GEOS-Chem meeting, 12 April 2007

Preliminary results for the year-Preliminary results for the year-to-year variation in satellite-to-year variation in satellite-

derived NOx sourcesderived NOx sourcesS. Koumoutsaris1, I. Bey1, N. Moore3, A. van Donkelaar3,

R. Martin3,4, L. Jaeglé2

1Laboratoire de Modélisation de la Chimie Atmosphérique, Lausanne, Switzerland

2Department of Atmospheric Sciences, University of

Washington, Seattle, (WA) USA.3Department of Physics and Atmospheric Science,

Dalhousie University, Halifax (NS), Canada4Harvard-Smithsonian Centre of Astrophysics, Cambridge

(MA), USA.

GEOS-Chem meeting, 12 April 2007

ObjectivesObjectives

Use GOME satellite data to derive Use GOME satellite data to derive multi-year inventory of NOx sources multi-year inventory of NOx sources (1995-2000).(1995-2000).

Examine the trends in fossil fuel NOx Examine the trends in fossil fuel NOx emissions.emissions.

Implement the new inventory into Implement the new inventory into GEOS-Chem, examine the effects in GEOS-Chem, examine the effects in OO33 and compare with the standard and compare with the standard simulation and with observations.simulation and with observations.

GOMEMODEL

MODELNO

t NONO

EE x

22

22

22

)(ln)(ln

)(lnln)(lnlnln

ta

taatapost

EEE

GEOS-Chem NO2 column/NOx emissions v7-02-04 30 levels, 2°x2.5°, 50 tracers, from 1995 to 2000GEOS-4 meteorological fieldsBiomass burning emissions IAV using AATSR Fire [Generoso et al., 2003]Anthropogenic emissions: fixed for 1995Lightning NOx emissions: 5.7-6.3 TgN/yr

GOME NO2 column (data from Aaron van Donkelaar, Dalhousie University)Improved filteringAccounts for meridional changes in stratospheric columnOnly when clouds contribute <50% of the backscattered radiation

GOME Error(data from Aaron van Donkelaar, Dalhousie University )

GEOS-Chem ErrorFossil fuel+biofuel errors: using EDGAR2000 emissionsBiomass burning error: using RETRO 1995-2000 emissionsSoil error = ±200%

Method: inversion Method: inversion [[Martin et al.Martin et al., 2003], 2003]

Eapost: “a posteriori”Ea: “a priori” NOx emissionsEt: “top-down”

Method: global partitioning Method: global partitioning [[Jaeglé Jaeglé et al.et al., 2005], 2005]

We obtain “a posteriori” NOx inventories for :

a)Biomass burning emissions

b)Fossil fuel and biofuel emissions

c)Soil emissions

9.0BFFFapriori

apriori

E

Etopdown

BFFFapriori EE

YES

NO

or

0,0

SoilBB

topdownBFFF

EE

EEBFFFapriori

BFFF EE

>0=0

ATSR/VIRS firecounts

0

BB

BFFFSoil

E

EEE

SoilBFFFBB

SoilSoil

EEEE

lonlatEMedianE

)),(( 106

of boxes with fires=0

NONOxx emissions over the biomass burning emissions over the biomass burning regions (1)regions (1)

BB “a priori” error = ±200%

CCD TOMS

Control simulation S0

Simulation with “a posteriori” emissions

Tropical tropospheric ozone column (DU)

22

22

)(ln)(ln

)(lnln)(lnlnln

ta

taat EEE

a posterioria priori

Tota

l N

Ox

em

issi

on

s (T

gN

)

November 199710 DU 46 DU

Does GOME see the diurnal variations of the fires?

How does that affect the NO2 column over the fire regions?

a posterioria priori

IAV BB “a priori” error =

BB “a priori” emissionsBB “RETRO” emissions

NONOxx emissions over the biomass burning emissions over the biomass burning regions (2)regions (2)

South America: -8% |39%|

Mediterranean Europe: +100% |107%|

Russia-Siberia: -6% |34%|

region: mean% |max%|

Biomass burning NOx emissions Biomass burning NOx emissions (TgN/month): “a priori” vs. “a (TgN/month): “a priori” vs. “a

posteriori”posteriori”

a posterioria priori

1996

2001

Soil NOx emissions (TgN/month): Soil NOx emissions (TgN/month): “a priori” vs. “a posteriori” “a priori” vs. “a posteriori”

USA: +46% |78%|

South Africa: +20% |154%|

Australia: +42% |138%|

region: mean% |max%|

a posterioria priori

1996

2001

Fossil fuel/biofuel NOx emissions Fossil fuel/biofuel NOx emissions (TgN/month): “a priori” (TgN/month): “a priori”

vs. “a posteriori”vs. “a posteriori”

Middle East: -14% |19%|

Australia: +5% |30%|

region: mean% |max%|

a posterioria priori

Mediterranean Europe: +2% |15%|

1996

2001

““a posteriori” FF+BF trendsa posteriori” FF+BF trends

North Europe: -1.51 %/yr

China/Japan: +1.4 %/yr

Australia: +1.3 %/yr

Mediterranean Europe: +0.7 %/yr

<0.5%/yr

1996

2001

““a posteriori” FF+BF trendsa posteriori” FF+BF trends

North Europe: -1.51 %/yr

China:+1.4 %/yr

Australia: +1.3 %/yr

Mediterranean Europe: +0.7 %/yr

China/Japan:+1.4 %/yr

Bottom-up: 1996 to 2005: 74% (~8.2%/yr) & 1998 to 2005: 71% (~10.1%/yr) [Streets, personnal communication] No trend in FF+BF in our “a priori” Slowdown of increase during the late ’90s

1996

2001

NONOxx emissions / NO emissions / NO22 column column trendtrend

GOMEMODEL

MODELNO

t NONO

EE x

22

Control Run: 0.67 % / yr

NO

x e

mis

sion

s /

NO

2 c

olu

mn

(T

g N

)

China/Japan Control Run

30 levels, 4°x5°, 24 tracers GEOS-4

Top-down using GOME NO2 column: 1996 to 2002: 50% (~8.3%/yr) [Irie et al., 2005; Richter et al., 2005]

NONOxx emissions / NO emissions / NO22 column column trendtrend

Control Run: 0.67 % / yr

Meteorology fixed in 1988 : 0.83 % / yr Anthropogenic emissions fixed in 1988 : ~ 0.0 % /yr Lightning emissions fixed (6TgN/yr) : 0.73 % /yr

NO

x e

mis

sion

s /

NO

2 c

olu

mn

(T

g N

)

GOMEMODEL

MODELNO

t NONO

EE x

22

China/Japan Control Run

30 levels, 4°x5°, 24 tracers GEOS-4

Top-down using GOME NO2 column: 1996 to 2002: 50% (~8.3%/yr) [Irie et al., 2005; Richter et al., 2005]

North America South America

North Europe

China/Japan

Australia

Mediterannean Europe

India-Indonesia-SEAsia

South Africa

““a posteriori” FF+BF a posteriori” FF+BF seasonal trendsseasonal trends

Preliminary conclusionsPreliminary conclusions Issue with the NOIssue with the NO22 column in GOME over fire column in GOME over fire

regions. regions. The “a posteriori” soil NOx emissions show higher The “a posteriori” soil NOx emissions show higher

interannual and seasonal variation and are found to interannual and seasonal variation and are found to be globally 16% higher than the “a priori”.be globally 16% higher than the “a priori”.

Positive trends have been found in the “a Positive trends have been found in the “a posteriori” fossil fuel+biofuel inventory over China, posteriori” fossil fuel+biofuel inventory over China, Australia and the Mediterranean Europe.Australia and the Mediterranean Europe.

Positive trend in the simulated NOPositive trend in the simulated NOX X emissions/NOemissions/NO22 column ratio which prevents from using directly column ratio which prevents from using directly NONO22 column to derive trends in emissions. column to derive trends in emissions.

Additional slidesAdditional slides

The change in bb error changes the The change in bb error changes the total and the bb (ff & soil unaffected)total and the bb (ff & soil unaffected)

1997: bb error=3 1997: bb error=using RETRO

NONOxx emissions / NO emissions / NO22 trend trendGOME

MODEL

MODELNO

t NONO

EE x

22

RUN 2x2.5

China, ENOx/[NO2]:

-0.49 % / yr

NO

x e

mis

sion

s /

NO

2

(Tg

N)