Interannual variability in CO2 fluxes derived from 64-region inversion of atmospheric CO2 data
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Transcript of Interannual variability in CO2 fluxes derived from 64-region inversion of atmospheric CO2 data
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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
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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
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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
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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.
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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
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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
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Development of 64-Regions Inverse Model
Patra et al., Global Biogeochem. Cycles, submitted...)( 2
2213 t
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Testing Inversion results: fitting to the data
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Testing Inversion results: χ
2 tests
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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
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Land and Ocean Flux - sensitivityP
atra
et a
l., 2
004a
, Glo
bal B
ioge
oche
m. C
ycle
s, s
ubm
itte
d
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Comparison of Ocean Flux Anomalies…
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Anomaly in Land
and Ocean
CO2 Fluxes
– ENSO effect
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Region-aggregated Fluxes
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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
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Regional Land Fluxes
Patra et al., 2004b, Global Biogeochem. Cycles, Submitted
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Various types of fires
Indonesia ablaze, 1998. These widespread fires released massive amounts of carbon into the atmosphere
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Comparisonof land flux anomalies:
Observations /estimations,
and Biome-BGC ecosystem
model fluxes
Patra et al., 2004b
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Capturing the time evolution of fires
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Correlation Analysis
CO2 Flux AnomalyWith MEI ENSO Index
CO2 Flux AnomalyWith IOD Index
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EOF Distribution
of Flux Anomaly
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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
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Stu
dyin
g C
O2 G
row
th R
ate at M
LO
(! A C
lassic Pro
blem
!)
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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
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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)