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Preindustrial, Anthropogenic, and

Contemporary Air-Sea Carbon Flux

S. Mikaloff Fletcher

Department of Atmospheric and Oceanic Sciences

The University of California, Los Angeles


• Inversion

– UCLA- Nicolas Gruber

Co-authors

– Princeton- Andrew Jacobson, Manuel Gloor, and Jorge Sarmiento

• Ocean Basis Functions

– Bern3D - Markus Gerber, Fortunat Joos, and Simon Mueller

– ECCO - D. Menemenlis

– MIT - Stephanie Dutkiewicz and Mick Follows

– NCAR - Scott Doney and Keith Lindsay

– Princeton - Andrew Jacobson

– UL - Anne Mouchet


Overview

• Review of air-sea CO 2 flux estimates

• Introduction to the ocean interior data

• The inverse approach

• Air-sea carbon fluxes

• Ocean interior carbon transports

• Assessing the robustness of the inversion


A (Very) Brief History of CO 2

• 1896- Arrhenius hypothesized that

humans could cause global warming

through CO 2 emissions

• 1938- Callendar speculated that the

observed warming between 1890 and

1935 could due to CO 2

Oceans

thought to

take up CO 2

emissions

• 1956-Revelle & Seuss found that ocean

chemistry limits the amount of CO 2 that

the ocean can absorb


1957- Keeling Observes CO 2


The Atmospheric CO 2 Increase

IPCC, 2001


How are oceanic CO 2 fluxes

estimated

• Observations of ∆pCO 2 and bulk formulas

• Ocean General Circulation Models (OGCMs)

Atmospheric inversions

Atmospheric observations of CO 2

Atmospheric transport models

• Ocean interior observations of dissolved inorganic

carbon (DIC) and other nutrients

– Inventory only

Oceanic inversions


The GLODAP dataset


Dissolved Inorganic Carbon

60N 30N Equator 30S 60S 60S 30S Equator 30N 60N

Atlantic Ocean

Pacific Ocean

In units of µmol/kg, normalized by subtracting a

constant such that the mean at the surface is zero


Preindustrial

The C*

Method

60N 30N Equator 30S 60S 60S 30S Equator 30N 60N

Atlantic Ocean

Pacific Ocean

Anthropogenic

• Effects of biology

removed using

nutrient

observations and

stoichiometric

ratios

• Effects due to airsea

gas exchange

divided into

preindustrial and

anthropogenic

60N 30N Equator 30S 60S 60S 30S Equator 30N 60N

Atlantic Ocean

Pacific Ocean


Inverse Modeling

The inverse model finds the combination of carbon

fluxes from a discrete number of ocean regions that

are optimally fit the observations

C j

=


i=1,nreg

H i, j

s i

+ E

• C j = Carbon signal due to gas exchange calculated

from observations € at site j

• x i = Magnitude of the flux from region I

• H i,j = The modeled response of a unit flux from

region i at station j, called the basis functions

• E=Error associated with the method


Pre-industrial Basis Function

Anthropogenic Basis Function

60N 30N Equator 30S 60S 60S 30S Equator 30N 60N

Atlantic Ocean

Pacific Ocean


Potential Sources of Error

• OGCM representation of ocean transport

• Data-based estimates

• Implicit assumptions about variability

– Anthropogenic carbon flux

– Ocean Circulation

• Inverse methodology


Contributing Models

Model

Bern-3D

ECCO

MIT

NCAR

PRINCE-LL

PRINCE-HH

PRINCE-LHS

PRINCE-2

PRINCE-2a

UL

Radiocarbon Skill Score

NA

NA

NA

0.94

0.653

0.968

0.757

0.921

0.912

0.855

CFC-11 Skill Score

NA

NA

0.85

0.91

0.80

0.87

0.86

0.87

0.85

0.76


Pre-Industrial Carbon Flux

59S-44S 44S-18S 18S-18N

~18N-49N

North


Inverse Flux

Estimates

OCMIP

Forward Flux

Estimates

Bern-3D

ECCO

MIT

NCAR

PRINCE-LL

PRINCE-HH

PRINCE-LHS

PRINCE-2

PRINCE-2a

UL

Latitude


Data-Based ∆C Estimates

gas ex SAMW

AAIW

CDW

AAIW

60N 30N Equator 30S 60S 60S 30S Equator 30N 60N

Atlantic Ocean

Pacific Ocean


Global Anthropogenic Uptake

(Pg C/yr, scaled to 1995)

Model Inverse Estimates OCMIP Forward Estimates

Bern-3D

2.05

NA

ECCO

2.01

NA

MIT

2.22

NA

NCAR

2.17

2.36

PRINCE-LL 1.85

1.90

PRINCE-HH 2.33

2.44

PRINCE-LHS 1.99

2.04

PRINCE-2

2.16

2.24

PRINCE-2a 2.25

2.36

UL

2.81

2.95

Mean

2.2 ± 0.25

2.3±0.33


Anthropogenic Carbon Flux

59S-44S 44S-18S 18S-18N

~18N-49N

North


Integrated (1765-1995)

Anthropogenic Carbon

Uptake

Inverse Estimates

Bern-3D

ECCO

MIT

NCAR

PRINCE-LL

PRINCE-HH

PRINCE-LHS

PRINCE-2

PRINCE-2a

UL

Forward Estimates

Latitude


Data-Based ∆C Estimates

ant SAMW

AAIW

CDW

AAIW

60N 30N Equator 30S 60S 60S 30S Equator 30N 60N

Atlantic Ocean

Pacific Ocean


Contemporary Carbon Flux

59S-44S 44S-18S 18S-18N

~18N-49N

North


Jacobson et al., GBC,

submitted

• Joint ocean-atmosphere inversion

• Vigorous oceanic uptake at southern midlatitudes

leads to a strong land source in the

southern hemisphere

– 77% probability > 1 Pg C/yr

• Similar magnitude to estimates of emissions

due to tropical land use change alone


Conclusions

• The ocean inversion is the only method currently

available to estimate air-sea fluxes from ocean

interior data

– Spatial patterns are remarkably robust

– But, large uncertainty in the Southern Ocean

• Preindustrial

– Outgassing in the tropics and Sub-polar S. Ocean

– Uptake at mid-latitudes

• Anthropogenic

– Greatest uptake in the Sub-polar S. Ocean and tropics

• Contemporary

– Recent pCO2 observations seem to support S.O. result


Southern Ocean Data

Inverse Flux

Estimates

Preliminary

∆pCO 2 Data From

T. Takahashi


CO 2 Budget-IPCC 2001

Atmospheric Increase

Fossil Fuels and

Cement

Oceans

Land

Land Use Change

Terrestrial sink

1980’s

3.3±0.1

5.4±0.3

-1.9±0.6

-0.2±0.6

1.7 (0.6-2.5)

-1.9 (-3.8 to 0.3)

1990’s

3.2±0.1

6.4±0.3

-1.7 ±0.5

-1.4 ±0.7


The C* Approach

C * = DIC − r c:p

( [ ])

[

3−

PO ] 4

− 1 2 Alk − r PO 3−

n:p 4

60N 30N Equator 30S 60S 60S 30S Equator 30N 60N

Atlantic Ocean

Pacific Ocean


Soft Tissue

Pump

60N 30N Equator 30S 60S 60S 30S Equator 30N 60N

Atlantic Ocean

Pacific Ocean

Carbonate Pump

60N 30N Equator 30S 60S 60S 30S Equator 30N 60N

Atlantic Ocean

Pacific Ocean


Uncertainty in Anthropogenic Carbon

Estimates

• Matsumoto and Gruber

(submitted, 2004)

• Stoichiometric ratios used to

remove the signal due to

biological activity

(Anderson and Sarmiento,

1994)

– Low R c:o

– High R c:o

0

Anthropogenic Carbon Bias

Potential Density

Bias=10%

27.4

Bias=7%


Anthropogenic Carbon Bias (PRINCE-

RDS model)

0.6

0.5

Standard Matsumoto and Gruber (2004) Low Rc:o High Rc:o

Pg C/yr (Scaled to 1995)

0.4

0.3

0.2

0.1

0

South

of

44S-

58S

- South

Atl.

South

Pac.

South

Ind.

- Eq.

Atl.

Eq.

Pac.

Eq.

Ind

North

Atl.

North

Pac.

- North

of

58S

49N

S. Ocean S. Mid. Lat.

18S-44S

Tropics

18S-18N

N. Mid. Lat.

18N-49N

N. Ocean

North


Preindustrial Carbon Bias

-Biological Terms

Effects of Remineralization Ratio Baises in Removal of Biological Effects

1

0.8

0.6

No Bias

Low Rc:p

High Rc:p

Low Rc:p in Southern Ocean

Depth dependent Rc:p

0.4

0.2

Pg C/yr

0

-0.2

-0.4

-0.6

-0.8

-1

S.

Ocean

- Sub.

Pol. Atl.

Sub.

Pol.

Pac. &

Ind.

- S. Mid-

Lat. Atl.

S. Mid-

Lat.

Pac.

S. Mid-

Lat. Ind.

- Trop.

Atl.

Trop.

Pac.

Trop.

Ind.

N. Mid-

Lat. Atl.

N. Mid-

Lat.

Pac.

N. High-

Lat. Atl.

N. High-

Lat.

Pac.


Preindustrial Carbon Bias

-Anthropogenic Term

Effects of Biases in Remineralization Ratios Used to Remove Anthropogenic Carbon

0.6

No Bias

Low Rc:o

High Rc:o

Matsumoto

0.4

0.2

0

Pg C/yr

-0.2

-0.4

-0.6

-0.8

S.

Ocean

- Sub.

Pol. Atl.

Sub.

Pol.

Pac. &

Ind.

- S. Mid-

Lat. Atl.

S. Mid-

Lat.

Pac.

S. Mid-

Lat. Ind.

- Trop.

Atl.

Trop.

Pac.

Trop.

Ind.

N. Mid-

Lat. Atl.

N. Mid-

Lat.

Pac.

N. High-

Lat. Atl.

N. High-

Lat.

Pac.


Preindustrial Carbon

Transport

Northward Transport (Gg C/m/yr

Latitude

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