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Terrestrial emissions of isoprene

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Terrestrial emissions of isoprene. Paul Palmer Division of Engineering and Applied Sciences, Harvard University. http://www.people.fas.harvard.edu/~ppalmer. hv. O 3. NO 2. NO. OH. HO 2. HC+OH  HCHO + products. NOx, HC, CO. Tropospheric O 3 is an important climate forcing agent. - PowerPoint PPT Presentation
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Terrestrial emissions of isoprene Paul Palmer Division of Engineering and Applied Sciences, Harvard University http://www.people.fas.harvard.edu/~ppalmer
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Page 1: Terrestrial emissions of isoprene

Terrestrial emissions of isoprene

Paul Palmer

Division of Engineering and Applied Sciences, Harvard University

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

Page 2: Terrestrial emissions of isoprene

NO

HO2OH

NO2

O3hv

HC+OH HCHO + products

NOx, HC, CO

Tropospheric O3 is an important climate forcing agent

IPCC, 2001

Level of Scientific Understanding

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

Page 3: Terrestrial emissions of isoprene

GEIA EPA BEIS2

7.1 Tg C 2.6 Tg C

MEGAN

3.6 Tg C

[1012 atom C cm-2 s-

1]

Isoprene emissions July 1996

Isoprene oxidation products (e.g. HCHO) provide constraints on

estimated emissions

Page 4: Terrestrial emissions of isoprene

GOME isoprene emissions (July 1996) agree with surface measurements

ppb0 12

r2 = 0.53

Bias -3%

GEIA BEIS2

r2 = 0.65

Bias -30%

Mod

ele

d H

CH

O [

pp

b]

Observed HCHO [ppb]

Page 5: Terrestrial emissions of isoprene

Modeling the terrestrial biosphere

April Sep

LA

IPAR – direct and diffuse (GMAO)

MODIS/AVHRR LAI

Canopy model (Guenther 1995)

Alt

itu

de

Emission

Temperature:

Instantaneous (G95)

10-day avg (Petron ‘01)

Fixed base emission factors (Guenther 2004)

Emissions

Page 6: Terrestrial emissions of isoprene

Monthly mean LAI (AVHRR/MODIS)

MEGAN (isoprene)Canopy model

Leaf ageLAI

TemperatureBase factors

MODEL BIOSPHERE

GEIAMonoterpenes

MBOAcetoneMethanol

GEOS-CHEMGlobal 3D CTM

PAR, T

Emissions

Global 3-D Modeling Overview•Driven by NASA GMAO met data

•2x2.5o resolution/30 vertical levels

•O3-NOx-VOC-aerosol chemistry

Page 7: Terrestrial emissions of isoprene

May

Jun

Jul

Aug

Sep[1012 atom C cm-2 s-

1]

VOC emissions

during 2001

growing season

IsopreneMonoterpenes MBO

Page 8: Terrestrial emissions of isoprene

GEOS-CHEM

NOx = 1 ppb

NOx = 0.1 ppb

isoprene

Use this analysis to parameterise source of HCHO from monoterpenes

HCHO production from biogenics using the MCM

0 20 40 60 80 100 120 1400.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0.55

Cum

mu

lativ

e H

CH

O Y

ield

fro

m is

opr

ene

oxi

da

tion

(per

C)

TIME (HOURS)

NOX = 0.1 PPB

NOX =1 PPB

Figure 18. Formation of HCHO from isoprene. Vertical lines denote midnight of each day

HOURS

0.5

Cu

mu

lati

ve H

CH

O y

ield

[p

er

C]

0 2 4 6 8 10 12 14 16 18 20 220.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

HC

HO

YIE

LD

PE

R C

RE

AC

TE

D

DAYS

NOX= 1 PPB NOX= 100 PPT pinene (

pinene similar)

DAYS

0.4

0.33

Y become closer at t progresses further

Mike Pilling and Jenny Stanton, Leeds University

Page 9: Terrestrial emissions of isoprene

•Nadir-viewing SBUV instrument

•Pixel 320 x 40 km2

•10.30 am cross-equator time (globe in 3 days)

•O3, NO2, BrO, OClO, SO2, HCHO, H2O, cloud

Global Ozone Monitoring Experiment

•HCHO slant columns fitted: 337-356nm

•Fitting uncertainty < continental signalsHCHO JULY 1997

Isoprene

Biomass Burning

Page 10: Terrestrial emissions of isoprene

May

Jun

Jul

GEOS-CHEM GOME

Aug

Sep

GEOS-CHEM GOME

HCHO column [1016 molec cm-

2]

GROWING SEASON 2001

HCHO column signal from monoterpenes is comparable to GOME

column uncertainty

Page 11: Terrestrial emissions of isoprene

HCHO data over the Ozarks

c/o Y-N. Lee, Brookhaven National Lab.

MissouriIllinois

Kansas

[ppb]

Aircraft data @ 350 m July 1999

OZARKS

SOS 1999

[1016 molec cm-

2]

GOME

Page 12: Terrestrial emissions of isoprene

Relating HCHO Columns to VOC Emissions

kHCHO HCHOEVOC = _______________kVOCYieldVOCHCHO

VOC source

Distance downwind

HCHO Isoprene

-pinenepropane

100 km

VOC

HCHOhours

OH

hours

h, OH

Ultimate Yield Y (per

C)

Approx. Time to Y

isoprene

~0.5 2-3 hrs

pinene

~0.3 3-4 days

pinene

~0.25 3-4 days

MBO ~0.4 3-4 days

Master Chemical Mechanism

Page 13: Terrestrial emissions of isoprene

                 

Wind direction associated with largest [HCHO] in 1998

intensive

EVALUATE GOME DATA USING LONG-TERM ISOPRENE FLUX DATA

PROPHET RESEARCH SITE (MI)Maple, beech, birch, basswood, mixed aspen, bog conifers (lower, wet areas), and pine and red oak (drier upland regions).  Average height near 20 m.  Overstory age of the hardwood forest is approximately 75 years.

Page 14: Terrestrial emissions of isoprene

Long term in situ isoprene flux measurements at PROPHET site during

2001

Y2K1 Day

HCHO column [1016 molec cm-2]

Isoprene flux [1012 molec cm-2 s-1] Measured

(WSU) MEGAN GOME

+/- uncertainty

Isoprene flux [1012 molec cm-2 s-1]

Measured (WSU) MEGAN GOME

Using observed isop flux:HCHO column regression better agreement with GOME

Page 15: Terrestrial emissions of isoprene

May June July August September

1996

1997

1998

1999

2000

2001

HCHO column [1016 molec cm-

2]

Page 16: Terrestrial emissions of isoprene

Interannual variability of the seasonal cycle

GO

ME H

CH

O C

olu

mn

[10

16 m

ole

c c

m-2]

Days 2K1

Southeast US 32-38N; 265-280W

Page 17: Terrestrial emissions of isoprene

In situ observations over Atlanta GA provide some verification of large

interannual variability

GO

ME H

CH

O C

olu

mn

[1

01

6 m

ole

c cm

-2]

PAMS (EPA) Isoprene Concentration (10-12 LT)

[ppbC]

r = 0.75 n=14

Mean values associated with individual values > 30 ppbC

Lance McCluney,

EPA

Page 18: Terrestrial emissions of isoprene

What is driving this variability?

Curve based on greenhouse data (Guenther)

2nd-order polynomial fit to HCHO columns

r=0.9

Page 19: Terrestrial emissions of isoprene

Closing Remarks

• GOME HCHO data provide constraints on natural VOC emissions

• Data consistent with seasonal and interannual variability observed with in situ measurements

• Improved understanding and quantification of air quality and climate

• Just the beginning…need to relate model-observation discrepancy to a better understanding of the underlying processes


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