Seasonal changes in the tropospheric carbon monoxide ...jennyf/talks/jaf_accomc_14_talk.pdf · Cape...

Seasonal changes in the tropospheric carbon monoxide profile over the remote Southern Hemisphere evaluated using multi-model simulations and aircraft observations

Jenny A. Fisher, Stephen R. Wilson University of Wollongong

Guang Zeng National Institute of Water and Atmospheric Research

Jason E. Williams Royal Netherlands Meteorological Institute

Louisa K. Emmons National Center for Atmospheric Research

Ray L. Langenfelds, Paul B. Krummel, L. Paul Steele CSIRO Oceans and Atmosphere Flagship

ACCOMC 12 November 2014

Jenny A. Fisher ([email protected]) 2014 ACCOMC

GOAL: Evaluate model CO in the remote Southern Hemisphere free troposphere

• Much model evaluaCon has focused on the Northern Hemisphere (NH)

• Models generally capture CO amounts, seasonality in the Southern Hemisphere (SH)

• However, CO verCcal distribuCon poorly represented, and difficult to constrain from satellite

In situ data from the free troposphere are necessary to evaluate model backgrounds!

MOPITT, mul>-‐model mean

Shindell et al., 2006


GOAL: Evaluate model CO in the remote Southern Hemisphere free troposphere

• Much model evaluaCon has focused on the Northern Hemisphere (NH)

• Models generally capture CO amounts, seasonality in the Southern Hemisphere (SH)

• However, CO verCcal distribuCon poorly represented, and difficult to constrain from satellite

In situ data from the free troposphere are necessary to evaluate model backgrounds!

MOPITT, mul>-‐model mean

Ra>o of CO(350 hPa) / CO(850hPa)

MOPITT

Mul>-‐model mean

Shindell et al., 2006


50°S

40°S

30°S

20°S

10°S

150°E 180° 150° W 120° W 90°W

CGOP

HIPPO

Aircraft data provide a unique opportunity

Cape Grim Overflight Program (CGOP) • 1991-‐1999, ~monthly flights • Melbourne —> Bass Strait —> Cape Grim • 0-‐8 km profiles west of Cape Grim • 85 flights total, ~17-‐20 flasks per flight

HIAPER Pole-‐to-‐Pole Observa>ons (HIPPO) • 2009-‐2011, 5 deployments • ArcCc —> Pacific —> AntarcCc • ConCnuous 0-‐8 km profiles • 4-‐6 SH flights/deployment, conCnuous sampling


GEOS−Chem

45oS

40oS

35oS

140oE 150oE

NIWA−UKCA

45oS

40oS

35oS

140oE 150oE

45oS

40oS

35oS

140oE 150oE

TM5 CAM−chem

45oS

40oS

35oS

140oE 150oE

40 50 60 70 80 ppbv

SHMIP: Southern Hemisphere Model Intercomparison Project

4 atmospheric chemistry models • GEOS-‐Chem • NIWA-‐UKCA • TM5 • CAM-‐chem

5-‐year simula>on (2004-‐2008)

Iden>cal emissions* • MACCity-‐REAS fossil fuels • GFEDv3 biomass burning • MEGANv2.1-‐CLM biogenic • *except parameterised lightning NOx, soil NOx, volcanic SO2

Different chemistry, meteorology


20

40

60

80

100

CO

(ppbv)

n=

35

n=

35

n=

25

n=

40

n=

22

n=

17

n=

26

n=

31

n=

37

n=

36

n=

31

n=

26

20

40

60

80

100

CO

(ppbv)

n=

60

n=

53

n=

39

n=

40

n=

35

n=

27

n=

32

n=

53

n=

65

n=

52

n=

51

n=

44

1 2 3 4 5 6 7 8 9 10 11 12

Month

20

40

60

80

100

CO

(ppbv)

n=

80

n=

87

n=

39

n=

54

n=

42

n=

21

n=

40

n=

57

n=

59

n=

56

n=

75

n=

49

0-2 km

2-5 km

5-8 km

Observations

Seasonal cycle of CO near Cape Grim


20

40

60

80

100

CO

(ppbv)

n=

35

n=

35

n=

25

n=

40

n=

22

n=

17

n=

26

n=

31

n=

37

n=

36

n=

31

n=

26

20

40

60

80

100

CO

(ppbv)

n=

60

n=

53

n=

39

n=

40

n=

35

n=

27

n=

32

n=

53

n=

65

n=

52

n=

51

n=

44

1 2 3 4 5 6 7 8 9 10 11 12

Month

20

40

60

80

100

CO

(ppbv)

n=

80

n=

87

n=

39

n=

54

n=

42

n=

21

n=

40

n=

57

n=

59

n=

56

n=

75

n=

49

0-2 km

2-5 km

5-8 km

Cape Grim Obs.

TM5

GEOS-Chem

NIWA-UKCA

CAM-chem


Comparison to SHMIP models shows both large-‐scale biases (OH-‐driven?) & differences in verCcal structure


20

40

60

80

100

CO

(ppbv)

n=

35

n=

35

n=

25

n=

40

n=

22

n=

17

n=

26

n=

31

n=

37

n=

36

n=

31

n=

26

20

40

60

80

100

CO

(ppbv)

n=

60

n=

53

n=

39

n=

40

n=

35

n=

27

n=

32

n=

53

n=

65

n=

52

n=

51

n=

44

1 2 3 4 5 6 7 8 9 10 11 12

Month

20

40

60

80

100

CO

(ppbv)

n=

80

n=

87

n=

39

n=

54

n=

42

n=

21

n=

40

n=

57

n=

59

n=

56

n=

75

n=

49

0-2 km

2-5 km

5-8 km

Cape Grim Obs.

TM5

GEOS-Chem

NIWA-UKCA

CAM-chem


TM5 overesCmates



20

40

60

80

100

CO

(ppbv)

n=

35

n=

35

n=

25

n=

40

n=

22

n=

17

n=

26

n=

31

n=

37

n=

36

n=

31

n=

26

20

40

60

80

100

CO

(ppbv)

n=

60

n=

53

n=

39

n=

40

n=

35

n=

27

n=

32

n=

53

n=

65

n=

52

n=

51

n=

44

1 2 3 4 5 6 7 8 9 10 11 12

Month

20

40

60

80

100

CO

(ppbv)

n=

80

n=

87

n=

39

n=

54

n=

42

n=

21

n=

40

n=

57

n=

59

n=

56

n=

75

n=

49

0-2 km

2-5 km

5-8 km

Cape Grim Obs.

TM5

GEOS-Chem

NIWA-UKCA

CAM-chem


TM5 overesCmatesCAM-‐chem underesCmates



20

40

60

80

100

CO

(ppbv)

n=

35

n=

35

n=

25

n=

40

n=

22

n=

17

n=

26

n=

31

n=

37

n=

36

n=

31

n=

26

20

40

60

80

100

CO

(ppbv)

n=

60

n=

53

n=

39

n=

40

n=

35

n=

27

n=

32

n=

53

n=

65

n=

52

n=

51

n=

44

1 2 3 4 5 6 7 8 9 10 11 12

Month

20

40

60

80

100

CO

(ppbv)

n=

80

n=

87

n=

39

n=

54

n=

42

n=

21

n=

40

n=

57

n=

59

n=

56

n=

75

n=

49

0-2 km

2-5 km

5-8 km

Cape Grim Obs.

TM5

GEOS-Chem

NIWA-UKCA

CAM-chem


TM5 overesCmatesCAM-‐chem underesCmatesGEOS-‐Chem, NIWA-‐UKCA reasonable… but with differences in verCcal structure



20

40

60

80

100

CO

(ppbv)

n=

35

n=

35

n=

25

n=

40

n=

22

n=

17

n=

26

n=

31

n=

37

n=

36

n=

31

n=

26

20

40

60

80

100

CO

(ppbv)

n=

60

n=

53

n=

39

n=

40

n=

35

n=

27

n=

32

n=

53

n=

65

n=

52

n=

51

n=

44

1 2 3 4 5 6 7 8 9 10 11 12

Month

20

40

60

80

100

CO

(ppbv)

n=

80

n=

87

n=

39

n=

54

n=

42

n=

21

n=

40

n=

57

n=

59

n=

56

n=

75

n=

49

0-2 km

2-5 km

5-8 km

Cape Grim Obs.

TM5

GEOS-Chem

NIWA-UKCA

CAM-chem


TM5 overesCmatesCAM-‐chem underesCmatesGEOS-‐Chem, NIWA-‐UKCA reasonable… but with differences in verCcal structure


We use the CO ver>cal gradient as a metric for model evalua>on


0

2

4

6

8

Altitude (

km

)

DJF MAMJ

0 10 20∆CO (ppbv)

JA SONCGOPHIPPO

0 10 20∆CO (ppbv)

0 10 20∆CO (ppbv)

0 10 20∆CO (ppbv)

The CO vertical gradient — observed

ΔCO = (CO) -‐ (median surface CO) in ppbv

Very close correspondence between Cape Grim & HIPPO —> gradients from both datasets are representa>ve of large-‐scale, long-‐term drivers


0

2

4

6

8

Altitu

de

(km

)

n=135

n=81

n=32

n=42

n=79

n=31

n=30

n=35DJF

n=102

n=54

n=35

n=34

n=70

n=28

n=32

n=44MAMJ

−5 0 5 10 15 20 25∆CO (ppbv)

0

2

4

6

8

Altitu

de

(km

)

n=65

n=32

n=17

n=18

n=49

n=20

n=20

n=17JA

−5 0 5 10 15 20 25∆CO (ppbv)

n=132

n=58

n=40

n=42

n=86

n=34

n=35

n=35SONCape Grim Obs.

TM5

GEOS-Chem

NIWA-UKCA

CAM-chem

The CO vertical gradient — observed & modelled

In austral winter/spring, all models reproduce verCcal gradient of 1.9-‐2.2 ppbv km-‐1 driven by primary biomass burning emissions


0

2

4

6

8

Altitu

de

(km

)

n=135

n=81

n=32

n=42

n=79

n=31

n=30

n=35DJF

n=102

n=54

n=35

n=34

n=70

n=28

n=32

n=44MAMJ

−5 0 5 10 15 20 25∆CO (ppbv)

0

2

4

6

8

Altitu

de

(km

)

n=65

n=32

n=17

n=18

n=49

n=20

n=20

n=17JA

−5 0 5 10 15 20 25∆CO (ppbv)

n=132

n=58

n=40

n=42

n=86

n=34

n=35


TM5

GEOS-Chem

NIWA-UKCA

CAM-chem

The CO vertical gradient — observed & modelled

In austral summer/autumn, most models underes>mate verCcal gradient of ~1.6-‐1.9 ppbv km-‐1 and show a wider inter-‐model spread.

WHY?

0

2

4

6

8

Altitu

de

(km

)

n=135

n=81

n=32

n=42

n=79

n=31

n=30

n=35DJF

n=102

n=54

n=35

n=34

n=70

n=28

n=32

n=44MAMJ

−5 0 5 10 15 20 25∆CO (ppbv)

0

2

4

6

8

Altitu

de

(km

)

n=65

n=32

n=17

n=18

n=49

n=20

n=20

n=17JA

−5 0 5 10 15 20 25∆CO (ppbv)

n=132

n=58

n=40

n=42

n=86

n=34

n=35


TM5

GEOS-Chem

NIWA-UKCA

CAM-chem


∆CO (ppbv)

0

2

4

6

8

Altit

ude

(km

)

b. Fixed-lifetime CO25 tracerDJF MAMJ JA SON

0 10 20 0 10 200 10 200 10 20

0

2

4

6

8

Altit

ude

(km

)

d. LPJ-GUESS biogenic emissionsDJF MAMJ JA SON

0 10 20 0 10 200 10 200 10 20

a. Standard simulation

0

2

4

6

8

Altit

ude

(km

)

DJF MAMJ JA SON

0 10 20 0 10 200 10 200 10 20

0

2

4

6

8

Altit

ude

(km

)

c. OH-loss COOH tracerDJF MAMJ JA SON

0 10 20 0 10 200 10 200 10 20

Role of meteorology / transport (2004-2005 only)

Total CO

CO25

(CO emissions, 25-‐day lifeCme)


∆CO (ppbv)

0

2

4

6

8

Altit

ude

(km

)


0 10 20 0 10 200 10 200 10 20

0

2

4

6

8

Altit

ude

(km

)


0 10 20 0 10 200 10 200 10 20


0

2

4

6

8

Altit

ude

(km

)

DJF MAMJ JA SON

0 10 20 0 10 200 10 200 10 20

0

2

4

6

8

Altit

ude

(km

)


0 10 20 0 10 200 10 200 10 20

∆CO (ppbv)

0

2

4

6

8

Altit

ude

(km

)


0 10 20 0 10 200 10 200 10 20

0

2

4

6

8

Altit

ude

(km

)


0 10 20 0 10 200 10 200 10 20


0

2

4

6

8

Altit

ude

(km

)

DJF MAMJ JA SON

0 10 20 0 10 200 10 200 10 20

0

2

4

6

8

Altit

ude

(km

)


0 10 20 0 10 200 10 200 10 20

Role of chemical loss (2004-2005 only)

Total CO

COOH

(CO emissions, OH-‐driven loss)


∆CO (ppbv)

0

2

4

6

8

Altit

ude

(km

)


0 10 20 0 10 200 10 200 10 20

0

2

4

6

8

Altit

ude

(km

)


0 10 20 0 10 200 10 200 10 20


0

2

4

6

8

Altit

ude

(km

)

DJF MAMJ JA SON

0 10 20 0 10 200 10 200 10 20

0

2

4

6

8

Altit

ude

(km

)


0 10 20 0 10 200 10 200 10 20

∆CO (ppbv)

0

2

4

6

8

Altit

ude

(km

)


0 10 20 0 10 200 10 200 10 20

0

2

4

6

8

Altit

ude

(km

)


0 10 20 0 10 200 10 200 10 20


0

2

4

6

8

Altit

ude

(km

)

DJF MAMJ JA SON

0 10 20 0 10 200 10 200 10 20

0

2

4

6

8

Altit

ude

(km

)


0 10 20 0 10 200 10 200 10 20

Role of biogenic sources (2004-2005 only)

Total CO

Total COLPJ-‐GUESS isoprene

MEGAN-‐CLM isoprene


a. CO

0

100

200 ppbGEOS−Chem NIWA-UKCA

c. CH2O

0

2.5

5 ppb

0

5

10 ppb

b. Isoprene

0

6

12

e. HO2

d. OH

0

100

200 ppq

f. PCO-LCO -3.75•106

0

3.75•106

molec cm-3 s-1

ppt

Chemistry in biogenic source regions key to downwind CO

Surface concentra>onsGEOS-‐Chem NIWA-‐UKCA


a. CO

0

100


c. CH2O

0

2.5

5 ppb

0

5

10 ppb

b. Isoprene

0

6

12

e. HO2

d. OH

0

100

200 ppq

f. PCO-LCO -3.75•106

0

3.75•106

molec cm-3 s-1

ppt


Surface concentra>onsa. CO

0

100


c. CH2O

0

2.5

5 ppb

0

5

10 ppb

b. Isoprene

0

6

12

e. HO2

d. OH

0

100

200 ppq

f. PCO-LCO -3.75•106

0

3.75•106

molec cm-3 s-1

ppt

GEOS-‐Chem NIWA-‐UKCA


a. CO

0

100


c. CH2O

0

2.5

5 ppb

0

5

10 ppb

b. Isoprene

0

6

12

e. HO2

d. OH

0

100

200 ppq

f. PCO-LCO -3.75•106

0

3.75•106

molec cm-3 s-1

ppt



0

100


c. CH2O

0

2.5

5 ppb

0

5

10 ppb

b. Isoprene

0

6

12

e. HO2

d. OH

0

100

200 ppq

f. PCO-LCO -3.75•106

0

3.75•106

molec cm-3 s-1

ppt

a. CO

0

100


c. CH2O

0

2.5

5 ppb

0

5

10 ppb

b. Isoprene

0

6

12

e. HO2

d. OH

0

100

200 ppq

f. PCO-LCO -3.75•106

0

3.75•106

molec cm-3 s-1

ppt



a. CO

0

100


c. CH2O

0

2.5

5 ppb

0

5

10 ppb

b. Isoprene

0

6

12

e. HO2

d. OH

0

100

200 ppq

f. PCO-LCO -3.75•106

0

3.75•106

molec cm-3 s-1

ppt



0

100


c. CH2O

0

2.5

5 ppb

0

5

10 ppb

b. Isoprene

0

6

12

e. HO2

d. OH

0

100

200 ppq

f. PCO-LCO -3.75•106

0

3.75•106

molec cm-3 s-1

ppt

a. CO

0

100


c. CH2O

0

2.5

5 ppb

0

5

10 ppb

b. Isoprene

0

6

12

e. HO2

d. OH

0

100

200 ppq

f. PCO-LCO -3.75•106

0

3.75•106

molec cm-3 s-1

ppt


Second & later stages of isoprene oxida>on chemistry proceed faster in NIWA-‐UKCA than in GEOS-‐Chem —> less downwind CO produc>on at low alCtude


c. CO

b. CH2O

a. Isoprene

−90 0 90Longitude

4

8

12

Altit

ude

(km

)

0

GEOS−Chem

0

100

200 ppt

−90 0 90Longitude

NIWA-UKCA

0

250

500 ppt

−90 0 90Longitude

−90 0 90Longitude

4

8

12

Altit

ude

(km

)

0

0

50

100 ppb

−90 0 90Longitude

−90 0 90Longitude

4

8

12

Altit

ude

(km

)

0

1 2 3 1 2 3

Transport of biogenic-sourced CO also critical

GEOS-‐Chem NIWA-‐UKCA15-‐45°S cross-‐sec>ons

S. America

Africa

Australia

CGOP Profiles


c. CO

b. CH2O

a. Isoprene

−90 0 90Longitude

4

8

12

Altit

ude

(km

)

0

GEOS−Chem

0

100

200 ppt

−90 0 90Longitude

NIWA-UKCA

0

250

500 ppt

−90 0 90Longitude

−90 0 90Longitude

4

8

12

Altit

ude

(km

)

0

0

50

100 ppb

−90 0 90Longitude

−90 0 90Longitude

4

8

12

Altit

ude

(km

)

0

1 2 3 1 2 3



S. America

Africa

Australia

CGOP Profiles

NIWA-‐UKCA vs GEOS-‐Chem:

More deep convecCve injecCon of isoprene over South American max


c. CO

b. CH2O

a. Isoprene

−90 0 90Longitude

4

8

12

Altit

ude

(km

)

0

GEOS−Chem

0

100

200 ppt

−90 0 90Longitude

NIWA-UKCA

0

250

500 ppt

−90 0 90Longitude

−90 0 90Longitude

4

8

12

Altit

ude

(km

)

0

0

50

100 ppb

−90 0 90Longitude

−90 0 90Longitude

4

8

12

Altit

ude

(km

)

0

1 2 3 1 2 3



S. America

Africa

Australia

CGOP Profiles



More UT producCon of CH2O and subsequently CO


c. CO

b. CH2O

a. Isoprene

−90 0 90Longitude

4

8

12

Altit

ude

(km

)

0

GEOS−Chem

0

100

200 ppt

−90 0 90Longitude

NIWA-UKCA

0

250

500 ppt

−90 0 90Longitude

−90 0 90Longitude

4

8

12

Altit

ude

(km

)

0

0

50

100 ppb

−90 0 90Longitude

−90 0 90Longitude

4

8

12

Altit

ude

(km

)

0

1 2 3 1 2 3



S. America

Africa

Australia

CGOP Profiles



More zonal transport of CO to UT regions downwind

More UT producCon of CH2O and subsequently CO


What’s next for SHMIP?• Fisher et al. (this work) in ACPD now: www.atmos-‐chem-‐phys-‐discuss.net/14/27531/2014/

• Zeng et al. in prep., focus impact of biogenic emissions on CO from surface in situ and ground-‐based total column measurements

• Jason Williams (KNMI) invesCgaCng sensiCvity of NOY in UTLS to biogenic emissions

• Kaitlyn Lieschke (UOW, 2015 Honours student) evaluaCng UT NOX and O3 to invesCgate impacts of model differences in lightning NOX parameterisaCons.

SHADOZGEOS−ChemCAM−ChemNIWA−UKCATM5

January

0 100

O3 (ppb)

1000

800

600

400

200

0

Pre

ssu

re (

hP

a)

April July October

0 100 0 100 0 100

San Cristobal ozonesonde


Conclusions

• Aircrai in situ data provide a rare & valuable perspecCve to evaluate global model representaCons of the chemical state of the background atmosphere

• The CO ver>cal gradient is a sensi>ve test of the combined impacts of model emissions, chemistry, and transport

• Models & observaCons agree in winter-‐spring, when primary biomass burning emissions dominate the SH CO budget

• Large model-‐model & model-‐observaCon discrepancies in summer-‐autumn, when gradients are driven by secondary CO of biogenic origin

• Disambigua>ng model error in emissions, chemistry, and transport requires broader in situ sampling of mul>ple species, across a range of al>tudes, in different chemical environments

Acknowledgements: UOW Vice Chancellor’s Fellowship, NCI Na8onal Facility, CSIRO GASLAB, Australian Bureau of Meteorology/Cape Grim Baseline Air Pollu8on Sta8on, HIPPO Science Team, NeSI high performance compu8ng facili8es, UKMO, UCAR, Na8onal Science Founda8on, Jingqiu Mao, Dagmar Kubis8n, Clare Murphy.

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