCaribbean

NADW

STMW

STMW

acdom325 [1/m]

AOU vs. CDOM(z >100m)

r2 = 0.17, n=617

Controls on the open ocean CDOM distribution

CDOM distribution is controlled by the relative strengths of:

• transport (ventilation, advection, upwelling)

• production (microbial transformations of DOC & POC)

• loss(photolysis in surface waters)

= loss of CDOM absorption per unit of light absorbed

Determination of the apparent quantum yield ()

• Moderates global surface distribution of CDOM• Moderates photochemistry • (e.g., CO2, CO, COS release, DMS photolysis)

• 15 samples from the major ocean basins

• Shore-based laboratory incubations using simulated solar irradiation

CDOM Photolysis

see Swan et al. OCRT POSTER

Experimental Design:

=Simulates spectrum and intensity of terrestrial irradiance

Solar Light Co. LS1000 Solar Simulator

(Dark Control)

=

• 2 days in simulator ≈ 11 days* in surface ocean ≈ 57 days* in mixed layer

*estimates based on mean daily insolation at 325nm, MLD, and CDOM/light attenuation in mid-Atlantic in spring [Zafiriou et al. 2008]

• Time course of CDOM absorption = photolysis rate = daCDOM(λo)/dt

CDOM Photolysis

in situ T°C

d(aCDOM(λo))/dt = ∫ Φ(λo;λi) Eo (λi) āCDOM (λi) dλi

CDOM Photolysis

Analytical Approach:

A and B coefficients solved by inversion

daCDOM/dt = m-1 s-1 A = m2 mol photons-1 λref = 300nmΦ = m2 mol photons-1 B = nm-1

Eo = mol photons m-2 s-1 nm-1 λo = observation (nm)āCDOM = m-1 λi = irradiation (nm)

Φ(λo;λi) = A(λo) e - B(λo)(λi – λref)

d(aCDOM(325))/dt

Eo(λi)*āCDOM(λi) (325;λi)

aCDOM(λo)Eo(λi)

d(aCDOM(λo))/dt = ∫ Φ(λo;λi) Eo (λi) āCDOM (λi) dλi

Schematic of inversion terms:

(325;λi)*Eo(λi)*āCDOM(λi)

λi (nm) λo (nm)

λi (nm)λi (nm) λi (nm)

exposure time (days)

Controls on quantum yield (Φ) variability in the open ocean?

Is Φ = f (z, T, salinity, O2, N, P, Si, Fe2+, DOC, Chl-a, initial aCDOM, initial S, N:P, Si:N, AOU) ?

Φ(325,λi)

A model for apparent quantum yield (Φ) for CDOM photolysis in the open ocean:

(o;i) = 0(o;i) + 1(o;i) (AOU) + 2(o;i) (N:P) + 3(o;i) (S) 0(325;325) = -0.1826 m2 mol photons-1

1(325;325) = -0.0002 m2 mol photons-1 mol-1 kg

2(325;325) = 0.0035 m2 mol photons-1

3(325;325) = 4.3485 m2 mol photons-1 nm

• Up to 95% of variability in apparent quantum yield is explained by AOU, N:P and initial S of the samples

i=300 i=310 i=320 i=325 i=330 i=340 i=350 i=360 i=375 i=400

o=300 0.64 0.70 0.75 0.76 0.77 0.73 0.68 0.63 0.57 NS

o=310 NS 0.58 0.69 0.74 0.77 0.77 0.72 0.64 0.54 NS

o=320 0.59 0.69 0.77 0.79 0.80 0.77 0.69 0.59 NS NS

o=325 0.68 0.75 0.80 0.82 0.83 0.82 0.78 0.72 0.61 NS

o=330 0.67 0.73 0.78 0.81 0.82 0.84 0.85 0.83 0.79 0.70

o=340 NS NS 0.69 0.78 0.84 0.90 0.91 0.90 0.87 0.80

o=350 0.82 0.85 0.87 0.88 0.87 0.86 0.85 0.83 0.80 0.74

o=360 0.90 0.91 0.91 0.91 0.91 0.91 0.90 0.90 0.88 0.85

o=375 0.90 0.92 0.94 0.94 0.95 0.95 0.95 0.94 0.91 0.84

o=400 0.86 0.88 0.88 0.89 0.89 0.89 0.89 0.89 0.89 0.87

Table of r2 values (p < 0.04, n = 14)

[Swan et al., submitted]

(325;λi)*Eo(λi)*āCDOM(λi)

Action Spectrum

• 310 – 350 nm wavelengths primarily responsible for CDOM photolysis

• Need CDOM and Eo measurements in the UV

• Remote-sensed estimates of colored dissolved and detrital materials (‘CDM’) are at 443 nm

d(aCDOM(λo))/dt = ∫ Φ(λo;λi) Eo (λi) āCDOM (λi) dλi

(325;λi)*Eo(λi)*āCDOM(λi)

How to apply ocean color data to estimate global CDOM photolysis rate?

Extrapolating satellite-retrieved absorption by colored dissolved and detrital materials, ‘CDM’ (m-1, 443 nm), into the UV

Ŝ = 0.013 + 0.017*e-75.184*CDOM(443)

r2 = 0.73, n = 7611 CLIVAR global field CDOM data

Spectral slope (S, nm-1) as a function of the CDOM absorption coefficient (m-1, 443 nm)

Estimating CDM UV absorption from satellite:

all cruisessurf. data (z < 7m)

n = 277, p<0.001

325nm: r2 = 0.79

340nm: r2 = 0.79

380nm: r2 = 0.75

412nm: r2 = 0.65

Ŝ = 0.013 + 0.017*e-75.1842.*aCDM(443)

aCDM(λ) = aCDM(443)*e –Ŝ(λ-443)

extrapolated CDM vs. measured CDOM

Se

aWiF

S(G

SM

) a

CD

M (m

-1)

spectroscopic aCDOM (m-1)

Estimating CDM absorption in the UV from satellite:

Next step: monthly climatologies of CDM(UV)

Estimate depth-resolved CDOM photolysis rates in the global ocean:

d(aCDM(λo))/dt =

∫ ∫ Es (λi)e-kd(λi)z aCDM (λi) Φ(λo;λi;AOU;N:P;S) dλi dz(integrated over λi and z = surf – MLD

FUTURE STEPS

PROPOSED DATA SOURCESES(UV-VIS): TOMS, SeaWiFSaCDM(UV-VIS) and S: GSM output (443nm) and Global S Modelkd = model (Bonhommeau et al., in prep)z = MLD from FNMOCO2, N, P = NODC