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JOURNALOF GEOPHYSICALRESEARCH,VOL. 103,NO. D9, PAGES10,697-10,711,
MAY 20, 1998
Global OH trend inferred from methylchloroformmeasurements
Maarten Krol, Peter Jan van Leeuwen, and Jos Lelieveld
Institute
forMarineandAtmospheric
Research,
Utrecht,Netherlands
Abstract. Methylchloroform(MCF) measurements
takenat the AtmosphericLifetime
Experiment/ GlobalAtmosphericGasesExperiment(ALE/GAGE) measurement
stations
are usedto deducethe troposphericOH concentrationand its linear trendbetween1978
and 1993. Globalthree-dimensional
fieldsof OH arecalculatedwith a transportmodelthat
includesbackgroundphotochemistry.
Despitethe largeuncertaintiesin theseOH fields,the
simulatedMCF concentrations
at the five ALE/GAGE stationscomparereasonablywell
to the measurements.
As a nextstep,the OH fieldsare adjustedto fit the measurements
optimally.An ensemble(MonteCarlo)techniqueis usedto optimizethe OH scalingfactor
andto derivethe lineartrendin OH. The optimizedOH fieldsandtrendimply a MCF
lifetimein the troposphere
of 4.7 yearsin 1978 andof 4.5 yearsin 1993. For CH4 these
lifetimes(due to OH destructiononly) are 9.2 and 8.6 yearsin 1978 and 1993, respectively.
Uncertaintiesin theseestimatesarediscussed
usingbox-modelcalculations.The optimized
OH concentrationis sensitiveto the strengthof other MCF sinks in the model and is
constrained
to 1...
an+o.09
o.•7 x 106molecules
o.•sx 106 molecules
cm-a in 1978andto1...
07+0.09
cm-a in 1993. The deduced OH trend is sensitiveto the trend in the MCF emissions
andis confined
to theintervalbetween
-0.1 and+1.1%yr-• witha mostlikelyvalueof
0.46%yr-•. Possible
causes
of a globalincrease
in OH arediscussed.
A positiveOH
trendis calculateddue to stratospheric
ozonedepletion,decliningCO concentrations,
increasedwater vapor abundance,and enhancedNO• emissions.Althoughthe changes
in the atmosphericcompositionare to a large extentunknown,it seemsthat the observed
changesare consistentwith significantincreasesin OH over the pastdecades.
1. Introduction
The oxidizingcapacityof the atmosphere
is largelydeterminedby the OH radical. This radicalis producedasa result
of O3 photolysisin the presenceof watervapor:
O3+ h•,(,X< 320nm)
O(1D)q-H20
> O(•D) + 02
> 2OH
(1)
(2)
Other mechanismsproducingOH include the reaction of
HOe with NO [e.g., Eisele et al., 1997]. On a global scale
the mostimportantsinkfor OH is its reactionwith eitherCO
or CH4, whichare relativelywell-mixedgasesthat limit the
lifetime of OH to 1-10 s. In the planetaryboundarylayer,
however,nonmethanehydrocarbonsmay be the dominant
OH sink, particularlyin pollutedand forestedareas. The
lifetimes of many trace gases,and most notably thoseof
CH4 andCO, aredeterminedby thereactionwith OH. In orderto understand
thetropospheric
trendsandconcentrations
of thesegases,one needsto havedetailedknowledgeof the
tropospheric
OH distributionandits temporalvariation.
Copyright
1998bytheAmerican
Geophysical
Union.
Methodsfor measuringOH in situin the troposphere
have
beendevelopedrecently. Thesemeasurements,
as well as
modelingstudies,
showthattheOH concentration
is highly
variablein spaceandtime [Mountand Williams,1997;Poppe
et al., 1994]. This may be expected,sinceOH production
is determinedmainlyby 03, watervaporand UV-B radiation, which are extremelyvariable in the troposphere.For
instance,the variabilityof UV-B is causedby changesin
the solarzenith angle,clouds,aerosols,surfacereflections,
and the overhead ozone column.
that the tropospheric
OH concentration
on a globalscaleis
subjectto considerable
variation.Dlugokencky
et al. [1996]
claim thatthe SO2 thatwasinjectedby Mount Pinatubointo
the lower stratosphere
and uppertropospherecausedsubstantialblockingof UV-B radiation. This may have led
to lower than usualtroposphericOH concentrations;
these
weretracedin thehigherthannormalCH4 growthratesduring 1991 and early 1992. On the otherhand,stratospheric
ozonedepletionleadsto moreUV-B radiationin the troposphere. Sincestratospheric
ozonehas decreasedsincethe
early 1980sand evenmore dramaticallysincethe eruption
of Mount Pinatubo,this may have led to increasingtroposphericOH concentrations
[Bekkiet al., 1994; Madronich
and Granier, 1992; Granier et al., 1996].
It should be realized
Papernumber98JD00459.
0148-0227/98/98JD-00459509.00
It is reasonable to assume
that the OH concentration
is also
a strongfunctionof cloud abundance(via Oa photolysis),
10,697
10,698
KROL ET AL.: OH TREND FROM METHYLCHLOROFORM
water vapor, O3 and NOx. Therefore,possibletrendsand
variability in thesequantitiesare alsoaffectingOH concentrations.Nevertheless,stratospheric
ozonedepletionis held
partly responsiblefor the rapid declinein the CO and CH4
growthratesduring 1992 and 1993 [Bekkiet al., 1994] (following the increasesin 1991 and early 1992).
Since reaction with OH is the most important sink for
MEASUREMENTS
quencies,and distributionof OH precursorsand sinks,the
accuracyof the calculatedOH fieldsis limited [e.g., Mount
and Williams, 1997]. Therefore some deviation between the
measurements
and the model resultscan be expected. For
instance,Thompson
and Stewart[ 1991] estimatedthata typ-
ical simulationof globalmeanOH contains• 9•5%uncertaintydueto imprecisionsof the kinetics.
CH4, measurements of CHa can be used to estimate the
In this studywe simulatethe ALE/GAGE MCF measureglobally averagedOH concentration.This requiresdetailed mentsusingan OH field obtainedfrom a troposphericphoknowledgeof the CHa sources. Since methaneis emitted tochemistrycalculationwith a three-dimensionaltransport
by manyanthropogenic
andnaturalsources,estimatesof the model.In section3 we adjustthe globalOH field until a best
total sourceare rather uncertain [Prather et al., 1994].
fit is obtained with the MCF
The 1,1,1 trichloroethane (CH3CC13 or methylchloroform, hereafter called MCF) is more likely to be
able to constrain troposphericOH concentrations[Prinn
et al., 1992, 1995]. Sourcesof thiscompoundare purelyanthropogenic,and it is claimedthat the emissionsare known
with high accuracy[Midgleyand McCulloch, 1995]. Most
sourcesare locatedin the northernhemisphere(NH). For a
few yearsnow, the MCF concentrations
in the troposphere
have been declining. It has been suggested[Spivakovsky
et al., 1990, C. M. Spivakovsky,Past and future observations of CH3CC13 as a constraintfor the interhemispheric
asymmetry in OH, submittedto GeophysicalResearch
Letters, 1997] that the resultingdecline in the latitudinal
gradientcan be usedas an extratestof ourunderstanding
of
mine the optimal linear OH trend between 1978 and 1993.
An ensemble(Monte Carlo) method is used to estimatethe
aforementioned
scalingfactorandOH trend,alongwith their
associatederrors. The sensitivityof the result to assump-
OH.
Prinn et al. [ 1995] usedthe 1978-1994AtmosphericLifetime Experiment/ Global AtmosphericGasesExperiment
(ALE/GAGE) measurements[Prinn et al., 1983a, b] to constrainthe averagelifetime of MCF (1000-200 hPa) to ,1.640.3 years. They also estimatedthe 1978-1994 trend in OH
tions made about MCF
measurements.
We also deter-
emissions and other MCF
sinks is
discussedin section4, and the sensitivityof global OH is
discussedin section5. Finally, in sections6 and 7 we discuss the results and summarize
the main conclusions.
2. Method
2.1. Model Description
The time series of MCF
measured since 1978 at five
ALE/GAGE stations(see section 3) have been simulated
with the MOGUNTIA model [Zimmermann, 1984; Crutzen
and Zimmermann,1991]. This model was originallydevelopedat theMax-Planck-Institute
(MPI) for chemistryin
Mainz, FRG (Germany),but severalaspectsof the model
have been modified. First, the advection scheme was re-
to be0.0 4- 0.9•%yr-1. Theseauthors
pointed
outthatthe placedby the lessdiffusiveup-windschemeQUICKEST
increasesin the lower atmosphericOH levels, expectedas describedby Vestedet al. [1992]. The original Oort clia result of recent acceleratedtotal ozone depletion,must, matology[0ort, 1983] was replacedby EuropeanCentre
at least at low latitudes,be offset by other factors. Prinn for Medium RangeWeatherForecasts(ECMWF) 1987 anet al. [1995] useda coarse-gridtwo-dimensionalmodel in alyzed wind data. As in the originalmodel, the advection
which the OH concentrationwas estimatedusingthe MCF is run with monthlyaveragedwinds. The variabilityof the
measurements.There hasbeen somedisputeaboutseveral winds duringone month is translatedinto diffusioncoeffiaspectsof themethod[Spivakovsky
et al., 1990;Hartleyand cientsby meansof Lagrangianmixing timescales[ZimmerPrinn, 1991; Spivakovsky
et al., 1991; Cunnoldand Prinn, mann, 1984].
1991; Spivakovsky,1991]. The inversemethodsof Prinn
Deep convectionis parameterizedby the schemedevelet al. [ 1995] focuson the measuredtemporaltrends,global opedby Feichterand Crutzen[ 1990]. The chemistry,which
content,and latitudinaldistributionsof MCF. Spivakovsky is usedonly to derivethe initial OH field, is the sameasde[ 1991] statesthat thesemethodsare not truly independent scribedby Dentenetand Crutzen[ 1993]. It describesthe
and that the determination of both the MCF lifetime and a background
CHa-CO-NOx-HOz chemistryandaccountsfor
calibrationfactoris an ill-posedproblembecausetheresults heterogeneous
removalof NOz by sulfateaerosol[Dentener
aresensitiveto smallchangesin theemissions
priorto 1978. and Crutzen, 1993]. The model is run for three consecuThe latterproblemhasbeensolvedby the recalibrationof tive yearswith the initial conditionsand emissionsgivenin
themeasurements
reportedby Prinn et al. [ 1995]. However, Table 1. Note that nonmethanehydrocarbonchemistryis
to our knowledge,no attemptshavebeenmadeto verify the not considered.Instead,a surrogatefor theseemissionshas
resultsof Prinn et al. [1995] by an independent
modeling been includedthroughCO (50%) and CH20 (50%) emisapproach.This is the goalof thispaper.
sions.Houwelinget al. [1998] showthat OH concentrations
In globalthree-dimensional
tropospheric
chemistrymod- are lower over the continentswhen more sophisticatednoneling it hasbecomecommonto testthe calculatedOH fields methanehydrocarbon
chemistryis included.However,these
by simulatingtheMCF concentrations
andcomparingthese changesare compensated
for by higherOH overthe oceans,
with the ALE/GAGE
measurements. Because of the uncer-
taintiesin the OH chemistry,rate constants,
photolysisfre-
and the calculated small net effect in OH does not influence
MCF simulations[Houwelinget al., 1998]. Specialcarehas
KROL ET AL.: OH TREND FROM METHYLCHLOROFORM
Table 1. Initial Conditions and Sources for the 3-Year Model
Simulation
Species Initial,ppb
NOx
50
1500
60
0.1
HNOa
CH20
C2H6
Call8
0.0
0.0
---
03
CH4
'CO
Source,
yr- x
Table 2. MCF
MEASUREMENTS
10,699
Emissions
Year
Emission
Year
Emission
Year
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
0.1
0.2
0.9
0.6
7.5
13
19
20
29
35
37
54
55
57
75
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
105
133
147
156
149
170
214
266
305
309
382
462
513
511
537
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
Emission
Boundary
473 Tg
523 Tg
1320 Tg
31.7 Tg N
4.7 Tg N
fixedconcentration
300 Tg
13.9 Tg
17.1 Tg
100 hPaa
surface
surface
surface
lightning
100 hPab
surface
as CO and CH20
as CO and CH20
All otherspeciesare initialized with zero concentration.
aAccountingfor downwardO3 transportfrom the extratropical
stratosphere.
548
522
536
585
594
603
623
666
690
719
636
592
387
bAccording
tomeasurements
[Murphyet al., 1993].
MCFEmissions
arein unitsof kt yr-1.
beentakento treatphotolysisproperly,sincethe globalOH
distributionis very sensitiveto ozonephotolysis.Therefore
three-dimensional
fields of photolysisrates are calculated,
takinginto accounttheeffectsof clouds,stratospheric
ozone
HRTo
Lo-eAZ1
(AZ2kH
+v/D2kH)(4)
columnsand surfacealbedo [Krol and van Weele, 1997]. The
cloud data are taken from the International
Satellite
Cloud
ClimatologyProject(ISCCP) database[Rossowand Schiffer, 1991] for the year 1987 and the ozone is taken from
the firstyear of Nimbus7 Total OzoneMapping Spectrometer (TOMS) observations[McPetersand Labow, 1996], processedwith theversion7 algorithm.The chemistryis calculatedwith a 2 hourtime step,andthe diurnalvariationin the
photolysisratesis takeninto account.
The transportpart of the model is used to simulate
the MCF distribution. Monthly averagedOH fields are
adoptedfrom the last year of the 3-year run with chemistry. The emissionestimatesof MCF are takenfrom Prinn
et al. [ 1987] (1951-1969) andfrom Midgley and McCulloch
[1995] (1970-1993). The subdivisionof the emissionsinto
five distinctregionsis adaptedfrom Midgleyand McCulloch
[1995]. Within eachregion, the emissionis distributedaccordingto the populationdistributiongiven by Fung et al.
[ 1991]. Emissionsprior to 1975 are distributedas in 1975.
Global MCF
emissions are listed in Table 2.
where e is the oceanfractionin the grid box, H is Henry's
lawcoefficient
(M atm-1),/7 is thegasconstant
(0.083atm
M- 1 K- 1), kH is thehydrolysis
rate(s- 1), andD2 is the
diffusion coefficientin the deep (non-mixed layer) ocean
(1.7 cm2 s- 1). Thetemperature
dependent
hydrolysis
rate
kH and Henry's law coefficientH of MCF are calculated
through[McLinden, 1989; Gerkensand Franklin, 1989]
1013
H = 133.-----•
exp(-20.29
q-4655/To)(5)
kH -- 3.1x 10-8 exp(--10000(1/To- 1/298)).
(6)
To is givenin Kelvin, andthevaluefor H is correctedfor the
effectof seasaltby multiplyingwith a factorof 0.8 [McLinden, 1989]. The oceanmixed-layerdepth and temperatures
were obtainedfrom the MPI Hamburgoceanmodel (K. Six,
personalcommunication,1997). The lifetime of MCF toward oceanicloss,as calculatedwith this parameterization,
is about83 years,which is in good agreementwith the esti-
TroposphericOH is the dominantsink for MCF through mate of Butler et al. [ 1991].
the reaction
The net flux of MCF to the stratospherehas been parameterizedas a hemisphericmeanand a monthlyvaryingloss
rate at the 100 hPa level [Kanakidou et al., 1995]. The strato-
MCF+OH k>products
;
-1550
k: 1.8 x 10-12exp•
T
(3)
sphericlossthusrepresents
thenet effectof the outfluxin the
IntertropicalConvergence
Zone (ITCZ) and a smallerreturn
flux into the model domain
[Talukdaret al., 1992] with T the temperaturein Kelvin.
at midlatitudes.
The assumed
lifetime of MCF due to stratospheric
destructionis about50
Two other sinks are taken into account: an ocean sink and
years,in agreementwith Kanakidouet al. [ 1995].
destructionin the stratosphere.
The hydrolysisin oceanwaThe MCF simulationsare carried out with a timestepof
ter is computedaccordingto Kanakidouet al. [1995]. We 2 hours and are comparedwith MCF measurementson the
accountfor monthlyvaryingfieldsof the heightof the atmo- basisof monthly averagedMCF fields. In the model, the
sphericmixedlayer(AZ1), the heightof the oceanicmixed exchangetime between the NH and SH is 1.05 year, in
layer(AZ2), andtheoceantemperature
(To). The oceanic goodagreementwith otherestimates[Ma'ller and Brasseur,
losstermis parameterized
as[Kanakidou
etal., 1995]
1995].
10,700
2.2.
Data
KROLET AL.' OH TREND FROM METHYLCHLOROFORMMEASUREMENTS
160
Treatment
It is not easyto comparemodel resultsto measurements.
First, MCF concentrations
in a modelrepresentaverageconcentrationsovera grid box, whereasmeasurements
aretaken
at a singlelocation.Second,the transportin the model uses
monthly averagedwind fields augmentedby diffusionand
convectionparameterizations.This approachneglectsthe
role of singlemeteorologicaleventswhichmay affectmean
concentrations.
Finally, due to the limited resolutionof the
model, sharp gradientsin the MCF concentrationare not
properly resolved. Sharp gradientsare expectedto occur
140
120
80
ß
Barbados
Tasmania
60
40
I
•
980
,
[
•
I
•
I
1985
1990
i
i
1995
close to sourcesand close to the ITCZ, which forms an MCF
transportbarrierbetweenthe NH andthe SH.
For most MCF measurement stations, however, these
problems are only minor. The ALE/GAGE measurement sitesare positionedin remoteregions,and pollution
events,causedby transportfrom nearbysources,can be removedby a screeningprocedure[Prinn et al., 1992, 1995].
The monthly averagedwinds are expectedto blow from
pollution-freewind sectors,which favorsa comparisonbetween the screened measurements
and the model.
Further-
more,far awayfrom sourcesthe MCF fieldsareexpectedto
be rather smooth. Thereforeone doesnot expectlarge gradientswithin the corresponding
model grid box. In other
words, the measurementstationsare representativefor a
largetropospheric
compartment[Prinn et al., 1995].
Hence we directly comparethe monthly averagedmodel
resultsto the monthly averagedMCF measurements
(nonpolluted). We simply take the model grid box in which
the measurementstationis located.However,two problems
arise. The Ireland station(Adrigole: 52øN, 10øW,replaced
in 1987 by Mace Head: 53øN, 10øW) is located exactly
on an east-westboundaryof a model grid. We selectthe
grid box locatedwest of the stationbecausethis grid cell
is free of emissionsand is locatedupwindof MCF sources
(box 20øW-10øW,50øN-60øN). The Oregonstation(Cape
Meares:45øN, 124øW) is locatedin a grid box in whichthe
model emits a considerable amount of MCF. Since the mea-
160
i
....
140
120
.
80
60
40
980
1985
1990
1995
Figure 1. Monthly averagedALE/GAGE measurements.
Error barsdenote1rr variationsduringthe month.The solid
lines correspondto the functionfit throughthe measurements(equation(7)).
tropicalstationsshowlarge interannualvariations. These
variationscannotbe reproducedby our model,sincewe use
only 1 yearof analyzedwind dataanda convection
parameterizationwhichdoesnot vary from yearto year. Therefore
we describe the measurements with a function which effec-
cycleaswell astheinterannual
surementsite is positionedat the coast(i.e., upwindfrom tivelyfiltersoutthe seasonal
thesesources,assuminga steadywesterlyflow), we moved variations.We are interestedin the long-termconcentration
OH, and this functhe emissionsfrom the above-mentioned
grid box (120øW- of (andthe linear trendin) tropospheric
130øW, 40øN-50øN) one box toward the east. The other tion containsexactlytheinformationneededfor comparison
model grid boxesthat are used for the comparisonare as with our model results.
Prinn et al. [1995] fitted the measurementsat the five
follows: Barbados(RaggedPoint: 13øN, 59øW): grid box
50øW-60øW, 10øN-20øN; American Samoa (Point MatatALE/GAGE stationsby meansof Legendrepolynomials.
ula: 14øS, 171øW): grid box 170øW-180øW,10øS-20øS; We follow this approachand for eachstationdescribethe
X by the function
and Tasmania(CapeGrim: 41øS, 145øE):grid box 140øE- MCF measurements
150øE, 40øS-50øS.
The monthly mean mixing ratios measuredat the five
ALE/GAGE stationsaredepictedin Figure 1. Obviouslocal
MCF pollutionoccurringat the Ireland,Oregon,andTasma-
x(t)
--ao
+k=l
E Nk
2/•k]
(2k)
a•P••t - 1
(7)
where N is half the lengthof the time seriesof the particin years),and t runsfrom 0 to 2N.
arealsoincludedin Figure 1 andarecalculatedfrom thecon- ular station(expressed
centrationfluctuations
duringthemonth[Prinnet al., 1992]. As a result,the argumentof the Legendrefunctionalways
to a LegenThe solid lines correspondto a functionfit to the measure- has a value between-1 and 1. P& corresponds
ments, which will be discussedlater.
dre polynomialof orderk (P0 = 1). The coefficients
a• :in
The latitudinalgradientand the larger standarddeviation front of the Legendrefunctionreceivea properdimension
in the NH are clearly visible in Figure 1. In particular,the (seeTable3) by the factorin front of ak. Here kmaxCO1Tenia stations is omitted from the data. The standard deviations
KROL ET AL.: OH TREND FROM METHYLCHLOROFORM MEASUREMENTS
10,701
Table3. OptimizedLegendreCoefficients
Corresponding
to Equation(7)
Ireland
Oregon
Barbados
Samoa
Tasmania
Start
End
Numberof points
ao (ppt)
July/1978
Dec./1993
142
126.7 q- 0.7
Dec./1979
July/1989
124
121.6 q- 0.6
July/1978
Dec./1993
183
114.6 q- 0.5
July/1978
Dec./1993
170
95.7 q- 0.4
July/1978
Dec./1993
221
93.9 q- 0.3
al (pptyr-1)
a2(10-2 pptyr-2)
a3 (10-4 pptyr-3)
a4(10-6 pptyr-4)
as(10-s pptyr-5)
a6(10-m pptyr-•)
a7 (10-12 pptyr-7)
4.0q-0.1
-4.0 q-0.8
-9.0 q-2
-30 q-5
-12 q-7
4.7q-0.2
1.2q-2.0
3.8q-0.1
-4.6 q-0.6
-5 q-2
-12 q-4
-10 q-6
3.7q-0.1
-3.7 q-0.5
-1 q-2
-11 q-3
-1 q-5
-0 q-7
-5 q-7
3.8q-0.1
-3.5 q-0.4
0 q- 1
-11 q-3
-7 q-4
-1 q-5
The resultingfunctionfit is depictedin Figure 1.
spondsto the maximumorderthat improvesthe overallfit
the period 1975-1993 with the initial MCF concentration
between the measurements and the functional
taken from the base run. Errors in this initial MCF field,
fit still further
(as measuredby the sum of the squareddistancesbetween which are causedby errorsin emissionsand sinksprior to
1975, are accountedfor by the introductionof a third variable. This variable,called AMCF (expressed
in %), is also
The coefficientsak are optimizedby weighting each X chosenrandomlyfrom a normal distributionarounda mean
the function valuesand the actual measurements).The value
for kmaxdiffers from station to station.
by theinverse
of a2. As a consequence,
thefit is lesstight
value.
for the northernmidlatitudeand the tropicalstationswhich
In more generalterms, a vector c• is defined,which conexperiencea lot of variationduringonemonth(seeFigure 1). tains elementswhich are randomly varied around a mean
We include in the standard deviations an additional 5% error
value. An independentmodelrun is performedfor eachredue to uncertainties in the absolute calibration
of the MCF
measurements
[Prinn et al., 1995]. The equationusedhere
includesmoretermsthanthe oneusedby Prinn et al. [ 1995],
but it doesnot include terms that describea seasonalcycle.
The functionalfit throughthe measurements
is depictedin
Figure 1.
Table3 liststhe coefficientsak per stationand the associated 1crerrors.Note that the periodoverwhich equation(7)
is evaluatedis differentfor the Oregonstationdueto the differentmeasurement
periodfor thisstation.Note alsothatthe
periodoverwhichthe functionis evaluatedrunsto the end
of 1993. Emissionestimatesfor the yearsafter 1993 were
not availableto us, andwe preferto comparethe modeland
the dataoverthe sametime period.
2.3.
Ensemble
Method
To eliminatebiases,e.g., from a singleyear'smeteorology
(1987) and systematicerrorsin the global mean OH calculations,we adjustthe globalthree-dimensional
OH field in
order to obtain the best comparisonwith the ALE/GAGE
measurements.
In additionto adjustingthe global mean OH
(hereaftercalled AOH, expressedin %), we also estimate
alization of this vector ((•i). After a number of model inte-
grations(n), the bestestimate• is obtainedfrom a weighted
averageof the vectors
•_ Ei=iWi(•i
,
EiL1Wi
(8)
The associatederror is givenby
O'&
-- Ei--1
Wi
((•i--•)2
Ei-- 1Wi
n
n
(9)
After sufficientmodelruns,the valuesof • and rra converge
to their final values. The weightswi are a measureof how
well the ith model result matches the measurements.
If the
matchis good,a large weight is calculated,whereasa poor
matchleadsto a low weight. Thus the weightscan be interpreted as the probability of finding integrationi, given the
measurementsand the error in the measurements.The appendixcontainsa more elaborateexplanationof the ensemble method.
The MCF measurementsare describedby equation (7).
theoptimallineartrendin OH (in % yr-1) for theperiod This equation can also be applied to the modeled MCF
1978-1993.
The base run is defined as the simulation with
zero AOH and zero trendandcoversthe 1951-1993 period,
assumingzero MCF concentrations
in 1951.
AOH and the OH trend are varied randomly arounda
meantrendand AOH, assuminga normaldistribution.To
avoid excessiveCPU time consumptionfor simulatingthe
entiretime span1951-1993repeatedly,the modelis run for
concentrations.Insteadof making a direct comparisonbetween the measured and modeled MCF concentrations, we
comparethe coefficientsof the function. The coefficients
that representthe measurements
are comparedwith the coefficientsthat representthe modeledMCF concentrations.
When the MCF concentrations
that are simulatedby a model
integrationi are fitted to the samefunction, this yields the
10,702
KROLET AL.: OH TRENDFROMMETHYLCHLOROFORM
MEASUREMENTS
OH (106molecules
cm-3,Jan)
coefficients
bs,k,ifor the Legendretermsk at stations. A
"penalty"functiondefineshow well the modelcoefficients
200
(mean, trend, and higher order terms) compareto the coefficientspresentedin Table 3. The penaltyof a particular
model integrationi is calculatedas
,
, .... -
s=l
)2
/ //
--3-
--•-•------
400
600
(10)
k=0
8OO
in whichas,kandos,karethevaluesgivenin Table3. The
1ooo
model usesmonthly averagedtransportparameters. The
variabilityof themodeledMCF concentrations
overa month
is only smallanddoesnot representthe samevariabilityas
that in the measurements.Therefore the monthly averaged
modelconcentrations
all receiveequalweightingwhenthey
are fitted to equation(7).
The "global"penaltyPi of a particularmodelintegration
-85
0
85
latitude
OH (106molecules
cm-3,Jul)
20O
400
canbe subdivided
into five "station"penaltiesPi,,. Penalties can be translatedinto weightsthat are requiredin the
ensembleaverageof the modelruns(equation(8)):
wi = exp (-Pi).
--
600
(11)
800 ....
Theseweightscan alsobe calculatedby usingthe penalties
lOOO
of an individualstation(Pi,s). The methodthat hasbeen
outlinedhereformallycorresponds
to theminimumvariance
estimatormethod,assumingGaussianerrorstatistics(seethe
appendix).
-85
0
85
latitude
OH (106molecules
cm-3,yearlyaveraged)
In summary,theALE/GAGE MCF measurements
arerepresentedby a functionthat retainsonly the long-termvariations. The ensemblemethodgeneratesmodel integrations
200
400
with random variations in the OH trend, AOH, and initial
MCF concentration.
Subsequently,
eachmodelrun is treated
....
600
in the same manner as the measurements, which leads to
weightsthat are appliedto an ensembleaverageof the random perturbations.Finally, this ensembleaverageleadsto
optimalvaluesfor thetrend,AOH, andinitialMCF concentration,togetherwith the associated
errors.
3. Results
3.1.
Global
8OO
lOOO
-85
0
latitude
85
Figure2. Zonallyand24-houraveraged
OH concentrations.
OH
Distribution
Figure 2 displaysthe OH concentrationobtainedfrom
the third year of a 3-year model run with chemistry. The
monthly averagedOH concentrationsare usedin the MCF
simulations. The yearly averagedfield clearly showsan
asymmetricOH distributionwith more OH in the NH due
to higher O3 and NOz concentrations.
Previously,OH distributionshave beenpresentedby Kanakidou et al. [1995], M•iller and Brasseur [1995], Spivakovskyet al. [1990], and Crutzen and Zimmermann
[1991]. These distributions all show an OH maximum in
the summerhemispherelocatedaround800 hPa, which is
morepronounced
in theNH summer.If we compareourOH
fieldsto the widely usedfieldsof Spivakovsky
et al. [1990],
we noticethat the OH concentrations
generallyagreewell,
our valuesbeing somewhathigher. Also, our maximumin
the NH summer is located closer to the surface. These dif-
ferencesmaybe explainedby theneglectof nonmethane
hydrocarbonsin our chemistryscheme.Anotherfactorof importanceis the quantumyield of the ozonephotolysis(reaction (1)). In our calculations,we usethe valuesproposedby
Michelsen et al. [ 1994]. These values result in increasedOH
concentrations,especiallyin the lower tropospheredue to
enhancedozonephotolysisat wavelengthslongerthan 310
nm. Recent measurementsof Silvente et al. [ 1997] indicate
even higherquantumyields, which would lead to more OH
in the troposphere.This is an additionaluncertaintyin the
OH chemistry.
3.2.
MCF
Simulations
Figure 3 showsthe 1975-1993 model-simulatedMCF
concentrations(base run). Although it is not the subject
KROLET AL.: OH TREND FROM METHYLCHLOROFORMMEASUREMENTS
160
10,703
Samoa
120
140
lOO
120
Ireland
80
100
60
80
40
1975
60
1980
1985
1990
1995
Year
40
1975
1980
1985
1990
1995
Year
Figure 4. Solidlinesarefunctionalfit (equation(7)) through
the first five MCF simulations for the Samoa station. Dashed
line is corresponding
fit throughthe MCF measurements
at
Samoa.
160
140
the integrationsare drawn from the normal distributionsas
120
follows:-5 + 1.4%for AOH; 0.47 + 0.18%yr-1 for the
trend; and 5 q- 7% for AMCF.
lOO
80
60
The uncertainties
/• Samoa
40
1975
1980
1985
1990
1995
Year
indicated
tions. These distributions
refer to l rr standard devia-
are rather narrow.
If the distribu-
tionshad beenchosenwider, only a few integrationswould
have receiveda significantweight in equation(8), which
would have made the method very uneconomical. Therefore pilot calculationswere performed,in which the penalty
function(equation(10)) wascrudelyminimizedwith respect
to the three variables. As we will see later, the distributions
Figure 3. MCF concentrationsimulatedat the ALE/GAGE
are still wide enoughfor a reliable optimizationof the varistationsby the model underbaserun conditions(thin line, ables.
see text). The solid lines depict the functionalfit (equaTable 4 showsthat the total penalty for this stationand
tion (7)) through the simulatedMCF concentrations.The
dashedlinesrepresentthe functionalfit throughthe measure- these integrationsranges from 1.38 to 9.87. From equaments.
tion (11) it follows that the weight that is receivedby integration4 is about5000 timeslargerthan the weight of integration1 becausethe weightsdependexponentiallyon the
penalties. It is obviousthat it is predominantlythe lower
of this paper, we note that the simulatedseasonalvaria- order terms (the mean and the trend) which are sensitive
tionscomparewell with the measuredvariations.In general, the fit of the empiricalfunctionto the measurements
is in fair agreementwith the corresponding
fit of the threedimensionalsimulation.Nevertheless,
in particular,for the
to changesin the OH trend, AOH and AMCF. Higher order terms,however,may contributesignificantlyto the total
penalty,as is shownfor the Ps term in Table 4. Note that
thesehigher order terms do not vary stronglybetweenthe
Oregon station the model underestimatesMCF concentra- differentintegrationsbecauseonly the globalOH concentrations.Giventhe largenumberof uncertaintiesin the model- tion, the trend in OH, and the initial MCF concentration are
ing approach,however,thesedifferencesbetweenthe model varied.
Table 5 lists the AOH, trend, and AMCF values obandthe measurements
canbe expected.If we try to explain
thedifferencesin termsof the OH field only (ignoringother tained by taking an ensembleaverageof 200 integrations.
errorsin themodelandinputdata),the comparison
suggests The resultsare given for all stationsgroupedtogetherand
a smalloverestimate
of OH in the model.Also, at firstsight for the individual stations. Note that the result for all stait seemsthatthe simulatedtrenddifferssystematically
from tionsgroupedtogethercan be differentfrom the averageof
thetrendin themeasurements,
whichsuggests
thatthesedif- the individual stations. Figure 5 providesa visualization
of the ensemblemethod. In the left panels, both the iniferencescanbe explainedby imposinga trendin OH.
To determine the OH concentration and trend for which
tial random distributions(dotted histograms)and the final
the bestcomparisonwith the measurements
is obtained,we ensemble-weighted
distributionsare given (histogramwith
use the ensemble method outlined in section 2.3. As an exsolidlines). The distributionsarebinnedfor thispurpose.To
ample,Figure4 showsthe empiricalfunctionalfit to the first obtainthe solidline histogram,we averagedthe weightsof
five of 200 integrationsfor the Samoastation. Table 4 lists the integrationsin eachbin. Both distributionspeakat about
the dominantpenaltiesfor thesefive integrations,as calcu- the sameposition.The weightsdisplaya narrowerdistribulatedwith equation(10). The randomnumberswhich define tion, whichconfirmsthatthe samplinghasbeentakenfrom a
10,704
KROL ET AL.' OH TREND FROM METHYLCHLOROFORM MEASUREMENTS
Table 4. Dominant Penalties Obtained for the First Five MCF Simulations for the
Samoa Station
i
AOH, %
Trend,% yr-•
AMCF,%
Po
P•
P5
Total
-1.9
-5.9
-8.1
-5.1
-5.8
0.40
0.50
0.63
0.46
0.74
11.4
4.6
6.5
6.4
3.6
5.97
0.47
5.78
0.07
1.24
2.48
0.07
0.16
0.19
2.53
0.82
0.83
0.84
0.83
0.81
9.87
1.63
7.08
1.38
4.93
1
2
3
4
5
,
o
.
Thecorresponding
functional
fitsaredisplayed
in Figure4.
sufficientlywidedistribution.
Many of therandomnumbers thusfar havebeenobtained
assuming
thattheemissions
and
otherMCF sinksthatare appliedin the modelare free of
significantlyto the final weightedaverage.In contrast,the errors.
threecentralbins of the trendhistogramaccountfor more
than60% of thetotalweightin theensembleaverage.
4. SensitivityAnalysis
The shapeof the ensemble-weighted
distributionreveals
To assess
the sensitivityof the modelto uncertainties
in
an anticorrelationbetween the trend and AOH. Indeed, the
in the tail of the initial random distributions do not contribute
covariance between the trend and AOH is -0.88. This anti-
the sourcesand sinksof MCF, we condensethe model into a
of theMCF concencorrelationcanbe understood
as follows.A highertrendin single"global"box.Therateof change
tration
in
the
box
is
described
by:
OH requiresa smallerAOH valueto arriveat the sameaverageMCF destruction
rate. For similarreasons,
a positive
dMCF
covariance of 0.75 is found between AOH and AMCF.
dt
The panelson the right-handsideof Figure5 showthe
convergenceof the variablesas a functionof the numberof
integrations.The vertical bars showthe lrr standarddevia-
=
EMCF(t) -- MCF
x
[LoH(1 + trend(t))+ Lo + L,] (12)
whereEMCF denotesthe time dependent
emission(ppt
tions,It is observed
thatafteronlyabout80 integrations
the yr- x),andLoll, Lo,andL8 (yr-x) denote
theMCFremoval
ensembleaverageis closeto thefinal result.However,small by the reaction with OH, the ocean sink, and the stratofluctuations remain as a result of the random nature of the
sphericdestruction,respectively.Thesevaluesare calcuestimation
method.After 200 integrations,
theprobability latedasaveragesof the MCF sinksin the three-dimensional
is 95% that AOH and the OH trend are estimated with an
model. The OH sink is varied in time with a linear trend in
accuracybetterthan5%. For AMCF thisaccuracyis 7%. theperiod1978-1993.Foursensitivity
runsarediscussed
in
It shouldbe stressed
thattheseerrorsarepurelymathemati- which the emissions and sinks of MCF are varied.
cal, since model uncertainties are not considered. In the next
section we will show that the errors in the variables associ-
atedwith the modeluncertainties
are muchlargerthanthe
1rr values listed in Table 5.
Table 5 shows that the best match between the model re-
sultsand the measurements
is obtainedif the globalOH
concentration
is scaleddownby about5%. The AOH val-
1. Thestratospheric
sinkis increased
(1/L8 = 25 years),
theoceansinkis increased
(1/Lo -- t52years)andtheemissionsaredecreased
by 1rr (- 2.2%).
2. Thestratospheric
sinkis decreased
(1/Ls = 75.years),
the oceansinkis decreased
(1/Lo = 134 years)andthe
emissions
areincreased
by la (+2.2%).
ues differ between the stations and run from -3.5% to -7%.
Valuesfor AOH, OH Trend,
Thesesmalldifferences
maybe explainedby modelerrors, Table 5. Ensemble-Weighted
suchasa wrongOH distribution,
transport
errors,errorsin andAMCF After200Integrations
the emission distribution, and the limited resolution. Nev-
ertheless,it is importantto notethatthe AOH valuewould
changeonly by a few percentif fewermeasurement
stations
are taken into account. For the OH trend the differences be-
tween the stations are even smaller. On the basis of each
individual measurementstation,a similar trend in OH is ob-
tained.Thisconsistent
resultimpliesthatan averagetrend
of 0.46q-0.09%yr- xin OHhasoccurred
ona globalscale
Station
AOH, %
Trend,% yr-•
AMCF,%
Ireland
-3.5
+ 1.0
0.44 + 0.11
3.8 q- 5.6
Oregon
-6.9 + 0.8
0.42 + 0.14
11,1 + 5.2
Barbados
Samoa
Tasmania
All
-3.5 +
-5.0 +
-5.3 +
-4.8 +
0.49
0.45
0.43
0.46
2.8
3.4
0.8
5.5
0.9
0.9
0.8
0.7
+
+
+
•:
0.11
0.10
0.09
0,09
+ 5.5
-l- 5.8
+ 5.4
+ 3.7
in theperiod1978-1993.Anotherpossible
explanation,
i.e.,
thattheassumed
trendin theMCF emissions
is wrong,will
The resultsare given for the individualstationsand for all stabe investigated
in the next section.The resultspresented tionsgrouped
together
(seetext).Uncertainty
rangesreferto 1a.
KROL ET AL.: OH TREND FROM METHYLCHLOROFORM MEASUREMENTS
0.3
-4.0
0.2
-5.0
0.1
-6.0
0.0
-6.5
-7.0
10,705
-5.5
-10
-8
-6
-4
0
-2
50
1O0
150
200
150
200
100
150
# realisations
200
# realisations
AOH (%)
0.30
0.25
0.20
0.50
•
0.15
0.40
•
O.lO
=
¸
-,•
0.30
0.05
0.00
0.20
0.0
0.4
0.2
0.6
0.8
0
1.0
50
Trend(% yr'1)
10
0.25
8
0.20
•' 6
0.15
0.10
--
0.051'
....
0.00 :•--•
-10
100
# realisations
0
'
10
20
AMCF (%)
0
0
50
Figure 5. (left panels)Histogramsof the initial normaldistributions
andof theensemble-weighted
distributions(seetext). (rightpanels)Ensemble-weighted
variablesasa functionof thenumberof integrations.
Errorbarsreferto 1crstandard
deviations.
Top,AOH; middle,OH trend;bottom,AMCF.
3. The emission trend is increased(--4.4% in 1975 to
+4.4% in 1993).
4. The emissiontrend is decreased(+4.4% in 1975 to
-4.4% in 1993).
Thus, the MCF lifetime toward oceanic loss is varied be-
ulation. Since more MCF is destroyedin the stratosphere
and the ocean,and lessMCF is emitted,this run producesa
lowerboundfor OH in thetroposphere.
The secondsensitivity run mirrorsthis effect and producesan upperboundfor
OH. The OH trendcalculationis expectedto be sensitiveto
tween62 and 134years,accordingto theestimatesof Butler the trend in the emission estimates. For the third and fourth
et al. [ 1991]. The lifetime towardstratospheric
lossis var- sensitivityrunsthe MCF deviationsremain within 2%, with
ied between25 and75 years,which is a somewhatarbitrary zero deviation in 1988.
Figure6c showsthe MCF perturbationfor changesin the
choice. Errors in the emissiontrend (runs 3 and 4) may be
ZXOH,
OH trend (T), and AMCF. The same box model is
due to changesin unreportedMCF productionor to uncertaintiesin the translationof industrialMCF productioninto used,but nowthe termsLo• andtrend in equation(12) are
emissionestimates[Midgley and McCulloch, 1995]. Fol- perturbedas well asthe initial MCF concentration.
First, AOH is considered. We calculate with the box
lowing Prinn et al. [1992, 1995], in sensitivityrun 4 (or 3)
we perturbthe emissionsby +2a (or-2a) at the startof the model that a 10% increase in the OH concentration (from
simulationperiodandby -2a (or +2a) at the end of the sim- 1975 on) results in a 7% MCF reduction at the end of the
ulation. These numbers are based on the differences between
simulationperiod. The time evolutionof the perturbation
two emissionscenarios
asdiscussed
by Prinn et al. [ 1992]. matchesthe shapeof the sensitivityruns t and 2 discussed
Figure 6a displaysthe results obtainedfrom the box above. As expected,an incorrectestimateof the MCF
model. The AOH, trend, and AMCF values from Table 5 source,stratospheric
sink,and oceanicsink can be compenare used for the MCF simulations in the box model. The
satedfor by a changein AOH only.
The influence of a trend in OH on the MCF concentration
figure showsthat the calculatedMCF concentrations,averagedover the entiremodeldomain,hardly displayseasonal differs from the AOH influence. If the trend in OH (i.e.,
variations.
0.46%yr-1) is removed,
it followsthattheMCF concen-
Figure6b showstheperturbations
of the MCF concentra- trationincreasessteadilyfrom 0% in 1978 to 5% at the end
tion for the differentsensitivityruns. The first sensitivity of the simulationperiod(Figure6c). It is clearthat the sigrun leads to a 9% smaller MCF burden at the end of the simnalsfrom sensitivityruns3 and 4 can only be explainedby
10,706
140
KROLET AL.: OH TRENDFROMMETHYLCHLOROFORM
MEASUREMENTS
in the emissionis perturbedfrom -4.4% in 1975 to +4.4%
in 1993. The trend is much less sensitiveto the perturbationsappliedin sensitivityruns 1 and 2. The emissionsused
by Prinn et al. [ 1995] resultin a slightlylower trendand a
lessnegativeAOH value.It is unlikelythatothermodeluncertainties(OH distribution,transport,resolution)influence
'a
120
•
80
the deduced OH trend.
The MCF
emissions assumed here
therefore limit the OH trend to the interval between -0.1 and
40
1975
1980
1990
1985
1995
+1.1%yr-•.
The results obtained for the OH trend and AOH would not
!
i
i
d2
3
.........
-10
1975
i
1980
.-•--
,
,
,
,
!
1985
Year
'-"----..4
,
,
,
,
i
1990
,
,
,
1995
changemuchwhenthe AMCF valuewouldhavebeenkept
fixed. As shownby the box modelsimulations,the AMCF
perturbationdiminishesat a rate that equalsthe MCF lifetime (about5 years). Sincewe startthe modelrunsat 1975,
the low sensitivityto AMCF is not surprising.
Usingtheseresults,the calculatedatmospheric
lifetime of
MCF is 4.7 q- 0.1 yearsin 1978 and4.5 q- 0.1 yearsin 1993
(lrr errorsfrom emissionuncertaintiesonly). This lifetime
consistsof a part that is determinedby dissolutionin the
oceans(83 years),a lifetimetowed stratospheric
destruction
(50 ye•s), anda p•t thatis dueto oxidationby tropospheric
OH (5.5 ye•s in 1978 and 5.2 ye•s in 1993). The MCF
destructionby OH changessignificantlywith height. The
averagelifetime at the surface(1000-950 hPa) is about 3
ye•s, whereasat 200 hPa a lifetime of almost24 ye•s is
calculated.This is causedby the highertemperatures
and
OH levels close to the surface.
If the uncertainties in emissions and other MCF
sinks are
taken into account, the MCF lifetime towed OH oxidation
1975
1980
1985
Year
1990
1995
iscalculated
tobe5 q+l.oyearsin 1978(fullrange
e•or).
Theco•esponding
OHnumber
density
equals
1...nn+o.oo
o.15 x
cm-a (100•100 hPa). For CH4, theseOH
Figure 6. (a) Box modelsimulationof the globalMCF con- 10a molecules
centration;(b) sensitivityof the global MCF concentration concentrations
imply a lifetime throughOH destructionof
to the four runs(seetext); (c) sensitivityof the globalMCF 9.2}• ye•s. Owing
to thetrendin OH,these
numbers
concentration
to perturbations
in AOH, the OH trend(T), change
to1.07+0'00
and AMCE
-o.17x 10amolecules
cm-a and... o.8ye•s
in 1993,respectively.The uncertainties
•e calculatedusing
theresultsof the sensitivityruns 1 and2 listedin Table6. In
paaicul•, the largeuncertaintyassumedin the stratospheric
adjustingthe OH trend.In otherwords,the trendin OH is lifetime (50 ß 25 years)is responsiblefor the uncertainty
sensitive to an incorrect estimate of the emission trend. Fiin the global averagedOH concentrationand the methane
nally,the signalfrom a perturbedinitialMCF concentration lifetime. In fact, this uncertaintyin s•atosphericlossmay
(+10% in 1975)decaysrelativelyrapidly.The exponential be muchsmaller,thusprovidingan uppere•or limit.
decaytimeequalstheMCF lifetime,whichis about5 years.
Now, we return to the full three-dimensionalmodel and
reestimateAOH, the OH trend, and AMCF under the con-
ditions of the same four sensitivityruns. The resultsare
5. Sensitivity of the Global OH Concentration
tained with simulations that are based on the emissions used
Basedon publishedemissionestimates[Midgleyand McCulloch,1995; Prinn et al., 1995], we infer a positivetrend
in OH, althoughwe have shownthat the calculatedtrend
byPrinnetal. [ 1995].It is shownthatAOH runsfrom- 19%
in OH is sensitive to the assumed trend in the emissions.
givenin Table6. Alsogivenin the tablearetheresultsob-
(run 1) to +4% (run 2). As expected,theseextremesoccur Moreover, the deduced trend is almost uniform for all stawhentheperturbations
of theemissionandtheMCF sinks tions. Theseresultsthusshowthat the comparisonbetween
improvessigall lead to a largeror smallertropospheric
MCF concentra- the modelandthe ALE/GAGE measurements
tion,respectively.
Consequently,
significantly
more(or less) nificantlyif we apply a linear trendto the globalOH field.
OH is requiredto restoretheagreement
betweenthemodel The optimizedtrendindicatesan increaseof almost7% in
results and the measurements.
theglobalOH concentration
overtheperiod1978-1993. Can
The calculated trend in the OH concentration of 0.46%
sucha trendbe explainedon the basisof reasonableassumpyr- • vanishes
whentheemission
trendis decreased.
Alter- tions?To answerthis question,we analyzethe responseof
natively,
a trendof about1%year-• isfoundwhenthetrend the globalOH concentrationin our modelto severalpertur-
KROLET AL.: OH TRENDFROMMETHYLCHLOROFORM
MEASUREMENTS
10,707
Table 6. Ensemble-Weighted
Valuesfor/.XOH, OH trend,andAMCF, Obtainedfor
the Four SensitivityRuns(See Text) and With the EmissionsUsed by Prinn et al.
[19951
Run
Table 5
1
2
3
AOH, %
-4.8
-19.3
Trend,% yr-a
q- 0.7
q- 0.8
0.46 q- 0.09
0.37 q- 0.10
+4.2 q- 0.7
-8.7 q- 0.7
0.44 q- 0.08
1.08 q- 0.08
4
-1.6
Prinn et al. [1995]
-3.8 q- 0.8
q- 0.7
AMCF,%
5.5 q- 3.7
9.6 q- 2.8
1.6 q- 2.7
q- 0.07
9.0 q- 3.1
-1.5 q- 3.2
0.42 q- 0.10
3.3 q- 4.0
-0.11
The resultsobtainedin section3 are alsogiven. The valuesrefer to all stationsgrouped
together.Uncertaintyrangesrefer to l rr.
bations. It is not our goal here to make an independentes- Troposphericphotolysisrate perturbationsoccur predomitimate of the global OH changesbut to investigatewhich nantlyat higherlatitudesduringspring.
factorsmighthavecontributedto a positivechangein OH.
4. Temperaturesin the model lower atmosphereare inFirst we calculatethe global OH concentrations
by fixing creasedover the 1978-1993 periodby 0.2øC basedon metethe CHa and CO concentrations to the 1978 levels. Subse- orologicalobservations.Althoughit is very difficult to isoquently,the following six perturbations
are applied.
late the anthropogenic
signalfrom the largenaturalvariabil1. The global mean CHa concentrationin the model is ity, thesetemperatureanomaliesare expectedto be at least
increasedto that observedin 1993, i.e., from 1550 ppbv to partly causedby the global warming effect of increasing
1722 ppbv [Schimelet al., 1996]. It may be expectedthat greenhouse
gases[Schimelet al., 1996]. Increasingtemperthis contributesto an OH reduction [Crutzen and Zimmer- aturesare expectedto be accompaniedby increasingwater
mann, 1991].
vaporconcentrations,
which are calculatedfrom the change
2. The globalCO concentrations
in the model are reduced in water vapor saturationpressure.
to those in 1993. Khalil and Rasmussen[1994] and Novelli
5. Water vapor increaseshave been observedover the
et al. [1994] reporta rapid declineof atmosphericCO up to oceans,which may in part be due to increasingwind ve8% yr-• since1990. However,duringtheearly1980san locities [Nichollset al., 1996]. In fact, water vaporincreases
increase
of almost1%yr- • wasobserved
in themidlatitude ashighas 13%decade
-• havebeenobserved
in thetropNH, while no trends could be detected in the SH [Zander ics [Gaffen et al., 1992], which is likely to be importantfor
et al., 1989; Brunke et al., 1990]. In our sensitivitystudy OH formationthroughreactions(1) and (2). Here we apply
we adopta globalCO decreaseof 6.5% overthe 1978-1993
period.
3. The photolysisrates are recalculatedto accountfor
stratospheric
ozonedepletion,henceincreasedUV penetration into the troposphereand consequentenhancementof
OH throughreactions(1) and (2). The stratospheric
ozone
lossesobservedby the TOMS satelliteinstrument(version7
data) are usedto calculatelatitudinaland heightdependent
trends in the photolysisrates [De Winter-Sorkina, 1997].
Table 7. Responsesof Global OH Concentrationsto
Perturbations
in the Model
Perturbation(SeeText)
OH Change,%
1 (11% CHa increase)
2 (6.5% CO decrease)
3 (stratospheric
ozoneloss)
-1.1
4 (0.2øCtemperature
increase)
+0.1
5 (10% tropicalH20 increase)
6 (10% NOx emissionincrease)
7 (simultaneous
perturbation)
+1.7
+1.7
+2.0
+2.0
+6.0
an overall 10% water vapor increasein the tropics(30øN30øS).
6. The modelOH levelsappearto be very sensitiveto the
abundanceof NOx. IncreasingNOx enhancesboth the formationof ozoneand the recyclingof OH radicals[Van Dop
and Krol, 1996; Eisele et al., 1997]. Since reportsof trends
in troposphericozone concentrationsare sparseand sometimesambiguous[Logan, 1994], we prefernot to perturbthe
calculated ozone field but rather the NOz emissions,increas-
ing them by 10%.
Table7 liststhe sensitivityof the global OH concentration
to the variousperturbations.As notedpreviously[Van Dop
and Krol, 1996], the atmosphericchemistry systemoften
acts as a negativefeedbacksystemso that the responseto
a perturbationis smaller than the perturbationitself. Nevertheless, we calculate that the small decrease in OH due
to the CH4 increase(-1.1%) is overcompensated
by OH increasesdue to higher photolysisrates (+2%), higher NOz
emissions(+2%), decreasedCO, and increasedwater vapor
(both +1.7%). The temperatureincreasehas only a minor
effecton globalOH. When all perturbationslistedin Table7
are applied simultaneously,we calculatea 6% net OH increase, which is close to the value of 7% we estimated from
the MCF
simulations.
10,708
KROL ET AL.' OH TREND FROM METHYLCHLOROFORM MEASUREMENTS
It shouldbe noted,however,that only a few of the assumptions
in thissensitivity
studyarewell documented
(e.g.,
the CH4 increaseandthe stratospheric
ozoneloss). Others
havea weakerexperimental
basis(e.g.,theincreasein water
vapor) and a potentiallylarge effect on the OH concentrations.Thereforetheperturbation
calculations
presented
here
shouldbe conceivedas a sensitivityanalysis.It shouldbe
emphasized
thatchanges
in theglobalatmospheric
compositionsince1978 are to a largeextentunknown.This is especiallytruefor themostimportantregionfor OH, thelower
troposphere
in thetropics.In thisregion,largechanges
may
be occurringdueto biomassburningandrapidlyemerging
industrial activities. Nevertheless,it is shown here that ob-
and measurements),
differencesin the methodologymight
giverise to differentresults.In particular,the functionalfit
of both the measurements and the model simulations differs
from the methoddescribedby Prinn et al. [ 1995].
Finally,the calculatedOH distributionis a possiblecause
of the differences.Whereasin the Prinn et al. [ 1995] model
more OH is presentin the SH, our tropospheric
chemistry
modelcalculatesmoreOH in theNH dueto the higherNO:•
and03 levels,consistent
with previousestimates[e.g.,Crutzen and Zimmermann,1991]. It is unlikely, however,that
differences in the OH distribution influence the calculated
OH trend. The consistencyof the trendscalculatedfor all
ALE/GAGE
measurement stations indicates an OH increase
servedchangesin theatmospheric
composition,
in combina- on a global scale. Therefore it seemsthat the downward
tion with somereasonable
assumptions,
areconsistent
with trend sincethe start of industrialization[e.g., Crutzenand
Zimmermann,1991] may have reversedsincethe past sevsignificantincreases
in OH overthepastdecades.
eral decades.
6. Discussion
The resultsthatarepresented
heredifferin somerespects
from thoseof Prinn et al. [1995]. The lower atmospheric
lifetime (200-1000 hPa)of MCF givenby Prinn et al. [ 1995]
equals4.6 + 0.3 years,which agreeswell with our estimate
(1000-100 hPa,4.7 yearsin 1978, 4.5 yearsin 1993, and 1oerror due to emissionuncertaintyof 0.1 year). Prinn et al.
[1995]reporta trendin OH of0.0 q-0.2%yr-l, whereas
we
constrained
theOH trendto therangebetween-0.1 and 1.1%
7. Summary and Conclusions
An ensemble(Monte Carlo) techniquewasusedto obtain
a bestestimatefor the globalOH concentration
and for the
linear trend in OH between 1978 and 1993, simulated with a
three-dimensional
transportmodelfor the troposphere.The
OH fieldswere adoptedfrom a modelsimulationwith backgroundCH4-CO-NO•-HO• chemistry. The monthly averagedOH fieldswere adjusteduntil a bestfit with the mea-
yr-1 witha mostlikelyvalueof 0.46%yr-1. Thisrange
surements was obtained.
was obtainedby adjustingthe emissiontrend. It is difficult
to unambiguouslyestablishthe causeof the differences,but
severalpossiblereasonscan be mentioned.
One possiblecause is related to the model resolution.
Prinn et al. [1995] resolvethe atmosphereinto 12 boxes.
The OH concentrations
in theboxesshouldthereforebe representativefor a very large domain. The OH concentration
may vary considerablywith longitude,for instancedue to
NO•: emissionsand watervaporvariability[Kanakidouand
also determined.
Crutzen, 1993; Kanakidou et al., 1995]. Moreover, most
MCF emissionsare accompanied
by NO•: emissions,which
might lead to a positivecorrelationbetweenOH and MCF.
Also, the ALE/GAGE stationsusuallysamplecleanbackgroundair. In the model of Prinn et al. [ 1995] thesemeasurementsare comparedto the concentrationsin semihemisphericboxes,in whichtheMCF emissions
alsotakeplace.
Anotherpossiblecausemay be associatedwith the treatment of stratosphericMCF breakdown. In our model,
stratospheric
MCF lossesare appliedat the •top of the
model.
These losses are calculated on the basis of two-
dimensionalmodel calculations[Kanakidou et al., 1995].
Prinn et al. [ 1995] explicitlyrepresentthe stratosphere,
althoughcrudely resolved. The MCF destructionrate in the
stratosphere,
as well as the the stratosphere-troposphere
exchangetime, appearto significantlyaffecttheMCF lifetime.
A third causemay be the estimationtechnique. Prinn
et al. [ 1995] usean optimalestimationinversiontechnique
to obtainthe MCF lifetime and its linear trend. Although
our methodshould,in principle,givecomparableresults(it
also minimizes
the differences between model simulations
A linear trend in the OH field was
Ratherfast convergence
was obtainedwith the ensemble
technique.However,it appearedthat the choiceof the initial random distributions
from which the random variables
(trend,AOH) are drawnis very critical.
A positive
trendin OH of 0.46 q- 0.6% yr-1 wascalculated(full range error). We deducedthat he lifetime of
MCF in the troposphere(1000-100 hPa) has changedfrom
4.7+0.1 years in 1978 to 4.5q-0.1 years in 1993 (lo-error includesonly the uncertaintyin emissions). The correspondingglobal mean OH number densitiesindicateincreasesfrom 1....
nn+0.00
0.15x 106molecules
cm-3 in 1978to
1....
•-/+0.09
o.• X 106 molecules
cm-a in 1993.Thecalculated
methanelifetime due to destructionby OH in the samedo-
main(1000-100
hPa)decreases
froma
•+•.•8 yearsin 1978
-•.z-,_0.
to 8"-'-{3.8
fi+•.6yearsin 1993 Theerrors
nowalsoinclude
the
uncertainties
in the stratospheric
andoceanMCF sinks.The
errorrangeadoptedfor theMCF lifetimedueto stratospheric
loss (50+25 years) is particularly large and leads to relatively large uncertaintiesin the OH numberdensityand
methanelifetime. These error rangesare thereforeconsidered to be full rangeerrors.
The estimated OH trend is sensitive to the trend in MCF
emissionestimates.If the trendis perturbedby -4.4% at the
startof the simulationand by +4.4% at the end of the sim-
ulation,thecalculated
OH trendincreases
to 1.! % yr-•. If
the trendis perturbedin the oppositedirection,however,the
calculated trend vanishes.
This indicates that the OH trend
calculationdependscriticallyon the appliedMCF emission
trend. Simplebox model calculationssupportthis conclu-
KROL ET AL.' OH TREND FROM METHYLCHLOROFORM
measurement
and is consistent for all
stations.
10,709
One is generallynotinterested
in thecompleteprobability
sion. The estimatedtrend in OH is expectedto be insensitive to other model uncertainties
MEASUREMENTS
densityf(qJId), but only in its firstfew moments.These
can be obtainedby usinga frequencyinterpretationof the
probability
densityf (q;).Forinstance,
theminimalvariance
estimate
of f(qJId) is themeanof thisprobability
density,
The optimizedOH trend suggeststhereforea global increasein OH of almost7% over the period 1978-1993. A
sensitivityanalysiswith our globalchemistrymodel shows givenby
thatreasonableassumptions
aboutglobalemissionestimates
andthemodelboundaryconditionsareconsistent
with a sig-
N
•i=x •'if(dl•i)
• - ff •Pf(•P)f(dI•P)&P
f (•P)f (dl•P)&P
nificant increasein OH. Prinn et al. [1995] estimateda zero
(A3)
trendin OH overthe sameperiod. Sincewe usedthe same
emission data, the different results must be due to the mod-
Its variance can be obtained as
eling approach.Other inversemodelingstudieson the MCF
data are requiredbefore definiteconclusionsabout an OH
trend can be drawn.
2_
O'½
--
Ei51
f(d•bi)
(A4)
This frequencydescriptionof the first two momentsof
f(bld) is obtained
fromanensemble
of N modelrunsin a
Appendix: Ensemble Method
The data-assimilation
Monte
Carlo simulation.
Returning
to thespecific
examplediscussed
in thispaper,
method used here is a so-called en-
(A3), (A4), and(A2) correspond
to equations
(8),
semblesmoother.The lifetime of MCF is about 4.5 years. equations
Therefore,the present-dayMCF concentrationdependson (9) and (11) in section2.3.
the OH concentrationof the past few years. A smoother
Acknowledgments.We thankSanderHouwelingandFrank
methodis an inversemethodthat accountsfor this dependence.
An ensemble
method uses statistical Monte
Carlo
Dentenerfor their helpful advice and Maria Kanakidoufor the
oceanicMCF removalcode. We thankRonPrinnandXuepeng
methodsto estimatetheprobabilitydensityof themodelevo-
Zhao for providingtheir MCF emissionsestimates.We thank the
lution.
CarbonDioxideInformationAnalysisCenterfor the ALE/GAGE
Van Leeuwen and Evensen [1996] showed that data as-
similationessentiallycombinesthe probability densitiesof
a model and measurement data [see also Tarantola, 1987;
MCF measurements.
Thisresearch
wassupported
by SpaceResearch
Organization
Netherlands.
P.J.vanLeeuwenwassupported
by theNationalResearch
Program(NOP II) grant951237.
Lorenc,1988]. The resultingprobabilitydensitycontainsinformation
about both the model and the data. The essential
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