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Estimating regional C fluxes by exploiting observed correlations between CO and CO 2. Paul Palmer Division of Engineering and Applied Sciences Harvard University. http://www.people.fas.harvard.edu/~ppalmer. + ballpark flux estimates for fast exchange processes (10 9 tonnes C). 61. 60. 1.6.

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estimating regional c fluxes by exploiting observed correlations between co and co 2
Estimating regional C fluxes by exploiting observed correlations between CO and CO2

Paul Palmer

Division of Engineering and Applied Sciences Harvard University

http://www.people.fas.harvard.edu/~ppalmer

chinese government statistics shown downward trend in chinese co2 emissions

?

China GDP (Billion 1995 yuan constant)

Year

China Energy Databook v6, 2004

IPCC

Chinese Government Statistics Shown Downward Trend in Chinese CO2 Emissions

(Streets et al., Science, 294, 1835-1837, 2001)

slide4

Bottom-up Emission Inventories are Very Uncertain

Emission Factor (TgC / Tg fuel)

Emissions (Tg C yr-1)

E = AF

Coal-burning cook stoves in Xian, China

Activity Rate (Tg fuel yr-1) (amount of fuel burned)

slide5

Remote data have limitations in estimating regional C budgets

RH + OH … CO  CO2

Direct & indirect emissions

Many 100s km

10s km

CMDL site

1000s km

Increasing model transport error

slide6

Aircraft data can improve level of disaggregation of continental emissions

Feb – April 2001

NASA TRACE-P

Main transport processes:

  • DEEP CONVECTION
  • OROGRAPHIC LIFTING
  • FRONTAL LIFTING

warm air

cold air

cold front

slide7

Offshore China

Over Japan

Slope (> 840 mb) = 22

R2 = 0.45

Slope (> 840 mb) = 51

R2 = 0.76

ATMOSPHERIC CO2:CO CORRELATIONS PROVIDE UNIQUE INFORMATION ON SOURCE REGION AND TYPE

A priori bottom-up

CO2

CO2

CO

CO

Top-down

- CO2:CO emission ratios vary with combustion efficiency

- Range in regional emission ratios reflect mix of sources and variation in fossil fuel combustion ratio

Suntharalingam et al, 2004

modeling overview
Modeling Overview

Forward model

(GEOS-CHEM)

Inverse model

^

x = xa + (KTSy-1K + Sa-1)-1 KTSy-1(y – Kxa)

y = Kxa + 

State vector (Emissions x)

Observation vector y

x = Fluxes of CO and CO2 from Asia (Tg C/yr)

y = TRACE-P CO and CO2 concentration data

slide9

Consistent CO and CO2 Emissions Inventories

Biomass burning: Variability from observed daily firecount data (AVHRR)

Heald et al, 2003

Anthropogenic emissions for 2001: domestic ff, biofuel, transport, industrial ff Streets et al, 2003

seasonal cycle of chinese co and co 2 emissions during trace p
Seasonal Cycle of Chinese CO and CO2 Emissions during TRACE-P

TERRESTIAL BIOSPHERE: CASA (Randerson, et al, 1997) OCEAN BIOSPHERE: Takahashi et al, 1999

TOTAL

TOTAL

CO

BIOFUEL

FOSSIL

Gt C yr-1

Fraction of annual emissions

BIOBURN

BIOSPHERE

Annual Mean

TRACE-P

Streets et al, 2003

slide11

Estimating the Jacobian

Global 3D CTM 2x2.5 deg resolution

Boreal Asia (BA)

China (CH)

Japan (JP)

Rest of World (ROW)

Korea (KR)

x = emissions from individual countries and individual processes (BB, BF, FF)

Southeast Asia (SEA)

[OH] from full-chemistry model (CH3CCl3 = 6.3 years)

slide12

TRACE-P Observations

Remove CO2 bias using 10th percentile of [CO2]: 4-4.5 ppm

GEOS-CHEM

4-6 km

2-4 km

CO [ppb]

CO2 [ppm]

0-2 km

Latitude [deg]

slide13

^

x = xa + (KTSy-1K + Sa-1)-1 KTSy-1(y – Kxa)

S = (KTSy-1K + Sa-1)-1

Xs = retrieved state vector (the CO sources)

Xa= a priori estimate of the CO sources

Sa = error covariance of the a priori

K = forward model operator

Sy = error covariance of observations

= instrument error + model error + representativeness error

Gain matrix

^

Linear Inverse Model

slide14

GEOS-CHEM

All latitudes

RRE

Mean bias

TRACE-P

GEOS-CHEM

2x2.5 cell

Altitude [km]

(measured-model) /measured

Error specification for CO and CO2

SaAnthropogenic (c/o Streets): China (78%), Japan (17%), Southeast Asia (100%), Korea (42%) – uniform 25%Biomass burning: 50% 30%Chemistry (~CH4): 25% Biosphere: 75%

SyMeasurement accuracyRepresentation

Model error (most important)

RRE = total observation error

(y*RRE)2 ~38ppb (CO)

~1.87ppm (CO2)

CO

slide15

NUMBER OF EIGENVALUES OF PREWHITENED JACOBIAN  1 = DOF

~

-1/2

1/2

K =S KS

a

Rodgers, 2000

CO: CH ANTH*, KRJP&, SEA, CH BB, BA [email protected], ROW

CO2: CH ANTH*, KRJP&, CH BB$, BA [email protected], BS, ROW (inc SEA$)

*Collocated sources; &coarse resolution forces merging; $observed gradients too weak to resolve source; @not well resolved

slide16

Independent Inversion of CO and CO2 emissions

A priori A posteriori

Anthropogenic CO2

CO emissions [Tg yr-1]

Biospheric CO2

K

~

CO2 emissions [Tg March 2001]

  1

Results consistent with [CO2]:[CO] analysis

slide17

Japan

China

Slope (> 840 mb) = 22

R2 = 0.45

Slope (> 840 mb) = 51

R2 = 0.76

Offshore China

Over Japan

50% CO increase from inverse model not enough

Reconciliation with observations: decrease a CO2 source withhigh CO2:CO

CO2/CO

Observed CO2:CO correlations are consistent with Chinese biospheric emissions of CO2 40% too high

  • Problem: Modeled Chinese CO2:CO slopes are 50% too large

biosphere

Suntharalingam et al, 2004

slide18

^

C

A posteriori correlation matrix illustrates the ambiguity between anthropogenic and biospheric CO2 emissions

Chinese anthropogenic CO2

Chinese biospheric CO2

CO2 state vector

slide19

N

N

Modeling correlations between CO and CO2

ECO =  (A + AA) (FCO + COFCO)

ECO2 =  (A + AA) (FCO2 + CO2FCO2)

Carbon Conservation (CO+CO2 ~ 0.9-1.0)

Perturbed F

0.9 

 1

Unperturbed F

slide20

A >> F

A << F

Interpretation of correlations

F

F

A

CO2 Emissions

CO2 Emissions

A

CO Emissions

CO Emissions

r > 0

r < 1

slide21

VALUES OF UNCERTAINTY FROM STREETS’ INCONSISTENT WITH DATA ANALYSIS AND LEAD TO SMALL CO2:CO CORRELATIONS

E = AF

A: CO 5-25%; CO2 5-20%

F: CO 50 - 200%; CO2 5-10%

Correlations: China ~0 Korea/Japan -0.2 Southeast Asia ~0

Correlations within sectors > lumped sectors

slide22

Alternative Correlations Tested…

Streets’

Min(A 25%)

Min(A 50%)

CO2:CO Correlation

Korea + Japan

Southeast Asia

Chinese anthropogenic

Also r = 0.5,…,1.0

slide23

A correlation of > 0.7 is needed to start decoupling biospheric and anthropogenic CO2

Anthropogenic CO2

Anthropogenic CO

A posteriori Uncertainty [unit]

Biospheric CO2

Lowest correlations correspond to those calculated using Monte Carlo method

future satellite missions
Future satellite missions

The “A Train”

1:38 PM

1:30 PM

1:15 PM

Aura

Cloudsat

OCO

CALIPSO

Aqua

PARASOL

OCO - CO2 column

OMI - Cloud heights

OMI & HIRLDS – Aerosols

MLS& TES- H2O & temp profiles

MLS & HIRDLS – Cirrus clouds

CALPSO- Aerosol and cloud heights

Cloudsat - cloud droplets

PARASOL - aerosol and cloud polarization

OCO - CO2

MODIS/ CERES

IR Properties of Clouds

AIRS Temperature and H2O Sounding

  • Due for launch in 2004
  • IR, high res. Fourier spectrometer (3.3 - 15.4 mm)
  • Has 2 viewing modes: nadir and limb
  • Spatial resolution of nadir view = 8x5 km2

C/o M. Schoeberl

slide25

New Concept: Testing science objectives of satellite instruments before launch

Orbiting Carbon Observatory (OCO)

Tropospheric Emission Spectrometer (TES)

  • Launch date in 2007.
  • Will provide column CO2 measurements
  • 3 spectrometers that measure CO2 at 1.61m and 2.05m and O2 at 0.76m
  • Field of view of spectrometers is 1x1.5 km2
  • Sun-synchronous orbit with 16-day repeat cycle and 1:15 pm equator crossing time
  • Launched in July 2004
  • An IR, high resolution Fourier spectrometer
  • Measures spectral range 3.3 - 15.4m
  • Limb and nadir view (footprint is 8x5 km2)
  • Sun-synchronous orbit with 16-day repeat cycle

Will measurements of CO and CO2 from TES and OCO provide accurate constraints on carbon fluxes from different regions in Asia?

Jones et al, 2004

simulation constraining asian carbon fluxes from space
Simulation: Constraining Asian Carbon Fluxes from Space

Generate pseudo-data from the satellites for March 1-31, 2001

CO (825 mb) along TES orbit (1 day)

CO2 column along OCO orbit (1 day)

ppm

ppb

Inverse model with realistic instrument and model errors, and which accounts for data loss due to cloud cover and the vertical sensitivity of the instruments

Jones et al, 2004

a posteriori error estimates

BorealAsia BB

BorealAsia BB

BorealAsia BB

BorealAsia BB

BorealAsia BB

BorealAsia BB

BorealAsia BB

BorealAsia BB

BorealAsia BB

IndiaFuel

IndiaFuel

IndiaFuel

IndiaFuel

IndiaFuel

IndiaFuel

IndiaFuel

IndiaFuel

IndiaFuel

JP/KRFuel

JP/KRFuel

JP/KRFuel

JP/KRFuel

JP/KRFuel

JP/KRFuel

JP/KRFuel

JP/KRFuel

JP/KRFuel

SE AsiaFuel

SE AsiaFuel

SE AsiaFuel

SE AsiaFuel

SE AsiaFuel

SE AsiaFuel

SE AsiaFuel

SE AsiaFuel

SE AsiaFuel

India

BB

India

BB

India

BB

India

BB

India

BB

India

BB

India

BB

India

BB

India

BB

ChinaBB

ChinaBB

ChinaBB

ChinaBB

ChinaBB

ChinaBB

ChinaBB

ChinaBB

ChinaBB

SE Asia

BB

SE Asia

BB

SE Asia

BB

SE Asia

BB

SE Asia

BB

SE Asia

BB

SE Asia

BB

SE Asia

BB

SE Asia

BB

ChinaFuel

ChinaFuel

ChinaFuel

ChinaFuel

ChinaFuel

ChinaFuel

ChinaFuel

ChinaFuel

ChinaFuel

Japan

Korea

Japan

Korea

Japan

Korea

Japan

Korea

Japan

Korea

Japan

Korea

Japan

Korea

SE

Asia

SE

Asia

SE

Asia

SE

Asia

SE

Asia

SE

Asia

SE

Asia

Rest of

world

Rest of

world

Rest of

world

Rest of

world

Rest of

world

Rest of

world

Rest of

world

India

India

India

India

India

India

India

China

China

China

China

China

China

China

Boreal

Asia

Boreal

Asia

Boreal

Asia

Boreal

Asia

Boreal

Asia

Boreal

Asia

Boreal

Asia

Significant reduction in uncertainty in estimates of the dominant Asian biospheric fluxes (China and Boreal Asia)

A Posteriori Error Estimates [%]

CO Sources

A priori

A priori

A priori

A priori

A priori

A priori

A priori

A posteriori

A posteriori

A posteriori

A posteriori

A posteriori

A posteriori

A posteriori

CO2 Sources

Biospheric CO2

Chinese biospheric fluxes weakly coupled to anthropogenic emissions

Jones et al, 2004

slide28

Closing Remarks

  • Estimated Chinese anthropogenicCO(CO2) sources are currently too low (high).
  • Chinese biospheric CO2 fluxes are estimated too high but they are coupled to anthropogenic CO2. Correlations between CO2 and CO can decouple these signals.
  • Emission correlations summed over sectors are too weak – need r > 0.7, impossible with current inverse model configuration.
  • Work in progress – much still to explore.
slide30

Increased Domestic Coal

Fractional Increase in Residential Coal Sector A

Fractional Increase in Chinese Anthropogenic CO2 Emissions

Increased Biofuel

Fractional Increase in Biofuel Sector A

CO2:CO analysis shows that A and F responsible for E

Adjusting only E  unrealistic values: 15/30% for biofuel and domestic coal

Suntharalingam et al, 2004

slide31

Domestic coal

Biofuel

Transport

r=-0.13 n = 16893

r=-0.0019 n = 11387

r=-0.43 n = 4754

CO2 Emissions

Industrial Coal

Lumped sectors

r=-0.12 n = 13954

r=-0.03 n = 4754

CO Emissions

Correlations within sectors > lumped sectors

e.g., China

Low or negative correlations reflect relatively large uncertainties in emission factors

slide33

Improvements to Inverse Model

xs = xa + (KTSy-1K + Sa-1)-1 KTSy-1(y – Kxa)

SS = (KTSy-1K + Sa-1)-1

Xs = retrieved state vector (the CO sources)

Xa= a priori estimate of the CO sources

Sa = error covariance of the a priori

K = forward model operator

Sy = error covariance of observations

= instrument error + model error + representativeness error

Gain matrix

  • Choice of x…
  • Aggregate anthropogenic emissions (colocated sources)
  • Aggregate Korea/Japan (coarse model grid resolution)
chinese government statistics shown downward trend in chinese co2 emissions35

IPCC

Chinese Government Statistics Shown Downward Trend in Chinese CO2 Emissions

(Streets et al., Science, 294, 1835-1837, 2001)

slide36

CO inverse modeling

  • Product of incomplete combustion; main sink is OH
  • Lifetime ~1-3 months
  • Relative abundance of observations

CMDL network for CO and CO2

  • Big discrepancy between Asian emission inventories and observations

TRACE-P (Transport And Chemical Evolution over the Pacific) data can improve level of disaggregation of continental emissions

slide37

Seasonal cycles of CO2 sources

Biospheric emissions are 65% of Chinese total, according to bottom-up inventories

Gt C yr-1

TRACE-P

slide38

Fuel consumption(Streets)

Biomass burning AVHRR (Heald/Logan)

Global 3D CTM 2x2.5 deg resolution

China

Japan

Korea

Southeast Asia

Rest of World

[OH] from full-chemistry model (CH3CCl3 = 6.3 years)

x = emissions from individual countries and individual processes (BB, BF, FF)

atmospheric co 2 co correlations provide unique information on source region and type
ATMOSPHERIC CO2:CO CORRELATIONS PROVIDE UNIQUE INFORMATION ON SOURCE REGION AND TYPE

Regional CO2/CO Emissions Ratios March 2001 (from a priori inventories)

Observed CO2:CO Correlations

(TRACE-P Flight DC8 #16, March 29, 2001)

CHINA

Ascent out of Japan (slope = 65)

Measurements above 500hPa (slope = 60)

Boundary layer, off China (slope = 12)

CO2 (mol)

JAPAN

- The emissions ratio varies with combustion efficiency

- Range in regional emissions ratios reflects mix of sources and variation in fossil fuel combustion ratio

CO (mol)

Suntharalingam et al, 2004

slide41

CO [ppb]

Observation

A priori

A priori

A posteriori

A posteriori

Lat [deg]

Best estimate is insensitive to inverse model assumptions

1-sigma uncertainty

A posteriori emissions improve agreement with observations

China (BB)

China (BB)

Rest of World

Southeast Asia

Korea + Japan

China (anthropogenic)

slide43

A posteriori correlation matrix

^

^

^

^

Ci,j = Si,j / (Si,iSj,j)

^

^

^

Ci,j = Si,j / (Si,iSj,j)

^

Eigen decomposition of prewhitened Jacobian

~

-1/2

1/2

K =S KS

a

N()  1 = dof

Diagnostics Used

slide44

Bottom-up Emission Inventories are Very Uncertain

Emission Factor (TgC / Tg fuel)

Emissions (Tg C yr-1)

E = AF

Coal-burning cook stoves in Xian, China

Activity Rate (Tg fuel yr-1) (amount of fuel burned)

inversion of spring 2001 mopitt co column data is consistent with trace p
Inversion of Spring 2001 MOPITT CO column data is consistent with TRACE-P

MOPITT

GEOS-CHEM

[1018 molec cm-2]

MOPITT – GEOS-CHEM

Inverse model analysis agrees with TRACE-P data

Large differences over NW Indian & SE Asia

[1018 molec cm-2]

c/o Heald, Emmons, Gille

slide46

A priori emissions have a large negative bias in the boundary layer

A priori

Observation

CO [ppb]

Lat [deg]

slide47

CO2 [ppm]

CO2 model evaluation

Remove CO2 bias using 10th percentile of [CO2]: 4-4.5 ppm

slide48

GEOS-CHEM

All latitudes

RRE

Mean bias

TRACE-P

GEOS-CHEM

2x2.5 cell

Altitude [km]

(measured-model) /measured

Error specification is crucial – CO and CO2

SaAnthropogenic (c/o Streets): China (78%), Japan (17%), Southeast Asia (100%), Korea (42%) Biomass burning: 50% Chemistry (~CH4): 25%

SyMeasurement accuracy (2%) Representation (14ppb or 25%)

Model error (y*RRE)2 ~38ppb (>70% of total observation error)

potential of tes nadir observations of co an observing system simulation experiment
Potential of TES nadir observations of CO: An Observing System Simulation Experiment

New Concept: test science objectives of satellite instruments before launch

Objective: Determine whether nadir observations of CO from TES have enough information to reduce uncertainties in estimates of continental sources of CO

Inverse model with realistic errors

After 8 days of observations (operating half time)

Jones et al, 2004

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