Interannual variability in CO2 fluxes derived from 64-region inversion of a

tmospheric CO2 data

Prabir K. Patra*, Shamil Maksyutov*, Misa Ishizawa*, Takakiyo Nakazawa#, Taro Takahashi$, and Gen Inoue&

*Frontier Research System for Global Change, Yokohama #Graduate School of Science, Tohoku University, Sendai

$Lamont-Doherty Earth Observatory, Columbia University, New York&National Institute for Environmental Studies, TSukuba

Acknowledgment: TranCom-3 Developers for the TDI CODE

TransCom-3 Meeting, Tsukuba, June 2004

Plan of the Talk

• Basic tools– Transport model (simulation of fluxes)– Inverse model (least-squares fitting to data)

• Results and Discussion– Testing of the results (networks, resolutions)– Comparisons with previous results– Climate controls on flux anomaly

• Conclusions

The transport equations is:

where, qk is the tracer concentration with index k, S is the source function, V () denote the horizontal (vertical) components of winds, Fk represents the PBL flux or convective transport.

We have used:

the NCEP/NCAR reanalysis data for pressure level fields

monthly PBL heights are cyclostationary (from NASA - DAO)

global distribution of yearly or monthly sources (cyclostat.)

kkk

kk

SFq

qt

q

V

NIES/FRSGC Tracer Transport Model: Basic Principles

Background CO2 fluxes:

Three Types

The fossil fuel emission do not have seasonality.Oceanic sources and sinks are weaker compared to the land and less heterogeneous.

Transport Model Simu

lations:

Combined (FOS, NEP, OCN) signals of CO2 at various layers of the atmosphere (left panels) and the estimated RSDs

(right panels).

Patra et al., J. Geophys. Res., 2003

The problem of surface source (S) inversion is mathematically the inversion of the forward problem:

, where the G a linear operator representing atmospheric transport (no chemistry).

The results are CO2 fluxes with uncertainty:

Inverse Model: Basic Equations

0

1 1 1( )TS D SC G C G C

1

0

1 10 0( ) ( );

D

T TD SS S G C G C G C D GS

EstimatedFlux

EstimatedFlux Cov.

A PrioriFlux

A PrioriFlux Cov.

AtmosphericCO2 Data

00 . SGD

Development of 64-Regions Inverse Model

Patra et al., Global Biogeochem. Cycles, submitted...)( 2

2213 t

Testing Inversion results: fitting to the data

Testing Inversion results: χ

2 tests

Averages of CO2 Fluxes for 1990s

Estimates This Work

IPCC 2001

Gurney et al., 2004

Bousquet et al, 2000

Rodenbeck et al., 2003

Land - 1.15 {0.14*}

-1.40

(0.70)

-1.54 (0.73)

-1.40 (0.80)

-1.20 (0.4)

Ocean -1.88 {0.18*}

-1.70 (0.50)

-1.35 (0.76)

-1.80 (0.60)

-1.70 (0.40)

Global -3.03 -3.10 -2.89 -3.20 -2.90

Patra et al., 2004a, Global Biogeochem. Cycles, submitted

* Spread based on sensitivity tests

Land and Ocean Flux - sensitivityP

atra

et a

l., 2

004a

, Glo

bal B

ioge

oche

m. C

ycle

s, s

ubm

itte

d

Comparison of Ocean Flux Anomalies…

Anomaly in Land

and Ocean

CO2 Fluxes

– ENSO effect

Region-aggregated Fluxes

Comparison of oceanic

flux anomaly:

observation and model

Patra et al., 2004a, Global Biogeochem. Cycles, Submitted

F

lux

anom

. (P

g-C

per

Yea

r)

Equatorial Pacific

North Pacific

Regional Land Fluxes

Patra et al., 2004b, Global Biogeochem. Cycles, Submitted

Various types of fires

Indonesia ablaze, 1998. These widespread fires released massive amounts of carbon into the atmosphere

Comparisonof land flux anomalies:

Observations /estimations,

and Biome-BGC ecosystem

model fluxes

Patra et al., 2004b

Capturing the time evolution of fires

Correlation Analysis

CO2 Flux AnomalyWith MEI ENSO Index

CO2 Flux AnomalyWith IOD Index

EOF Distribution

of Flux Anomaly

Flux Anom./PC vs. Met./Clim. IndexRegion/PC ENSO IOD Rain*

Temp.

Temp. N. A. -0.41 -0.34 -0.39 0.30

Trop. S. A. 0.49 0.51 -0.51 0.66

Temp. Asia -0.19 0.17 0.11 -0.15

Trop. Africa 0.73 0.44 0.37 0.47

South Africa 0.66 0.54 0.00 0.07

Trop. Asia 0.53 0.46 -0.68 0.55

Australia 0.36 0.35 -0.19 0.18

PC - 1 0.91 0.76

PC - 2 -0.46 -0.22 PC - 3 0.25 -0.13

* CO2 flux anomaly lags 3-month the rainfall anomaly.

In Tropics:CO2 flux & Temp: +veCO2 flux & Rain : -ve

Stu

dyin

g C

O2 G

row

th R

ate at M

LO

(! A C

lassic Pro

blem

!)

In search of simple empirical relations

Increase Rates

1972 1987 2003

El Nino 4.7 2.3 0.8

Boreal Fire

0.0 0.5 1.1-3.3

CO2 Gr. Rate

1.8 1.4 1.8

Green diamond: van der Werf et al.Vertical bar: Kasischke and Bruhwiler

Conclusions

1. We have derived CO2 fluxes from 42 land and 22 ocean regions.

2. The inverse method fairly successfully captures the flux variability due to climate variation.

3. The highest influence of weather/climate is observed over the tropical lands.

4. Major modes of CO2 flux variability are connected to ENSO/IOD, Biomass burning (indirect climate forcing?).

5. Interannual variability in MLO CO2 growth rates are mostly of natural origin (as in Keeling et al., 1995)