1. Paul Palmer University of Edinburgh
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

2. Current model estimates show that tropical ecosystems represent 75% of global biogenic NMVOC emissions
Guenther et al, 2007
But how accurate are these estimates? How well do we understand observed surface flux variability?

3. Because measurements are sparse including individual data points (and extrapolating them to plant functional types) have a big effect on bottom-up models
Barkley et al, in prep., 2007

4. Contribution of isoprene to Amazon chemical budget
From CH4
From isoprene
From other
NCAR MOZART-4 CTM
LAI/PFT maps
MODIS #1
CLM
MODIS #2
Pfister et al, in review, 2007

5. An integrative perspective is required
Column abundance
d[HCHO]/dt = [VOC][OH]k – [HCHO][OH]k’
Concentration(z)
Net canopy VOC flux
Three/four prong attack:
Chemistry; Emissions: magnitude and controls; Transport
In-canopy sinks

6. Palmer et al, Barkley et al
GOME HCHO columns: July 1998
Biogenic emissions
Palmer et al, Abbot et al, Millet et al
Curci et al
Fu et al, Shim et al
Data: c/o Chance et al
South Atlantic Anomaly
Palmer et al, Barkley et al
Biomass burning
*Columns fitted: nm *Pixel: 320km x 40km * Fit uncertainty < continental signals * Only use cloud fraction<40%
[1016 molec cm-2]

7. GEOS-Chem chemistry transport model Chemistry and transport
MODEL BIOSPHERE
Monthly mean AVHRR LAI
MEGAN (isoprene)
Canopy model; Leaf age;
LAI; Temperature;
Fixed Base factors
GEIA
Monoterpenes; MBO;
Acetone; Methanol
Parameterized HCHO source from monoterpenes and MBO using the Master Chemical Mechanism
PAR, T
Emissions
Chemistry and transport
run at 2x2.5 degrees AND
sampled at GOME scenes
GFED biomass burning emissions
d[HCHO]/dt = [VOC][OH]k –[HCHO][OH]k’

8. Master Chemical Mechanism yield calculations
0.5
NOx = 1 ppb
NOx = 0.1 ppb
Isoprene
C5H8+OH(i) RO2+NOHCHO, MVK, MACR
(ii) RO2+HO2ROOH
ROOH recycle RO and RO2
Cumulative HCHO yield [per C]
HOURS
Higher CH3COCH3 yield from monoterpene oxidation  delayed (and smeared) HCHO production
0.4
Explain reasons for delayed production of HCHO from pinenes
Parameterization (1ST-order decay) of HCHO production from monoterpenes in global 3-D CTM – MAX 5-10% of column
 pinene
( pinene similar)
DAYS
Palmer et al, JGR, 2006.

9. Significant pyrogenic HCHO source over South America
Good: Additional trace gas
measurement of biomass
burning; effect can be
identified largely by
firecounts.
ATSR Firecount
Monthly ATSR Firecounts
Slant Column HCHO [1016 molec cm-2]
Day of Year
Bad: Observed HCHO is a mixture of
biogenic and pyrogenic – difficult to
separate without better temporal and
spatial resolution

10. Remove HCHO if concurrent NO2 > 8x1015 molec/cm2
Firecounts and GOME NO2 columns are used to remove pyrogenic HCHO signal over western South America
1015, 1016 [molec/cm2]
NO2 HCHO
Remove HCHO if concurrent NO2 > 8x1015 molec/cm2
Barkley et al, in prep., 2007

11. Model HCHO columns are typically 20% higher than GOME data
Model and observed columns are better correlated in the dry season

12. Ground-based and aircraft measurements of isoprene and/or HCHO are sparse but invaluable for evaluating satellite data
Kuhn et al, ACP, 2007
Trostdorf et al, ACPD, 2007
Helmig et al, JGR, 1998
Kuhn et al, JGR, 2002

13. Barkley et al, in prep, 2007. Kuhn et al, ACP, 2007
Helmig et al, JGR, 1998
MEGAN 2004 Ts
MEGAN 2004 T(1)
MEGAN 2006
Barkley et al, in prep, 2007.

14. Annual cycle of isoprene
In situ isoprene 2002
Trostdorf et al, 2004
Isoprene [ppb]
Dry season
Trostdorf et al, ACPD, 2007
Hypothesis: water availability has a role in determining the magnitude of isoprene emission in the dry season

15. Other factors affecting phenology?
In situ isoprene 2002
Trostdorf et al, 2004
Isoprene [ppb]
Dry season
Other factors affecting phenology?
Kuhn et al, 2004
1999
LAI
Vegetation seasonal phenology (mean +/- sd). Satellite EVI and local tower GPP at Tapajos primary forest (km 67 site, ).
Carswell, et al, 2002
Huete et al, 2006

16. GEOS-Chem(MEGAN) has only a weak annual cycle compared with data, symptomatic of model deficiency
Dry season
Bias = +102%; r2 = 0
Bias = +38%; r2 = -0.2
Bias = +180%; r2 = 0
Barkley et al, in prep, 2007.

17. Are bottom-up inventories biased towards dry season measurements?
GEOS-Chem over estimates surface [HCHO] during (1) the wet season and (2) night time
Kuhn et al, JGR, 2002
Model does NOT account for in-canopy chemistry and not a fair data comparison
Are bottom-up inventories biased towards dry season measurements?

18. HCHO Columns Over NW South America
In situ isoprene 2002
Trostdorf et al, 2004
Isoprene [ppb]
Dry season
2.5
2.0
1.5
Slant Column HCHO [1016 molec cm-2]
1.0
Use GOME NO2 and ATSR firecounts to remove pyrogenic HCHO
0.5
0.0
Month
Q: What’s driving this seasonal distribution of HCHO?

19. Relating HCHO Columns to VOC Emissions
hours OH
h, OH
VOC
Net
kHCHO
EVOC =
(kVOCYVOCHCHO)
HCHO
___________
Local linear relationship between HCHO and E
Three/four prong attack:
Chemistry; Emissions: magnitude and controls; Transport
VOC source
Distance downwind
WHCHO
Isoprene
a-pinene
propane
100 km
EVOC: HCHO from GEOS-CHEM CTM and MEGAN isoprene emission model
Palmer et al, JGR, 2003.

20. LL-VOC ELL-VOC + SL-VOCESL VOC = HCHO
kHCHO
(kVOCYVOCHCHO)
___________
 =
, GEOS-Chem chemistry mechanism
LL-VOC ELL-VOC + SL-VOCESL VOC = HCHO
Background due to CH4, CH3OH
May
Aug
Jul
Jun
r = 0.9
r = 0.8
Model HCHO [1016 molec cm-2]
Low slope typical of a low Nox situation
Slope  = s
Intercept (background) = 5-6x1015 molec/cm2
Isoprene emission E [1013 atomC cm-2 s-1]

21. Isoprene emissions [1013 molec/cm3/s]
MEGAN
GOME
MODIS EVI
Apr
Jun
Megan is 20-85% too high
Aug
Oct
Isoprene emissions [1013 molec/cm3/s]

22. 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
Provided GEOS-CHEM d[HCHO]/dt is correct then canopy fluxes of VOCs inferred from HCHO columns are more suitable for global models
d[HCHO]/dt = [VOC][OH]k – [HCHO][OH]k’
Column abundance
Three/four prong attack:
Chemistry; Emissions: magnitude and controls; Transport
Concentration(z)
Net canopy VOC flux
In-canopy sinks

23. 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.

24. 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?

25. SPARE SLIDES

26. Vertical column retrievals
8 x 1016 molec cm-2
Transmission
Chance et al, GRL, 2000
nm (O3, NO2, BrO, O2-O2)
1) Direct fit of observed radiances: slant columns
Estimated Error Budget
Slant column fitting: x1015 molec cm-2
AMF:
UV albedo (8%)
Model error (10%)
Clouds (20%)
Aerosols (20%)
Subtotal 30%
For a vertical column of 2x1016 molec cm-2 and AMF of 0.7
TOTAL = 9x1015 molec cm-2
2) Air-mass factor calculation: vertical columns
AMF = AMFG  w() S() d 1
Radiative transfer
Normalised HCHO profile
Palmer et al, JGR, 2001

27. Month of 2000 50
Model bias [%]