3-D global simulations of tropospheric CO distributions...

3-D global simulations of tropospheric CO distributions ± resultsof the GIM/IGAC intercomparison 1997 exercise

M. Kanakidou a,*, F.J. Dentener b, G.P. Brasseur c, T.K. Berntsen d, W.J. Collins e,D.A. Hauglustaine c,f, S. Houweling b, I.S.A. Isaksen d, M. Krol b, M.G. Lawrence g,

J.-F. Muller h, N. Poisson a,1, G.J. Roelofs b, Y. Wang i,2, W.M.F. Wauben j

a Laboratoire des Sciences du Climat et de lÕEnvironnement, Unite Mixte CNRS/CEA, Orme des Merisiers, CE Saclay,

F-91191 Gif-sur-Yvette Cedex, Franceb Utrecht University, IMAU, Utrecht, The Netherlands

c Atmospheric Chemistry Division ± NCAR, Boulder, CO, USAd University of Oslo, Oslo, Norway

e Atmospheric Chemistry Modelling, Meteorological O�ce, Bracknell, UKf Service dÕAeronomie, Universit�e Paris 6, Jussieu, Paris, France

g Max Planck Institute for Chemistry, Atmospheric Chemistry Division, Mainz, Germanyh Belgian Institute for Space Aeronomy, Brussels, Belgium

i Harvard University, Cambridge, MA, USAj KNMI, De Bilt, The Netherlands

Received 17 April 1998; accepted 7 January 1999

Importance of this Paper: The current generation of 3-D global models used for chemistry and climate global simulations

have been compared in the frame of the Tropospheric Ozone (O3) Global Model Intercomparison Exercise performed in

1997. The objective was to systematically evaluate their capabilities to simulate tropospheric ozone and their precursor

gases, including carbon monoxide, and to identify key areas of uncertainty in our understanding of the tropospheric O3

budget. This exercise was organised by Global Integration Modelling (GIM) Activity of the International Global At-

mospheric Chemistry (IGAC) Project.

Abstract

The objective of the Tropospheric Ozone (O3) Global Model Intercomparison Exercise performed in 1997 was to

systematically evaluate the capabilities of the current generation of 3-dimensional global models to simulate tropo-

spheric ozone and their precursor gases, and to identify key areas of uncertainty in our understanding of the tropo-

spheric O3 budget. This exercise has been organised by Global Integration Modelling (GIM) Activity of the

International Global Atmospheric Chemistry (IGAC) Project. The present paper focuses on the capability of the

Chemosphere: Global Change Science 1 (1999) 263±282

* Corresponding author. Department of Chemistry, Environmental Chemical Process Laboratory, University of Crete, P.O. Box

1470, 71409 Heraklion, Greece. Tel.: +30-81-393-633; fax: +30-81-393-601; e-mail: [email protected] Present address: Harvard University, Cambridge, MA, USA.2 Present address: Georgia Institute of Technology, Atlanta, USA.

1465-9972/99/$ ± see front matter Ó 1999 Elsevier Science Ltd. All rights reserved.

PII: S 1 4 6 5 - 9 9 7 2 ( 9 9 ) 0 0 0 2 9 - X

models to simulate carbon monoxide, which is an important pollutant in the troposphere. The intercomparison of

twelve 3-dimensional global chemistry/transport models shows signi®cant di�erences between the models although all

of them capture the general patterns in the global distribution of CO. Ó 1999 Elsevier Science Ltd. All rights reserved.

Keywords: 3-dimensional; Global; Models; Intercomparison; Tropospheric chemistry; IGAC; Carbon monoxide; CO;

Budget; Methane lifetime

1. Introduction

It is now recognised that the atmospheric con-centrations of several chemical and radiativelyimportant trace constituents (gases and particles)are changing in the atmosphere (IPCC, 1994).Human activity is the main reason for thesechanges, climate changes a�ecting natural emis-sions and the radiative balance of the earth beingthe second forcing agent. Indeed, several traceconstituents like aerosols, methane (CH4), nitrousoxide (N2O), nitrogen oxides (NO + NO2), carbonmonoxide (CO), Non-Methane Volatile Com-pounds (NMVOC) including Non-Methane Hy-drocarbons (NMHC), sulphur dioxide (SO2),dimethylsulphide (DMS) and halocarbons havedirect anthropogenic and/or natural emissions tothe atmosphere. Others, like ozone (O3) and sec-ondary aerosols, are produced from chemical re-actions initiated by their precursors. In turn, thechanging atmospheric concentrations of thesetrace constituents a�ect the radiative balance ofthe earth and may lead to a climate change.

To understand and reliably predict chemicaland climate changes in the atmosphere a thoroughunderstanding of the chemical, physical, biologicaland climatic processes which a�ect the distribu-tions of trace compounds in the atmosphere, andof the interactions between these processes is re-quired. Only then, the global budgets of traceconstituents, their evolution due to natural andanthropogenic forcing and the related feedbackmechanisms can be realistically simulated by themean of numerical models.

The use of numerical models is required to takeinto account the physical processes governing thebudget of trace gases in the troposphere and thecomplex non-linear behaviour of chemical reac-tions in the troposphere. Such models integrate themost recent information on chemical kinetics of

homogeneous and heterogeneous reactions as wellas photodissociation coe�cients of atmosphericmolecules. They also account for interactions be-tween transport, chemistry and the radiative bud-get in the troposphere, and speci®cally for: (i)transport of trace constituents like nitrogen species(NOy) and O3 from the stratosphere, (ii) emissionsof trace gases like O3 precursors: nitrogen oxides,carbon monoxide, NMVOCs and aerosols occur-ring mainly in the low troposphere, (iii) dry de-position of various trace gases and particles on theearthÕs surface, (iv) wet removal of soluble gasesand particles, (v) convection, (vi) planetary boun-dary layer mixing, (vii) synoptic mixing in thetroposphere, (viii) cloud microphysics and (ix)homogeneous and heterogeneous chemical reac-tions.

Recent assessments of the global-scale, radia-tive e�ects of greenhouse gases and of aerosolshave been performed on the basis of global modelswhich, in most cases, are still under development.The accuracy of such calculations relies stronglyon the capability of the models to simulate theglobal budget of trace gases and aerosols. A majorconcern for the calculation of the chemically re-active trace gases budget is associated with: (1) theinaccuracies in the transport parameterisation(Jacob et al., 1997), (2) the simpli®cation of thechemistry in the troposphere, required due to theprohibitive number of chemical reactions andspecies and (3) the uncertainties in the trace gasesemissions used in the global models.

Even though current models ignore some im-portant chemical and radiative processes in theatmosphere, it seems crucial for the time being toimprove our model estimate of the budgets of O3,CH4, CO and NOx. Indeed, O3 is a greenhouse gas,a pollutant and one of the major oxidising agentsin the troposphere. CH4 is the most chemicallyimportant greenhouse gas with direct emissions to

264 M. Kanakidou et al. / Chemosphere: Global Change Science 1 (1999) 263±282

the atmosphere. NOx, which is also a pollutant,and in particular NO and NO2, controls O3

chemical production and destruction in the tro-posphere. CO is a chemically important gas withprimary and secondary emissions in the atmo-sphere. Depending on the ambient NOx mixingratios, CO oxidation by hydroxyl (OH) radicalsforming hydrogen peroxy radicals (HO2) enhances(high NOx) or depletes (low NOx) ozone in thetroposphere (Crutzen, 1994). CO changes havefeedbacks into the abundance of CH4 through thealteration of OH (Prather, 1996). Thus, althoughCO itself is not a signi®cant greenhouse gas,changes in its concentration a�ect ozone and CH4,both signi®cant greenhouse gases (Daniel andSolomon, 1998).

E�ort has already started by WMO/IPCC in1994 (Stordal et al., 1995; Olson et al., 1997) toevaluate the capability of the chemical models tosimulate O3, NOx, HOx and VOC chemistry. Thise�ort is continued by the Global IntegrationModelling (GIM) Activity within the InternationalGlobal Atmospheric Chemistry (IGAC) Project ofIGBP in 1996±1997, and focuses on the capabilityof the global 3-D chemistry/transport models usedfor global chemical and climate simulations tocalculate the O3 budget. Ground-based and in situobservations from several regions of the globe areused for validation of the model results. Thepresent paper focuses on the model capability tosimulate carbon monoxide since it is a key com-pound for tropospheric chemistry. Moreover,carbon monoxide has a more simple and betterunderstood chemistry and better documented di-rect sources than the shorter lived NOx and VOCs.It is therefore a good tool to evaluate the chemistryand the transport characteristics of photochemicalmodels. Ozone simulations are the topic of anotherpaper in preparation.

2. The GIM/IGAC tropospheric ozone chemistry/

transport 3-D models intercomparison exercise

The objective of the Tropospheric OzoneGlobal Model Intercomparison Exercise perform-ed in 1997 was to systematically evaluate the ca-pabilities of the current generation of 3-D global

models to simulate tropospheric O3 and otherchemical compounds, and to identify key areas ofuncertainty in our understanding of the tropo-spheric O3 budget. This exercise has been organ-ised by GIM Activity within IGAC Project. Thestrategy was to investigate the consistency of thepredicted results between models, which use a vari-ety of inputs and mechanisms, to compare modelresults to the real atmosphere and identify areas forfurther research in the close future.

To conduct this exercise, several modellinggroups were asked to provide the Ôbest caseÕ outputof their models, and speci®cally:· O3, CO and NOx (NO + NO2) monthly mean

mixing ratios at surface, 500 and 200 mbar inJanuary and July;

· surface monthly mean concentrations at Bar-row, Mace Head, Hohenpeissenberg, MaunaLoa, Barbados Samoa, Cape Grim, Cape Pointfor O3; and at Barrow, Mace Head, MaunaLoa, Samoa, Cape Grim for CO;

· monthly varying vertical O3 pro®les at selectedozone sonde stations (Resolute, Hohenpeissen-berg, Wallops island, Bermudas, Hilo, Natal,Samoa, Lauder, Syowa, S. Pole);

· seasonal and annual mean terms of O3 budgetup to 300 mbar for the 4 seasons annual meanCH4 lifetime and mean hemispheric and globalburdens.

3. Description of the chemistry transport models

The 12 global 3-D Chemistry Transport Modelsthat participated at this exercise are listed in Table1 together with the contributing scientists and keyreferences. All these models are global 3-D models.Three of them are climatological models, i.e., theysimulate atmospheric transport based on pre-cal-culated or observed monthly mean winds andtemperatures. One of the models is a general cir-culation model (ECHAM) and the remainingmodels are chemistry/transport models using pre-calculated/observed meteorology provided every4±12 h. This meteorological input is derived fromgeneral circulation models or weather predictionmodels. Models also di�er in the parameterisationof the main processes controlling chemical tracer

M. Kanakidou et al. / Chemosphere: Global Change Science 1 (1999) 263±282 265

budgets, i.e. transport by advection, di�usion andconvection, chemistry (homogeneous and hetero-geneous), wet and dry deposition and emission oftrace compounds (including CO) by natural andanthropogenic sources. Detailed model descrip-tions can be found in the literature (see referencesin Table 1).

Some of the model characteristics together withthe annual global emissions of ozone precursors:NOx, CO, CH4 and NMVOC used for the Ôbestcase present dayÕ simulations are summarised inTables 2 and 3, respectively. Note that the directannual amount CO emissions adopted in eachmodel can be signi®cantly di�erent: Total primarysources vary from 1040 to 2362 Tg-CO/yr amongthe various models, as shown in Table 3. Theseemissions are primarily associated with techno-logical sources, in the northern hemisphere, andwith biomass burning, in the tropics. In somemodels, those not explicitly considering NMVOCchemistry, CO pseudo-primary emissions are usedto represent the photochemical source fromNMVOC oxidation. However, the major reasonfor the large range of CO emissions is linked to theuncertainties in the global CO emission estimatesdeduced from smaller scale observations. The at-mospheric dynamics (Table 2a) and the spatial andtemporal resolutions of each model are di�erent,implying di�erences in the propagation of theemissions in each model. Finally, the chemicalschemes adopted for NMVOC chemistry repre-

sentation (Table 2b) and consequently the leveland distributions of the hydroxyl (OH) radical andthe photochemical production of CO also di�er ineach model. In addition, one should note that thenumerical solvers used to integrate the chemicaldi�erential equations are di�erent between models.

One model (GFDL) which participated in theoverall intercomparison exercise, uses paramete-rised chemistry and imposes pre-calculated COdistributions. Therefore the results and the char-acteristics of this particular model will not beconsidered in the present paper. All other modelsprovided:· the calculated CO monthly mean mixing ratios

from the best case simulation at the EarthÕs sur-face, 500 and 200 mbar in January and July.

· the CO monthly mean concentrations (andstandard deviation) at 5 selected surface sta-tions (or the grid points of the models closestto these stations) where observations are avail-able.

4. Results and discussion

The 3-D CO distributions calculated by the 11models show important di�erences as demon-strated in Figs. 1±4 for January and Figs. 5±8 forJuly ((a) at surface and (b) at 500 hPa). CalculatedCO distributions re¯ect CO direct emissions,photochemical production by NMVOC and CH4

Table 1

3-D models that participated at the Tropospheric Ozone (O3) Global Integration Modelling (GIM) intercomparison exercise (3-D

models)

IMAGES J.-F. Muller, G. Brasseur, C. Granier Muller and Brasseur, 1995

GFDL H. Levy Kasibhatla et al., 1996

HARVARD Y. Wang, D. Jacob Wang et al., 1998a,b,c

ECHAM G.-J. Roelofs Roelofs and Lelieveld, 1995, 1997

TM3 F. Dentener, S. Houweling Houweling et al., 1998

IMAU3 M. Krol Krol and Weele, 1997

CTMK W.M.F. Wauben Wauben et al., 1997, 1998

MATCH M.G. Lawrence, P.J. Crutzen Lawrence, 1996, Lawrence et al., 1995, Lawrence

and Crutzen, 1998

MOGUNTIA N. Poisson, M. Kanakidou Poisson, 1997, Poisson et al, 1999

MOZART D.A. Hauglustaine, G.P. Brasseur, S. Walter Brasseur et al., 1996, 1998

UKMETO R.G. Derwent, C.E. Johnson, W.J. Collins, D.S.

Stevenson

Collins et al., 1997

UIO T.K. Berntsen, I.S.A. Isaksen Bernsten and Isaksen, 1997


Tab

le2

MO

DE

LIM

AU

3IM

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HA

R-

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UK

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ME

TO

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MC

TM

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97

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97

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97

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97

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97

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Sep

tem

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97

Marc

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Oct

ob

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Ap

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97

(a)

Mo

del

des

crip

tio

n

GC

M/C

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(Y/N

)N

ON

ON

ON

OY

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NO

NO

NO

NO

NO

NO

Syn

op

tic

NO

NO

YE

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Up

per

bo

un

da

ry1

00

mb

50

mb

10

mb

100

mb

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10

mb

Nu

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10

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15

28

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10

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Ch

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2h

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late

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of

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15

days

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Tab

le3

Em

issi

on

so

fo

zon

ep

recu

rso

rsu

sed

inth

eC

TM

s

MO

DE

LIM

-

AU

3

IMA

G-

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(I)

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R-

VA

RD

UK

-

ME

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CT

MK

MA

T-

CH

MO

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ZA

RT

TM

3M

O-

GU

N-

TIA

UIO

Ran

ge

CO

dir

ect

emis

sio

ns

(Tg

-CO

/yr)

13

50

12

83

1040

1168

1250

1427

1680

a1218

1110

1185

2362

b1040±2362

Fu

elco

nsu

mp

tio

n,

ind

ust

ry5

40

47

1520

303

450

383

560

a382

400

500

893

Bio

ma

ssb

urn

ing

49

06

26

520

725

700

716

920

a662

450

515

714

Veg

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n/s

oil

28

01

66

NO

100

100

166

150

a162

75

c130

387

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an

s4

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0N

O40

162

50

13

40

40

378

CO

seco

nd

ary

sou

rce

10

63

14

59

1090

1320

840

dN

C884

d;a

NC

1350

1235

NC

840±1459

CO

surf

ace

loss

NC

26

4N

O270

NO

NC

233

NC

320

330

NC

0±330

NO

emis

sio

ns

(Tg

-N/y

r)3

7.1

36

.85

45

47.5

37.5

47.6

939

39.9

538.4

38

42.7

236.8

±47.7

Fu

elco

nsu

mp

tio

n,

ind

ust

ry2

1(G

)2

121

(G)

21

(G)

21

22

21.3

(G)

21.4

(G)

22

(G)

21.3

(G)

21

(G)

Bio

ma

ssb

urn

ing

6e

4.9

12

96

e5.3

e8.5

e4.5

5e

6e

4.4

So

il4

.85

.56

12

f5.5

3.9

5.5

f6.6

5.5

f5.5

12.2

g

Lig

htn

ing

4.7

53

5h

55

2.2

h7

5.3

55.5

Air

cra

ft0

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0.5

0.5

NO

0.8

50.5

0.4

50.6

NO

0.5

2

Tra

nsp

ort

fro

mst

rato

sph

ere

NC

NC

0.5

i0.4

5N

C0.6

41.0

NC

NC

0.2

NC

CH

4em

issi

on

s(T

g-C

H4/y

r)5

23

48

5460

j452

kj

540

j469

j540

304

304±540

NM

VO

Cem

issi

on

s(T

g-C

/yr)

NO

l1

25

4680

442.4

650

NO

NO

741

520

748.8

442±1254

An

thro

po

gen

icN

O1

75

80

58.4

330

(CO

)

NO

225

a

(CO

)

125

(EG

)120

114

m

Na

tura

lN

O1

07

9600

384

320

(CO

)

NO

350

a

(CO

)

516

400

400

+-

150

634.8

m

a8

80

Tg

CO

/yr

an

thro

po

gen

icb

urn

ing

sa

nd

40

Tg

CO

/yr

wil

d®

res;

225

Tg

CO

/yfr

om

oxid

ati

on

of

NM

VO

Cfr

om

tech

no

logic

al

sou

rces

;350

Tg-C

O/y

fro

mn

atu

ral

NM

VO

Co

xid

ati

on

.b

Glo

ba

lv

alu

efo

rmH

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gh

(19

91)

an

dg

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rap

hic

al

dis

trib

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on

as

for

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w3

00

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eD

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ibu

tio

nta

ken

fro

mH

ao

eta

l.(1

99

0).

fY

ien

ger

an

dL

evy

(19

95).

gB

iom

ass

bu

rnin

g+

soil

s.h

Pri

cea

nd

Rin

d(1

99

2);

Law

ren

ceet

al.

(19

95).

iS

um

of

NO

xa

nd

HN

O3.

jP

resc

rib

edsu

rface

con

cen

tra

tio

ns

for

CH

4a

cco

rdin

gto

Fu

ng

etal.

(1991).

kP

resc

rib

edsu

rface

con

cen

tra

tio

ns

of

17

72

pp

bv

NH

an

do

f1680

pp

bv

SH

.lIn

clu

des

on

lyC

2H

6a

nd

C6H

8ch

emis

try

,a

lso

dir

ect

CH

2O

emis

sio

ns

300

Tg/y

r.

(G)

GE

IAin

ven

tory

;(E

G)

ED

GA

R/G

EIA

inv

ento

ry;

(I)

Mu

ller

an

dB

rass

eur

(1995),

up

date

d;

Tu

rman

an

dE

dgan

;(N

C)

no

tca

lcu

late

d;

(NO

)n

ot

use

d;

(CO

)em

issi

on

s

intr

od

uce

din

the

form

of

carb

on

mo

no

xid

e;m

An

thro

po

gen

icem

issi

on

sa

cco

rdin

gto

EP

A;

na

tura

lem

issi

on

sacc

ord

ing

toIs

ak

sen

an

dH

ov

(1987).


Fig. 1. CO concentrations (ppbv) as computed by the eleven 3-dimensional global models for the month of January at the surface

(1000 mbar).


Fig. 2. CO concentrations (ppbv) as computed by the eleven 3-dimensional global models for the month of January at the surface

(continue).


Fig. 3. CO concentrations (ppbv) as computed by the eleven 3-dimensional global models for the month of July at the surface (1000

mbar).


Fig. 4. CO concentrations (ppbv) as computed by the eleven 3-dimensional global models for the month of July at the surface

(continue).


Fig. 5. CO concentrations (ppbv) as computed by the eleven 3-dimensional global models for the month of January at 500 mbar.


Fig. 6. CO concentrations (ppbv) as computed by the eleven 3-dimensional global models for the month of January at 500 mbar

(continue).


Fig. 7. CO concentrations (ppbv) as computed by the eleven 3-dimensional global models for the month of July at 500 mbar.


Fig. 8. CO concentrations (ppbv) as computed by the eleven 3-dimensional global models for the month of July at 500 mbar (con-

tinue).


oxidation and destruction by the OH radical, aswell as advective and convective transport awayfrom the source regions and losses to the surface.

4.1. Surface CO distributions

Maximum surface CO concentrations are cal-culated by all models over technologically devel-oped areas of the world and over biomass burningregions. The calculated maximum CO concentra-tions depend on the model resolution. The low-resolution models are arti®cially more di�usivethan the higher resolution models by splittingemissions in their relatively large grid boxes. Fur-thermore, the vertical resolution of the surfacelayers is also di�erent between models and rangesbetween 50 and 400 m. The models with theshallowest surface layers generally show the high-est concentrations. Convective redistributions ofemissions, chemistry and surface loss strongly af-fect the ultimate surface concentrations.

Comparisons between Figs. 1 and 2 and Figs. 3and 4 (surface for January and July, respectively)show that lower CO concentrations are calculatedin July than in January in the northern hemisphere(NH) due to the strong photochemical sink of COduring NH summertime although the photo-chemical production of CO is also enhanced dur-ing that season. This is not the case in biomassburning areas, where CO concentrations remainhigh although their maximum values are shiftedfrom the NH tropics in January to the southernhemisphere (SH) tropics in July. Almost all modelscapture this pattern. However, signi®cant di�er-ences exist in the magnitude of CO seasonal cycleand absolute levels as shown in Figs. 1±4. Thesedi�erences will be further discussed when com-paring model results to observations.

Sharper interhemispheric gradients are calcu-lated for NH winter since CO increases in the NHdue to technological emissions (about 95% in thishemisphere) and relatively low photochemical de-struction under wintertime conditions. The longerlifetime of CO during winter than during summerallows CO to be transported over long distances,so that large concentrations of CO are calculatedduring winter downwind of the CO source areas.On the contrary, in July, high CO concentrations

are more restricted to source regions. These pat-terns are calculated by all models.

All models capture the general pattern of COlinked to the distribution of its surface sources andphotochemical production and destruction of CO.However, the calculated CO levels vary signi®-cantly between models due to (i) the annualamount of emissions adopted by each model (Ta-ble 3) as discussed above, (ii) the formulation ofmodel dynamics (Table 2a), (iii) the spatial reso-lution, and (iv) the chemical schemes (Table 2b)resulting in signi®cant di�erences in the hydroxyl(OH) radical distributions calculated by the mod-els. As a consequence the photochemical produc-tion of CO from VOCs and its photochemical sinkby reaction with OH radical di�er signi®cantlyfrom model to model (Table 3). Di�erences in theOH levels are re¯ected (i) on the photochemicalsource of CO that varies between 840 Tg-CO/yrand 1459 Tg-CO/yr and (ii) on the methane (CH4)lifetimes due to reaction with OH radical in thetroposphere below 300 mbar or the closest modellevel, calculated by the models to vary between 6.6and 10 yr (median 7.95 yr). Di�erences in thecalculated CO surface distributions can be partlyattributed to the surface losses of CO neglected inseveral models (see Table 3). Daniel and Solomon(1998) show that accurate assessment of thisquantity is important at a climatic time-scale.However, as shown in Table 3, this loss couldcontribute about 10% to the CO removal in theatmosphere, which is in the range of the uncer-tainties of CO sources.

4.2. Middle troposphere CO distributions

The calculated CO distributions in the middleand upper troposphere are of particular interest (i)because they a�ect the OH maximum in the middletropical troposphere and thus CH4 levels and (ii)they modify ozone concentrations in the uppertroposphere where ozone radiative impact maxi-mises. These CO distributions are even more dif-ferent between the models than the surfacedistributions since they strongly depend on thetransport of CO in the atmosphere by advection,di�usion and convection. In particular di�erencesin the convection parameterisation result in dif-


ferent patterns in the middle Figs. 5 and 6 andFigs. 7 and 8 and upper tropical troposphere (notshown).

In the middle troposphere at 500 mbar Figs. 5and 6 and Figs. 7 and 8 the detailed signature ofthe surface sources of CO is lost since transportzonally mixes CO. The sharp interhemisphericgradient of CO remains obvious in the free tro-posphere in January. This pattern is almost lost inJuly in most models both in the boundary layerand the free troposphere, due to the relativelyshort lifetime of CO in NH summer that prohibitse�cient CO transport far from its technologicalsources. Simultaneously in the SH, the CO mixingratio is more uniform (wintertime conditions).

A notable feature of most models is that theyrepresent the propagation of the CO biomassburning sources to the free troposphere by deepconvection, which is very intense in the tropics.However, this phenomenon is represented verydi�erently by the various models, re¯ecting thedi�erences in the model parameterisation of theconvection.

In general, models calculate an important de-crease in CO concentrations between the boun-dary layer and the middle troposphere, themagnitude of which depends on the season, thegeographical location and the model. Thus, theCO concentrations at 500 mbar are 0.2±0.8 timesthose calculated for the boundary layer with the

lowest ratios calculated over continental intensivesource areas. The reductions in CO between thesurface and the 500 mbar over oceans range from0.6 to 1.0 (no reduction). At some tropical loca-tions models calculate higher CO concentrationsin the middle troposphere than in the boundarylayer, re¯ecting strong vertical convective trans-port and possibly di�erences in boundary layersinks of OH.

4.3. Comparison of model results to observations

The observed monthly averages between dif-ferent years (Novelli et al., 1992) have been com-pared to the calculated seasonal variation ofsurface CO concentrations at ®ve surface stations:Barrow (71°N, 157°W), Mace Head (53°N, 10°W),Mauna Loa (20°N, 155°W), Samoa (14°S, 171°W)and Cape Grim (41°S, 145°E). Table 4 depicts theannual mean calculated and observed CO con-centrations at these ®ve stations.

To compare the seasonal trends in the modelsafter removing the di�erences in annual meansbetween the models shown in Table 4, in Figs. 9and 10 the seasonal variation of the normalised tothe annual mean CO concentrations as calculatedby the eleven models and as observed at the ®vesurface stations have been plotted. These normal-ised concentrations correspond to the ratio of thedi�erence between the monthly mean concentra-

Table 4

Calculated and observed annual mean CO concentrations (in ppbv) at ®ve surface stations

Model Barrow Mace Head Mauna Loa Samoa Cape Grim

IMAU3 117.5 114.2 66.0 54.0 59.8

IMAGES 114.4 127.4 76.1 50.3 62.9

HARVARD 121.5 130.8 89.5 58.8 62.2

UKMETO 101.0 118.3 73.9 57.8 66.0

ECHAM 153.6 162.3 92.2 68.1 79.9

CTMK 111.4 107.7 67.3 53.2 58.5

MATCH 149.1 222.8 77.7 59.8 71.5

MOZART 160.2 150.8 78.4 58.1 68.9

TM3 124.6 184.7 79.5 51.0 61.5

MOGUNTIA 135.3 161.8 90.4 54.5 69.2

UIO 131.6 130.2 73.7 60.9 74.1

Observations 148.3 133.1 96.3 62.7 53.4

(138.6±148.3) (128.8±137.4) (89.6±103.0) (57.6±67.7) (49.9±57.0)


tion of CO from its annual mean value divided bythe annual mean.

It is notable that the di�erences between modelsare larger than the inter-annual variability associ-

ated with the observations. Most models tend tounderestimate CO levels at Barrow (NH) (Table 4)but capture the seasonal trend within 50% (Fig. 9),which indirectly is an indication for OH levels. At

Fig. 9. Seasonal variation of the surface CO concentrations normalised to their annual mean: Comparison between model results and

observations at Barrow, Mace Head and Mauna Loa.


Mace Head, an NH coastal station, two modelslargely overestimate the CO levels whereas theremaining are within 30% of the observed values.More models have di�culties in capturing theobserved seasonal trend at this location (Fig. 9).At Mauna Loa station, more representative of thefree troposphere, all models underestimate COlevels by 4±30 ppbv (i.e. 6±30%, Table 4) depend-ing on the model and overestimate the CO sea-sonal amplitude in particular in January (Fig. 9).At Samoa (SH) most models (only one exception)underestimate the CO levels by 3±20% and they donot capture its seasonal cycle. On the contrary, atCape Grim (SH) they overestimate the CO con-centrations by 10±50% as well as the seasonaltrend (Fig. 9).

These patterns can be related to (i) the adoptedemissions (see Table 3) and emission distributions

in time and space, (ii) to the calculated di�erencesin OH radical concentrations between the NH andthe SH which are also re¯ected in the lifetime ofmethane (CH4). As a matter of fact, all modelscalculate the photochemical CH4 lifetime in thetroposphere up to 300 mbar or the closest modellevel, to be higher in the SH (median 9.2 yr) thanin the NH (median 7.2 yr). This implies a lowerconcentration of OH in the SH than in the NH.However, the opposite trend in OH levels is ex-pected from the lower CO and CH4 concentra-tions in the SH as well as from 14COmeasurements (Warneck, 1988; Brenninkmeijer etal., 1992) which are indirect indications for OHlevels. This could indicate that NH industrialemissions might be underestimated in the models,in particular those related to biofuel and wasteburning.

Fig. 10. Seasonal variation of the surface CO concentrations normalised to their annual mean: Comparison between model results and

observations at Samoa and Cape Grim.


The overestimate of CO concentrations at CapeGrim may also result from the fact that the ob-servations depicted in this ®gure are clean sectordata. The model results contain all sectors andinclude air masses originating the Australian con-tinent. They are therefore expected to be higherthan the clean sector observations. Sector selectionis also performed at the Mace Head station data,where some models tend to overestimate CO levelsand the range of model results largely exceeds thatof the observations.

5. Conclusions

The intercomparison of eleven 3-D globalchemistry/transport models shows signi®cant dif-ferences between the models although all of themcapture the general patterns in the observed globaldistribution of CO. The comparison betweenmodel results and observations at selected stationsallows the evaluation of the model deviations fromthe observed annual mean CO concentrations atabout �50%, whereas di�erences between modelsare more important since models can deviate fromtheir annual mean values by up to 80%.

More accurate emission inventories of CO andother ozone precursors, measurements of theglobal 3-D distribution of tropospheric CO, andbetter assessment of the chemical processes deter-mining the oxidizing capacity of the troposphereand especially governing the OH levels could helpimproving model simulations and reducing relateduncertainties and model deviations from reality.

Unfortunately within the present exercise theimportance of emission inventories and chemicalprocesses cannot be separated from other contrib-uting factors like transport and deposition di�er-ences. In future intercomparison exercises, modelsshould preferably use the same emission inventor-ies as input, thereby ruling out di�erences betweeninventories as cause of di�erences between models.

Acknowledgements

We thank Elena Abilova from the University ofNorway for centralising and drawing the 2-D plots

of CO, Ra� Semercyan for compiling the stationdata in 1997. The constructive comments of ananonymous reviewer are kindly acknowledged. Weexpress gratitude to the European Union, theNASA and the PNCA/INSU of the CNRS forsupporting this exercise. MK expresses gratitudeto IDRIS for computing facilities.

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Maria Kanakidou is Assistant Professor of EnvironmentalChemistry at the Department of Chemistry of the University ofCrete, Heraklion, Greece. She has been co-convenor of theGlobal Integration Modeling (GIM) activity of IGAC of theIGPB since summer 1996 and a member of the Commission onAtmospheric Chemistry and Global Pollution (CACGP) of theIAMAS since summer 1998. In the past, she has worked as aresearch scientist at the Max±Planck-Institute for Chemistry inMainz, Germany, and at the CNRS in Gif-sur-Yvette, France.Her research concerns atmospheric chemistry and physics witha focus on tropospheric chemistry and climate changes due tohuman activities and on gas/particle interactions (http://ecpl.chemistry.uch.gr/cv/kanakidou.html).


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