Global budget of methanol Constraints from atmospheric observations

Global budget of methanol:Constraints from atmospheric observations

Daniel J.Jacob,1Brendan D.Field,1Qinbin Li,1,2Donald R.Blake,3Joost de Gouw,4

Carsten Warneke,4Armin Hansel,5Armin Wisthaler,5Hanwant B.Singh,6

and A.Guenther7

Received28June2004;revised5January2005;accepted2February2005;published26April2005.

[1]We use a global three-dimensional model simulation of atmospheric methanol to

examine the consistency between observed atmospheric concentrations and current

understanding of sources and sinks.Global sources in the model include128Tg yrà1from

plant growth,38Tg yrà1from atmospheric reactions of CH3O2with itself and other

organic peroxy radicals,23Tg yrà1from plant decay,13Tg yrà1from biomass burning

and biofuels,and4Tg yrà1from vehicles and industry.The plant growth source is a

factor of3higher for young than from mature leaves.The atmospheric lifetime of

methanol in the model is7days;gas-phase oxidation by OH accounts for63%of the

global sink,dry deposition to land26%,wet deposition6%,uptake by the ocean5%,and

aqueous-phase oxidation in clouds less than1%.The resulting simulation of atmospheric concentrations is generally unbiased in the Northern Hemisphere and reproduces the

observed correlations of methanol with acetone,HCN,and CO in Asian outflow.

Accounting for decreasing emission from leaves as they age is necessary to reproduce

the observed seasonal variation of methanol concentrations at northern midlatitudes.

The main model discrepancy is over the South Pacific,where simulated concentrations are

a factor of2too low.Atmospheric production from the CH3O2self-reaction is the

dominant model source in this region.A factor of2increase in this source(to50–100Tg

yrà1)would largely correct the discrepancy and appears consistent with independent

constraints on CH3O2concentrations.Our resulting best estimate of the global source of

methanol is240Tg yrà1.More observations of methanol concentrations and fluxes are

needed over tropical continents.Better knowledge is needed of CH3O2concentrations in

the remote troposphere and of the underlying organic chemistry.

Citation:Jacob,D.J.,B.D.Field,Q.Li,D.R.Blake,J.de Gouw,C.Warneke,A.Hansel,A.Wisthaler,H.B.Singh,and A.Guenther(2005),Global budget of methanol:Constraints from atmospheric observations,J.Geophys.Res.,110,D08303, doi:10.1029/2004JD005172.

1.Introduction

[2]Methanol is the second most abundant organic gas in the atmosphere after methane.It is present at typical con-centrations of1–10ppbv in the continental boundary layer and0.1–1ppbv in the remote troposphere[Singh et al., 1995;Heikes et al.,2002].It is a significant atmospheric source of formaldehyde[Riemer et al.,1998;Palmer et al., 2003a]and CO(B.N.Duncan et al.,Global model study of the interannual variability and trends of carbon monoxide (1988–1997):1.Model formulation,evaluation,and sensi-tivity,submitted to Journal of Geophysical Research,2004, hereinafter referred to as Duncan et al.,submitted manu-script,2004),as well as a minor term in the carbon cycle [Heikes et al.,2002]and in the global budgets of tropo-spheric ozone and OH[Tie et al.,2003].Most of the observations of atmospheric methanol concentrations con-sist of short-term records in surface air[Heikes et al.,2002]. Recent aircraft missions have added a new dimension to our knowledge of methanol concentrations in the global tropo-sphere[Singh et al.,2000,2001,2003a,2004;Lelieveld et al.,2002].We use here a global3-D chemical transport model(CTM)to examine the constraints that these aircraft observations provide on current understanding of methanol sources and sinks.

[3]Global budgets of atmospheric methanol have been presented previously by Singh et al.[2000],Galbally and Kristine[2002],Heikes et al.[2002],Tie et al.[2003],and von Kuhlmann et al.[2003a,2003b].They are summarized in Table1.Plant growth is the principal source.Additional

JOURNAL OF GEOPHYSICAL RESEARCH,VOL.110,D08303,doi:10.1029/2004JD005172,2005 1Division of Engineering and Applied Science,Harvard University,

Cambridge,Massachusetts,USA.

2Now at Jet Propulsion Laboratory,Pasadena,California,USA.

3Department of Chemistry,University of California,Irvine,California,

USA.

4NOAA Aeronomy Laboratory,Boulder,Colorado,USA.

5Institute of Ion Physics,University of Innsbruck,Innsbruck,Austria.

6NASA Ames Research Center,Moffett Field,California,USA.

7Atmospheric Chemistry Division,National Center for Atmospheric

Research,Boulder,Colorado,USA.

Copyright2005by the American Geophysical Union.

0148-0227/05/2004JD005172$09.00

sources include plant decay,biomass burning,atmospheric oxidation of methane and other volatile organic com-pounds(VOCs),vehicles,and industrial activities.Chemi-cal loss by oxidation by OH results in an atmospheric lifetime for methanol of about10days.Deposition, exchange with the ocean,and heterogeneous reactions in aerosols and clouds are additional sinks but are poorly constrained.

[4]Large discrepancies are apparent between the different budgets of Table1.Singh et al.[2000]found a factor of three imbalance between their independently estimated sources and sinks,implying a missing sink.Heikes et al. [2002]achieved closure between sources and sinks in their budget but with large uncertainties.Their estimated plant growth source is four times larger than that of Singh et al. [2000]and is partly compensated by an assumed large deposition sink.Galbally and Kirstine[2002]enforced closure by simulating methanol in a six-box global model of the atmosphere-ocean system.Tie et al.[2003]conducted global3-D model simulations of methanol with two esti-mates for the plant growth source,a high value of312Tg yrà1from their bottom-up source inventory and a low value reduced by a factor of3to match that of Galbally and Kirstine [2002].They presented order-of-magnitude comparisons to observations but did not conclude as to the implied constraints on the methanol budget.Von Kuhlmann et al.[2003b] evaluated a global3-D model simulation of methanol with observations from two aircraft campaigns(SONEX and PEM-Tropics B).We present here a more detailed evaluation with a large ensemble of measurements from aircraft and ships(Figure1).

2.Model Description

2.1.General Description

[5]We conducted a1-year simulation of atmospheric methanol for2001with the GEOS-CHEM CTM v4.32 (https://www.wendangku.net/doc/5912081097.html,/chemistry/trop/geos)[Bey et al.,2001].The model is driven by GEOS-3assimilated meteorological observations(including convective mass fluxes)from the NASA Global Modeling and Assimilation Office.The GEOS-3data have a temporal resolution of 6hours(3hours for mixing depths and surface quantities), a horizontal resolution of1°?1°,and48layers in the vertical.We average them here over a4°latitude?5°longitude grid for input to GEOS-CHEM.

[6]The simulation is conducted for18months starting from low concentrations in July2000.The first six months are used for initialization and we focus on the1-year results for2001.The methanol budget in the model is given in Table1and details are given below.Methanol originating from each source in Table1is transported as a separate species,for a total of5species transported in the model.The model losses of methanol are linear,so that the sum of transported species adds up to the total methanol concen-tration within a few percent(nonlinearity in the semi-Lagrangian advection algorithm prevents perfect closure). We use the model results to compare to observations not just

Table1.Global Atmospheric Budgets of Methanol Reported in the Literature

Reference Singh et al.

[2000]a

Heikes et al.

[2002]a

Galbally and Kirstine

[2002]a

Tie et al.

[2003]b

von Kuhlmann et al.

[2003a,2003b]This Work c

Sources(Tg yrà1)

Plant growth75(50–125)280(50–>280)100(37–212)104–31277128(100–160) Plant decay20(10–40)20(10–40)13(5–31)23(5–40) Biomass burning d6(3–17)12(2–32)13(6–19)1513(10–20) Urban e3(2–4)8(5–11)4(3–5)24(1–10) Atmospheric production f18(12–24)30(18–30)19(14–24)312838g(50–100) Total source122(75–210)350(90–490)149(83–260)135–343123206h(170–330)

Sinks(Tg yrà1)

Gas-phase oxidation by OH100(25–150)109(60–203)59–14977i129j

In-cloud oxidation by OH(aq)10(5–20)5(2–15)<1

Dry deposition(land)70(35–210)24(11–43)32–853755

Ocean uptake k50(à20–150)0.3(0.2–0.6)10

Wet deposition10(4–36)11(5–20)16–50912

Total sinks40–50l270(160–570)149(82–273)107–284123206 Atmospheric inventory(Tg) 3.9 3.4 1.9–4.7 4.0 Atmospheric lifetime(days)9(5)m8127(5–10)

a Best estimates and ranges in parentheses.

b The authors present two budgets,one with their best estimate of the plant growth source(312Tg yrà1),and one with that source reduced by a factor of3 to match the estimate of Galbally and Kirstine[2002].The numbers given here are the range defined by these two budgets.

c Values use

d in th

e GEOS-CHEM CTM simulation presented in the text,with best estimates o

f ranges in parentheses.Singh et al.[2004]reported a preliminary version of this budget in their study comparin

g GEOS-CHEM model results to TRACE-P aircraft observations(see text).

d Including biofuel use.

e Including vehicles,solvent use,and manufacturing.

f From reactions of CH

3O2with organic peroxy radicals.The self-reaction CH3O2+CH3O2accounts for85%of this source on a global basis according to

our work.

g Although a value of38Tg yrà1is used in our simulation,comparison to methanol observations over the remote oceans suggests that this source is too low by a factor of two(see text).Our resulting best estimate for the range is50–100Tg yrà1.

h After doubling of the source from atmospheric production(see footnote g),our best estimate for the total source is240Tg yrà1.

i Von Kuhlmann et al.[2003b]give relative contributions from each sink,which we convert here to absolue values on the basis of their global source. j Including3Tg yrà1in the stratosphere.

k Net ocean uptake;the ocean is both a source and a sink of methanol[Heikes et al.,2002].

l From combined effects of gas-phase oxidation by OH and dry deposition(deposition velocity of0.1cm sà1).

m Heikes et al.[2002]give a lifetime of9days in the text;however their global budget table implies a lifetime of5days.

from 2001but from other years as well,assuming that interannual variability is a relatively small source of error.[7]Correlations of the methanol simulation with consis-tent simulations of CO,HCN,and acetone for 2001are also presented for comparison with ship and aircraft observa-tions.The CO and HCN simulations are as described by Li et al.[2003].The acetone simulation is as described by Jacob et al.[2002]but without an ocean source (for reasons to be discussed in section 4.2).

2.2.Sources of Methanol 2.2.1.Plant Growth

[8]We use the plant physiology model of Galbally and Kirstine [2002]in which methanol emission scales as net primary productivity (NPP)with emission factors per unit carbon of 0.020%for grasses and 0.011%for other plants.This offers a process-based parameterization for global mapping of the methanol source from plant growth.We apply the emission factors to a monthly NPP database from the CASA 2biosphere model with 1°?1°spatial resolution [Potter et al.,1993;Randerson et al.,1997].The global NPP in that database is 17Pg C yr à1for grasslands and 41Pg C yr à1for other plants.The resulting methanol emission is 128Tg yr àhttps://www.wendangku.net/doc/5912081097.html,parison to previous studies in Table 1would suggest at least a factor of 2uncertainty on this global source but our simulation of atmospheric obser-vations implies a narrower range,as discussed later.

[9]The Galbally and Kirstine [2002]relationship of methanol emission to NPP has yet to be tested with field observations,and NPP estimates are themselves subject to substantial uncertainty [Karl et al.,2004].Our initial

simulations used monthly mean NPP values to distribute methanol emissions seasonally,but there resulted large model overestimates of observed methanol concentrations at northern midlatitudes in late summer and https://www.wendangku.net/doc/5912081097.html,boratory and field data indicate in fact that methanol emissions from young leaves are a factor of 2–3higher than from mature leaves [McDonald and Fall ,1993;Nemecek-Marshall et al.,1995;Karl et al.,2003].We fit these results by scaling our monthly mean NPP-based emissions with the following parameterization adapted from A.Guenther:

a i ?

b 1t2max L i àL i à1

L i

;0

e1T

where L i is the local leaf area index for month i ,a i is the monthly scaling factor,and b is a normalizing factor such

that

P

12i ?1

a i =12for each grid square.The normalization ensures consistency with the Galbally and Kirstine [2002]

NPP-based algorithm on a yearly basis.Leaf area indices in the model are computed monthly as a function of ecosystem type,NPP,and global vegetation index (GVI),following the algorithm of Guenther et al.[1995]as implemented by Wang et al.[1998].The resulting methanol emissions at midlatitudes peak in spring,when they may be as much as a factor of three larger than in summer.Within a given month we distribute the methanol source evenly over the daytime hours,assuming zero emission from green plants at night [Nemecek-Marshall et al.,1995;Schade and Goldstein ,2001;Warneke et al.,

2002].

Figure 1.Atmospheric observations of methanol used for comparison with model results.Ship cruises indicated by lines include INDOEX over the Indian Ocean in March 1999[Wisthaler et al.,2002]and AOE-2001over the Arctic Ocean in July August 2001(A.Hansel and A.Wisthaler,unpublished data,2001).Aircraft missions indicated by lines include TRACE-P over the North Pacific in March–April 2001[Singh et al.,2003a,2004]and TOPSE over the North American Arctic in February–May 2000(D.R.Blake,unpublished data,2000).Additional aircraft missions indicated by boxes include SONEX over the North Atlantic in October–November 1997(regions 1–2)[Singh et al.,2000],MINOS over the eastern Mediterranean in August 2001(region 3)[Lelieveld et al.,2002],ITCT 2K2over the northeast Pacific in April–May 2002(region 4)[Nowak et al.,2004],and PEM-Tropics B over the South Pacific in February–March 1999(regions 5–9)[Singh et al.,2001].The symbol labeled ‘‘10’’indicates the locations of Innsbruck (Austria)[Holzinger et al.,2001]and Zugspitze (Germany,2650m ASL)(A.Hansel and A.Wisthaler,unpublished data,2003),both at (47N,11E).

2.2.2.Plant Decay

[10]Warneke et al.[1999]reported the abiotic emission of methanol from decaying plant matter with an emission factor of3–5?10à4g per g of C oxidized.We apply this emission factor to monthly mean heterotrophic respiration rates with1°?1°resolution from the CASA2model.The global heterotrophic respiration rate is58Pg C yrà1and the resulting methanol source is17–29Tg yrà1(best estimate 23Tg yrà1).Galbally and Kirstine[2002]point out that part of the methanol produced by plant decay may be consumed within the litter,but they also point out that additional biotic processes contribute to the methanol source from plant decay.Their best estimate for the total source from plant decay is13Tg yrà1(range5–31).

2.2.

3.Biomass Burning and Biofuels

[11]We use a methanol/CO emission factor of0.018mol molà1for combustion of different types of biomass,based on compilations of literature data[Yokelson et al.,1999; Andreae and Merlet,2001].Yokelson et al.[1999]find little variability in the emission factor between different types of fires(range0.006–0.031mol molà1).Singh et al.[2004] find a mean emission factor of0.016±0.002mol molà1for fire plumes from Southeast Asia sampled over the NW Pacific.Christian et al.[2003]find a mean value of 0.024mol molà1for Indonesian fuels.Holzinger et al. [2004]find a mean value of0.038mol molà1for aged biomass burning plumes sampled over the Mediterranean Sea,which is relatively high and which they attribute to secondary production;but the fire plumes sampled by Singh et al.[2004]were also aged.

[12]We apply the0.018mol molà1emission factor to gridded CO emission inventories for biomass burning (climatological,monthly)[Duncan et al.,2003]and bio-fuels(aseasonal)[Yevich and Logan,2003].Global emis-sions in these inventories are440Tg CO yrà1for biomass burning and161Tg CO yrà1for biofuels,and the resulting methanol sources are9and4Tg yrà1respectively.Inverse modeling estimates of the global biomass burning source of CO constrained with surface air observations fall in the range600–740Tg CO yrà1[Bergamaschi et al.,2000; Pe′tron et al.,2002],about50%higher than used here. 2.2.4.Urban

[13]We refer to‘‘urban’’as the ensemble of methanol sources from fossil fuel combustion,other vehicular emissions,solvents,and industrial activity[Galbally and Kirstine,2002].We use the aseasonal gridded(1°?1°) EDGAR V2.0global anthropogenic emission inventory for1990,which gives a total urban alkanol emission of 8.2Tg yrà1[Olivier et al.,1994],and assume that methanol accounts for half of this total or4.1Tg yrà1.Goldan et al. [1995]reported a concentration ratio of methanol to nitro-gen oxides(NO x)of0.17mol molà1in urban air in Colorado in winter;scaling of this source to a global fossil fuel combustion NO x source of23Tg N yrà1would yield an anthropogenic source of methanol of9.1Tg yrà1. Aircraft measurements by J.deGouw(unpublished)indi-cate methanol/CO enhancement ratios of0.050mol molà1 in Denver but0.011–0.014mol molà1in other U.S.cities. An emission ratio of0.013mol molà1,combined with a global fossil fuel source of CO of480Tg yrà1(Duncan et al., submitted manuscript,2004),would imply a global methanol source of7.1Tg yrà1.Holzinger et al.[2001]report a methanol:benzene enhancement ratio of0.8mol molà1for urban air in Innsbruck,Austria.The EDGAR V2.0inventory gives a global benzene emission of1.2Tg yrà1from vehicles and industry,which would imply a methanol source of only 0.4Tg yrà1.The urban source of methanol is thus highly uncertain but is clearly small on a global scale.

2.2.5.Atmospheric Production

[14]Methanol is produced in the atmosphere by reactions of the methylperoxy(CH3O2)radical with itself and with higher organic peroxy(RO2)radicals[Madronich and Calvert,1990;Tyndall et al.,2001]:

CH3O2tCH3O2!CH3OHtCH2OtO2

eR1T

CH3O2tRO2!CH3OHtR0CHOtO2

eR2T

Alternate branches for these reactions,not producing methanol,are

CH3O2tCH3O2!CH3OtCH3OtO2

eR10T

CH3O2tRO2!CH3OtROtO2

eR20T

The CH3O2and RO2radicals are produced in the atmosphere by oxidation of VOCs.The sum of reactions (R1),(R10),(R2),and(R20)typically accounts for less than 10%of the CH3O2sink in current chemical mechanisms. The dominant atmospheric sinks are the reactions with HO2 and NO,which do not produce methanol:

CH3O2tHO2!CH3OOHtO2

eR3T

CH3O2tNO!CH3OtNO2

eR4T

[15]We calculate the atmospheric source of methanol from(R1)and(R2)with a GEOS-CHEM simulation of tropospheric ozone-NO x-VOC chemistry[Fiore et al., 2003].Primary VOCs in that simulation include methane, ethane,propane,higher alkanes,>C2alkenes,isoprene, acetone,and methanol.The simulation uses recommended data from Tyndall et al.[2001]for the kinetics and methanol yields of(R1),and also for(R3)involving the acetonylperoxy radical(CH3C(O)CH3O2)produced by oxidation of acetone. The methanol yields at298K for these two reactions are0.63 and0.5,respectively.We assume a0.5yield for all other CH3O2+RO2reactions,following Madronich and Calvert [1990].The resulting global source of methanol is38Tg yrà1. Reaction(R1)contributes85%of that global total.The remaining15%,contributed by(R2),mainly involves RO2 radicals produced from biogenic isoprene and is largely confined to the continental boundary layer,where it is much smaller than the primary emission from plant growth.In the remote atmosphere,where the atmospheric production of methanol is of most interest,the CH3O2 radicals driving(R1)originate mainly from the oxidation of methane.

[16]Previous literature estimates of the atmospheric source of methanol from(R1)and(R2),similarly obtained with global tropospheric chemistry models,are in the range

18–31Tg yrà1(Table1),lower than our estimate.These include30Tg yrà1from Heikes et al.[2002]computed with an earlier version of GEOS-CHEM.Discrepancies between estimates reflect differences in the abundances of NO and HO2,the CH3O2reaction rate constants,the yield of methanol from(R1)and(R2),and the importance of (R2).Uncertainties in the(R1)rate constant and in the corresponding methanol yield are each about30%at298K [Tyndall et al.,2001]and higher at colder temperatures.As we will see,observations from the PEM-Tropics B aircraft mission over the South Pacific suggest an even larger atmospheric source of methanol than is used here.

2.3.Sinks of Methanol

2.3.1.Gas-Phase Oxidation by OH

[17]We use the rate constant k=3.6?10à12exp[à415/T] cm3moleculeà1sà1recommended by Jimenez et al.[2003] with an uncertainty of20%.We apply this rate constant to a 3-D archive of monthly mean OH concentrations from the Fiore et al.[2003]GEOS-CHEM simulation of tropospheric ozone-NO x-VOC chemistry.The lifetime of methylchloro-form against tropospheric oxidation by OH(proxy for the global mean tropospheric OH concentration)is5.6years in that simulation[Martin et al.,2003].This is within the range constrained by the methylchloroform observations,which imply a25%uncertainty in global mean OH concentrations [Intergovernmental Panel on Climate Change(IPCC), 2001].Stratospheric loss of methanol is computed using OH concentrations archived from a global2-D stratospheric chemistry model[Schneider et al.,2000]and amounts to only2%of the loss in the troposphere.Our computed lifetime of methanol against oxidation by OH is11days. Addition of errors in quadrature implies an uncertainty of 30%on this value.

2.3.2.Aqueous-Phase Oxidation by OH(aq)

[18]Methanol dissolves in aqueous aerosols(Henry’s law constant H=1.6?10à5exp[4900/T]M atmà1)and can then be oxidized by OH(aq)(k=1.0?109exp[à590/T] Mà1sà1;Elliot and McCracken[1989]).The corresponding first-order loss rate constant(sà1)for atmospheric methanol is k0=HLRTk[OH(aq)]where L is the dimensionless liquid water content,R is the gas constant,and[OH(aq)]is the OH(aq)concentration in units of M(moles per liter of water).The reaction takes place mainly in clouds,where L is more than3orders of magnitude higher than in a clear-sky atmosphere.The GEOS-3meteorological archive pro-vides3-D cloud optical depths t from which we estimate L= 4t r/3Q D Z where r is the effective cloud droplet radius(taken to be10m m),Q%2is the cloud droplet extinction efficiency, and D Z is the vertical thickness of the gridbox.The OH(aq) concentration in cloud droplets is largely determined by aqueous-phase cycling with HO2(aq)/O2àand depends in a complicated way on cloud composition[Jacob,1986].We assume here a simple parameterization[OH(aq)]=d[OH(g)] where[OH(g)]is the gas-phase concentration calculated in GEOS-CHEM without consideration of aqueous-phase cloud chemistry,and d=1?10à19M cm3moleculeà1is chosen to fit the cloud chemistry model results of Jacob[1986].The resulting lifetime of methanol against in-cloud oxidation by OH(aq)is longer than1year.Heikes et al.[2002]and Galbally and Kirstine[2002]found a larger role for in-cloud oxidation by OH(aq)in their global methanol budgets,amounting to5–10%of the gas-phase loss (Table1).Their assumed OH(aq)concentrations and aqueous-phase CH3OH+OH rate constants are higher than ours.

[19]A few studies have raised the possibility of a missing heterogeneous sink for methanol in aerosols or clouds.Singh et al.[2000]suggested that such a sink might explain their observed decrease of methanol con-centrations from the middle to upper troposphere at north-ern midlatitudes(SONEX aircraft mission).Jaegle′et al. [2000]found that unexpectedly high formaldehyde con-centrations measured in the upper troposphere during SONEX correlated with methanol,and speculated that reactive uptake of methanol in cirrus clouds with a reaction probability g=0.01could provide an explanation.Yokelson et al.[2003]observed the depletion of methanol in a cloud polluted by biomass burning smoke,and Tabazadeh et al. [2004]proposed that surface reactions of methanol on cloud droplets could be responsible.However,statistical compari-son of in-cloud versus clear-sky methanol concentrations in the large data set from the TRACE-P aircraft mission indi-cates no significant differences at any altitude[Singh et al., 2004].Upper tropospheric observations from the SONEX mission indicate no depletion in air processed by deep convection or cirrus clouds[Jaegle′et al.,2000].Laboratory studies indicate no significant reactive uptake of methanol by sulfuric acid aerosols[Iraci et al.,2002],ice[Hudson et al.,2002;Winkler et al.,2002],or mineral oxide surfaces [Carlos-Cuellar et al.,2003].Kane and Leu[2001]report fast reaction of methanol with sulfuric acid in concen-trated solutions but Iraci et al.[2002]attribute this result to an experimental artifact.At present,the weight of evidence does not support a fast heterogeneous sink for methanol.

2.3.3.Deposition to Land

[20]Microbial and foliar uptake of methanol by vegeta-tion and soils is difficult to separate from the plant growth and decay sources,either observationally or for model purposes.Investigators have placed soil and leaf litter [Schade and Goldstein,2001]and foliage[Nemecek-Marshall et al.,1995]in enclosures and observed a net emission of methanol.However,these studies used enclo-sures that were flushed with methanol-free air.Recent measurements by A.Guenther(unpublished)indicate that there can be a net uptake of methanol when enclosures are flushed with air containing ambient levels of methanol. [21]Measurement of the diurnal cycle of methanol con-centrations at continental surface sites provides some sepa-ration of microbial uptake from plant growth emission, which is restricted to daytime.Kesselmeier et al.[2002] observed no significant diurnal variation at a site in the Amazon forest,but other observations at sites in the tropics and northern midlatitudes indicate decreases over the course of the night ranging from about30%[Goldan et al.,1995]to several-fold[Riemer et al.,1998;Holzinger et al.,2001; Karl et al.,2004],suggesting surface uptake.A30% decrease over the course of an8-hour summer night,and for a typical nighttime mixing depth of100m,would imply a methanol deposition velocity of0.12cm sà1.Karl et al. [2004]measured methanol deposition to a tropical forest ecosystem by using a combination of eddy covariance and vertical gradients,and found a mean methanol deposition

velocity of0.27±0.14cm sà1.On the basis of the above information,and acknowledging the large uncertainty,we assume here a constant dry deposition velocity of0.2cm sà1 to land.We assume that this deposition velocity also applies to ice-covered surfaces since Boudries et al.[2002]found that Arctic snow is a net sink for methanol.The resulting atmospheric lifetime of methanol against dry deposition is 26days.Including a methanol sink from dry deposition improves significantly the ability of the model to reproduce observations at northern midlatitudes in fall and winter. 2.3.4.Wet Deposition

[22]We simulate wet deposition of methanol with the GEOS-CHEM wet deposition scheme described by Liu et al.[2001].This scheme accounts for scavenging of water-soluble species by convective updrafts,convective anvils, and large-scale precipitation.The scavenging coefficients used by Liu et al.[2001]are scaled here to the dissolved fraction of methanol inferred from the local liquid water content and Henry’s law constant.Scavenging is assumed to take place in warm clouds only(T>268K).The retention efficiency of methanol upon cloud droplet freezing is taken to be0.02,by analogy with previous assumptions for CH3OOH and CH2O[Mari et al.,2000],so that scavenging is ineffi-cient in mixed liquid-ice clouds where the precipitation involves https://www.wendangku.net/doc/5912081097.html,boratory studies indicate that methanol coverage of ice surfaces is low,less than10à4of a monolayer at equilibrium[Hudson et al.,2002;Winkler et al.,2002], supporting the assumption of a low retention efficiency.The resulting lifetime of methanol against wet deposition is 120days.With such a long lifetime,wet deposition does not significantly affect the vertical profile of methanol in the troposphere[Crutzen and Lawrence,2000].

2.3.5.Uptake by the Ocean

[23]Simple solubility considerations imply that the ocean mixed layer must be a large reservoir for methanol[Galbally and Kirstine,2002;Singh et al.,2003a].Heikes et al.[2002] point out that methanol is both produced and consumed in the ocean.Singh et al.[2003a]observed a gradient of increasing methanol concentrations from the marine boundary layer to the free troposphere over the remote North Pacific during TRACE-P,implying an ocean sink with a mean deposition velocity of0.08cm sà1.Measurements taken at the Mace Head coastal site in Ireland also show evidence of methanol uptake by the ocean,with a similar deposition velocity [Carpenter et al.,2004].Our GEOS-CHEM results presented in the work of Singh et al.[2003a]show that an ocean saturation ratio of90%for methanol,combined with a standard two-layer parameterization of ocean-atmosphere exchange,gives a good simulation of the observed TRACE-P vertical gradients.In the absence of better infor-mation we assume that the90%saturation ratio holds globally.The resulting lifetime of methanol against net uptake by the ocean is130days.

3.Global Model Budget and Atmospheric Distribution of Methanol

[24]Table1summarizes our global model budget of methanol.The global source of206Tg yrà1includes contributions from plant growth(62%),atmospheric oxida-tion of VOCs(18%),plant decay(11%),biomass burning and biofuels(6%),and vehicles and industrial activities (2%).The global sink balancing that source is dominated by gas-phase oxidation by OH(63%)with minor contributions from dry deposition to land(26%),wet deposition(6%),net uptake by the ocean(5%),and aqueous-phase oxidation in clouds(<1%).The resulting atmospheric lifetime of meth-anol is7days and the global atmospheric burden is4.0Tg.

A preliminary version of our model budget was presented by Singh et al.[2004]in a study comparing GEOS-CHEM results to their TRACE-P aircraft observations(reference to our budget is given there as B.D.Field et al.,manuscript in preparation,2003).That preliminary version did not include deposition of methanol to land or the dependence of methanol emissions on leaf age.

[25]Our global source of methanol is in the range of previous literature cited in Table1(122–350Tg yrà1). Differences reflect principally the magnitude of the plant growth source.Our value for that source(128Tg yrà1)is close to that of Galbally and Kirstine[2002](100Tg yrà1), as would be expected since we followed their algorithm. Our atmospheric source of methanol from CH3O2reactions (38Tg yrà1)is larger than previous estimates(18–31Tg yrà1).

[26]Gas-phase oxidation by OH is a major methanol sink in all the inventories of Table1.However,the principal methanol sink in the Heikes et al.[2002]budget is dry deposition to land and oceans,with an assumed deposition velocity of0.4cm sà1.As mentioned above,the TRACE-P aircraft data of Singh et al.[2003a]imply a much lower deposition velocity to the ocean.Fast deposition to land in the Heikes et al.[2002]budget(90Tg yrà1)partly com-pensates for their high terrestrial biogenic source(280Tg yrà1).The relative contributions to the methanol sink from gas-phase oxidation by OH,dry deposition,and wet depo-sition in our model are essentially the same as in the previous global3-D model study by von Kuhlmann et al. [2003a](63%,30%,and7%respectively).That study did not report separate deposition terms to land and ocean. [27]Figure2shows the mean concentrations of methanol simulated by the model in surface air and at500hPa,for January and July.Surface air concentrations over land exceed5–10ppbv in the tropics and at northern midlatitudes in summer.The particularly high concentrations over Siberia in July are due to the long continental fetch and the late emergence of leaves.Surface air concentrations at northern midlatitudes in winter drop to0.5–2ppbv.Concentrations over land decrease typically by a factor of2–5from the surface to500hPa,reflecting the surface source and the7-day lifetime.There is by contrast little vertical gradient over the oceans.Concentrations over the northern hemi-sphere oceans(0.5–1ppbv)show little seasonal variation, because faster chemical loss in summer balances the effect of the larger continental source.Concentrations over the south-ern hemisphere oceans are higher in winter(0.5–1ppbv) than in summer(0.2–0.5ppbv)since the continental source in the southern hemisphere is mainly in the tropics and has little seasonal variation.Concentrations are lowest(below 0.2ppbv)in surface air over Antarctica in summer.

4.Evaluation With Observations

[28]We examine in this section how the above estimates of methanol sources and sinks,when implemented in a

global CTM,succeed in reproducing the observed atmo-spheric concentrations of methanol.Our primary focus is on observations from aircraft and ships,which integrate source information over large regions.Observations from surface continental sites,reviewed by Heikes et al.[2002],are few and show high spatial variability that we cannot expect to reproduce given our crude model representation of sources.We use these surface observations mainly to examine large-scale features,including seasonal variations and tropical concentrations,that are of particular interest for testing the model.

4.1.Surface Concentrations

[29]The review of Heikes et al.[2002]gives representa-tive surface air concentrations of 20(range 0.03–47)ppbv in urban air,10(1–37)ppbv over forests,6(4–9)ppbv over grasslands,2(1–4)ppbv for continental background,and 0.9(0.3–1.4)ppbv over the northern hemispheric oceans.Our model values are roughly consistent with these ranges,as shown in Figure 2.Urban air is not resolved on the scale of the model.

[30]The Heikes et al.[2002]compilation includes no surface data for tropical land ecosystems,where the model predicts high concentrations year-round.Measurements by Kesselmeier et al.[2002]in the Rondo ?nia tropical forest of Brazil (10S,63W)for a 7-day period in October 1999(end of dry season)indicate a range of 1to 6ppbv.Our mean

simulated concentration for that site and month is much higher,10ppbv,with dominant contributions from plant growth (6ppbv),plant decay (2ppbv),and biomass burning (2ppbv).Measurements by Karl et al.[2004]at a tropical forest site in Costa Rica (10N,84W)for a 20-day period in April–May 2003(peak of dry season)indicate a mean concentration of 2.2ppbv.Our mean simulated concentra-tion for that site and 2-month period is 2.1pppbv,in agreement with observations,with a major contribution from plant growth (1.2ppbv)and minor contributions of about 0.3ppbv each from plant decay,biomass burning,and atmospheric production.The lower model value in Costa Rica than in Rondo ?nia reflects the shorter continental fetch.The model shows little seasonal variation at the Rondo ?nia site (monthly means range from 8to 10ppbv)but more at the Costa Rica site (1.2to 2.7ppbv).At the latter site,the seasonal maximum is in the wet season (June–October)when the plant growth source is high,and the seasonal minimum is in the early dry season (January–February)before biomass burning.Karl et al.[2004]also made methanol flux measurements at the Costa Rica site,which indicate a value 50%higher than the parameterization of Galbally and Kristine [2002]when normalized to the estimated tropical forest NPP.On the other hand,the data from Kesselmeier et al.[2002]would suggest that the NPP-based parameterization of Galbally and Kristine [2002]is too high for the Amazon forest.More observations

of

Figure 2.Simulated monthly mean concentrations of methanol (ppbv)in surface air and at 500hPa for January and July 2001.

methanol concentrations and fluxes are clearly needed for tropical land ecosystems.

[31]To our knowledge,the only methanol observations in the literature extending over a full yearly cycle are those of Holzinger et al.[2001]taken in the outskirts of Innsbruck,Austria (47N,11E).These authors used concurrent mea-surements of benzene (emitted by vehicles)to separate the local urban from the regional nonurban components of methanol.Figure 3shows the observed ranges for the nonurban component.There is a spring-summer maximum and fall-winter minimum,reflecting biogenic emission.Concentrations drop by more than a factor of 2from June to September.Also shown in Figure 3are the simulated methanol concentrations and the contributions from the individual model sources.Plant growth is the main model source except in winter,when plant decay and urban sources become relatively important.The model reproduces the seasonal variation in the observations,including the rise from winter to spring and the decrease from spring to fall.The latter reflects in the model the weaker emission from mature leaves.

[32]Ship measurements from Wisthaler et al.[2002]over the Indian Ocean during INDOEX 1999(March 1999)provide another important data set for methanol in surface air.The cruise track extended from 20N to 12S,starting from the west coast of India and extending south of the intertropical convergence zone (Figure 1).Observed meth-anol concentrations exceed 1ppbv in Indian outflow and drop to 0.5–0.6ppbv in southern hemispheric air.Wisthaler et al.[2002]reported a strong correlation of methanol with CO,which we compare in Figure 4to our mean monthly model results for 2001sampled along the cruise track.The agreement is remarkably good.The model captures

the

Figure 3.Seasonal variation of methanol concentrations at Innsbruck,Austria.The ranges of observations reported by Holzinger et al.[2001]for different times of year in 1996–1997are shown as vertical lines with symbols.These observations are for the non-urban component of methanol,after subtraction of the urban component based on correlation with benzene.Model results are shown as the solid line,with additional lines identifying contributions from individual sources in the model:plant growth (short dashes),plant decay (dots),urban (thin solid),biomass burning and biofuels (long dashes),and atmospheric production

(dash-dot).

Figure 4.Methanol-CO correlations along the INDOEX 1999cruise track over the Indian Ocean (Figure 1).Observations from Wisthaler et al.[2002]are shown as small dots,with linear regression as solid line.Monthly mean GEOS-CHEM results along the cruise track are shown as large open circles,with linear regression as dashed line.Coefficients of determination (r 2)and slopes of the linear regressions (S )are shown inset.

clean 0.5–0.6ppbv background and the observed relation-ship with CO,although it does not capture the population of observations with relatively high CO but low methanol (hence the stronger methanol-CO correlation in the model,r 2=0.76versus r 2=0.47).Over India in the model the methanol is mostly from plant growth,while CO is mostly from combustion;the methanol-CO correlation in Indian outflow reflects varying degrees of dilution with the marine background rather than a commonality of methanol and CO source processes.

[33]Additional unpublished methanol measurements have been made by A.Wisthaler and A.Hansel on a ship cruise in the Arctic in July August 2001(AOE-2001),and at Zugspitze in southern Germany (47N,11E,2650m altitude)for four months in October 2002to January 2003.These are shown in Figure 5together with the corresponding model results.The model reproduces the low Arctic cruise obser-vations north of 85N (0.30–0.35ppbv),reflecting deposi-tion to the ocean,but not the even lower concentrations (0.1–0.2ppbv)observed further south;these low concen-trations are from only a few measurements and suggest a strong local ocean sink.Observations at Zugspitze show a decrease from 0.7ppbv in October to 0.3ppbv in January.The model also shows a decrease,mostly from the deposi-tion sink,but not as strong as observed.This would suggest a nonphotochemical sink of methanol missing from the model.However,the TOPSE aircraft observations over the North American Arctic in winter,to be discussed in section 4.3,show higher concentrations than at Zugspitze and do not suggest such a missing sink.

https://www.wendangku.net/doc/5912081097.html,n Outflow (TRACE-P Mission)

[34]The TRACE-P aircraft mission in March–April 2001provided extensive data for methanol and other species in

Asian outflow over the North Pacific [Jacob et al.,2003].The outflow included a major contribution from seasonal biomass burning in Southeast Asia [Heald et al.,2003a].Flight tracks are shown in Figure 1.Our model simulation is for the same meteorological year as TRACE-P.We sample the model along the flight tracks and for the flight days for comparison to observations.GEOS-CHEM simu-lations for the TRACE-P period have been evaluated previously with observations for a number of species including in particular CO [Kiley et al.,2003;Palmer et al.,2003b]and HCN [Li et al.,2003],which we discuss below in the context of their correlations with methanol.The model gives a good simulation of Asian outflow pathways with no evident transport bias [Liu et al.,2003;I.Bey et al.,Characterization of transport errors in chemical forecasts from a global tropospheric chemical transport model,submitted to Journal of Geophysical Research ,2005].Simulation of the CO 2observations indicates a 45%underestimate of the net biospheric carbon emission for China in the CASA 2model during the TRACE-P period [Suntharalingam et al.,2004].However,as we will see below,there is no evident bias in our methanol simulation.

4.2.1.Vertical Profiles

[35]Figure 6compares the mean simulated and observed vertical profiles of methanol concentrations for the four TRACE-P quadrants separated at 30N,150E.The four quadrants contrast fresh Asian outflow west of 150E to more background North Pacific air to the east.

[36]The observed methanol concentrations in the NW quadrant decrease from 1.5–2ppbv in the boundary layer to 0.9–1.3ppbv in the free troposphere,rise again to a maximum in excess of 2ppbv in the upper troposphere,and then decline to 0.1ppbv above the tropopause at 12

km.

Figure 5.Mean methanol concentrations from the AOE-2001Arctic cruise in July August 2001as a function of latitude and from Zugspitze (southern Germany,2650m ASL)in October 2002to January 2003as a function of month.Unpublished observations from A.Wisthaler and A.Hansel (closed circles)are compared to model results (open circles).Standard deviations on the observations are shown for Zugspitze.

The model reproduces these features although it is up to a factor 2too low in the middle troposphere.Stratospheric concentrations cannot be compared because of excessive stratosphere-troposphere exchange in the GEOS assimilat-ed meteorological data used to drive the model [Liu et al.,2001;Tan et al.,2004].The plant growth source in the model contributes a relatively featureless background of 0.3ppbv throughout the troposphere in this NW quadrant.The boundary layer enhancement reflects a mix of Chi-nese emissions from plant decay,biofuels,and urban sources.The upper tropospheric enhancement is due to outflow from deep convection in Southeast Asia and is contributed mainly by the plant growth and biomass burning components.

[37]The observed mean profile for the SW quadrant in Figure 6shows greater influence from Southeast Asia than the NW quadrant.Concentrations decrease gradually with altitude,from 1.5ppbv in the boundary layer to 0.5–1ppbv in the middle and upper troposphere but with a convective enhancement apparent at 8–9km.The model reproduces this structure with no evident bias.

[38]Observations in the NE quadrant show a marked increase with altitude,in contrast to the western https://www.wendangku.net/doc/5912081097.html,n influence is principally in the middle and upper troposphere.The model underestimates this Asian influ-ence,by about the same factor as for the free tropospheric Asian outflow in the NW quadrant.The relatively low methanol concentrations in the lower troposphere,both in the model and in the observations,reflect gas-phase oxida-tion and ocean uptake of methanol as the Asian air masses subside [Singh et al.,2003a].

[39]Asian influence during TRACE-P was weakest in the SE quadrant,which lies south of the dominant outflow track.The observed layer of high methanol concentrations at 2–4km altitude is from one single Hawaii-Guam flight where the aircraft sampled repeatedly an Asian outflow plume that had traveled southward and subsided [Crawford et al.,2004].High CO concentrations (200ppbv)were observed in that layer.Previous analysis of the GEOS-CHEM CO simulation shows that this layer is displaced upward and diluted in the model relative to the observations [Heald et al.,2003b].A similar bias is found for methanol,as the model enhancement is at 4–5km altitude and peaks at only 0.7ppbv.The model is also low in the marine boundary layer (0.4ppbv,versus 0.6ppbv in the observa-tions);atmospheric production is the most important model source there and is probably too weak,as discussed later in the context of the PEM-Tropics B observations over the South Pacific.

[40]Singh et al.[2004]previously compared results from our preliminary model version (not including dry deposition to land or the decrease in the plant growth source as leaves age)to their mean observed background vertical profile of methanol concentrations for the ensemble of the mission.This showed agreement within 10%up to 6km altitude,but a growing model overestimate at higher altitudes (up to a factor of 3at 11km)that they speculated could reflect heterogeneous chemical loss not captured by the model.

In

Figure 6.Vertical profiles of methanol concentrations from the TRACE-P aircraft mission over the North Pacific (March–April 2001)averaged over four quadrants separated at 30N,150E (Figure 1).Means and standard deviations of observations are shown as symbols and horizontal lines;the number of observations contributing to each average is indicated.Model results are shown as solid lines,with additional lines identifying contributions from individual model sources:plant growth (short dashes),plant decay (dots),urban (thin solid),biomass burning and biofuels (long dashes),and atmospheric production (dash-dot).

the Singh et al.[2004]comparison,observed methanol concentrations decrease from 0.8ppbv at 6km to 0.35ppbv at 11km (filtered against stratospheric influence);whereas model values increase from 0.9ppbv at 6km to 1.1ppbv at 11km.Our comparisons in Figure 6do not show such a model bias in the upper troposphere,but instead consider-able regional difference in simulated and observed vertical profiles for the different quadrants,as discussed above.The decreasing trend from 6to 11km in the observations is found only for the southern quadrants.In addition,the model values presented here are lower than in the prelim-inary simulation reported by Singh et al.[2004]due to our subsequent inclusion of a land deposition sink in the model.This affects in particular the simulation of upper tropospheric concentrations,which include a major contribution from convective outflow of tropical continental air.4.2.2.Correlations With Other Species

[41]Further insight can be gained by examining the observed correlations of methanol with other species.In the ensemble of TRACE-P observations we find that methanol correlates most strongly with acetone (r 2=0.83,slope =0.38mol mol à1),HCN (r 2=0.78,slope =0.12mol mol à1),and CO (r 2=0.63,slope =64mol mol à1).These correlations are shown in Figure 7.The slopes are given

with methanol as denominator.We generated corresponding correlations from the model results sampled along the flight tracks.These are also shown in Figure 7.The CO and HCN simulations are as described in the work of Li et al.[2003].The acetone simulation presented here is that of Jacob et al.[2002]applied to the TRACE-P period,but without an ocean source since the TRACE-P observations indicate that the ocean was in fact a net sink for acetone [Singh et al.,2003a].We find that an ocean source for acetone in the model would destroy the correlation with methanol since the ocean is a sink for methanol.

[42]The model reproduces the correlation between meth-anol and acetone found in the observations (r 2=0.49,slope =0.48mol mol à1in the model).The biogenic acetone source in the model is scaled to isoprene emission [Jacob et al.,2002],while that of methanol is scaled to NPP.The stronger correlation in the observations suggests that the sources of acetone and methanol are governed by more similar processes than is assumed in the model.Measure-ments at rural U.S.sites in summer have previously shown a strong correlation between acetone and methanol with an acetone/methanol slope of 0.21–0.27mol mol à1[Goldan et al.,1995;Riemer et al.,1998].The higher slope observed here is due to anthropogenic sources of

acetone.

Figure 7.Correlations of methanol with CO,acetone,and HCN concentrations in the ensemble of TRACE-P data.Observations (top panels)are compared to model results (bottom panels).Coefficients of determination (r 2),regression lines,and corresponding slopes (S )are indicated.

[43]The observed correlation between methanol and HCN is well simulated by the model(r2=0.43,slope= 0.10mol molà1).The dominant source of HCN variability in the TRACE-P data is biomass burning in Southeast Asia [Li et al.,2003;Singh et al.,2003b].The observed HCN/ CO molar emission ratio from biomass burning varies in the literature over a large range from0.03%to1.1%,with a best fit for the TRACE-P conditions of0.27%[Li et al.,2003]. Combined with our estimated methanol/CO molar emission ratio from biomass burning of1.8%mol molà1this yields an HCN/methanol emission ratio of0.15mol molà1.The weaker slope in the model and in the observations likely reflects biogenic methanol emissions in Southeast Asia that are collocated with the biomass burning emissions. [44]Good agreement between model and observations is also found in the correlation of methanol with CO(r2= 0.44,slope=69mol molà1in the model).CO here serves as a general tracer of Asian outflow and the correlation mainly provides support for the overall magnitude of methanol export from the Asian continent.Although the model underestimates the TRACE-P methanol observations in the middle troposphere(Figure6),the methanol-CO correlation is principally driven by strong outflow events in the boundary layer[Liu et al.,2003].

4.3.Other Aircraft Observations

[45]We now compare model results to methanol obser-vations from other aircraft missions including TOPSE at North American high latitudes in February–May2000 (D.R.Blake,unpublished data),SONEX over the North Atlantic in October–November1997[Singh et al.,2000], MINOS over the eastern Mediterranean in August2001 [Lelieveld et al.,2002],ITCT2K2over the Northeast Pacific in April–May2002[Nowak et al.,2004],and PEM-Tropics B over the South Pacific in February–March 1999[Singh et al.,2001].Aside from MINOS,these missions were conducted for years other than the2001 model year.We use monthly mean vertical profiles in the model over the flight regions(Figure1)to compare to the mean observations.The measurements in TRACE-P, SONEX,and PEM-Tropics B were made by real-time gas chromatography(GC).The measurements in ITCT2K2and MINOS were made by proton transfer mass spectrometry (PTR-MS).A ship-based intercomparison of online PTR-MS and real-time GC-MS methods indicates high correla-tion between the two and agreement within a few percent [de Gouw et al.,2003].Calibration differences of up to20% may be expected between the GC measurements of Singh et al.from different missions.The TOPSE measurements were made by GC analysis from collected air canisters,all with the same relative humidity and the same lapse of time between collection and analysis.The accuracy is estimated to be30%and the precision is much better.

[46]Additional aircraft methanol data are available from the PEM-West B mission over the NW Pacific in February–March1994[Singh et al.,1995]and from the LBA-CLAIRE mission over the rain forest in Surinam in March 1998[Williams et al.,2001].The PEM-West B data are from the same region and season as TRACE-P,and show similar concentrations[Singh et al.,2004],but represent a much sparser data set.The LBA-CLAIRE data indicate low methanol concentrations,averaging1.1ppbv in the bound-ary layer and0.6ppbv in the free troposphere.These would suggest,consistent with Kesselmeier et al.[2002],that methanol emission from tropical forests of South America is much lower than predicted from the Galbally and Kirstine [2002]parameterization.However,quantitative comparison to the model is difficult because the observations were

taken https://www.wendangku.net/doc/5912081097.html,titudinal profiles of methanol concentrations at North American high latitudes during the TOPSE mission(Figure1),for February and April and for0–2and4–6km altitude.Mean observations

(D.R.Blake,unpublished data,2000)are shown as solid circles;the number of observations used to compute the mean is also shown.Model results are shown as open circles.

under conditions of marine inflow and only about 12hours downwind of the coast.

[47]The TOPSE measurements are of particular interest as they characterize the latitudinal gradient at high northern latitudes during the transition from winter to spring [Atlas et al.,2003].We show in Figure 8the latitudinal profiles measured at 0–2and 4–6km altitude in February and April (only a few observations are available in May).Also shown are the corresponding model profiles,sampled as monthly means along the TOPSE flight tracks.Boundary layer concentrations in February show a latitudinal decrease from 1ppbv at northern midlatitudes to 0.4ppbv in the Arctic,both in the model and the observations,due to the shutting down of the biogenic source and the effect of surface deposition.The same latitudinal gradient is found in April but concentrations are about 0.2ppbv higher,similarly in the model and in the observations,reflecting the springtime source.Concentrations at 4–6km altitude show by contrast little latitudinal gradient,which the model explains as reflecting a weaker influence of the dry deposition sink.The February observations at 4–6km (0.5ppbv)are higher than the wintertime Zugspitze observations discussed in section 4.1and are more consistent with the model results.

The model misses the observed April enhancement at 4–6km south of 60N although it captures it in the 0–2km data;this could be due to model error in the springtime onset of the source.We have no explanation for the high concentrations (above 1ppbv)observed north of 80N at 4–6km in April.

[48]Aircraft observations at northern midlatitudes are available from the SONEX,MINOS,and ITCT 2K2mis-sions.These are shown in Figure 9together with the corresponding model results.Observed concentrations in the middle and upper troposphere in SONEX are low,0.4ppbv on average,and this is reproduced in the model with only a slight positive bias.Reduced methanol emission from mature leaves is critical for simulation of the SONEX observations;without this reduction the simulated methanol concentrations would be much higher in fall because of the weak photochemical sink.The global 3-D model study of von Kuhlmann et al.[2003b]also found good agreement with the SONEX observations using a global methanol source 40%lower than ours (Table 1).They did not account for the decrease of methanol emission with leaf age.

[49]The model reproduces the free tropospheric concen-trations observed in MINOS (0.6–1ppbv)over the

Medi-

Figure 9.Vertical profiles of methanol concentrations from the SONEX,MINOS,ITCT 2K2,and PEM-Tropics B aircraft missions averaged over the regions of Figure 2.Symbols and lines are as for Figure 6.The MINOS observations are taken from Lelieveld et al.[2002]without information on the number of observations.

terranean in July,and also the high values in surface air.It is too low in the lower free troposphere(2–4km),possibly reflecting difficulties in simulating vertical mixing and transport over this highly heterogeneous region.Salisbury et al.[2003]observed a mean concentration of3.3ppbv at a site in Crete during MINOS,and as shown in Figure9this is consistent with model https://www.wendangku.net/doc/5912081097.html,parisons to the von Kuhlmann et al.[2003a,2003b]model presented in that paper found it to be too low by a factor of four on average, possibly reflecting its low plant growth source.

[50]Observations from the ITCT2K2campaign off the California coast in April–May average0.9–1.1ppbv in the 1–7km column with lower values near the surface, consistent with an ocean sink.The model is0.2–0.6ppbv too low.The comparison near the surface is subject to uncertainty because of land-ocean contrast(the model has more continental influence than the observations,but this may be due to numerical diffusion).The free tropospheric observations in ITCT2K2are similar in magnitude to the TRACE-P observations for the northeast quadrant (Figure6),but the model shows lower values in ITCT 2K2because of the longer distance from the Asian conti-nent.Atmospheric production makes a relatively large contribution(0.2ppbv)to the simulated concentrations in ITCT2K2,reflecting the strong radiation and low NO x concentrations over the subsiding northeast Pacific.A possibility,discussed further below,is that this atmospheric source in the model is too weak.

[51]Observations from the PEM-Tropics B campaign over the South Pacific are typically0.6–1.2ppbv with little horizontal or vertical structure(Figure9).These values are remarkably high considering the remoteness from land.The model is about a factor of2too low, averaging about0.4–0.5ppbv with little structure.A unique feature of model results for this region is that atmospheric production is the dominant source,contribut-ing0.2–0.4ppbv.This source is favored by the high UV radiation(stimulating CH3O2production),low NO x con-centrations(suppressing competition from the CH3O2+NO reaction),and low CO/CH4ratio(resulting in a high CH3O2/HO2ratio).The model underestimate in this region suggests that atmospheric production in the model is too low.A doubling of this source would largely correct the discrepancy and would be consistent with the featureless character of the observations.

[52]We previously discussed in section2the uncertain-ties in computing the atmospheric source of methanol from CH3O2reactions.Examination of model results for the PEM-Tropics(B)region indicates that self-reaction(R1) accounts for5–10%of the CH3O2sink,reaction(R4)with NO for20%,and reaction(R3)with HO2for the rest; reaction(R2)is negligible.CH3OOH produced by(R3)has an atmospheric lifetime of about a day against losses by reaction with OH and photolysis:

CH3OOHtOH!CH3O2tH2O

eR5aT

CH3OOHtOH!CH2OtOHtH2O

eR5bT

CH3OOHth n!CH3OtOH

eR6T

[53]Reaction(R5a)recycles CH3O2while reactions (R5b)and(R6)do not.For typical atmospheric conditions, (R5a)accounts for about50%of CH3OOH loss and(R5b) and(R6)each for about25%.Assuming chemical steady state for CH3OOH,the effective sink of CH3O2from(R3)is determined by k3(1àf),where the CH3O2recycling efficiency f=k5a/(k5a+k5b+k6)is about50%.Jet Propulsion Laboratory(JPL)[2003]gives uncertainty esti-mates of30%for k3,40%for k5,and50%for k6(it gives no uncertainty estimate for the recommended branching ratio 70/30for k5a/k5b).Thus the overall uncertainty on the effective CH3O2sink from(R3)could be a factor of two. In addition,Martin et al.[2002]showed that the NO concentrations simulated by GEOS-CHEM over the South Pacific are about50%higher than the PEM-Tropics(B) https://www.wendangku.net/doc/5912081097.html,bination of these two factors could easily allow a doubling of the source of methanol from(R1), considering that this source is quadratic in the CH3O2 concentration.Photochemical model calculations by Olson et al.[2001]along the PEM-Tropics(B)flight tracks indicated a50%overestimate of observed CH3OOH,which would be consistent with an overestimate of(R3).They also indicated a factor of2underestimate of CH2O(common product of CH3O2degradation pathways)[Heikes et al., 2001].As discussed by Heikes et al.[2001],the latter discrepancy suggests a major contribution from VOCs other than methane to the supply of CH3O2.This would provide an alternate explanation for increasing the methanol source from the CH3O2self-reaction.

5.Discussion:Constraints on the Methanol Budget From Atmospheric Observations

[54]Observed atmospheric concentrations of methanol are consistent with the view that plant growth provides the principal global https://www.wendangku.net/doc/5912081097.html,boratory and field studies indicate a factor of2–3decrease in this source from young to mature leaves[McDonald and Fall,1993;Nemecek-Marshall et al.,1995;Karl et al.,2003].We find that this is consistent with the observed seasonal variation of meth-anol concentrations at northern midlatitudes.A global plant growth source of128Tg yrà1,computed in our model using the NPP-based algorithm of Galbally and Kirstine[2002], provides an overall unbiased simulation at northern midlat-itudes.The tropics are the largest global contributor to this source but observations there are few.Data for tropical South America[Williams et al.,2001;Kesselmeier et al., 2002]suggest that our model source is too high,while data for Costa Rica[Karl et al.,2004]are consistent with the model.More work is clearly needed to improve understand-ing of methanol emission from tropical land ecosystems. The model also predicts high surface air concentrations (>10ppbv)over Siberia in July,reflecting a large seasonal plant growth source as well as a long continental fetch. Measurements in this region would be of great value. [55]The net flux of methanol from the terrestrial bio-sphere in the model is96Tg yrà1including128Tg yrà1 from plant growth and23Tg yrà1from plant decay, balanced by55Tg yrà1dry deposition to land(Table1). The atmospheric observations offer limited constraints for separating these three different terms.The plant growth source appears to be responsible for the observed decline of

concentrations from spring to fall,while the deposition sink appears to be responsible for the observed diurnal variation of concentrations as well as the decline at northern latitudes over the course of the winter.

[56]Observed concentrations of methanol over the South Pacific during the PEM-Tropics B aircraft mission(0.6–1.2ppbv)are much higher than would be expected from the terrestrial vegetation source and show little vertical or longitudinal structure.Diffuse atmospheric production of methanol from the CH3O2self-reaction(R1),where CH3O2 originates mainly from methane oxidation,is the principal model source in the region and contributes a0.2–0.4ppbv model background.Our computed global magnitude of this source(38Tg yrà1)is larger than previous estimates(18–31Tg yrà1)but still would need to be doubled to approach the South Pacific observations.Methanol observations over the remote north tropical and subtropical Pacific(TRACE-P and ITCT2K2aircraft missions)also suggest that atmo-spheric production of methanol from(R1)in the model is too low.

[57]Computation of the methanol source from(R1)in global tropospheric chemistry models is affected by uncer-tainties in the concentration of CH3O2and in the branching ratio of CH3O2reactions.A doubling to50–100Tg yrà1 does not appear to be inconsistent with independent con-straints,and is specifically within the constraints offered by ancillary PEM-Tropics B observations of CH3OOH and CH2O.It could reflect errors in kinetic rate constants,or the presence of VOCs other than methane in the remote marine atmosphere to provide sources of CH3O2.Some observations of aldehydes in remote marine air[Heikes et al.,2001;Singh et al.,2001,2004]suggest the latter,and imply a more active organic photochemistry than is cur-rently described in models,while other observations are more consistent with current understanding[Fried et al., 2003].Measurements of HO2and total peroxy(HO2+ CH3O2+RO2)radical concentrations by the chemical amplifier method during TRACE-P indicated concentration ratios of organic peroxy radicals to HO2that are consistent with current models[Cantrell et al.,2003],but there is substantial uncertainty in these measurements.Direct mea-surements of CH3O2concentrations in the remote atmo-sphere are needed.

[58]Our model yields a global mean atmospheric lifetime for methanol of7days,with losses from gas-phase reaction with OH(63%of total loss),dry deposition to land(26%), wet deposition(6%),and uptake by the ocean(5%). Aqueous-phase oxidation by OH(aq)in clouds is negligible. The general ability of the model to reproduce the observed variability of methanol concentrations(cf.Figure7)sug-gests a relatively narrow range of uncertainty in the lifetime, 5–10days.Chemical loss of methanol by reaction with OH is uncertain by only about30%[Jimenez et al.,2003]and most likely represents the dominant global sink for metha-nol.The low methanol concentrations observed at high northern latitudes in winter imply an additional nonphoto-chemical sink,which we attribute in the model to dry deposition to land.This attribution is supported by obser-vations of methanol fluxes and of the diurnal variation of methanol concentrations at vegetated sites,although there is considerable variability from site to site.Heterogeneous loss in aerosols and clouds could provide an alternate explana-tion for the observed wintertime depletion of methanol but is not supported by independent evidence.Aircraft obser-vations of vertical profiles over the oceans suggest that ocean uptake is only a weak sink,although ship data from the AOE-2001summertime Arctic campaign suggest the possibility of rapid local uptake.Wet deposition,con-strained by the Henry’s law solubility for methanol,is only

a weak sink.

[59]Our best estimate of the global source of methanol (after allowing for doubling of atmospheric production to 76Tg yrà1)is240Tg yrà1.Singh et al.[2004]estimated a much lower global source,110Tg yrà1,by scaling the global acetone source of95Tg yrà1from Jacob et al. [2002]to the mean acetone/methanol concentration ratio of 2.1g gà1that they measured in TRACE-P and to the ratio of lifetimes of acetone(15days,from Jacob et al.[2002]) and methanol(9days,from Heikes et al.[2002]).However, the concentration ratio measured in TRACE-P is lower than the expected global mean because of the large regional anthropogenic contribution to acetone[Jacob et al.,2002] and the relatively weak biogenic methanol emission from East Asia at that time of year.A methanol source of110Tg yrà1in our model would result in a low bias relative to observations.Singh et al.[2004]acknowledge that their emission estimate is subject to large uncertainty.

6.Implications for Atmospheric Chemistry [60]Our budget implies a minor but nonnegligible role for methanol in global tropospheric chemistry.Oxidation of methanol by OH produces CH2OH(major pathway)and CH3O(minor),which both go on to produce CH2O and HO2[Jimenez et al.,2003].The global tropospheric rate of methanol oxidation calculated in our model on a per-carbon basis,47Tg C yrà1,amounts to12%of the corresponding value of380Tg C yrà1for methane oxidation[IPCC, 2001].Methanol provides a significant source of CH2O both in the continental boundary layer[Riemer et al.,1998; Palmer et al.,2003a]and in the remote troposphere[Heikes et al.,2001].The production of CO from methanol oxida-tion amounts to4–6%of the global CO source of1800–2700Tg yrà1(Duncan et al.,submitted manuscript,2004). Inclusion of methanol in GEOS-CHEM at the levels simu-lated here decreases the global tropospheric OH concentra-tion by2%,an effect similar to that reported previously by Tie et al.[2003].Regional variations in this effect on OH are discussed by Tie et al.[2003]and are relatively small.

[61]Acknowledgments.This work was funded at Harvard by the Atmospheric Chemistry Program of the U.S.National Science Founda-tion.H.B.Singh acknowledges support from the NASA IDS program.

A.Wisthaler and A.Hansel thank Caroline Leck(MISU),the coordi-nator of the Atmospheric Program of AOE-2001,and Ulrich Poeschl (TU-Munich),the coordinator of the Zugspitze SCA VEX Program.The GEOS-CHEM model is managed at Harvard University with support from the NASA Atmospheric Chemistry Modeling and Analysis Pro-gram.We thank the reviewers for their very useful comments. References

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Yokelson,R.J.,J.G.Goode,D.E.Ward,R.A.Susott,R.E.Babbitt,D.D. Wade,I.Bertschi,D.W.T.Griffith,and W.M.Hao(1999),Emissions of formaldehyde,acetic acid,methanol,and other trace gases from biomass fires in North Carolina measured by airborne Fourier transform infrared spectroscopy,J.Geophys.Res.,104,30,109–30,125.

Yokelson,R.J.,I.T.Bertschi,T.J.Christian,P.V.Hobbs,D.E.Ward,and W.M.Hao(2003),Trace gas measurements in nascent,aged,and cloud-processed smoke from African savanna fires by airborne Fourier trans-form infrared spectroscopy(AFTIR),J.Geophys.Res.,108(D13),8478, doi:10.1029/2002JD002322.

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D.R.Blake,Department of Chemistry,University of California,570 Rowland Hall,Irvine,CA92697-2025,USA.

J.de Gouw and C.Warneke,NOAA Aeronomy Laboratory,325 Broadway,R/AL7,Boulder,CO80305,USA.

B. D.Field and D.J.Jacob,Division of Engineering and Applied Science,Harvard University,Pierce Hall,29Oxford St.,Cambridge,MA 02138,USA.(djacob@https://www.wendangku.net/doc/5912081097.html,)

A.Guenther,Atmospheric Chemistry Division,National Center for Atmospheric Research,PO Box3000,Boulder,CO80307-3000,USA. A.Hansel and A.Wisthaler,Institute of Ion Physics,University of Innsbruck,Technikerstrasse25,A-6020Innsbruck,Austria.

Q.Li,Jet Propulsion Laboratory,M/S183-501,4800Oak Grove Dr., Pasadena,CA91109,USA.

H.B.Singh,NASA Ames Research Center,Mail Stop245-5,Moffett Field,CA94035,USA.

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① It’s cheaper to buy goods in quantity / in large quantities. ② A large quantity of silk is sold in Japan. ③ A large quantity of drugs are found in his home. ④ Large quantities of rain are needed in this area. ① He tends to get angry when others disagree with her. ② His views tend towards the extreme（极端）. ③ He was tending (to) his son when I saw him in the hospital. ④ Jane is nice but has a tendency to talk too much. =Jane is nice but she tends to talk too much. ① The price of the new house in our area has gone up by 1,000 yuan per square meter（平方米）。That is（也就是说）it has gone up to 5,000 yuan per square meter. ② The wind has gone down a little. ④ The country has gone through too many wars.

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站prodsklad.kg比较活跃，也在一定程度上刺激了中亚电商的发展。另外，由于（威胁）在中亚地区，信用卡使用限制，网络支付存在很大风险，货到付款是目前主要的支付方式。 西亚及中东 一句话概况：中东市场有两个鲜明的形象：（机会）一是易被忽视的“金矿”，二是跨境电商伺机爆发。 从告了解到，中东地区电商市场从2011年的70亿美元增长至2012年的90亿美元，有29%的涨幅，2013年这一规模达到112亿美元，2014年达到128亿美元，2015年突破150亿美元。 中东地区各国电商渗透率为：阿联酋46%，沙特阿拉伯25%，黎巴嫩9%，埃及8%，科威特35%。根据Research and Markets的报告，从B2C电商销售额来看，阿联酋是中东地区最大的B2C电商市场，沙特阿拉伯是中东地区第二大B2C电商市场。 在电商基础设施上，物流方面，中东地区的公司只能通过公路传送货物而不是飞机，且目前没有公认的法律规定可参考。支付方面，大约80%的网上购物是货到付款，15%是信用卡和借记卡支付。 由于联系亚、欧、非三大洲，沟通大西洋、印度洋，中东地区自古以来就凭借十分重要的地理位置成为国际贸易要塞。这一地区给人的总体感觉是，居民有钱（石油输出国），但物资缺乏，所以人们跨境网购的热情非常高。 在这一背景下，Amazon、eBay等全球电商早早的就进入中东市场，而近年来，中国的电商企业在此也颇为活跃。根据中国跨境出口电商商

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土耳其的经典谚语

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藤制家居用品 Portable Grill Standard Drum Oven 背景为画的tableware 花纹典雅高尚的Dinnerware

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土耳其概况 【国名】土耳其共和国（Republic of Turkey）。 【面积】78.36万平方公里，其中97%位于亚洲的小亚细亚半岛，3%位于欧洲的巴尔干半岛。 【人口】7256万，土耳其族占80%以上，库尔德族约占15%；城市人口为4970多万，占总人口的70.5%。土耳其语为国语。99%的居民信奉伊斯兰教，其中85%属逊尼派，其余为什叶派（阿拉维派）；少数人信仰基督教和犹太教。 【首都】安卡拉（Ankara），人口447万，年最高气温31℃，最低气温－4℃。 【政体】共和制 【货币】土耳其里拉（Turkish Lira）。 【国家元首】总统阿卜杜拉·居尔（Abdullah Gül）, 2007年8月28日由议会选出，当日就任。 【主要节日】新年：1月1日；国家主权和儿童日：4月23日；青年和体育节：5月19日；胜利日：8月30日；共和国成立日：10月29日。 【简况】地跨亚、欧两洲，邻格鲁吉亚、亚美尼亚、阿塞拜疆、伊朗、伊拉克、叙利亚、希腊和保加利亚，濒地中海、爱琴海、马尔马拉海和黑海。海岸线长7200公里，陆地边境线长2648公里。南部沿海地区属亚热带地中海式气候，内陆为大陆型气候。

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Global warming全球变暖全英文介绍

One of the effects of global warming is the destruction of many important ecosystems.Changing and erratic climate conditions will put our ecosystems to the test, the increase in carbon dioxide will increase the problem. The evidence is clear that global warming and climate change affects physical and biological systems. There will be effects to land, water, and life. Already today, scientists are seeing the effects of global warming on coral reefs, many have been bleached and have died. This is due to warmer ocean waters, and to the fact that some species of plants and animals are simply migrating to better suited geographical regions where water temperatures are more suitable. Melting ice sheets are also making some animals migrate to better regions. This effects the ecosystems in which these plants and animals live. Several climate models have been made and they predict more floods (big floods), drought, wildfires, ocean acidification, and the eventual collapse of many ecosystems throughout the world both on land and at sea. There have been forecasts of things like famine, war, and social unrest, in our days ahead. These are the types of effects global warming could have on our planet. Another important effect that global warming will bring is the loss and endangerment of many species. Did you know that 30 percent of all plant and animal species alive in the world today are at risk of extinction by the year 2050 if average temperatures rise more than 2 to 11.5 degrees Fahrenheit. These mass extinctions will be due to a loss of habitat through desertification, deforestation, and ocean warming. Many plants and animals will also be affected by the inability to adapt to our climate warming.

globalwarming教案

Teaching Plan Contents: Reading Book 6 Unit 4 Global warming I.Analysis of the Teaching Material This article is from a magazine about global warming, which illustrates how global warming has come about and different attitudes to its effects. The passage is long, abstract and far away from their life. What’s more, there are many mouthful professional terms, which increases students’ difficulty while reading, although they have some knowledge about global warming. II. Analysis of the Students Students from Senior Two are the students in an excellent level, who have good abilities to read and speak. This unit talks about global warming, which has been taught in Geography. It will help students understand the text better and I believe the students will be interested in this class. However,because they pay little attention to this topic in the daily life, they may have few desire to speak something about global warming. III. Teaching objectives 1. Knowledge objective 1) Enable the students to analyze how global warming has come about； 2) Get students know different attitudes towards global warming and its effects. 2. Competence objective Improve the students’ reading and speaking abilities. 3. Emotion objective 1) Develop student s’ teamwork. 2) Raise their awareness of global warming. IV. Important points Enable the students to understand how global warming has come about. V. D ifficult points Get the students understand how global warming has come about. Let the students understand the difficult sentences better. ①It is human activity that has caused this global warming rather than a random but natural phenomenon. (Line 6) ②All scientists subscribe to the view that the increase in the earth’s temperature is due t o the burning of fossil fuels like coal, natural gas and oil to produce energy. (Line 18) ③This is when small amounts of gases in the atmosphere, like carbon dioxide, methane and water vapour, trap heat from the sun and therefore warm the earth.(Line 26-29) VI. Teaching aids: Multimedia classroom, printed material VII. Teaching methods: Task-based teaching, communicative teaching method VIII.Teaching procedures: Step 1. Lead in and pre-reading (5 mins ) It’s reported that global temperatures continue to rise, making July 2016 the hottest month in the history of the earth. Did you feel extremely hot in July? When you felt hot, what did you do? Did you feel global temperatures going up quietly? Let’s look at a flash (global temperatures from 1850 to 2016). What information can you get? The earth is becoming warmer and warmer. Is it natural or caused by human being? Do you think what effects global warming will bring about? Is global warming beneficial or harmful? Today we’re going to read a magazine article about global warming. It will work out your puzzles. Please open your book and turn to P26. Today we are going to

与土耳其人做生意应该注意什么

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