Using space-borne measurements of HCHO to test current understanding of tropical BVOC emissions Paul...

Using space-borne measurements of HCHO to test

current understanding of tropical BVOC emissions

Paul Palmer University of Edinburgh

xweb.geos.ed.ac.uk/~ppalmer

Current model estimates show that tropical ecosystems represent 75% of global biogenic

NMVOC emissions

Guenth

er

et

al,

200

7

But how accurate are these estimates? How well do we understand observed surface flux variability?

Barkley et al, in prep., 2007

Because measurements are sparse including individual data points (and extrapolating them to plant functional

types) have a big effect on bottom-up models

Pfister et al, in review, 2007

From CH4

From isoprene

From other

Contribution of isoprene to Amazon chemical budget

NCAR MOZART-4 CTM

MODIS #1CL

MMODIS

#2

LAI/PFT maps

An integrative perspective is required

Net canopy VOC flux

Con

cen

trati

on

(z)

Column abundance

d[HCHO]/dt = [VOC][OH]k – [HCHO][OH]k’

In-canopy sinks

GOME HCHO columns: July 1998

[1016 molec cm-

2]

Biogenic emissionsPalmer et al, Abbot et al, Millet et al

Biomass burning*Columns fitted: 337-356nm

*Pixel: 320km x 40km * Fit uncertainty < continental signals * Only use cloud fraction<40%

Data

: c/o C

hance

et

al

South Atlantic Anomaly

Fu et al, Shim et

al

Curci et al

Palmer et al, Barkley

et al

Monthly mean AVHRR LAIMEGAN (isoprene)

Canopy model; Leaf age; LAI; Temperature; Fixed Base factors

GEIAMonoterpenes; MBO;Acetone; Methanol

MODEL BIOSPHERE

GEOS-Chem chemistry transport model

Chemistry and transport run at 2x2.5 degrees ANDsampled at GOME scenes

PAR, T

Emissions

Parameterized HCHO source from monoterpenes and MBO using the Master Chemical Mechanism

d[HCHO]/dt = [VOC][OH]k –[HCHO][OH]k’

GFED biomass burning

emissions

Master Chemical Mechanism yield calculations

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 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

0.55C

umm

ula

tive

HC

HO

Yie

ld fr

om

iso

pren

e o

xid

atio

n (p

er C

)

TIME (HOURS)

NOX = 0.1 PPB

NOX =1 PPB

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

Isoprene

HOURS

0.5NOx = 1 ppb

NOx = 0.1 ppb

Parameterization (1ST-order decay) of HCHO production from monoterpenes in global 3-D CTM – MAX 5-10% of column

Higher CH3COCH3 yield from monoterpene oxidation delayed (and smeared) HCHO production

Palmer et al, JGR, 2006.

C5H8+OH(i) RO2+NOHCHO, MVK, MACR

(ii) RO2+HO2ROOH

ROOH recycle RO and RO2

Month

ly

ATSR

Fire

counts

Sla

nt

Colu

mn

HC

HO

[1

016 m

ole

c cm

-2]

Day of Year

Significant pyrogenic HCHO source over South America

Good: Additional trace gas measurement of biomass

burning; effect can beidentified largely by

firecounts.

Bad: Observed HCHO is a mixture of

biogenic and pyrogenic – difficult to

separate without better temporal and

spatial resolution

ATSR Firecount

Remove HCHO if concurrent NO2 > 8x1015 molec/cm2

Barkley et al, in prep., 2007

Firecounts and GOME NO2 columns are used to remove pyrogenic HCHO signal over western South America

NO2 HCHO

10

15,

10

16

[mole

c/cm

2]

HCHO NO2

Model HCHO columns are typically 20% higher than GOME data

Model and observed

columns are better

correlated in the dry season

Ground-based and aircraft measurements of isoprene and/or HCHO are sparse but invaluable for evaluating

satellite dataTrostdorf et al, ACPD, 2007

Helmig et al, JGR, 1998

Kuhn et al, JGR, 2002

Kuhn et al, ACP, 2007

Kuhn et al, ACP, 2007

Barkley et al, in prep, 2007.

Helmig et al, JGR, 1998 MEGAN 2004

Ts

MEGAN 2004 T(1)

MEGAN 2006

Annual cycle of isoprene

Hypothesis: water availability has a role in determining the magnitude of isoprene emission in the dry season

In situ isoprene 2002

Tro

stdorf e

t al,

200

4

Isop

ren

e [

pp

b] Dry season

Trostdorf et al, ACPD, 2007

Carswell, et al, 2002 Huete et al, 2006

In situ isoprene 2002

Tro

stdorf e

t al,

20

04

Isopre

ne [

ppb]

Dry seasonLA

I

1999

Vegetation seasonal phenology (mean +/- sd). Satellite EVI and local tower GPP at Tapajos primary forest (km 67 site, 2002-2004).

Other factors affecting phenology?

Kuhn e

t al, 2

00

4

Dry season

Barkley et al, in prep, 2007.

GEOS-Chem(MEGAN) has only a weak annual cycle compared with data, symptomatic of model

deficiency

Bias = +102%; r2 = 0

Bias = +38%; r2 = -0.2

Bias = +180%; r2 = 0

Kuhn et al, JGR, 2002

Are bottom-up inventories biased towards dry season measurements?

GEOS-Chem over estimates surface [HCHO] during (1) the wet season and (2) night time

Model does NOT account for in-canopy chemistry and not a fair data comparison

HCHO Columns Over NW South America

Use GOME NO2 and ATSR firecounts to remove pyrogenic HCHO S

lan

t C

olu

mn H

CH

O [

10

16 m

ole

c cm

-2]

Month

2.5

2.0

1.5

1.0

0.5

0.0

Q: What’s driving this seasonal distribution of HCHO?

In situ isoprene 2002

Tro

stdorf e

t al,

20

04

Isopre

ne [

ppb]

Dry season

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

LL-VOC ELL-VOC + SL-VOCESL VOC = HCHO

kHCHO

(kVOCYVOCHCHO)___________ =

Background due to CH4, CH3OH

, GEOS-Chem chemistry mechanism

Isoprene emission E [1013 atomC cm-2 s-1]

May

AugJul

Jun

r = 0.9

r = 0.9

r = 0.8

r = 0.9

Mod

el

HC

HO

[10

16 m

ole

c cm

-2]

Slope = 2000-2200 s

Intercept (background) = 5-6x1015 molec/cm2

Isoprene emissions [1013 molec/cm3/s]

MEGAN GOME

Apr

Jun

Aug

Oct

MODIS EVI

Bottom-up emission inventories typically represent within-canopy measurements:(1) Within-canopy turbulence and chemistry are sub-grid scale processes in global 3-D CTMs (2) Artificially increase [OH] to remove isoprene faster would be problematic in global CTMs

Con

cen

trati

on

(z)

Net canopy VOC flux

Column abundance

d[HCHO]/dt = [VOC][OH]k – [HCHO][OH]k’

In-canopy sinks

Provided GEOS-CHEM d[HCHO]/dt

is correct then canopy fluxes of VOCs inferred

from HCHO columns are more suitable for global

models

What we’ve shown….

Satellite observations of HCHO have strong (and distinct) pyrogenic and biogenic signatures.

GOME HCHO data are broadly consistent with the temporal variability observed by ground-based data, particularly the partitioning between wet and dry season.

GOME HCHO data are qualitatively consistent with bottom-up isoprene emissions in the dry season (when model bias is greatest).

Bottom-up models (here, we pick on MEGAN!) lack data to provide robust isoprene estimates over South America.

Isoprene emissions inferred from GOME represent the canopy-atmosphere flux – what global 3-D CTMs want.

Open questions that still need to be answered…

How do we reconcile the apparent discrepancy between ground-based measurements of isoprene flux and concentration and oxidation products?

Are GOME isoprene fluxes more consistent with ground-based data? [Calculations running as we speak]

Why are isoprene fluxes in the dry season higher than in the wet season? Light vs drought: are GOME isoprene fluxes more consistent with seasonal changes in EVI or drought indices?]

How important is isoprene to the regional carbon budget?

Will better spatial and temporal resolved satellite data improve estimates?

SPARE SLIDES

Vertical column retrievals

8 x 1016 molec cm-2T

ransm

issi

on

Chance et al, GRL, 2000

337-356 nm (O3, NO2, BrO, O2-O2)

1) Direct fit of observed radiances: slant columns

AMF = AMFG w() S() d1

0

Radiative transfer

Normalised HCHO profile

Palmer et al, JGR, 2001

2) Air-mass factor calculation: vertical columns

Estimated Error Budget

Slant column fitting: 4x1015 molec cm-2

AMF:

1) UV albedo (8%)

2) Model error (10%)

3) Clouds (20%)

4) Aerosols (20%)

Subtotal 30%

For a vertical column of 2x1016 molec cm-2 and AMF of 0.7

TOTAL = 9x1015 molec cm-2

Month of 2000

50

0

Mod

el b

ias

[%]