Global isoprene sources and chemistry: - PowerPoint Presentation

Slide1

Global isoprene sources and chemistry: constraints from atmospheric observations

Daniel J. Jacob

with Emily Fischer, Fabien

Paulot, Lei Zhu, Eloïse Marais, Chris Miller

and funding from NASA, HUCE

Slide2

Volatile organic compounds (VOCs) in the atmosphere:carbon oxidation chain

VOC

RO

2

NO

2

O

3

organic

peroxy

radical

NO

h



carbonyl

R’O

2

h



OH

+ products

organic aerosol

ROOH

organic

peroxide

OH

HO

2

OH,

h



OH

products

EARTH SURFACE

biosphere

combustion

industry

deposition

Increasing functionality & cleavage

sources of organic aerosol

sources/sinks of oxidants (ozone, OH)

Slide3

Volatile organic compounds (VOCs) in the atmosphere:effect on nitrogen cycle

NO

x

CH

3

C(O)OO

OH

EARTH SURFACE

combustion

deposition

Reservoirs for long-range transport of

NO

x

lightning

deposition

HNO

3

peroxyacetylnitrate

(PAN)

other organic nitrates

NO

x

OH

deposition

HNO

3

Long-range atmospheric transport

RO

2

N fixation

hours

Slide4

Why is isoprene such an important VOC?

Global emission,

Tg C a-1

1. Large emission:

2. Oxidation generates suite of volatile reactive products:

Isoprene

OH

~1 h

multistep

Formaldehyde

Other carbonyls

Dicarbonyls

Peroxides Epoxides Isoprene nitrates

Slide5

Contribution of isoprene to PAN

from GEOS-Chem global 3-D chemical transport model

Emily Fischer, Harvard

Anthropogenic

Open fires

Isoprene

Other biogenic VOCs

%

January July

Slide6

Sensitivity of nitrogen deposition to isoprene emission

Sensitivity for

Cayuhoga

National Park (Ohio)

computed with the GEOS-

Chem

adjoint

Local isoprene emission suppresses N deposition, upwind emission increases it

Fabien

Paulot

, Harvard

of local NOx emission)

Slide7

Estimating isoprene emissions:

bottom-up and top-down approaches

Bottom-up estimate

from plant model:E

ISOP

= f(plant type,

phenology

, LAI,

T

, PAR, water stress, …)

Isoprene

oxidationproductsEcosystem observations

Atmospheric observations

Top-down estimate

from Inversion of chemical transport model:

E

ISOP

= f(atmospheric concentrations,

transport, chemistry)

Slide8

Observing isoprene oxidation products from space:formaldehyde (HCHO) and glyoxal (CHOCHO)

Scattering by

atmosphere

and Earth surface

l

1

l

2

HCHO or

CHOCHO

absorption

spectrum

l

1

l

2

GOME (1995-2001), SCIAMACHY (2002-2012),

OMI (2004-), GOME-2 (2006-) instruments

Spectral fitting yields “slant” columns of HCHO, CHOCHO along light path

Air mass factor from

radiative

transfer model converts slant to vertical columns

HCHO

CHOCHO

Annual mean vertical columns from GOME-2, 2007-2008

HCHO

CHOCHO

Slide9

Relating HCHO columns to VOC emission

VOC

i

HCHO

h



(340 nm), OH

oxidation

k ~ 0.5 h

-1

Emission E

i

displacement

In absence of horizontal wind, mass balance for HCHO column

W

HCHO

:

yield

y

i

but

wind smears

this

relationship

depending on VOC lifetime

wrt

HCHO production:

Local linear relationship

between HCHO column and E

VOCsource

Distance downwind

W

HCHO

Isoprene

a

-

pinene

methanol

100 km

detection limit



HCHO is mainly sensitive to isoprene emission with smearing ~ 10-100 km

Slide10

Past use of 

HCHO vs. E

ISOP relationship over US

to constrain isoprene emission with OMI data

OMI HCHO (Jun-Aug 2006)

OMI-constrained isoprene emission

GEOS-

Chem

local relationship between

HCHO column and isoprene emission

Model slope (2400 s) agrees with

INTEX-A vertical profiles (2300),

PROPHET Michigan site (2100)

Palmer et al. [2003, 2006}, Millet et al. [2006, 2008]

Slide11

Temperature dominates variability of EISOP

seen by OMIcan’t pick up any other variable from multivariate correlations, case studies

Lei Zhu, Harvard

5 10 15

10

15

molecules cm

-2

HCHO column,

Jun-Aug 2005

2006

2007

2008

Correlation of monthly mean HCHO with air

T

NE Texas, JJA 2005-2008

Exponential fit

MEGANDaily data in Southeast US binned by air temperature 290 295 300 305 310 K

285 290 295 300 K

turnover

at 307 K

Slide12

After 2009 it’s curtains for OMI

…but GOME-2 provides consistent continuity

GOME-2 HCHO, 2007 OMI

June

July

August

GOME-2 vs. OMI correlation

monthly data in SE US JJA 2007-2008

Lei Zhu, Harvard

OMI

13x24 km

2

13:30

GOME-2 40x80 km2 9:30

nadir pixel time

slope = 0.91

r

2 = 0.82

Slide13

Using OMI HCHOto constrain isoprene emissions in Africa

MODIS leaf area index MODIS fire counts Earth lights AATSR gas flares

10

15

molecules cm

-2

OMI annual mean

HCHO slant columns

2005-2009

Observed HCHO distribution over Africa points to sources from (1) biosphere, (2) open fires, (3) oil and gas industry

Africa accounts for 20% of global biogenic isoprene emissions in MEGAN inventory…but based on little in situ data

Aug-Sep

Marais et al., in press

Slide14

10

15 molecules cm

-2

Isolating biogenic HCHO in the OMI data

Exclude open fire (and dust) influence using MODIS fire counts, OMI absorbing aerosol optical depth

Exclude oil/gas industry influence using AATSR gas flare product

Marais et al., in press

HCHO slant column

original data

HCHO vertical column

biogenic only

air mass factor

HCHO slant column

HCHO biogenic vertical column;

8-day product with 1

o

x1

o

resolution

Slide15

OH

NO

HO

2



-IEPOX

formaldehyde

h



Pathways for HCHO formation from isoprene oxidation

RO

2

OH

OH

Isomerization

C

1,5

-shift

ROOH

high-

NO

x

branch (RO

2

+NO) yields fast HCHO as 1

st

generation product

Peeters

Paulot

MVK

MACR

Epoxydiols

[

Paulot

et al., 2009]

More recently proposed low-

NO

x

pathways regenerate OH, produce HCHO:

Isomerization

[

Peeters and Muller, 2010]

standardGEOS-Chemmechanism

first-generation

high-NOx

low-

NO

x

low-

NO

x

branch (RO

2

+HO

2

) yields slower HCHO, depletes OH

OH

Slide16

Time-dependent HCHO yield from isoprene oxidation

DSMACC box model calculations

aging/smearing

Yield is sensitive to

NO

x

, not so much to mechanism except at very low

NO

x

Marais et al., in press

Slide17

Boundary layer NOx levels over Africa

Annual NO

2

tropospheric columns, fire influences excluded

Satellite observations Model

% isoprene RO

2

reacting with NO

(GEOS-

Chem

, July)

Boundary layer

NOx over Africa is typically 0.1-1 ppbv Expect NOx dependence of HCHO yield, moderate smearingMarais et al., in pressboundary layer

Slide18

Testing HCHO-isoprene smearing with AMMA aircraft data

Flight tracks (Jul-Aug 2006)

and MODIS leaf area index

Latitudinal profiles below 1 km

WIND

HCHO tracks isopre

ne with only ~50 km smearing

But

NO

x

measured in AMMA was relatively high (mean

0.3 ppb)OMI HCHO

Marais et al., in press

WIND

Slide19

Smearing

produces“shadow

” region 200-300 km downwind of rainforest

Marais et al., in press

OMI HCHO column

10

15

molecules cm

-2

WIND

July

Testing HCHO-isoprene smearing

in longitudinal transect across Congo:

high isoprene and low

NO

x

shadow

Slide20

Relationship between HCHO column and isoprene emission

Model sensitivity S

of HCHO column (ΔHCHO

) to isoprene emission (ΔE

ISOP

)

as function of

tropospheric

NO

2

column (NO2)

Standard

Paulot Use S = ΔHCHO / ΔEISOP

for local OMI NO2 to derive isoprene emission

Exclude “shadow” regions on basis of anomalously high S values

Marais et al., in press

Slide21

Error analysis on inferring EISOP from satellite HCHO data

Slant HCHO column

20% (spectral fitting)

Vertical HCHO column

20% (clouds, vertical distribution,

albedo

)

Isoprene emission

Estimated errors (8-day data, 1

o

x1

o

resolution)

15% (chemical mechanism)

25-60% (smearing)

15% (NO

2

column)

Total error: 40% (high-

NO

x

), 40-90% (low-NO

x ). Can be reduced by averaging Smearing is dominant error component. Need to resolve transport!

Marais et al., in press

Slide22

Isoprene emission (12-15 local time annual mean, 2006)

Comparison of OMI isoprene emissions to MEGAN

MEGAN is too low for equatorial forest, too high for savanna

Marais et al., in press

Slide23

2005-2009 monthly variability of isoprene emissionfor evergreen broadleaf forest of central Africa

Eloïse

Marais, Harvard

Variability is small and weakly correlated to temperature and LAI

Need to address uncertainty in meteorological and LAI products!

E

ISOP

,

temperature

E

ISOP , LAI

AVHRR

Slide24

2005-2009 monthly variability of isoprene emission

in open deciduous broadleaf forest of s. Africa

May-Sept dry season; LAI drops below 1 in Aug, driving

EISOP

down

Sept-Nov increase in LAI (greening) causes spike in

E

ISOP

Wet season cloudiness causes T to decrease after Nov, driving E

ISOP down even though LAI continues to increase Suggests saturation of

EISOP when LAI exceeds 1.5Eloïse Marais, HarvardEISOP , temperature

EISOP , LAI

Jan

Jan

Jan

AVHRR

Slide25

Glyoxal from space as additional constraint on VOC sources

GOME-2

Glyoxal sources in GEOS-Chem:

55% isoprene, 24% acetylene,

7% aromatics, 8% fire emission, 2%

monoterpenes

Glyoxal

lifetime ~1 h (photolysis)

Chris Miller, Harvard

Operational data available from SCIAMACHY, GOME-2 OMI retrieval in progress (Chris Miller, Harvard)GEOS-Chem

Slide26

Does glyoxal provide information complementary to HCHO?

GOME-2

GEOS-

Chem

Glyoxal

columns (Jun-Aug 2007)

Glyoxal

/HCHO column ratio

GOME-2 shows variability in

glyoxal

/HCHO ratio that GEOS-

Chem

doesn’t captureChris Miller, Harvard

Slide27

Glyoxal production from isoprene

Observed fast production with 2-3% yield [Galloway 2011] – Dibble isomerization

?

Chris Miller, Harvard

Dibble

isomerization

first-generation

Slide28

Tower data from CABINEX, northern Michigan (Jul-Aug 09)

Measured

GEOS-

Chem

with

E

ISOP

/2

isoprene

Glyoxal Pathways for

glyoxal formationDibbleObservations by Frank KeutschDibble isomerization is dominant model pathway for glyoxal formation

Chris Miller, Harvard

OH-

aldehydes

Slide29

Vision for the future: ecosystem monitoring

Adjoint inversion of isoprene emission using geostationary satellite observations of HCHO and

glyoxal

HCHO,

glyoxal

measurement



(x,

t

)

1-km chemical

transport model

inverse

model

Emission

E( x

’,

t’

)

Geostationary observation



diurnal information, higher precision daily data

GEMS (Korea), 2017; Sentinel-4 (Europe), 2019; GEO-CAPE (US), 2020+

Adjoint

inversion



solve smearing problem, allow isoprene emission monitoring

need to properly represent chemistry-transport coupling on scales of PBL mixing

Wind

boundary layer

mixing (~1 h)