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Constraining global isoprene emissions with GOME formaldehyde column

measurements

Constraining global isoprene emissions with GOME formaldehyde column

measurements

Changsub Shim, Yuhang Wang, Yunsoo Choi

Georgia Institute of Technology

​

Paul Palmer, Dorian Abbot

Harvard University

​

Kelly Chance

Harvard-Smithsonian Center for Astrophysics

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NO2

NO

OH

CO

O2

hv

hv

H2O

HO2

O3

ISOPRENE

C5H8

Most dominant biogenic

hydrocarbon

​

Global budget is highly

uncertain.

Emission dependence

- Temperature,

- Vegetation type,

- Leaf Mass

- Light intensity, etc…

Global Atmospheric Isoprene

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HCHO for constraining isoprene

  • It is a high-yield byproduct of the isoprene oxidation & VOCs
  • It also has a short lifetime (order of hours)
  • HCHO atmospheric columns have been measured by a satellite instrument (GOME) at 337 ~ 356 nm
  • HCHO is a good proxy for isoprene by remote sensing!

( Chance et al., 2000; Palmer et al., 2003)

Objectives

Obtaining better global isoprene emissions based on

GOME HCHO measurements (Sep. 1996 ~ Aug. 1997)

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Application of Inverse Modeling

8 regions for Inverse modeling

  • High Signal-to-noise ratio HCHO GOME observations
  • Account for ~65% of global a priori isoprene emissions

Tropical rain forest

Grassy lands

Savanna

Tropical seasonal forest

Mixed deciduous

Farm land & paddy rice

Dry evergreen

Regrowing wood (natural + artificial)

Drought deciduous

Other biogenic source

Biomass burning emission

Industrial emission

State vectors (Source parameters)

Isoprene

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Application of Inverse Modeling (10 biogenic state vector distribution)

V1: Tropical rain forest V2: Grass & shrub

V3: Savanna V4: Tropical seasonal forest & thorn woods

V5: Mixed deciduous V6: Farm land & paddy rice

V7: Dry evergreen V8: Regrowing wood

V9: Drought deciduous V10: Other biogenic source

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Inverse modeling ( Bayesian Least Squares, Rodgers, 2000)

y = Kx + e

y : observations (GOME HCHO)

x : defined source parameters: GEOS-CHEM

K : Jacobian matrix (sensitivity of x to y :GEOS-CHEM)

e : error term

​

GEOS-CHEM v5.05

- Resolution: 4ox5o

- GEOS-STRAT (26 vertical layers)

The solution,

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Results (Annual HCHO columns)

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Results:Monthly mean HCHO for 8 regions

A priori

A posteriori

GOME

Month : Sep96 Aug97

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Results (Annual isoprene emissions)

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Discrepancy over northern equatorial Africa.

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Results: Annual isoprene emissions

499

560

370

Total

43

14

22

17

60

178

133

32

50

30

43

15

55

125

189

53

43

19

28

11

37

95

103

36

69

96

63

122

110

75

102

96

291

287

280

285

298

337

332

302

N. America

Europe

East Asia

India

S. Asia

S. America

Africa

Australia

GEIA

A Posteriori

A Priori

A Posteriori

A Priori

Isoprene Annual Emissions ( Tg C yr-1)

Weighted Uncertainty(%)

Continent

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The impact of a posteriori isoprene emissions

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Conclusions

  • In order to constrain global isoprene emissions, source parameters for 10 vegetation groups, biomass burning, and industrial emissions are considered in inverse modeling over 8 regions with high signal-to-noise ratios in GOME measurements.

​

  • Global a posteriori isoprene annual emission is higher by 50% to 566 Tg/yr (a priori : 397 Tg/yr). The a posteriori global isoprene annual emissions are generally higher at mid latitudes and lower in the tropics when compared to the GEIA inventory

​

  • There is a significant discrepancy between the seasonality of GOME measured and GEOS-CHEM simulated HCHO columns over the northern equatorial Africa. We attribute this problem to the incorrect seasonal cycle in surface temperature used in GEOS-CHEM. As a result, isoprene emissions over the region are overestimated.

  • The a posteriori results suggest higher isoprene base emissions for agricultural land and tropical rain forest and lower isoprene base emissions for dry evergreen

​

  • The a posteriori biomass burning HCHO sources increase by a factor of 2 – 4 in most regions with significant emissions except for India. The industrial HCHO sources are higher by ~20% except for East Asia and India (~60%).

​

  • The a posteriori uncertainties of emissions, although greatly reduced, are still high (~90%) reflecting the relatively large uncertainties in GOME retrievals.

​

  • This higher isoprene emissions reduces the global mean OH concentration by 11%. The corresponding CH3CCl3 lifetime is increased to 5.7 years.

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Acknowledgements

We thank Alex Guenther for his suggestion of conducting inverse modeling on a regional basis.

​

We thank Daniel Jacob and Robert Yantosca for their help. We also thank Mark Jacobson for his suggestions.

​

The GEOS-CHEM model is managed at Harvard University with support from the NASA Atmospheric Chemistry Modeling and Analysis Program.

​

This work was supported by the NASA ACMAP program.

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Results ( Regional Statistics )

503

566

375

-35

0.68

60

Global

31.1

50.6

33.3

-24.8

-40

0.56

0.52

96

302

69

Australia

105.7

103.3

60.3

-23.6

-46.3

0.54

0.56

102

332

53

Africa

163.5

106.4

79.4

-12.6

-31.8

0.64

0.58

75

337

54

South America

38.2

29.1

20.2

-19.4

-35.8

0.69

0.66

110

298

54

Southeast Asia

15.2

14.4

10.5

-18.4

-33.2

0.56

0.57

122

285

59

India

12.8

24.8

17.4

-18.6

-39.2

0.75

0.63

63

280

56

East Asia

6.1

12.0

9.5

-11.9

-29.9

0.60

0.52

96

287

69

Europe

21.4

25.7

22.2

-3.6

-14.3

0.84

0.84

69

291

59

North America

GEIA

POST

PRI

POST

PRI

POST

PRI

POST

PRI

Ω (%)4

Regions

Isoprene emission (Tg C/yr)

Model bias (%)

Correlation coefficient(R)3

Weighted uncertainties2

GOME

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Results (Emission Type)

91

67

121

78

129

98

118

89

108

88

146

98

115

90

141

125

Total

-

-

1.2

1.2

1

1

5.1

4.2

7.7

5.2

8.1

4.8

6.4

5.4

7.2

6

IND

-

-

8.8

4.2

11.3

2.3

11.3

5.6

10.8

9.9

14

3.4

1.8

1.8

-

-

BB

35.9

16.2

21.8

8.4

17.9

9.9

11.7

3.7

8.3

3.4

48.5

28.5

38

34.6

40

30.7

RV

4.7

4.3

15.2

6.9

1.6

1.8

1.7

3.5

3.4

2.4

-

-

-

-

-

-

V9

5.9

-

3.4

3.4

1.6

1.6

8.3

8.3

13.5

7.5

5.2

1.3

4.1

3.7

18.1

22.6

V8

10.1

14.5

1.4

1.4

2.6

2.6

1.2

1.2

1.9

1.9

-

-

2

1.2

3.2

4.6

V7

1.8

-

2.1

2.1

1.2

1.2

14.3

4.9

7.4

8.3

12.8

4.9

16.3

4.8

8.9

5.6

V6

1.6

1.6

-

-

-

-

4.8

3.2

-

-

1.3

12.5

10.3

5.7

13.3

7.8

V5

-

-

4.5

4.5

16.2

8.5

6.9

4.6

-

-

-

-

-

-

-

-

V4

5

1.2

14.6

10.5

21.4

15.6

-

-

-

-

-

-

-

-

1

1

V3

2.9

4.8

10.3

2.7

4.3

8.6

1.6

1.6

6.5

2.5

21.8

8.1

5.4

2.3

8.5

6.12

V2

-

-

6.4

2

17.9

13.8

7.9

5

1.5

-

-

-

-

-

-

-

V1

pos

pri

Pos

pri

pos

Pri

pos

Pri

Pos

Pri

pos

Pri

Pos

Pri

pos

Pri

Australia

Africa

S. America

S. Asia

India

E. Asia

Europe

N. America