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What can we learn about the Earth system using space-based observations of tropospheric chemical composition? Paul Palmer, University of Leeds www.env.leeds.ac.uk/~pip
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What can we learn about the Earth system using space-based observations of tropospheric chemical composition?

Paul Palmer, University of Leedswww.env.leeds.ac.uk/~pip

+ UK/European missions + CMDL network = sparse data coverage

NO

HO2OH

NO2

O3hv

HC+OH HCHO + productsNOx, HC,

CO

Tropospheric O3 is an important climate forcing agent

IPCC, 2001

Level of Scientific Understanding

Natural VOC emissions (50% isoprene) ~ CH4 emissions.

Bottom-up Isoprene

emissions, July 1996

MEGAN

3.6 Tg C

GEIA

7.1 Tg C

[1012 atom C cm-2 s-

1]

Guenther et al, JGR, 1995

EPA BEIS2

2.6 Tg C

Pierce et al, JGR, 1998

Guenther et al, ACP, 2006

E = A ∏iγi

Emissions (x,y,t); fixed base emissions(x,y); sensitivity parameters(t)

Global Ozone Monitoring Experiment (GOME) &the Ozone Monitoring Instrument (OMI)

• GOME (European), OMI (Finnish/USA) are nadir SBUV instruments• Ground pixel (nadir): 320 x 40 km2 (GOME), 13 x 24 km2 (OMI)• 10.30 desc (GOME), 13.45 asc (OMI) cross-equator time • GOME: 3 viewing angles global coverage within 3 days• OMI: 60 across-track pixels daily global coverage• O3, NO2, BrO, OClO, SO2, HCHO, H2O, cloud properties

Launched in 2004

GOME HCHO columns

July 2001

[1016 molec cm-

2]

0 1 20.5

1.5

2.5

Biogenic emissions

Biomass burning

* Columns fitted: 337-356nm * Fitting uncertainty < continental signals

Data: c/o Chance et al

May

Jun Jul Aug

Sep

1996

1997

1998

1999

2000

2001

GOME HCHO column [1016 molec cm-2]

0 1 20.5

1.5

2.5

Palm

er

et

al, JG

R,

2006.

Tropospheric chemistry: a highly non-linear system, e.g. isoprene chemistry

Yield dependent on oxidant concentrations

•Models typically use a simplified (lumped) oxidation mechanism

•Assimilating HCHO possible but involves integrating many 100s of uncertain chemical reactions

•Neglected to mention other (more chemically uncertain) biogenic sources of HCHO!

•For now, we can extract lots of information from the HCHO using a simple method…

Relating HCHO Columns to VOC Emissions

VOC HCHOhours

OH

hours

h, OH

Local linear relationship between HCHO and E

kHCHO

EVOC = (kVOCYVOCHCHO)HCHO

___________

VOC source

Distance downwind

HCHO Isoprene

-pinenepropane

100 km

EVOC: HCHO from GEOS-CHEM CTM and MEGAN isoprene emission model

Palmer et al, JGR, 2003.

Net

Seasonal Variation of Y2001 Isoprene Emissions

•Good accord for seasonal variation, regional distribution of emissions (differences in hot spot locations – implications for O3 prod/loss).

•Other biogenic VOCs play a small role in GOME interpretation

May

Jun

Aug

Sep

Jul

0 3.5

7

1012 atom C cm-2s-

1

GOME MEGAN MEGAN GOME

Palmer et al, JGR, 2006.

GOME Isoprene Emissions: 1996-2001May Jun Jul Aug Sep

1996

1997

1998

1999

2000

2001

[1012 molecules cm-2s-1]0 5 10

Relatively inactive

Palm

er

et

al, JG

R,

2006.

Isop

ren

e fl

ux [

10

12 C

cm

-2 s

-1]

Julian Day, 2001

MEGANObsGOME

Sparse ground-truthing of GOME HCHO columns and derived isoprene flux estimates

Seasonal Variation: Comparison with eddy correlation isoprene flux measurements (B. Lamb) is encouraging

Atlanta, GA

May Jun July Aug Sep

PAMS Isoprene, 10-12LT [ppbC]

GO

ME H

CH

O [

10

16 m

ole

c c

m-2]

1996 1997 1998 1999 2000 2001Interannual Variation:

Correlate with EPA isoprene surface concentration data. Outliers due to local emissions.

Atlanta, GA

PROPHET Forest Site, MI

Surface temperature explains 80% of GOME-observed variation in HCHO

NCEP Surface Temperature [K]

GO

ME H

CH

O S

lant

Colu

mn

[10

16 m

ole

c cm

-2]

G98 fitted to GOME data

G98 Modeled curves

Time to revise model parameterizations of isoprene emissions?

Palm

er

et

al, JG

R,

2006.

Tropical ecosystems represent 75% of biogenic NMVOC emissions

1996

1997

1998

1999

2000

2001

What controls the variability of NMVOC emissions in tropical ecosystems?

Importance of VOC emissions in C budget?

Kesselmeier, et al, 2002

GOME HCHO column, July

Challenges: Cloud cover, biomass burning, and lack of fundamental understanding of NMVOC emissions…

em

issio

n r

ate

(C

)(µ

g g

-1 h

-1)

PA

R(µ

mol m

-2 s

-1)

assim

ilati

on

(C

)(m

g g

-1 h

-1) 0

123456

limonene myrcene b-pinene a-pinene sabinene

500

1000

1500

00:0006:0012:00

18:0000:00

06:0012:00

18:0000:00

0

2

4

local time [hh:mm]

10

20

30

40

tem

pera

ture

[°C

]

0

2

4

G93

for

isop

.[s

um

of

mon

ote

rpen

es]

tran

sp

irati

on

(mm

ol m

-2 s

-1)

monoterpene emission of Apeiba tibourbou

OMI, 24/9-19/10, 2004

13x24 km2

TES data @ 6km, 11/04

O3

CO

MODIS Firecount

O3-CO-NO2-HCHO-firecount correlations import to utilize when looking at the tropics

Improved cloud-clearing algorithms and better spatial resolution data help.

TES data c/o Bowman, JPL

A more integrated approach to understanding controls of NMVOCs, e.g., surface data, lab data,

Africa

Some aerosol-climate effects

South America Africa

Fe fertilization

deposition

Primary and secondary

aerosol sources: biomass burning,

biogenic, desert dust

Internally or externally mixed?

visibility

CCN

D Z

N POcean Ecosystem

SEVERI AOD

40

45

50

55

60

65

70

- 60 - 50 - 40 - 30 - 20 - 10 0 10

“Normal” airmass flow

44

46

48

50

52

54

56

- 20 - 15 - 10 - 5 0 5 10

Stagnant airmass flow

0

200

400

600

800

1000

1200

1400

27-Jul

29-Jul

31-Jul

2-Aug

4-Aug

6-Aug

8-Aug

10-Aug

12-Aug

14-Aug

16-Aug

18-Aug

20-Aug

22-Aug

24-Aug

26-Aug

28-Aug

30-Aug

0

5

10

15

20

25

30

35

40

Tem

pera

ture

(C

)

Isop

ren

e (

pp

t)

Estimated up to 700 extra deaths attributable to air pollution (O3 and PM10) in UK during this period

O3 > 100 ppb on 6 consecutive days

2pm, 6th Aug, 2003

Compiled from UK ozone network data

Isoprene c/o Ally Lewis

“Expect harmful levels of ozone and PM2.5 over the next couple of days; please keep small children and animals inside. Transatlantic pollution represents 20% of today’s UK surface ozone.”

2010

Resolution of new satellite data allows study UK AQ from space

SCIA NO2 @ 0.4x0.4o, Aug 2004

GOME NO2 @ 1x1o

Aug 1997

NAEI NOX emissions as NO2, 2002

GOME and SCIA NO2 c/o R. Martin; OMI (unofficial) NO2 c/o

T. Kurosu

Length

of

day

[hours

]

Day of Year

Edinburgh, 56N

Denver, 40N Cloud cover [%], ISCCP August 83-04

30 10070

OMI NO2 @ 0.1ox0.1o, Jul 2004

The increasing role of BVOCs: constraints from OMI HCHO?

Stewart et al, 2003Isoprene

MonoterpenesBVOC fluxes for a “hot, sunny” day

1016 [molec cm-

2]

OMI HCHO

2<0.3

• Unclear what PM characteristics affect health

• Secondary PM is formed from:– Oxidation of organic

compounds

– Oxidation of SO2

– Difficult to estimate offline – need models and data

MISRSurface PM2.5 =

ModelSurface [PM2.5] x MISR AOT

Model AOT2003

roadside (primary)

PM10

MISR AOT can help estimate total PM2.5:

Space-based aerosol optical properties can help map emissions of particulate matter

Liu

et

al,

2004

Case study of Oregon Fire using data from the Multi-angle Imaging SpectroRadiometer (MISR)

1

2

3

4

5

0.0 0.6 1.2 0.0 1.2 2.4 0 5000 10,0001

2

3

4

5

Kahn, et al., JGR, submitted

Plume Periphery: Higher AOT, Lower ANG, Lower SSA than BackgroundPlume Core: Too Thick & Variable for Standard AOT Retrieval

How do we optimally use this information operationally?

Current Development in Modelling UK AQ

• UK currently using MODELS 3 (MM5 + CMAQ) for AQ

1) UM mesoscale CTM

UKMO Unified Model

2) UKCA gas-aerosol chemistry scheme

AQ-climate links

AQ Model Chem-Clim Model

•Similar equations for data assimilation and inverse modelling

J(x) = ½(yo – H(x))T(E+F)-1(yo-H(x)) +½(x-xb)TB-1(x-xb)

•Multi-species analyses – inter-species error covariance?•Radiance versus retrieved products?•Limit of linearization of non-linear oxidant chemistry?

Global vs urban chemistry? Subgrid scale processes?

“First global space-based measurements of CO2 with the precision and spatial resolution needed to quantify carbon sources and sinks”

The Orbiting Carbon Observatory (OCO)

• Spectroscopic observations of CO2 (1.61 m and 2.06 m) and O2 (0.765 m) to estimate the column integrated CO2 dry air mole fraction,

XCO2 = 0.2095 x (column CO2) / (column O2)• Precisions of 1 ppm on regional scales • Global coverage in 16 days (nadir 1x1.5 km footprint)• JPL-based instrument: PI D. Crisp; Deputy PI: C. Miller (Crisp et al, 2004)

Launch in 2008

2-year mission


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