cam-chem: description and evaluation of interactive ... · conclusions are in sect. 8. 2 cam4...

Geosci Model Dev 5 369ndash411 2012wwwgeosci-model-devnet53692012doi105194gmd-5-369-2012copy Author(s) 2012 CC Attribution 30 License

GeoscientificModel Development

CAM-chem description and evaluation of interactive atmosphericchemistry in the Community Earth System Model

J-F Lamarque1 L K Emmons1 P G Hess2 D E Kinnison1 S Tilmes1 F Vitt 1 C L Heald3 E A Holland1P H Lauritzen1 J Neu4 J J Orlando1 P J Rasch5 and G K Tyndall1

1National Center for Atmospheric Research Boulder CO USA2Cornell University Ithaca NY USA3Colorado State University Fort Collins CO USA4Jet Propulsion Laboratory Pasadena CA USA5Pacific Northwest National Laboratory Richland WA USA

Correspondence toJ-F Lamarque (lamarucaredu)

Received 8 August 2011 ndash Published in Geosci Model Dev Discuss 16 September 2011Revised 24 February 2012 ndash Accepted 6 March 2012 ndash Published 27 March 2012

Abstract We discuss and evaluate the representation of at-mospheric chemistry in the global Community AtmosphereModel (CAM) version 4 the atmospheric component of theCommunity Earth System Model (CESM) We present a va-riety of configurations for the representation of troposphericand stratospheric chemistry wet removal and online and of-fline meteorology Results from simulations illustrating theseconfigurations are compared with surface aircraft and satel-lite observations Major biases include a negative bias inthe high-latitude CO distribution a positive bias in upper-troposphericlower-stratospheric ozone and a positive biasin summertime surface ozone (over the United States andEurope) The tropospheric net chemical ozone productionvaries significantly between configurations partly related tovariations in stratosphere-troposphere exchange Aerosol op-tical depth tends to be underestimated over most regionswhile comparison with aerosol surface measurements overthe United States indicate reasonable results for sulfate es-pecially in the online simulation Other aerosol species ex-hibit significant biases Overall the model-data comparisonindicates that the offline simulation driven by GEOS5 me-teorological analyses provides the best simulation possiblydue in part to the increased vertical resolution (52 levels in-stead of 26 for online dynamics) The CAM-chem code asdescribed in this paper along with all the necessary datasetsneeded to perform the simulations described here are avail-able for download atwwwcesmucaredu

1 Introduction

Atmospheric chemistry plays an integral role in the distri-bution of the non-CO2 radiatively active gases and aerosols(Forster et al 2007) In addition climate and its evolu-tion strongly influences atmospheric chemistry and air qual-ity (Chen et al 2009 Jacob and Winner 2009) Becauseof the nonlinear behavior of chemistry and the importanceof regionally-varying emissions it is critical to represent theinteractions between chemistry and climate using a globalthree-dimensional model

We discuss here the representation of atmospheric chem-istry (gas phase and aerosol species) in the global Commu-nity Atmosphere Model (CAM version 4 Neale et al 2011)the atmospheric component of the Community Earth Sys-tem Model (CESM) Due to its full integration in the CESMCAM-chem provides the flexibility of using the same codeto perform climate simulations and simulations with spec-ified meteorological fields Therefore CAM-chem can beused in three separate modes all embedded within CESM(1) a fully coupled Earth System model (ie with all cli-mate components active which offers the possibility to con-nect the chemistry with biogeochemical processes in the landand ocean models) (2) with specified sea-surface and sea-ice distributions and (3) with specified meteorological fieldsConfigurations (1) and (2) are usually referred to as onlinewhile configuration (3) is referred to as offline In configura-tions (2) and (3) only the atmosphere and land componentsof CESM are active When run in a fully coupled modethe ocean and ice dynamics are also allowed to respond to

Published by Copernicus Publications on behalf of the European Geosciences Union

370 J-F Lamarque et al CAM-chem description and evaluation

Atmosphere13 Model13 version13 413

Dynamics13

Physics13

Chemistry13

Coupler13

Lan13 Land13 Model13

Lan13 Ocean13 Model13

Lan13 Sea-shy‐ice13 Model13

Radiagton13

Analysis13

Fig 1 Schematic representation of the Community Earth System Model The dotted box indicates the features associated with the use ofspecified dynamics (use of meteorological analyses and lack of feedback through radiation)

changes in the atmosphere This allows ldquoslow responsesrdquo(for example in ocean currents and ice extent) within the cli-mate system to occur that are prevented in modes 2 and 3Mode 2 only allows fast atmospheric responses and land re-sponses to occur while mode 3 does not allow any meteoro-logical responses

The availability of a specified dynamics configuration ofCAM-chem offers a number of advantages over traditionalchemistry-transport models (1) It allows for consistent sim-ulations between the online and offline versions (2) it allowsthe offline version to be run in an Earth System frameworkso as to fully exploit other model components In particularit allows an incorporation of the biogeochemical algorithmsin the land components (3) it allows for the radiative algo-rithms incorporated into CAM4 to be fully exploited in theoffline version This allows a calculation of the instantaneousradiative forcing within the offline model version includinga calculation of the instantaneous radiative forcing for spe-cific events (eg forest fires) (Pfister et al 2008 Randersonet al 2006) (4) it allows for the strict conservation of tracermass by accounting for changes in mixing ratio as the watervapor concentration changes within the atmosphere In addi-tion CAM-chem uses a chemical preprocessor that providesextensive flexibility in the definition of the chemical mecha-nism allowing for ease of update and modification

Recent applications of CAM-chem have demonstratedits ability to represent tropospheric (Aghedo et al 2011Lamarque et al 2010 2011a b) and stratospheric (Lamar-que et al 2008 Lamarque and Solomon 2010) conditionsincluding temperature structure and dynamics (Butchart etal 2011) Offline CAM-chem has been used in the Hemi-spheric Transport of Air Pollution (HTAP) assessments(Anenberg et al 2009 Fiore et al 2009 Jonson et al 2010Shindell et al 2008 Sanderson et al 2008)

It is the purpose of this paper to document all these as-pects of CAM-chem along with results from associatedsimulations and their evaluation against observations The

paper is therefore organized as follows in Sect 2 we pro-vide a short introduction to CAM4 In Sect 3 we discussall the chemistry-specific parameterizations of CAM-chemThe implementation of the specified-dynamics version is dis-cussed in Sect 4 Section 5 presents the various chemicalmechanisms used in this study Model simulation setups in-cluding emissions are discussed in Sect 6 while the com-parison to observations is shown in Sect 7 Discussion andconclusions are in Sect 8

2 CAM4 description and definition of CAM-chem

CAM-chem refers to the implementation of atmosphericchemistry in the Community Earth System Model (CESM)All the subroutines responsible for the representation ofchemistry are included in the build of CESM only whenexplicitly requested therefore a user only interested in aclimate simulation (for which the atmospheric composi-tion is specified) is not impacted by the additional codeand additional cost to simulate chemistry The chemistryis fully integrated into the Community Atmosphere Model(Fig 1) meaning that the representation of dynamics (in-cluding transport) and physics (radiation convection andlarge-scale precipitation boundary-layer and diffusion) is thesame whether the model is using computed (online) or spec-ified (specified dynamics dotted box in Fig 1) meteorolog-ical fields CAM-chem is therefore CAM4 with chemistryactivated

In the configurations described in this paper atmosphericchemistry interacts with the climate only through radiationsince no cloud-aerosol interaction is available in CAM4 theimpact on temperature is of course overwritten in the case ofspecified dynamics Also the ocean and sea-ice componentsof the CESM are inactive due to the use of specified sea-surface temperatures and sea-ice distribution On the otherhand the land component is fully active and provides thechemistry with deposition velocities and biogenic emissions

Geosci Model Dev 5 369ndash411 2012 wwwgeosci-model-devnet53692012

J-F Lamarque et al CAM-chem description and evaluation 371

Table 1 Plant functional types in land model of the CESM

Index Plant functional type

1 desert ice and ocean2 needleleaf evergreen temperate tree3 needleleaf evergreen boreal tree4 needleleaf deciduous temperate tree5 broadleaf evergreen tropical tree6 broadleaf evergreen temperate tree7 broadleaf deciduous tropical tree8 broadleaf deciduous temperate tree9 broadleaf deciduous boreal tree10 broadleaf evergreen shrub11 broadleaf deciduous temperate shrub12 broadleaf deciduous boreal shrub13 C3 arctic grass14 C3 non-arctic grass15 C4 grass16 corn17 wheat

When CAM4 is run without chemistry it uses prescribedozone and aerosol fields usually monthly-averaged three-dimensional fields from a previously performed simulationwith CAM-chem

The Community Atmosphere Model version 4 (CAM4Neale et al 2011) was released as the atmosphere compo-nent of the Community Climate System Model version 4(CCSM4) and contains improvements over CAM3 (Collinset al 2006) In particular the finite volume dynamical coreoption available in CAM3 is now the default primarily due toits superior tracer transport properties (Rasch et al 2006)As in CAM3 deep convection is parameterized using theZhang-McFarlane approach (1995) but with modificationsas discussed below while shallow convection follows Hacket al (2006) Additional information on the representationof clouds and precipitation processes can be found in Bovilleet al (2006) Finally processes in the planetary boundarylayer are represented using the Holtslag and Boville (1993)parameterization The vertical coordinate is a hybrid sigma-pressure

Changes made to the representation of deep convectioninclude a dilute plume calculation and the introduction ofConvective Momentum Transport (CMT Richter and Rasch2007 Neale et al 2008) In addition the cloud fraction hasan additional calculation to improve thermodynamic consis-tency A freeze-drying modification is further made to thecloud fraction calculation in very dry environments such asArctic winter where cloud fraction and cloud water estimateswere somewhat inconsistent in CAM3

Altogether only marginal improvements over CAM3 arefound in the large-scale aspects of the simulated climate (seeNeale et al 2011 for a complete description of the modelperformance) Indeed it is found that the implementation

of the finite volume dynamical core leads to a degradation inthe excessive trade-wind simulation but with an accompany-ing reduction in zonal stresses at higher latitudes But CMTreduces much of the excessive trade-wind biases Plume di-lution leads to moister deep tropics alleviating much of themid-tropospheric dry biases and reduces the persistent pre-cipitation biases over the Arabian peninsula and the southernIndian ocean associated with the Indian Monsoon Finallythe freeze-drying modification alleviates much of the winter-time excessive cloud bias and improves the associated sur-face cloud-related energy budget

3 Chemistry-specific parameterizations

CAM-chem borrows heavily from MOZART-4 (Emmons etal 2010 referenced hereafter as E2010) In particular manyof the parameterizations needed to represent atmosphericchemistry in a global model are adapted or expanded fromtheir equivalents in MOZART-4 However for completenesswe will include a brief description of those parameterizations(and their updates whenever applicable)

31 Dry deposition

Dry deposition is represented following the resistance ap-proach originally described in Wesely (1989) as discussedin E2010 this earlier paper was subsequently updated andwe have included all updates (Walcek et al 1986 Walmsleyand Wesely 1996 Wesely and Hicks 2000) Following thisapproach all deposited chemical species (the specific list ofdeposited species is defined along with the chemical mecha-nisms see Sect 4) are mapped to a weighted-combination ofozone and sulfur dioxide depositions this combination rep-resents a definition of the ability of each considered speciesto oxidize or to be taken up by water In particular the latteris dependent on the effective Henryrsquos law coefficient Whilethis weighting is applicable to many species we have in-cluded specific representations for COH2 (Yonemura et al2000 Sanderson et al 2003) and peroxyacetylnitrate (PANSparks et al 2003 Turnipseed et al 2006) Furthermoreit is assumed that the surface resistance for SO2 can be ne-glected (Walcek et al 1986) Finally following Cooke etal (1999) the deposition velocities of black and organic car-bonaceous aerosols are specified to be 01 cm sminus1 over allsurfaces Dust and sea-salt are represented following Ma-howald et al (2006a b)

The computation of deposition velocities in CAM-chemtakes advantage of its coupling to the Community LandModel (CLM Oleson et al 2010 also seehttpwwwcesmucaredumodelscesm10clmCLM4TechNotepdf) Inparticular the computation of surface resistances in CLMleads to a representation at the level of each plant func-tional type (Table 1) of the various drivers for depositionvelocities The grid-averaged velocity is computed as the

wwwgeosci-model-devnet53692012 Geosci Model Dev 5 369ndash411 2012


O2 + hv =gt 2O

10-13 10-12 10-11 10-10 10-9 10-8

sec-1

100000

10000

1000

100

010

001

Pres

sure

hPa

CAMCHEM (26L)

WACCM (66L)

O3 + hv =gt products

10-5 10-4 10-3 10-2

sec-1

100000

10000

1000

100

010

001

O(1D) O(3P)

NO +hv =gt N + O

10-11 10-10 10-9 10-8 10-7 10-6 10-5

sec-1

100000

10000

1000

100

010

001

Cl2O2 +hv =gt Cl + ClOO

0001 0010sec-1

100000

10000

1000

100

010

001

Fig 2 Photolysis rates in CAM and WACCM for 1 January noon at 0 N conditions

weighted-mean over all land cover types available at eachgrid box This ensures that the impact on deposition veloc-ities from changes in land cover land use or climate can betaken into account

In addition the same dry deposition approach is sepa-rately applied to water surfaces ie lakes and oceans in-cluding sea-ice It is then combined with the land-basedvalue weighted by the oceansea-ice and land fractions ineach model grid cell

32 Biogenic emissions

Similar to the treatment of dry deposition over land bio-genic emissions of volatile organic compounds (isoprene andmonoterpenes) are calculated based upon the land coverThese are made available for atmospheric chemistry unlessthe user decides to explicitly set those emissions using pre-defined (ie contained in a file) gridded values Details ofthis implementation in the CLM3 are discussed in Heald etal (2008) we provide a brief overview here

Vegetation in the CLM model is described by 17 plantfunction types (PFTs see Table 1) Present-day land surface

parameters such as leaf area index are consistent withMODIS land surface data sets (Lawrence and Chase 2007)Alternate land cover and density can be either specified or in-teractively simulated with the dynamic vegetation model ofthe CLM for any time period of interest

Isoprene emissions follow the MEGAN2 (Guenther et al2006) algorithms for a detailed canopy model (CLM) Thisincludes mapped PFT-specific emission factors to accountfor species divergent emissions of isoprene These standardemission factors are modulated by activity factors accountingfor the effect of temperature radiation leaf age vegetationdensity (identified by the leaf-area index) and soil moistureThe annual totals are in the range of 500ndash600 Tg yrminus1 de-pending on model configuration and associated climate con-ditions (see Table 9)

Total monoterpene emissions follow the earlier work ofGuenther et al (1995) as implemented in the CLM by Leviset al (2003) Baseline emission factors are specified for eachplant function type and are scaled by an exponential functionof leaf temperature



Table 2 List of input fields required for specified dynamics in CAM

Variable Physical description (units geometric dimensions)

U zonal wind component (m sminus1 3-D)V meridional wind component (m sminus1 3-D)T temperature (K 3-D)PS surface pressure (Pa 2-D)PHIS surface geopotential (m2 sminus2 2-D)TS surface temperature (K 2-D)TAUX zonal surface stress (N mminus2 2-D)TAUY meridional surface stress (N mminus2 2-D)SHFLX sensible heat flux (W mminus2 2-D)LHFLX latent heat flux (W mminus2 2-D) computed from moisture fluxOCNFRAC Grid cell fraction over ocean used in dry deposition (2-D)ICEFRAC Grid cell fraction over ocean ice used in dry deposition based on mete-

orology surface temperatures (2-D)

33 Wet deposition

Wet removal of soluble gas-phase species is the combinationof two processes in-cloud or nucleation scavenging (rain-out) which is the local uptake of soluble gases and aerosolsby the formation of initial cloud droplets and their conver-sion to precipitation and below-cloud or impaction scav-enging (washout) which is the collection of soluble speciesfrom the interstitial air by falling droplets or from the liquidphase via accretion processes (eg Rotstayn and Lohmann2002) Removal is modeled as a simple first-order loss pro-cessXiscav=Xi times F times (1minus exp(minusλ 1t)) In this formulaXiscav is the species mass (in kg) ofXi scavenged in timestep1t F is the fraction of the grid box from which traceris being removed andλ is the loss rate In-cloud scaveng-ing is proportional to the amount of condensate convertedto precipitation and the loss rate depends on the amount ofcloud water the rate of precipitation formation and the rateof tracer uptake by the liquid phase Below-cloud scavengingis proportional to the precipitation flux in each layer and theloss rate depends on the precipitation rate and either the rateof tracer uptake by the liquid phase (for accretion processes)the mass-transfer rate (for highly soluble gases and smallaerosols) or the collision rate (for larger aerosols) In CAM-chem two separate parameterizations are available Horowitzet al (2003) from MOZART-2 and Neu and Prather (2011)

The distinguishing features of the Neu and Prather schemeare related to three aspects of the parameterization (1) thepartitioning between in-cloud and below cloud scavenging(2) the treatment of soluble gas uptake by ice and (3) ac-counting for the spatial distribution of clouds in a columnand the overlap of condensate and precipitation Given acloud fraction and precipitation rate in each layer the schemedetermines the fraction of the gridbox exposed to precipita-tion from above and that exposed to new precipitation for-mation under the assumption of maximum overlap of theprecipitating fraction Each model level is partitioned into

as many as four sections each with a gridbox fraction pre-cipitation rate and precipitation diameter (1) cloudy withprecipitation falling through from above (2) cloudy with noprecipitation falling through from above (3) clear sky withprecipitation falling through from above (4) clear sky withno precipitation falling from above Any new precipitationformation is spread evenly between the cloudy fractions (1and 2) In region 3 we assume a constant rate of evapora-tion that reduces both the precipitation area and amount sothat the rain rate remains constant Between levels we av-erage the properties of the precipitation and retain only twocategories precipitation falling into cloud and precipitationfalling into ambient air at the top boundary of each level Ifthe precipitation rate drops to zero we assume full evapora-tion and random overlap with any precipitating levels belowOur partitioning of each level and overlap assumptions are inmany ways similar to those used for the moist physics in theECMWF model (Jakob and Klein 2000)

The transfer of soluble gases into liquid condensate is cal-culated using Henryrsquos Law assuming equilibrium betweenthe gas and liquid phase Nucleation scavenging by ice how-ever is treated as a burial process in which trace gas speciesdeposit on the surface along with water vapor and are buriedas the ice crystal grows Karcher and Voigt (2006) havefound that the burial model successfully reproduces the mo-lar ratio of HNO3 to H2O on ice crystals as a function oftemperature for a large number of aircraft campaigns span-ning a wide variety of meteorological conditions We usethe empirical relationship between the HNO3H2O molar ra-tio and temperature given by Karcher and Voigt (2006) todetermine in-cloud scavenging during ice particle formationwhich is applied to nitric acid only Below-cloud scaveng-ing by ice is calculated using a rough representation of theriming process modeled as a collision-limited first order lossprocess Neu and Prather (2011) provide a full description ofthe scavenging algorithm



Online26 levels

GEOS5MERRA56 levels

Fig 3 Distribution of vertical levels in the various model configu-rations

On the other hand the Horowitz approach uses the raingeneration diagnostics from the large-scale and convectionprecipitation parameterizations in CAM equilibrium be-tween gas-phase and liquid phase is then assumed based onthe effective Henryrsquos law Below-cloud removal is applied tonitric acid and hydrogen peroxide only

While using the same information on rain formation andprecipitation the wet removal of aerosols is handled sep-arately using the parameterization described in Barth etal (2000)

34 Lightning

The emissions of NO from lightning are included as inE2010 ie using the Price parameterization (Price and Rind1992 Price et al 1997) scaled to provide a global an-nual emission of 3ndash5 Tg(N) yrminus1 (see Table 9) slightly lower

than the global estimate of Hudman et al (2007) Thisrange is due to interannnual variability in convective activityThe vertical distribution follows DeCaria et al (2006) as inE2010 In addition the strength of intra-cloud (IC) lightningstrikes is assumed to be equal to cloud-to-ground strikes asrecommended by Ridley et al (2005)

35 Polar stratospheric clouds and associated ozonedepletion

The representation of polar stratospheric clouds is an updateover the version used in all the CCMval-2 analysis papers(eg Austin et al 2010) in which it was shown that CAM-chem (identified in those studies as CAM35) was underes-timating the extent and depth of Antarctic ozone hole deple-tion In particular we are now using a strict enforcement ofthe conservation of total (organic and inorganic) chlorine andtotal bromine under advection Indeed it has been identifiedthat the existence of strong gradients in the stratosphere ledto non-conservation issues of the total bromine and chlorineas computed from the sum of their components related to in-accuracies in transport algorithms We are therefore forcingthe conservation through the addition of two additional trac-ers TCly and TBry These tracers are specified at the lowerboundary and reflect the total amount of Br and Cl atoms(organic and inorganic) in the atmosphere following the ob-served concentrations of all considered halogen species inthe model To ensure mass conservation at each grid pointthe total mass after advection of the summed Cl-containingspecies is scaled to be the same as the mass of TCly Uniformscaling is applied to each component The overall impact isto increase the amount of reactive bromine and chlorine inthe polar stratosphere and consequently of ozone loss

In addition we have updated the heterogeneous chemistrymodule to reflect that the model was underestimating the su-percooled ternary solution (STS) surface area density (SAD)Heterogeneous processes on liquid sulfate aerosols and polarstratospheric clouds are included following the approach ofConsidine et al (2000) This approach represents the sur-face area density effective radius and composition of liquidbinary sulfate (LBS) supercooled ternary solution (STS) ni-tric acid tri-hydrate (NAT) and water-ice There are six het-erogeneous reactions on liquid sulfate aerosol (LBS or STS)five reactions on solid NAT aerosol and six reactions on solidwater-ice aerosol The process of denitrification is derived inthe chemistry module the process of dehydration is derivedin the prognostic H2O approach used in CAM Details of theheterogeneous module are discussed in Kinnison et al 2007This previous version (used in the CCMval-2 simulations) al-lowed the available HNO3 to first form nitric acid trihydrate(NAT) then the resulting gas-phase HNO3 was available toform STS SAD In the new version the approach is reversedwith the available HNO3 to first form STS aerosol This en-hances the STS SAD that is used to derive heterogeneousconversion of reservoir species to more active odd-oxygen



Table 3 List of species in the considered chemical mechanisms In addition we list the chemical solver (Explicit or Implicit) the potentialuse of emissions of lower-boundary conditions and of deposition processes (wet and dry)

Number Species Formula Solver Emissions Boundary Wet Drynamelowast condition deposition deposition

1 ALKO2 C5H11O2 I2 ALKOOH C5H12O2 I X X3 BIGALD C5H6O2 I X4 BIGALK C5H12 I X5 BIGENE C4H8 I X6 C10H16 I X7 C2H2 I X8 C2H4 I X9 C2H5O2 I0 C2H5OH I X X X11 C2H5OOH I X X12 C2H6 I X13 C3H6 I X14 C3H7O2 I15 C3H7OOH I X16 C3H8 I X17 CH2O I X X X18 CH3CHO I X X X19 CH3CN I X X X20 CH3CO3 I21 CH3COCH3 I X X22 CH3COCHO I X23 CH3COOH I X X X24 CH3COOOH I X X25 CH3O2 I26 CH3OH I X X X27 CH3OOH I X28 CH4 E X29 CO E X X30 CRESOL C7H8O I31 DMS CH3SCH3 I X32 ENEO2 C4H9O3 I33 EO HOCH2CH2O I34 EO2 HOCH2CH2O2 I35 GLYALD HOCH2CHO I X X36 GLYOXAL C2H2O2 I37 H2 E X38 H2O2 I X X39 HCN I X X X40 HCOOH I X X X41 HNO3 I X X42 HO2 I43 HOCH2OO I44 HO2NO2 I X X45 HYAC CH3COCH2OH I X X46 HYDRALD HOCH2CCH3CHCHO I X X47 ISOP C5H8 I X48 ISOPNO3 CH2CHCCH3OOCH2ONO2 I X49 ISOPO2 HOCH2COOCH3CHCH2 I50 ISOPOOH HOCH2COOHCH3CHCH2 I X X

lowast The convention in the ldquoSpecies namerdquo column refers to the actual naming as it appears in the code (limited to 8 characters) All chemistry subroutines can identify the array indexfor a specific species through query functions associated with the name as listed here



Table 3Continued

Number Species Formula Solver Emissions Boundary Wet Dryname condition deposition deposition

51 MACR CH2CCH3CHO I X52 MACRO2 CH3COCHO2CH2OH I53 MACROOH CH3COCHOOHCH2OH I X X54 MCO3 CH2CCH3CO3 I55 MEK C4H8O I X56 MEKO2 C4H7O3 I57 MEKOOH C4H8O3 I X X58 MPAN CH2CCH3CO3NO2 I X59 MVK CH2CHCOCH3 I X60 N2O E X61 N2O5 I62 NH3 I X X X63 NO I X X64 NO2 I X X65 NO3 I66 O I67 O1D O I68 O3 I X69 OH I70 ONIT CH3COCH2ONO2 I X X71 ONITR CH2CCH3CHONO2CH2OH I X X72 Pb E X73 PAN CH3CO3NO2 I X74 PO2 C3H6OHO2 I75 POOH C3H6OHOOH I X X76 Rn E X77 RO2 CH3COCH2O2 I78 ROOH CH3COCH2OOH I X X79 SO2 I X X X80 TERPO2 C10H17O3 I81 TERPOOH C10H18O3 I X X82 TOLO2 C7H9O5 I83 TOLOOH C7H10O5 I X X84 TOLUENE C7H8 I X85 XO2 HOCH2COOCH3CHOHCHO I86 XOH C7H10O6 I87 XOOH HOCH2COOHCH3CHOHCHO I X X

Bulk aerosolspecies

1 CB1 C hydrophobic black carbon I X X2 CB2 C hydrophilic black carbon I X X X3 DST01 AlSiO5 I X X X4 DST02 AlSiO5 I X X X5 DST03 AlSiO5 I X X X6 DST04 AlSiO5 I X X X7 NH4 I X X8 NH4NO3 I X X9 OC1 C hydrophobic organic carbon I X X10 OC2 C hydrophilic organic carbon I X X X11 SOA C12 I X X12 SO4 I X X X13 SSLT01 NaCl I X X X14 SSLT02 NaCl I X X X15 SSLT03 NaCl I X X X16 SSLT04 NaCl I X X X



Table 3Continued


Stratosphericspecies

1 BRCL BrCl I2 BR Br I3 BRO BrO I4 BRONO2 BrONO2 I X5 BRY E X6 CCL4 CCl4 E X7 CF2CLBR CF2ClBr E X8 CF3BR CF3Br E X9 CFC11 CFCl3 E X10 CFC12 CF2Cl2 E X11 CFC13 CCl2FCClF2 E X12 CH3CL CH3Cl E X13 CH3BR CH3Br E X14 CH3CCL3 CH3CCl3 E X15 CLY E X

16 CL Cl I17 CL2 Cl2 I18 CLO ClO I19 CLONO2 ClONO2 I X20 CO2 E X21 OCLO OClO I22 CL2O2 Cl2O2 I23 H I24 H2O I X25 HBR HBr I X26 HCFC22 CHF2Cl E X27 HCL HCl I X28 HOBR HOBr I X29 HOCL HOCl I X30 N I

depleting species Observational studies have shown thatSTS is the main PSC for odd-oxygen loss and therefore thisis a better representation of stratospheric heterogeneous pro-cesses (eg Lowe and MacKenzie 2008) After formation ofSTS aerosol there is still enough gas-phase HNO3 availableto form NAT The effective radius of NAT is then used to set-tle the condensed phase HNO3 and eventual irreversible den-itrification occurs These updates have led to a considerableimprovement in the representation of the polar stratosphericozone loss (see Sect 74)

36 Photolysis

In CAM-chem for wavelengths longer than 200 nm (up to750 nm) the lookup table approach from MOZART-3 (Kin-nison et al 2007) is the only method available at this timeWe have also included the online calculation of photoly-sis rates for wavelengths shorter than 200 nm (121ndash200 nm)

from MOZART-3 this was shown to be important for ozonechemistry in the tropical upper troposphere (Prather 2009)Therefore the combined (online-lookup table) approach isused in all model configurations In addition because thestandard configuration of CAM only extends into the lowerstratosphere (model top isasymp 40 km) we have included anadditional layer of ozone and oxygen above the model topto provide a very accurate representation of photolysis ratesin the upper portion of the model (Fig 2) as compared tothe equivalent calculation using a fully-resolved stratosphericdistribution The fully resolved stratospheric module wasevaluated in Chapter 6 of the SPARC CCMVal report on theEvaluation of Chemistry-Climate Models This photolysismodule was shown to be one of the more accurate modulesused in CCMs (see SPARC 2010 for details)



Table 4 List of reactions Temperature (T ) is expressed in K pressure (P ) in Pa air density (M) in molec cmminus3 ki and ko incm3 molecminus1 sminus1

Tropospheric photolysis Rate

O2 +hvrarr 2OO3 +hvrarr O1D + O2O3 +hvrarr O + O2N2O +hvrarr O1D + N2NO +hvrarr N + ONO2 +hvrarr NO + ON2O5 +hvrarr NO2 + NO3N2O5 +hvrarr NO + O + NO3HNO3 +hvrarr NO2 + OHNO3 +hvrarr NO2 + ONO3 +hvrarr NO + O2HO2NO2 +hvrarr OH + NO3HO2NO2 +hvrarr NO2 + HO2CH3OOH +hvrarr CH2O + H + OHCH2O +hvrarr CO + 2HCH2O +hvrarr CO + H2H2O2 +hvrarr 2OHCH3CHO +hvrarr CH3O2 + CO + HO2POOH +hvrarr CH3CHO + CH2O + HO2 + OHCH3COOOH +hvrarr CH3O2 + OH + CO2PAN +hvrarr 6CH3CO3 + 6NO2 + 4CH3O2 + 4NO3 + 4CO2MPAN +hvrarr MCO3 + NO2MACR +hvrarr 67HO2 + 33MCO3 + 67CH2O + 67CH3CO3+ 33OH + 67COMVK + hvrarr 7C3H6 + 7CO + 3CH3O2 + 3CH3CO3C2H5OOH +hvrarr CH3CHO + HO2 + OHC3H7OOH +hvrarr 82CH3COCH3 + OH + HO2ROOH +hvrarr CH3CO3 + CH2O + OHCH3COCH3 +hvrarr CH3CO3 + CH3O2CH3COCHO +hvrarr CH3CO3 + CO + HO2XOOH +hvrarr OHONITR +hvrarr HO2 + CO + NO2 + CH2OISOPOOH +hvrarr 402MVK + 288MACR + 69CH2O + HO2HYAC +hvrarr CH3CO3 + HO2 + CH2OGLYALD + hvrarr 2HO2 + CO + CH2OMEK +hvrarr CH3CO3 + C2H5O2BIGALD +hvrarr 45CO + 13GLYOXAL + 56HO2 + 13CH3CO3+ 18CH3COCHOGLYOXAL + hvrarr 2CO + 2HO2ALKOOH +hvrarr 4CH3CHO + 1CH2O + 25CH3COCH3 + 9HO2+ 8MEK + OHMEKOOH +hvrarr OH + CH3CO3 + CH3CHOTOLOOH +hvrarr OH + 45GLYOXAL + 45CH3COCHO + 9BIGALDTERPOOH +hvrarr OH + 1CH3COCH3 + HO2 + MVK + MACR



Table 4Continued

Stratospheric only photolysis Rate

CH4 +hvrarr H + CH3O2CH4 +hvrarr 144H2 + 18CH2O + 18O + 66OH + 44CO2 + 38CO+ 05H2OH2O +hvrarr OH + HH2O +hvrarr H2 + O1DH2O +hvrarr 2H + OCL2 +hvrarr 2CLOCLO +hvrarr O + CLOCL2O2 +hvrarr 2CLHOCL +hvrarr OH + CLHCL +hvrarr H + CLCLONO2 +hvrarr CL + NO3CLONO2 +hvrarr CLO + NO2BRCL +hvrarr BR + CLBRO +hvrarr BR + OHOBR +hvrarr BR + OHBRONO2 +hvrarr BR + NO3BRONO2 +hvrarr BRO + NO2CH3CL +hvrarr CL + CH3O2CCL4 +hvrarr 4CLCH3CCL3 +hvrarr 3CLCFC11 +hvrarr 3CLCFC12 +hvrarr 2CLCFC113 +hvrarr 3CLHCFC22 +hvrarr CLCH3BR +hvrarr BR + CH3O2CF3BR +hvrarr BRCF2CLBR +hvrarr BR + CLCO2 +hvrarr CO + O

Odd-Oxygen Reactions Rate

O + O2 + Mrarr O3 + M 6Eminus34(300T)24O + O3rarr 2O2 800Eminus12exp(minus2060T)O + O + M rarr O2 + M 276Eminus34exp( 720T )O1D + N2rarr O + N2 210Eminus11exp(115T)O1D + O2rarr O + O2 320Eminus11exp( 70T)O1D + H2Orarr 2OH 220Eminus10O1D + H2rarr HO2 + OH 110Eminus10O1D + N2Orarr N2 + O2 490Eminus11O1D + N2Orarr 2NO 670Eminus11O1D + CH4rarr CH3O2 + OH 113Eminus10O1D + CH4rarr CH2O + H + HO2 300Eminus11O1D + CH4rarr CH2O + H2 750Eminus12O1D + HCNrarr OH 770Eminus11exp(100T)



Table 4Continued

Odd hydrogen reactions Rate

H + O2 + M rarr HO2 + M troe ko= 440Eminus32(300T)130ki = 470Eminus11(300T)020f =060

H + O3rarr OH + O2 140Eminus10exp (minus470T)H + HO2rarr 2OH 720Eminus11H + HO2rarr H2 + O2 690Eminus12H + HO2rarr H2O + O 160Eminus12OH + Orarr H + O2 220Eminus11exp(120T)OH + O3rarr HO2 + O2 170Eminus12exp(minus940T)OH + HO2rarr H2O + O2 480Eminus11exp(250T)OH + OHrarr H2O + O 180Eminus12OH + OH + M rarr H2O2 + M troe ko= 690Eminus31(300T)100

ki = 260Eminus11f =060

OH + H2rarr H2O + H 280Eminus12exp(minus1800T)OH + H2O2rarr H2O + HO2 180Eminus12OH + HCNrarr HO2 troe ko= 428Eminus33

ki = 930Eminus15(300T)minus442f =080

OH + CH3CNrarr HO2 780Eminus13exp(minus1050T)HO2 + Orarr OH + O2 300Eminus11exp(200T)HO2 + O3rarr OH + 2O2 100Eminus14exp(minus490T)HO2 + HO2rarr H2O2 + O2 (23Eminus13exp(600T) + 17Eminus33 [M] exp(1000T)) (1 + 14Eminus21[H2O]exp(2200T))H2O2 + Orarr OH + HO2 140Eminus12exp(minus2000T)

Odd nitrogen reactions Rate

N + O2rarr NO + O 150Eminus11exp(minus3600T)N + NO rarr N2 + O 210Eminus11exp(100T)N + NO2rarr N2O + O 580Eminus12exp(220T)NO + O + M rarr NO2 + M troe ko= 900Eminus32(300T)150


NO + HO2rarr NO2 + OH 350Eminus12exp(250T)NO + O3rarr NO2 + O2 300Eminus12exp(minus1500T)NO2 + Orarr NO + O2 510Eminus12exp(210T)NO2 + O + Mrarr NO3 + M troe ko= 250Eminus31(300T)180

ki = 220Eminus11(300T)070f =060

NO2 + O3rarr NO3 + O2 120Eminus13exp(minus2450T)NO2 + NO3 + Mrarr N2O5 + M troe ko= 200Eminus30(300T)440

ki = 140Eminus12(300T)070f =060

N2O5 + Mrarr NO2 + NO3 + M k(NO2+NO3+M) 3333E26 exp(10990T)NO2 + OH + Mrarr HNO3 + M troe ko= 180Eminus30(300T)300


HNO3 + OHrarr NO3 + H2O k0 + k3[M](1 + k3[M]k2)k0= 24Eminus14middotexp(460T)k2= 27Eminus17middotexp(2199T)k3= 65Eminus34middotexp(1335T)

NO3 + NOrarr 2NO2 150Eminus11exp(170T)NO3 + Orarr NO2 + O2 100Eminus11NO3 + OHrarr HO2 + NO2 220Eminus11NO3 + HO2rarr OH + NO2 + O2 350Eminus12



Table 4Continued

NO2 + HO2 +M rarr HO2NO2 + M troe ko =200Eminus31(300T)340ki = 290E-12(300T)110f =060

HO2NO2 + OHrarr H2O + NO2 + O2 130Eminus12exp(380T)HO2NO2 + Mrarr HO2 + NO2 + M k(NO2+HO2+M)middotexp(minusminus10900T)21Eminus27

C-1 Degradation (Methane CO CH2O and derivatives) Rate

CH4 + OHrarr CH3O2 + H2O 245Eminus12exp(minus1775T)CH3O2 + NOrarr CH2O + NO2 + HO2 280Eminus12exp(300T)CH3O2 + HO2rarr CH3OOH + O2 410Eminus13exp(750T)CH3OOH + OHrarr CH3O2 + H2O 380Eminus12exp(200T)CH2O + NO3rarr CO + HO2 + HNO3 600Eminus13exp(minus2058T)CH2O + OHrarr CO + H2O + H 550Eminus12exp(125T)CH2O + Orarr HO2 + OH + CO 340Eminus11exp(minus1600T)CO + OH + Mrarr CO2 + HO2 + M troe ko= 590Eminus33(300T)140

ki = 110Eminus12(300T)minus130f = 060

CO + OHrarr CO2 + HO2 ki= 21E09 (T300)61ko= 15Eminus13 (T300)06rate= ko(1+ko(kiM))06(1(1+log10(ko(kiM)2)))

CH3O2 + CH3O2rarr 2CH2O + 2HO2 500Eminus13exp(minus424T)CH3O2 + CH3O2rarr CH2O + CH3OH 190Eminus14exp(706T)CH3OH + OHrarr HO2 + CH2O 290Eminus12exp(minus345T)CH3OOH + OHrarr 7CH3O2 + 3OH + 3CH2O + H2O 380Eminus12exp(200T)HCOOH + OHrarr HO2 + CO2 + H2O 450Eminus13CH2O + HO2rarr HOCH2OO 970Eminus15exp(625T)HOCH2OOrarr CH2O + HO2 240E+12exp(minus7000T)HOCH2OO + NOrarr HCOOH + NO2 + HO2 260Eminus12exp(265T)HOCH2OO + HO2rarr HCOOH 750Eminus13exp(700T)

C-2 Degradation Rate

C2H2 + OH + Mrarr 65GLYOXAL + 65OH + 35HCOOH + 35HO2 troe ko= 550Eminus30+ 35CO + M ki= 830Eminus13(300T)minus200

f = 060C2H6 + OHrarr C2H5O2 + H2O 870Eminus12exp(minus1070T)C2H4 + OH + Mrarr 75EO2 + 5CH2O + 25HO2 + M troe ko= 100Eminus28(300T)080

ki = 880Eminus12f = 060

C2H4 + O3rarr CH2O + 12HO2 + 5CO + 12OH + 5HCOOH 120Eminus14exp(minus2630T)CH3COOH + OHrarr CH3O2 + CO2 + H2O 700Eminus13C2H5O2 + NOrarr CH3CHO + HO2 + NO2 260Eminus12exp(365T)C2H5O2 + HO2rarr C2H5OOH + O2 750Eminus13exp(700T)C2H5O2 + CH3O2rarr 7CH2O + 8CH3CHO + HO2 + 3CH3OH + 2C2H5OH 200Eminus13C2H5O2 + C2H5O2rarr 16CH3CHO + 12HO2 + 4C2H5OH 680Eminus14C2H5OOH + OHrarr 5C2H5O2 + 5CH3CHO + 5OH 380Eminus12exp(200T)CH3CHO + OHrarr CH3CO3 + H2O 560Eminus12exp(270T)CH3CHO + NO3rarr CH3CO3 + HNO3 140Eminus12exp(minus1900T)CH3CO3 + NOrarr CH3O2 + CO2 + NO2 810Eminus12exp(270T)CH3CO3 + NO2 + Mrarr PAN + M troe ko= 850Eminus29(300T)650

ki = 110Eminus11(300T)f = 060



Table 4Continued

CH3CO3 + HO2rarr 75CH3COOOH + 25CH3COOH + 25O3 430Eminus13exp(1040T)CH3CO3 + CH3O2rarr 9CH3O2 + CH2O + 9HO2 + 9CO2 + 1CH3COOH 200Eminus12exp(500T)CH3CO3 + CH3CO3rarr 2CH3O2 + 2CO2 250Eminus12exp(500T)CH3COOOH + OHrarr 5CH3CO3 + 5CH2O + 5CO2 + H2O 100Eminus12EO2 + NOrarr EO + NO2 420Eminus12exp(180T)EO + O2rarr GLYALD + HO2 100Eminus14EOrarr 2CH2O + HO2 160E+11exp(minus4150T)GLYALD + OH rarr HO2 + 2GLYOXAL + 8CH2O + 8CO2 100Eminus11GLYOXAL + OH rarr HO2 + CO + CO2 110Eminus11C2H5OH + OHrarr HO2 + CH3CHO 690Eminus12exp(minus230T)PAN + M rarr CH3CO3 + NO2 + M k(CH3CO3+NO2+M) 1111E28 exp(14000T)PAN + OHrarr CH2O + NO3 400Eminus14

Cminus3 Degradation Rate

C3H6 + OH + Mrarr PO2 + M troe ko= 800Eminus27(300T)350ki = 300Eminus11f = 050

C3H6 + O3rarr 54CH2O + 19HO2 + 33OH + 08CH4 + 56CO 650Eminus15exp(minus1900T)+ 5CH3CHO + 31CH3O2 + 25CH3COOHC3H6 + NO3rarr ONIT 460Eminus13exp(minus1156T)C3H7O2 + NOrarr 82CH3COCH3 + NO2 + HO2 + 27CH3CHO 420Eminus12exp(180T)C3H7O2 + HO2rarr C3H7OOH + O2 750Eminus13exp(700T)C3H7O2 + CH3O2rarr CH2O + HO2 + 82CH3COCH3 375Eminus13exp(minus40T)C3H7OOH + OHrarr H2O + C3H7O2 380Eminus12exp(200T)C3H8 + OHrarr C3H7O2 + H2O 100Eminus11exp(minus665T)PO2 + NOrarr CH3CHO + CH2O + HO2 + NO2 420Eminus12exp(180T)PO2 + HO2rarr POOH + O2 750Eminus13exp(700T)POOH + OHrarr 5PO2 + 5OH + 5HYAC + H2O 380Eminus12exp(200T)CH3COCH3 + OHrarr RO2 + H2O 382Eminus11 exp(2000T) + 133Eminus13RO2 + NOrarr CH3CO3 + CH2O + NO2 290Eminus12exp(300T)RO2 + HO2rarr ROOH + O2 860Eminus13exp(700T)RO2 + CH3O2rarr 3CH3CO3 + 8CH2O + 3HO2 + 2HYAC 710Eminus13exp(500T)+ 5CH3COCHO + 5CH3OHROOH + OHrarr RO2 + H2O 380Eminus12exp(200T)HYAC + OH rarr CH3COCHO + HO2 300Eminus12CH3COCHO + OHrarr CH3CO3 + CO + H2O 840Eminus13exp(830T)CH3COCHO + NO3rarr HNO3 + CO + CH3CO3 140Eminus12exp(minus1860T)ONIT + OH rarr NO2 + CH3COCHO 680Eminus13


BIGENE + OHrarr ENEO2 540Eminus11ENEO2 + NOrarr CH3CHO + 5CH2O + 5CH3COCH3 + HO2 + NO2 420Eminus12exp(180T)MVK + OH rarr MACRO2 413Eminus12exp(452T)MVK + O3 rarr 8CH2O + 95CH3COCHO + 08OH + 2O3 + 06HO2 752Eminus16exp(minus1521T)+ 05CO + 04CH3CHOMEK + OH rarr MEKO2 230Eminus12exp(minus170T)MEKO2 + NOrarr CH3CO3 + CH3CHO + NO2 420Eminus12exp(180T)MEKO2 + HO2rarr MEKOOH 750Eminus13exp(700T)MEKOOH + OHrarr MEKO2 380Eminus12exp(200T)MACR + OH rarr 5MACRO2 + 5H2O + 5MCO3 186Eminus11exp(175T)



Table 4Continued

MACR + O3rarr 8CH3COCHO + 275HO2 + 2CO + 2O3 + 7CH2O 440Eminus15exp(minus2500T)+ 215OHMACRO2 + NOrarr NO2 + 47HO2 + 25CH2O + 53GLYALD 270Eminus12exp(360T)+ 25CH3COCHO + 53CH3CO3 + 22HYAC + 22COMACRO2 + NOrarr 08ONITR 130Eminus13exp(360T)MACRO2 + NO3rarr NO2 + 47HO2 + 25CH2O + 25CH3COCHO + 22CO 240Eminus12+ 53GLYALD + 22HYAC + 53CH3CO3MACRO2 + HO2rarr MACROOH 800Eminus13exp(700T)MACRO2 + CH3O2rarr 73HO2 + 88CH2O + 11CO + 24CH3COCHO 500Eminus13exp(400T)+ 26GLYALD + 26CH3CO3 + 25CH3OH + 23HYACMACRO2 + CH3CO3rarr 25CH3COCHO + CH3O2 + 22CO + 47HO2 140Eminus11+ 53GLYALD + 22HYAC + 25CH2O + 53CH3CO3MACROOH + OHrarr 5MCO3 + 2MACRO2 + 1OH + 2HO2 230Eminus11exp(200T)MCO3 + NOrarr NO2 + CH2O + CH3CO3 530Eminus12exp(360T)MCO3 + NO3rarr NO2 + CH2O + CH3CO3 500Eminus12MCO3 + HO2rarr 25O3 + 25CH3COOH + 75CH3COOOH + 75O2 430Eminus13exp(1040T)MCO3 + CH3O2rarr 2CH2O + HO2 + CO2 + CH3CO3 200Eminus12exp(500T)MCO3 + CH3CO3rarr 2CO2 + CH3O2 + CH2O + CH3CO3 460Eminus12exp(530T)MCO3 + MCO3rarr 2CO2 + 2CH2O + 2CH3CO3 230Eminus12exp(530T)MCO3 + NO2 + Mrarr MPAN + M 11Eminus11 300T[M]MPAN + M rarr MCO3 + NO2 + M k(MCO3+NO2+M) 1111E28 exp(14000T)MPAN + OH rarr 5HYAC + 5NO3 + 5CH2O + 5HO2 troe ko= 800Eminus27(300T)350

ki = 300Eminus11f = 050


ISOP + OHrarr ISOPO2 254Eminus11exp(410T)ISOP + O3rarr 4MACR + 2MVK + 07C3H6 + 27OH + 06HO2 105Eminus14exp(minus2000T)+ 6CH2O + 3CO + 1O3 + 2MCO3 + 2CH3COOHISOP + NO3rarr ISOPNO3 303Eminus12exp(minus446T)ISOPO2 + NOrarr 08ONITR + 92NO2 + HO2 + 51CH2O + 23MACR 440Eminus12exp(180T)+ 32MVK + 37HYDRALDISOPO2 + NO3rarr HO2 + NO2 + 6CH2O + 25MACR + 35MVK 240Eminus12+ 4HYDRALDISOPO2 + HO2rarr ISOPOOH 800Eminus13exp(700T)ISOPOOH + OHrarr 8XO2 + 2ISOPO2 152Eminus11exp(200T)ISOPO2 + CH3O2rarr 25CH3OH + HO2 + 12CH2O + 19MACR + 26MVK 500Eminus13exp(400T)+ 3HYDRALDISOPO2 + CH3CO3rarr CH3O2 + HO2 + 6CH2O + 25MACR + 35MVK 140Eminus11+ 4HYDRALDISOPNO3 + NOrarr 1206NO2 + 794HO2 + 072CH2O 270Eminus12exp(360T)+ 167MACR + 039MVK + 794ONITRISOPNO3 + NO3rarr 1206NO2 + 072CH2O + 167MACR + 039MVK 240Eminus12+ 794ONITR + 794HO2ISOPNO3 + HO2rarr XOOH + 206NO2 + 794HO2 + 008CH2O + 167MACR 800Eminus13exp(700T)+ 039MVK + 794ONITRBIGALK + OH rarr ALKO2 350Eminus12ONITR + OHrarr HYDRALD + 4NO2 + HO2 450Eminus11ONITR + NO3rarr HO2 + NO2 + HYDRALD 140Eminus12exp(minus1860T)HYDRALD + OH rarr XO2 186Eminus11exp(175T)ALKO2 + NO rarr 4CH3CHO + 1CH2O + 25CH3COCH3 + 9HO2 420Eminus12exp(180T)+ 8MEK + 9NO2 + 1ONITALKO2 + HO2 rarr ALKOOH 750Eminus13exp(700T)



Table 4Continued

ALKOOH + OH rarr ALKO2 380Eminus12exp(200T)XO2 + NOrarr NO2 + HO2 + 5CO + 25GLYOXAL + 25HYAC 270Eminus12exp(360T)+ 25CH3COCHO + 25GLYALDXO2 + NO3rarr NO2 + HO2 + 05CO + 25HYAC + 025GLYOXAL 240Eminus12+ 25CH3COCHO + 25GLYALDXO2 + HO2rarr XOOH 800Eminus13exp(700T)XO2 + CH3O2rarr 3CH3OH + 08HO2 + 7CH2O + 2CO + 1HYAC 500Eminus13exp(400T)+ 1GLYOXAL + 1CH3COCHO + 1GLYALDXO2 + CH3CO3rarr 05CO + CH3O2 + HO2 + CO2 + 25GLYOXAL 130Eminus12exp(640T)+ 25HYAC + 25CH3COCHO + 25GLYALDXOOH + OHrarr H2O + XO2 190Eminus12exp(190T)XOOH + OHrarr H2O + OH T2 769Eminus17 exp(253T)


TOLUENE + OHrarr 25CRESOL + 25HO2 + 7TOLO2 170Eminus12exp(352T)TOLO2 + NOrarr 45GLYOXAL + 45CH3COCHO + 9BIGALD + 9NO2 420Eminus12exp(180T)+ 9HO2TOLO2 + HO2rarr TOLOOH 750Eminus13exp(700T)TOLOOH + OHrarr TOLO2 380Eminus12exp(200T)CRESOL + OHrarr XOH 300Eminus12XOH + NO2rarr 7NO2 + 7BIGALD + 7HO2 100Eminus11


C10H16 + OHrarr TERPO2 120Eminus11exp(444T)C10H16 + O3rarr 7OH + MVK + MACR + HO2 100Eminus15exp(minus732T)C10H16 + NO3rarr TERPO2 + NO2 120Eminus12exp(490T)TERPO2 + NOrarr 1CH3COCH3 + HO2 + MVK + MACR + NO2 420Eminus12exp(180T)TERPO2 + HO2rarr TERPOOH 750Eminus13exp(700T)TERPOOH + OHrarr TERPO2 380Eminus12exp(200T)

RadonLead Rate

Rnrarr Pb 210Eminus06

Aerosol precursors and aging Rate

SO2+ OHrarr SO4 ko= 30Eminus31(300T)33 ki= 1Eminus12f = 06DMS + OHrarr SO2 960Eminus12exp(minus234T)DMS + OHrarr 5SO2+ 5HO2 17Eminus42 exp(7810T) [M]

021 (1 + 55Eminus31 exp(7460T) [M] 021)DMS + NO3rarr SO2+ HNO3 190Eminus13exp(520T)NH3 + OHrarr H2O 170Eminus12exp(minus710T)CB1rarr CB2 710Eminus06OC1rarr OC2 710Eminus06



Table 4Continued

Heterogeneous reactions on tropospheric aerosols Rate γ = reaction probability

N2O5rarr 2HNO3 γ = 01 on OC SO4 NH4NO3 SOANO3rarr HNO3 γ = 0001 on OC SO4 NH4NO3 SOANO2rarr 05OH + 05NO + 05HNO3 γ = 00001 on OC SO4 NH4NO3 SOAHO2rarr 05H2O2 γ = 02 on OC SO4 NH4NO3 SOA

O1D reactions with halogens Rate

O1D + CFC11rarr 3CL 170Eminus10O1D + CFC12rarr 2CL 120Eminus10O1D + CFC113rarr 3CL 150Eminus10O1D + HCFC22rarr CL 720Eminus11O1D + CCL4rarr 4CL 284Eminus10O1D + CH3BRrarr BR 180Eminus10O1D + CF2CLBRrarr BR 960Eminus11O1D + CF3BRrarr BR 410Eminus11

Odd Chlorine Reactions Rate

CL + O3rarr CLO + O2 230Eminus11exp(minus200T)CL + H2 rarr HCL + H 305Eminus11exp(minus2270T)CL + H2O2rarr HCL + HO2 110Eminus11exp(minus980T)CL + HO2rarr HCL + O2 180Eminus11exp(170T)CL + HO2rarr OH + CLO 410Eminus11exp(minus450T)CL + CH2Orarr HCL + HO2 + CO 810Eminus11exp(minus30T)CL + CH4rarr CH3O2 + HCL 730Eminus12exp(minus1280T)CLO + Orarr CL + O2 280Eminus11exp( 85T)CLO + OHrarr CL + HO2 740Eminus12exp(270T)CLO + OHrarr HCL + O2 600Eminus13exp(230T)CLO + HO2rarr O2 + HOCL 270Eminus12exp(220T)CLO + NOrarr NO2 + CL 640Eminus12exp(290T)CLO + NO2 + Mrarr CLONO2 + M troe ko= 180Eminus31(300T)340

ki = 150Eminus11(300T)190f = 060

CLO + CLOrarr 2CL + O2 300Eminus11exp(minus2450T)CLO + CLOrarr CL2 + O2 100Eminus12exp(minus1590T)CLO + CLOrarr CL + OCLO 350Eminus13exp(minus1370T)CLO + CLO + M rarr CL2O2 + M troe ko= 160Eminus32(300T)450

ki = 200Eminus12(300T)240f = 060

CL2O2 + Mrarr CLO + CLO + M User defined HCL + OH rarr H2O + CL 260Eminus12exp(minus350T)HCL + O rarr CL + OH 100Eminus11exp(minus3300T)HOCL + Orarr CLO + OH 170Eminus13HOCL + CL rarr HCL + CLO 250Eminus12exp(minus130T)HOCL + OHrarr H2O + CLO 300Eminus12exp(minus500T)CLONO2 + Orarr CLO + NO3 290Eminus12exp(minus800T)CLONO2 + OHrarr HOCL + NO3 120Eminus12exp(minus330T)CLONO2 + CLrarr CL2 + NO3 650Eminus12exp(135T)



Table 4Continued

Odd Bromine Reactions Rate

BR + O3rarr BRO + O2 170Eminus11exp(minus800T)BR + HO2rarr HBR + O2 480Eminus12exp(minus310T)BR + CH2Orarr HBR + HO2 + CO 170Eminus11exp(minus800T)BRO + Orarr BR + O2 190Eminus11exp(230T)BRO + OHrarr BR + HO2 170Eminus11exp(250T)BRO + HO2rarr HOBR + O2 450Eminus12exp(460T)BRO + NOrarr BR + NO2 880Eminus12exp(260T)BRO + NO2 + Mrarr BRONO2 + M troe ko= 520Eminus31(300T)320

ki = 690Eminus12(300T)290f = 060

BRO + CLOrarr BR + OCLO 950Eminus13exp(550T)BRO + CLOrarr BR + CL + O2 230Eminus12exp(260T)BRO + CLOrarr BRCL + O2 410Eminus13exp(290T)BRO + BROrarr 2BR + O2 150Eminus12exp(230T)HBR + OHrarr BR + H2O 550Eminus12exp(200T)HBR + Orarr BR + OH 580Eminus12exp(minus1500T)HOBR + Orarr BRO + OH 120Eminus10exp(minus430T)BRONO2 + Orarr BRO + NO3 190Eminus11exp(215T)

Organic Halogens Reactions with Cl OH Rate

CH3CL + CLrarr HO2 + CO + 2HCL 217Eminus11exp(minus1130T)CH3CL + OHrarr CL + H2O + HO2 240Eminus12exp(minus1250T)CH3CCL3 + OHrarr H2O + 3CL 164Eminus12exp(minus1520T)HCFC22 + OHrarr CL + H2O + CF2O 105Eminus12exp(minus1600T)CH3BR + OHrarr BR + H2O + HO2 235Eminus12exp(minus1300T)

Sulfate aerosol reactions Comment

N2O5rarr 2HNO3 γ = 004CLONO2rarr HOCL + HNO3 f (sulfuric acid wt)BRONO2rarr HOBR + HNO3 f (T P HCl H2Or)CLONO2 + HCLrarr CL2 + HNO3 f (T P H2Or)HOCL + HCL rarr CL2 + H2O f (T P HCl H2Or)HOBR + HCLrarr BRCL + H2O f (T P HCl HOBrH2Or)

Nitric acid diminushydrate reactions Comment

N2O5rarr 2HNO3 γ = 00004CLONO2rarr HOCL + HNO3 γ = 0004CLONO2 + HCLrarr CL2 + HNO3 γ = 02HOCL + HCL rarr CL2 + H2O γ = 01BRONO2rarr HOBR + HNO3 γ = 03

Ice aerosol reactions Comment

N2O5rarr 2HNO3 γ = 002CLONO2rarr HOCL + HNO3 γ = 03BRONO2rarr HOBR + HNO3 γ = 03CLONO2 + HCLrarr CL2 + HNO3 γ = 03HOCL + HCL rarr CL2 + H2O γ = 02HOBR + HCLrarr BRCL + H2O γ = 03



Table 5 Bulk aerosol parameters used in calculation of surfacearea number distribution mean radius (rm) geometric standard de-viation (σg) and density

Aerosol rm (nm) σg (microm) ρ (g cmminus3)

CB1 CB2 118 200 10OC1 OC2 212 220 18SO4 695 203 17NH4NO3 695 203 17SOA 212 220 18

While the lookup table provides explicit quantum yieldsand cross-sections for a large number of photolysis rate de-terminations additional ones are available by scaling of anyof the explicitly defined rates This process is available in thedefinition of the chemical preprocessor input files (see Sect 5for a complete list of the photolysis rates available)

The impact of clouds on photolysis rates is parameterizedfollowing Madronich (1987) However because we use alookup table approach the impact of aerosols (troposphericor stratospheric) on photolysis rates cannot be represented

4 Offline meteorology and transport

CAM-chem has the capability to perform simulations us-ing specified dynamics where offline meteorological fieldsare input into the model instead of calculated online Thisprocedure can allow for more precise comparisons betweenmeasurements of atmospheric composition and model out-put To use input meteorological fields we follow the sameprocedure defined originally in the Model of AtmosphericTransport and Chemistry (MATCH) (Rasch et al 1997) andsubsequently applied in all versions of MOZART (E2010Kinnison et al 2007 Horowitz et al 2003 Brasseur et al1998) In this procedure only the horizontal wind compo-nents air temperature surface temperature surface pressuresensible and latent heat flux and wind stress (see Table 2) areread from the input meteorological dataset in all cases dis-cussed here the input datasets are available every 6 h Fortimesteps between the reading times all fields are linearlyinterpolated to avoid jumps These fields are subsequentlyused to internally generate (using the existing CAM4 param-eterizations) the variables necessary for (1) calculating sub-grid scale transport including boundary layer transport andconvective transport (2) the variables necessary for specify-ing the hydrological cycle including cloud and water vapordistributions and rainfall (see Rasch et al 2007 for more de-tails)

CAM4 (and therefore CAM-chem) uses a sub-steppingprocedure to solve the advection equations for the mass fluxtemperature and velocity fields over each timestep Regard-less of the configuration (online or specified dynamics) the

meteorological fields are allowed to evolve over each sub-step When using specified dynamics we have found that thissub-stepping dampens some of the inconsistencies betweenthe inserted and model-computed velocity and mass fieldssubsequently used for tracer transport The mass flux (atmo-spheric mass and tracer mass) at each sub-step is accumu-lated to produce the net mass flux over the entire time stepThis allows transport to be performed using a longer timestep than the dynamics computations A graphical explana-tion of the sub-stepping is given in Lauritzen et al (2011)

In addition an atmospheric mass fixer algorithm is nec-essary as the mass flux computed from the offline meteoro-logical winds input into CAM4 will not in general producea mass distribution consistent with the offline surface pres-sure field If uncorrected this may lead to spurious changesin tracer mass concentration or surface pressure (Rotman etal 2004) The mass fixer algorithm ensures that the calcu-lated surface pressure closely matches the surface pressure inthe offline meteorological dataset (see discussion in Rotmanet al 2004) The mass fixer algorithm makes the appropriateadjustments to the horizontal mass fluxes to produce a result-ing mass distribution consistent with the evolution of surfacepressure in the input meteorological dataset The procedurefollows the algorithm given in Rotman et al (2004) first ituses an efficient algorithm to find the correction to the ver-tically integrated mass flux then the corrected mass flux isdistributed in the vertical in proportion to the dependence ofeach model level on the surface pressure in a hybrid coor-dinate system The edge pressures of the Lagrangian masssurfaces are consistently adjusted to allow for the verticalremapping of the transported fields to the fixed hybrid pres-sure coordinate system Following the corrections in massflux and the edge pressures the constituent tracers are trans-ported by the large-scale wind fields

Currently we recommend using the NASA God-dard Global Modeling and Assimilation Office (GMAO)GEOS5 generated meteorology The meteorological fieldswere generated using the operational forecast model anddatasets (labeled below GEOS5) or under the ModernEra Retrospective-Analysis For Research And Applications(MERRA) setup (Rienecker et al 2011) These differ intheir assimilation methods and to a lesser extent assimi-lated datasets see Rienecker et al (2011) for more detailsUsing either of these meteorological datasets and the formu-lation of offline CAM-chem as described above multi-yearsimulations (see Sect 5 and Fig 7) do not seem to requirethe use of limiters of stratosphere-troposphere exchange suchas SYNOZ (McLinden et al 2000) All GEOS5MERRAmeteorological datasets used in this study are made avail-able at the standard CAM resolution of 19

times 25 on theEarth System Grid (httpwwwearthsystemgridorghomehtm) These files were generated from the original resolu-tion (12

times23) by using a conservative regridding proce-dure based on the same 1-D operators as used in the trans-port scheme of the finite-volume dynamical core used in



GEOS5 MERRA Online

GEOS5 MERRA Online

GEOS5 MERRA Online

Fig 4aAnnual (2006ndash2008) and zonal mean distribution of relative humidity (top ) zonal wind (middle m sminus1) and temperature (bottomK) in GEOS5 MERRA and the online configurations (shown as absolute difference with respect to GEOS5 note different color scale)

GEOS5MERRA and CAM (S-J Lin personal communi-cation 2009) Note that because of a difference in the signconvention of the surface wind stress (TAUX and TAUY) be-tween CESM and GEOS5MERRA these fields in the inter-polated datasets have been reversed from the original filessupplied by GMAO In addition it is important for users torecognize the importance of specifying the correct surfacegeopotential height (PHIS) to ensure consistency with theinput dynamical fields which is important to prevent unre-alistic vertical mixing

Over the range between the surface and 4 hPa the numberof vertical levels in the GEOS5MERRA fields is 56 insteadof 26 for the online dynamics (see Fig 3) In particular be-tween 800 hPa and the surface GEOS5MERRA has 13 lev-els while the online version has 4 The choice of 26 levelsis dictated by the use of CAM4 in climate simulations Noadjustment is made to the CAM4 physics parameterizationsfor this increase in the number of levels

5 Chemical mechanisms

As mentioned in the Introduction CAM-chem uses the samechemical preprocessor as MOZART-4 This preprocessorgenerates Fortran code specific to each chemical mecha-nism allowing for an easy update and modification of exist-ing chemical mechanisms In particular the generated codeprovides two chemical solvers one explicit and one semi-implicit which the user specifies based on the chemical life-time of each species Because the semi-implicit solver isquite efficient it is recommended to preferentially use it un-less the chemical species has a long lifetime everywhere

We describe in this paper two chemical mechanisms(1) extensive tropospheric chemistry and (2) extensive tro-pospheric and stratospheric chemistry All species and re-actions are listed in Tables 3 and 4 respectively In bothmechanisms described in this paper CAM-chem uses thebulk aerosol model discussed in Lamarque et al (2005) andE2010 This model has a representation of aerosols basedon the work by Tie et al (2001 2005) ie sulfate aerosol



GEOS5 MERRA Online

GEOS5 MERRA Online

GEOS5 MERRA Online

Fig 4b Annual (2006ndash2008) distribution of temperature at 700 hPa (top K) and total precipitation (bottom mm dayminus1) in GEOS5 MERRAand the online configurations (shown as absolute difference with respect to GEOS5 note different color scale)

Table 6 Summary of simulations All versions extend toasymp 40 km

Name Dynamics Period Chemistry Wet removal Resolution Levels

Online online 1992ndash2010 stratosphere-troposphere Neu and Prather 19times25 26

GEOS5 GEOS5 2004ndash2010 troposphere Horowitz 19times25 56

MERRA MERRA 1997ndash2010 troposphere Horowitz 19times25 56

MERRA Neu MERRA 2006ndash2008 troposphere Neu and Prather 19times25 56

is formed by the oxidation of SO2 in the gas phase (by re-action with the hydroxyl radical) and in the aqueous phase(by reaction with ozone and hydrogen peroxide) Further-more the model includes a representation of ammonium ni-trate that is dependent on the amount of sulfate present inthe air mass following the parameterization of gasaerosolpartitioning by Metzger et al (2002) Because only the bulkmass is calculated a lognormal distribution (Table 5 also seeE2010) is assumed for all aerosols with different mean ra-dius and geometric standard deviation (Liao et al 2003) foreach aerosol type The conversion of carbonaceous aerosols

(organic and black) from hydrophobic to hydrophilic is as-sumed to occur within a fixed 16 days (Tie et al 2005)Natural aerosols (desert dust and sea salt) are implementedfollowing Mahowald et al (2006a b) and the sources ofthese aerosols are derived based on the model calculatedwind speed and surface conditions Size-dependent gravi-tational settling is included for dust and sea-salt In addi-tion secondary-organic aerosols (SOA) are linked to the gas-phase chemistry through the oxidation of atmospheric non-methane hydrocarbons (NMHCs) as in Lack et al (2004)Note that because of the representation of SOA formation



minus50minus40minus30minus20minus1010203040

O3 Diff()


O3 Diff(ppb)


O3 Diff(ppb)

Online Annual 250 hPa GEOS5 Annual 250 hPa MERRA Annual 250 hPa



Fig 5 Mean annual bias of modeled ozone against ozone sonde climatology (Tilmes et al 2011) for 3 pressure levels each square indicatesthe bias at the location of the ozone sonde Averaging is done over the overlap period between the model simulations and the observationsfor each location

through a 2-product method there is no simple way to repre-sent that in our chemical mechanism (and therefore Table 4)this calculation is therefore performed in a separate portionof the code using information from the chemical mechanism(rate of VOC oxidation)

The extensive tropospheric chemistry scheme represents aminor update to the MOZART-4 mechanism fully describedin E2010 In particular we have included chemical reactionsfor C2H2 HCOOH HCN and CH3CN and minor changes tothe isoprene oxidation scheme including an increase in theproduction of glyoxal Reaction rates have been updated toJPL-2006 (Sander et al 2006) This mechanism is mainlyof relevance in the troposphere and is intended for simula-tions for which variability in the stratospheric composition isnot crucial Therefore in this configuration the stratosphericdistributions of long-lived species (see discussion below) arespecified from previously performed WACCM simulations(Garcia et al 2007 see Sect 63)

On the other hand in the case where changes in strato-spheric composition are important for which the dynamicsis calculated online we have added a stratospheric portionto the tropospheric chemistry mechanism described aboveThis addition (of species and reactions see Tables 3 and

4) is taken from the WACCM mechanism as this has beenshown to perform very well in the recent CCMval-2 analysis(SPARC 2010) The overall description of this chemistry isdiscussed in Kinnison et al (2011)

6 Simulations setup including emissions and otherboundary conditions

All simulations discussed in this paper are performed at thehorizontal resolution of 19 (latitude) and 25 (longitude)The number of vertical levels ranges from 26 levels (onlinedynamics) to 56 levels (GEOS5 and MERRA meteorology)in both cases the model extends to approximately 4 hPa (asymp

40 km) The computational cost of the various configura-tions scales (roughly) linearly with the number of tracers(103 for GEOS5MERRA and 133 for online) and levels (56for GEOS5MERRA and 26 for online) leading to a costfor GEOS5MERRA approximately 17 higher than the on-line configuration The online simulation is performed usingthe Neu and Prather removal scheme while the specified dy-namics simulations are performed using the Horowitz wetremoval scheme Therefore the label ldquoMERRArdquo refers tothe simulation with the Horowitz wet removal scheme an



Table 7 Yearly emission totals (Tg(species) yrminus1)

Species Sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010

BIGALK anthro 733 736 739 743 748 753 759 765 770 776 782 788 788 788bb 19 21 14 12 13 15 14 14 14 14 14 11 08 08total 752 757 753 755 760 768 773 779 785 790 796 799 795 796

BIGENE anthro 71 71 71 72 73 74 75 75 76 77 77 78 78 78bb 23 23 12 09 10 14 13 14 14 14 14 09 20 21total 95 95 84 81 82 88 88 89 90 91 91 86 98 99

C10H16 biogenic 813 813 813 813 813 813 813 813 813 813 813 813 813 813

C2H2 anthro 34 34 34 34 34 34 34 34 34 34 34 34 34 34bb 03 03 02 02 02 02 02 02 03 02 02 02 07 07total 38 38 37 36 36 37 37 37 37 37 37 36 41 42

C2H4 anthro 67 67 67 67 68 70 71 72 72 73 74 75 75 75bb 88 84 56 46 51 58 54 60 61 58 60 43 30 31biogenic 50 50 50 50 50 50 50 50 50 50 50 50 50 50total 204 201 172 163 169 178 174 182 184 181 184 168 154 156

C2H5OH anthro 54 54 54 54 54 54 54 54 54 54 54 54 54 54bb 06 06 04 04 04 05 04 05 05 04 05 04 00 00total 60 60 58 58 58 58 58 58 58 58 58 57 54 54




CB1 anthro 37 37 37 37 37 37 37 37 37 37 37 37 37 37bb 29 30 21 18 20 22 20 21 22 20 22 17 17 18total 66 67 58 55 57 59 58 59 59 58 59 54 54 55


CH2O anthro 09 09 09 10 10 10 10 10 10 11 11 11 11 11bb 42 49 27 22 23 31 31 28 28 29 28 21 44 47total 51 58 36 32 33 41 41 39 39 40 39 32 55 58

CH3CHO anthro 21 21 21 21 22 22 22 22 22 22 22 22 22 22bb 76 82 55 48 51 58 56 56 57 54 57 45 46 47total 98 103 76 69 73 80 78 78 79 76 79 67 69 70

CH3CN biofuel 07 07 07 07 07 07 07 07 07 07 07 07 07 07bb 16 17 11 10 11 12 12 12 12 11 12 09 11 12total 23 24 19 17 18 19 19 19 19 19 19 16 18 19

CH3COCH3 anthro 03 03 03 03 03 03 03 03 03 03 03 03 03 03bb 34 36 25 22 24 26 24 26 26 24 26 20 19 20biogenic 243 243 243 243 243 243 243 243 243 243 243 243 243 243total 281 282 271 268 270 273 271 272 273 271 273 267 266 267



Table 7Continued

species sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010

CH3COOH anthro 66 66 66 66 66 66 66 66 66 66 66 66 66 66bb 31 27 18 15 15 20 19 20 22 21 21 16 81 85total 97 93 84 82 81 86 86 86 88 87 87 82 147 151

CH3OH anthro 04 04 04 04 04 04 04 04 04 04 04 04 04 04bb 107 113 76 67 72 81 77 79 80 76 80 62 57 61biogenic 2288 2288 2288 2288 2288 2288 2288 2288 2288 2288 2288 2288 2288 2288total 2399 2405 2368 2358 2364 2373 2369 2371 2372 2368 2372 2354 2349 2353

CO anthro 5980 5953 5950 5988 6070 6209 6315 6351 6388 6425 6461 6498 6498 6498bb 5529 5866 3885 3348 3630 4153 3941 4030 4087 3875 4069 3133 3518 3781biogenic 1593 1593 1593 1593 1593 1593 1593 1593 1593 1593 1593 1593 1593 1593ocean 198 198 198 198 198 198 198 198 198 198 198 198 198 198total 13301 13610 11626 11127 11491 12154 12048 12173 12267 12091 12322 11423 11808 12071

DMS ocean 599 599 599 599 599 599 599 599 599 599 599 599 599 599

HCN biofuel 10 10 10 10 10 10 10 10 10 10 10 10 10 10bb 22 24 16 14 15 17 16 16 17 16 16 13 13 14total 32 34 26 23 25 27 26 26 26 26 26 23 23 24

HCOOH anthro 66 66 66 66 66 66 66 66 66 66 66 66 66 66bb 06 06 04 03 03 04 04 04 04 04 04 03 17 17total 72 72 70 69 69 70 70 70 71 70 70 70 83 83

MEK anthro 12 12 12 13 13 13 13 13 13 14 14 14 14 14bb 74 79 52 45 49 56 53 54 55 52 55 42 47 50total 86 91 65 58 62 69 66 68 69 66 68 57 61 64

NH3 anthro 479 482 486 488 489 490 493 497 502 507 511 516 516 516bb 79 85 59 52 56 62 59 60 61 57 61 48 42 46ocean 81 81 81 81 81 81 81 81 81 81 81 81 81 81soil 24 24 24 24 24 24 24 24 24 24 24 24 24 24total 663 672 650 645 651 657 657 663 669 669 677 670 663 667

NO anthro 593 597 603 609 616 629 639 644 649 654 659 665 665 665bb 140 160 114 104 111 120 116 113 115 107 115 97 55 59soil 171 171 171 171 171 171 171 171 171 171 171 171 171 171total 904 928 887 883 898 919 926 928 935 932 945 932 890 894

OC1 anthro 81 81 81 81 81 81 81 81 81 81 81 81 81 81bb 145 171 106 92 97 117 116 107 108 106 109 87 109 118total 225 251 186 173 178 198 197 188 188 186 189 168 190 198


SO2 anthro 1290 1286 1288 1303 1328 1367 1392 1392 1391 1391 1390 1390 1390 1390bb 32 37 23 20 21 26 25 23 24 23 24 19 23 25volcano 95 95 95 95 95 95 95 95 95 95 95 95 95 95total 1417 1419 1407 1419 1445 1488 1513 1511 1510 1509 1509 1504 1508 1510

TOLUENE anthro 287 288 290 292 295 300 304 308 312 316 320 324 324 324bb 38 45 28 25 26 31 31 29 29 28 29 23 118 126total 325 333 318 317 322 331 335 337 341 344 349 347 442 450

additional simulation (2006ndash2008) with MERRA but withthe Neu and Prather wet removal scheme is labeled MERRANeu A summary is provided in Table 6 We focus on theperiod post-Pinatubo to limit the influence of the eruptionon the chemical composition and meteorology The startingdates for the offline simulations are dictated by the availabil-ity of the respective meteorological datasets

61 Emissions

Available with the distribution of the CAM-chem are emis-sions for tropospheric chemistry that are an extension of thedatasets discussed in E2010 covering 1992ndash2010 More

specifically for 1992ndash1996 which is prior to satellite-basedfire inventories monthly mean averages of the fire emissionsfor 1997ndash2008 from GFED2 (van der Werf et al 2006 andupdates) are used for each year For 2009ndash2010 fire emis-sions are from FINN (Fire INventory from NCAR) (Wied-inmyer et al 2010) As discussed in E2010 most of theanthropogenic emissions come from the POET (Precursorsof Ozone and their Effects in the Troposphere) databasefor 2000 (Granier et al 2005) The anthropogenic emis-sions (from fossil fuel and biofuel combustion) of blackand organic carbon determined for 1996 are from Bond etal (2004) For SO2 and NH3 anthropogenic emissions arefrom the EDGAR-FT2000 and EDGAR-2 databases respec-



GEOS5MERRAOnline

1-Tropics2-SH Midlat3-SH Polar4-North America5-West Europe6-Japan7-NH Polar East8-NH Polar West9-Canada

117120108

Bias099112110

Bias

106121119

Bias

Fig 6 Taylor diagram of modeled ozone against ozone sonde climatology (Tilmes et al 2011) for 3 pressure levels the radial distanceindicates the normalized bias while the angle indicates the correlation of the average seasonal cycle Averaging is done over the overlapperiod between the model simulations and the observations for each location

tively (httpwwwmnpnledgar) For Asia these invento-ries have been replaced by the Regional Emission inventoryfor Asia (REAS) with the corresponding annual inventory foreach year simulated (Ohara et al 2007) Aircraft emissionshave global annual totals of 063 Tg yrminus1 (135 Tg N yrminus1)for NO 170 Tg yrminus1 for CO and 016 Tg yrminus1 for SO2 Forthe anthropogenic emissions only Asian emissions (fromREAS) are available each year all other emissions are there-fore repeated annually for each year of simulation The DMSemissions are monthly means from the marine biogeochem-istry model HAMOCC5 representative of the year 2000(Kloster et al 2006) SO2 emissions from continuously out-gassing volcanoes are from the GEIAv1 inventory (Andresand Kasgnoc 1998) Totals for each year and emitted speciesare listed in Table 7 All emissions but volcanoes are releasedin the model bottom layer and implemented as a flux bound-ary condition for the vertical diffusion

Note that while the emissions are provided at the modelresolution any emissions resolution can be used and the

model automatically interpolates to the model resolution Atthis point this interpolation is a simple bilinear interpolationand therefore does not ensure exact conservation of emis-sions between resolutions Errors are usually small and lim-ited to areas of strong gradients

62 Lower boundary conditions

For all long-lived species (see Table 3) the surface concen-trations are specified using the historical reconstruction fromMeinshausen et al (2011) In addition for CO2 and CH4 anobservationally-based seasonal cycle and latitudinal gradientare imposed on the annual average values provided by Mein-shausen et al (2011) These values are used in the model byoverwriting at each time step the corresponding model mix-ing ratio in the lowest model level with the time (and latitudeif applicable) interpolated specified mixing ratio



Alert

100200300400500600

Ozo

ne (p

pbv)

250hPa

Ny_Alesund

100200300400500600

250hPa

Edmonton

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

Payerne

100200300400500600

Ozo

ne (p

pbv)

250hPa

Sapporo

100200300400500600

250hPa

Boulder

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

San Cristobal

50100150200250300

Ozo

ne (p

pbv)

100hPa

Lauder

100200300400500600

250hPa

Neumayer

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 2008

Years

0

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

Fig 7 Long-term change in median observed (grey shading indicates variability within the season) and simulated tropospheric ozone (top250 hPa middle 500 hPa and bottom 800 hPa) for a variety of stations spanning the globe Seasonal averages are shown Black line isobservations red line is online green line is GEOS5 and blue line is MERRA

63 Specified stratospheric distributions

In the case where no stratospheric chemistry is explicitly rep-resented in the model it is necessary to ensure a proper distri-bution of some chemically-active stratospheric (namely O3NO NO2 HNO3 CO CH4 N2O and N2O5) species as isthe case for MOZART-4 This monthly-mean climatologi-cal distribution is obtained from WACCM simulations cov-ering 1950ndash2005 (Garcia et al 2007) Because of the vastchanges that occur over that time period our data distributionprovides files for three separate periods 1950ndash1959 1980ndash1989 and 1996ndash2005 This ensures that users can perform

simulations with a stratospheric climatology representativeof the pre-CFC era as well as during the high CFC and post-Pinatubo era Note that additional datasets can easily be con-structed if necessary

While transport and chemistry are applied to all species inthe stratosphere the concentration of the species listed aboveare explicitly overwritten from the model top to 50 hPa Be-tween that level and two model levels above the tropopause(computed from the temperature profile) a 10-day relaxationis applied to force the model concentrations towards the ob-servations



Table 8 Bias (ppb) and correlation coefficient (r) of ozone timelines between ozone sonde observations and model results for a matchingtime period

Sites Configuration 800 hPa 500 hPa 250 hPa

Alert online 925074 1106057 minus6025045MERRA 1162088 1443069 1224076GEOS5 103072 411068 minus2818084

NyAlesund online 483045 542075minus6821054MERRA 599077 898049 316069GEOS5 minus040078 minus131077 minus1912048

Edmonton online 1077036 1124065 4660068MERRA 1081068 1017050 5791076GEOS5 minus064086 minus178083 2252070

Payerne online 1373076 763087 3870044MERRA 967086 1186073 4608065GEOS5 230092 280092 4184073

Sapporo online 1104025 1397079 7398073MERRA 1225064 1788077 12797075GEOS5 491072 253090 11767063

Boulder online 407078 672071 2298045MERRA minus246082 786067 2168055GEOS5 minus283086 395085 3110064

Sancristobal online 859004 440069 533063MERRA 330012 453075 1300074GEOS5 minus337008 minus056067 1086074

Lauder online minus068091 165068 2295041MERRA 086092 544060 2625063GEOS5 minus318092 219078 3035066

Neumayer online minus148095 minus314085 minus5398070MERRA minus335091 minus159088 minus1976081GEOS5 minus1168087 minus815087 minus4451089

7 Comparison with observations

The purpose of this section is to document the model chem-istry performance against observations for the model setupsdescribed in Sect 6 (see Table 6 for a summary) Modelperformance in simulating climate and meteorological fea-tures can be found in Lamarque et al (2008) Lamarque andSolomon (2010) and in Neale et al (2011) Here we con-trast the fields generated by CAM4 in the simulations listedin Table 6

The zonal mean distribution (Fig 4a) of relative humid-ity is very similar between GEOS5 (considered here as thereference since it is the operational forecast product and as-similates the most observations) and MERRA except in theNorthern Hemisphere polar stratosphere and in the lower tro-posphere where MERRA is slightly drier than GEOS5 (notshown) On the other hand it is clear that the online distribu-tion is wetter in the tropical lower troposphere and drier inthe tropical upper troposphere

The zonal mean wind is essentially the same in theGEOS5 and MERRA simulations but the online polar jetsare stronger in both hemispheres leading to a more isolatedpolar stratosphere Other features such as the position andstrength of the mid-latitude jet are very similar between thesimulations

In terms of temperature the online configuration is char-acterized by a slightly higher-altitude tropical minimum aswell as a colder Southern Hemisphere polar stratosphererelated to the stronger jet We also find that the onlinesimulation tends to be colder in the Northern polar lowerstratosphere The online produces a shift of the tropicaltropopause with a warming in the upper-troposphere andcooling in the lower stratosphere similar to the findings ofCollins et al (2011) when using interactive ozone In thelower troposphere (700 hPa Fig 4b) there is no clear indi-cation of a bias except for slightly warmer land areas of theNorthern Hemisphere (1ndash2 K not shown) in online comparedto GEOS5MERRA



Many of the precipitation patterns are similar between theconfigurations (Fig 4b) but the difference with respect toGEOS5 indicates that MERRA has slightly different tropicalstructure (stronger precipitation) while the online configura-tion has an overall stronger precipitation in both the tropicaland midlatitude ocean regions Land masses tend to be drierin the online version except for Central Africa China andthe Himalayas

The chemical composition evaluation below makes use ofa variety of measurements surface airborne and satellite Inthe case of the online stratosphere-troposphere version thecomparison will include evaluation of modeled total ozonecolumn It is important to note that because the onlinemodel is only driven by the observed sea-surface tempera-tures there is no expectation that a single-year in the modelsimulation will be directly comparable with observations in-stead the most meaningful comparison is at the climatologi-cal level

71 Comparison with ozone sondes and surface

Due to its central role in tropospheric chemistry and the avail-ability of numerous ozone sonde measurements dating sev-eral decades (Logan 1994) we focus our first evaluation ontropospheric ozone using ozone sonde measurements both atspecific locations and averaged over representative regions(supplement Fig S1 Tilmes et al 2011) For a varietyof sites spanning the whole globe (especially in the merid-ional direction) the data coverage allows the comparison ofprofiles (Fig S2) seasonal cycles (Fig S3) and long-termchanges (Fig 7)

In order to provide a more concise description of the modelperformance under various configurations we first displaythe annual bias at specific pressure levels (250 hPa 500 hPaand 900 hPa) to span the troposphere and lower stratosphere(Fig 5) In particular at high latitudes the 250 hPa surfacewill be located in the stratosphere during a fraction of theyear

We find that the model tends to underestimate the ozoneconcentration at 250 hPa in the high latitudes On the otherhand most of the mid-latitude sites indicate a positive biasThis is an indication that the model seems to provide a posi-tion of the chemical tropopause that is lower than observedie an overestimate of the ozone mixing ratio in the lowerstratosphere (ie 200ndash300 hPa see Fig 5) This is confirmedby the Taylor diagrams (generated using regional averagesof ozone sondes and equivalent model results from monthlyoutput) which indicate that all versions are quite similar at250 hPa (Fig 6) In particular the seasonal cycle (quanti-fied by the correlation coefficient computed using monthly-averaged data and model output) ranges between 07 and 09indicating a reasonable representation of ozone variations at250 hPa

At 500 hPa GEOS5 is clearly providing the best perfor-mance with many stations with a bias smaller in absolute

Fig 8 (a)Comparison of surface ozone summertime 8-h maximumfor CASTNET sites over the United States(b) Same as(a) but forthe European EMEP sites

value than 5 ppbv It is interesting to note that MERRAbehaves quite differently than GEOS5 This is most likelydue to differences in the assimilation method and datasetsused In particular as discussed below the MERRA me-teorology leads to a much stronger stratosphere-troposphereflux of ozone likely leading to the 500 hPa biases In termsof bias the online simulation performs better than MERRAespecially in the Southern Hemisphere However the cor-relation is much worse (05ndash06) for many of the NorthernHemisphere stations The seasonal cycle in both MERRAand GEOS5 seems equally good



Fig 9 (a)Comparison of regional aircraft profiles (from Emmons et al 2000) averaged over 2ndash6 km to the online GEOS5 and MERRAmodel simulations Each symbol represents an aircraft campaign region in the climatology(b) Same as(a) but for additional species



Fig 10 (a)As Fig 9a except comparing the Horowitz and Neu and Prather washout schemes(b) Same as(a) but for additional species



Table 9 Tropospheric (ozonele 100 ppb) ozone budget methane lifetime isoprene and lightning emissions averaged for 2006ndash2008

Name Burden Production Loss Net Chem Deposition STE Lifetime CH4 lifetime Isoprene LightningUnits Tg Tg yrminus1 Tg yrminus1 Tg yrminus1 Tg yrminus1 Tg yrminus1 days years Tg yrminus1 TgN yrminus1

GEOS5 328 4897 4604 293 705 411 260 87 540 36MERRA Neu 345 4778 4682 96 770 674 269 87 540 44MERRA 346 4868 4760 108 781 673 265 91 540 44Online 349 5014 4657 357 773 416 274 98 602 43

Fig 11 (a)Comparison of the annual mean surface mixing ratioof CO to NOAAGMD observations(b) Comparison of the meanannual cycle (maximum minus minimum) of surface CO mixingratio

In the lower troposphere (900 hPa) the various configura-tions tend to exhibit similar regional annual biases especiallyin the Northern Hemisphere Correlation of the annual cycleis for most regions in the range 06ndash09 with some clearmisrepresentations especially the MERRA simulation overNorth America Surface ozone is discussed below

The analysis of long-term changes (Fig 7) indicates thatobserved meteorology is important in representing interan-nual variability especially in the upper troposphere as thecorrelation for GEOS5MERRA tends to be higher than inthe online configuration (Table 8 stations were selected forthe availability of fairly continuous long-term records) Nev-ertheless from Table 8 the bias at 250 hPa seems to be bet-ter captured by the online simulation for the analysis stationsequatorward of 40 possibly related to consistently repre-senting transport and chemistry Overall Table 8 confirmsthe previous analysis that in the free troposphere (500 hPa)GEOS5 provides the best representation of ozone Thatconfiguration also tends to provide a better simulation at800 hPa with a lower mean bias and usually high (gt07) cor-relation coefficient The seasonal cycle at the tropical stationSan Cristobal (Galapagos) is clearly misrepresented at thataltitude in all configurations

The annual budget for tropospheric ozone is summa-rized in Table 9 note that these are averaged numberswith an interannual variability on the order of 10 Wefind that the online and offline versions have similar tro-pospheric (defined here as the region of the atmospherewhere the ozone mixing ratio is lower than 100 ppbv) bur-dens and depositions but with an overall smaller net chem-ical ozone production in the case of the offline meteorol-ogy The corollary to this comparison is that the diagnosedstratosphere-troposphere flux of ozone (computed as the dif-ference between the deposition and net chemical produc-tion) ranges from 410ndash420 Tg yrminus1 (online and GEOS5) to675 Tg yrminus1 (MERRA) within the range of published model-derived estimates (eg 515ndash550 Tg yrminus1 Hsu et al 2005556plusmn 154 Tg yrminus1 Stevenson et al 2005) In the case ofthe online simulation this leads to an ozone lifetime ofasymp

27 days in very good agreement with Stevenson et al (2005)To discuss surface ozone we present in Fig 8a and b the

comparison of summertime (JunendashAugust) daily 8-h max-imum (usually afternoon) for the United States (from theCASTNET network) and Europe (from the EMEP network)Because of the very different chemical regimes betweenWestern and Eastern United States we have separated thestations using longitude 100 W as the line of demarcationWe find that all model configurations tend to reproduce theWestern sites quite well with a propensity for the online



Fig 12 Comparison of MOPITT (2004ndash2009) climatology of day-time CO (ppbv) retrievals at 500 hPa with model results (convolved witha priori and averaging kernels) for winter (DJF) and summer (JJA) Model results are averaged over the same period

Table 10CO evaluation against NOAAGMD stations

Annual Annual Annual Seasonal Seasonal Seasonalmean mean mean cycle cycle cyclebias rmsd correl bias rmsd correl(ppb) (ppb) (ppb) (ppb)

MERRA 28 28 099 minus38 53 047Online minus83 22 080 minus85 40 037



EPTOMS-OMI

Online

Fig 13 Time evolution of zonally and monthly-averaged totalozone column (in Dobson Units) from satellite measurements (EP-TOMS and OMI top) and online simulation (bottom)

configuration to be slightly higher Over the Eastern UnitedStates all configurations are biased high with the MERRAconfiguration leading to the highest biases (40ndash60 ppbv) Itis unclear why ozone is biased high over those regions andis likely to be a combination of incorrect emissions coarseresolution (Wild and Prather 2006) and misrepresentation ofphysical processes (Lin et al 2008) It is less likely to bemeteorology-driven since online and specified dynamics be-have similarly

Over Europe (Fig 8b) the model also tends to overesti-mate surface ozone It is however clear that the online mete-orology provides a much more biased ozone distribution (ascan be seen in Fig 6 region 5) than the specified dynamicsIt is interesting to note that in the model the concentrationssaturate at 80ndash90 ppbv depending on the configuration un-like the United States sites Further analysis is necessary tounderstand this behavior

Fig 14 (a) Timeseries of October total ozone column at 88 S(b) Timeseries of March total ozone column at 80 N

72 Comparison with aircraft observations

As a standard benchmark evaluation we have performedcomparison with the aircraft observations in the Emmons etal (2000) climatology (Fig S4) In this section we sum-marize the content of those figures (Fig 9) by focusing onthe regional averages for each campaign in the Emmons etal (2000) climatology We further concentrate our analysison a specific range of altitude 2ndash6 km in this case This ischosen as to be representative of the free troposphere andbe less directly influenced in possible emission shortcom-ings Because we focus on the regional averages on theclimatology we include all simulations In each case wehave used the monthly-averaged model results for the period2006ndash2008 although the use of a longer period does not alterthe conclusions (not shown)

As expected from the ozone analysis presented inSect 71 the GEOS5 simulation performs better than theother ones in its ozone distribution Also MERRA stronglyoverestimates ozone during the TOPSE (Northern Hemi-sphere high latitudes) campaigns reinforcing the view of toostrong mixing from the stratosphere



Table 11 Slope of linear fit of annual mean modeled aerosolsagainst IMPROVE data

Sulfate EC OC Amm Nit

Online 099 037 036 093GEOS5 212 041 039 091MERRA 190 043 041 098

Nitrogen oxides (NOx) tends to be highest in the onlineconfiguration (Fig 9a) although all versions seem to exhibitsimilar biases in particular there is clear low NOx bias in thecase of TOPSE Similarly PAN and nitric acid (HNO3) areconsistently simulated across configurations In most casesthe lowest CO distribution is associated with the GEOS5 sim-ulation This is discussed in more details in Sect 73

Except for the TOPSE campaign the non-methane hydro-carbons (Fig 9b) are quite well represented in the modelover most of the campaigns TOPSE being again the mostbiased with a strong underestimate in C2H6 There is alarger spread between configurations on methylhydroperox-ide (CH3OOH) where the online simulation is consistentlythe lowest and on hydrogen peroxide (H2O2) for whichMERRA tends to be the lowest

In order to identify the role of the wet removal parameteri-zation (see Sect 33) we perform the same analysis as aboveusing the MERRA simulations (with the Horowitz and Neuand Prather schemes see Table 6) Over the analysis regions(Fig 10) we find little impact on ozone NOx or PAN On theother hand CO is consistently smaller (and therefore worse)when the Neu and Prather scheme is used Furthermore thenitric acid and hydrogen peroxide distributions over TOPSEare more accurately represented with the Horowitz schemeHowever in most cases little difference can be found be-tween those simulations

73 Comparison with surface carbon monoxide

Surface mixing ratio of carbon monoxide represents one oflongest records of tropospheric composition for this compar-ison we use all years available from the National Oceanic andAtmospheric Administration data (available atftpftpcmdlnoaagovccgcoflaskevent Novelli and Masarie 2010)Furthermore because of its strong link to the hydroxyl rad-ical OH the overall concentration and seasonal cycle of CO(which in turn depends on emissions of CO and its precur-sors) is an important indicator of the representation of thetropospheric oxidative capacity (Lawrence et al 2001) Forthat purpose we compare the model results (in this case theonline and MERRA simulations in order to have a suffi-ciently long record) in terms of the latitudinal distributionof the annual mean and seasonal cycle (Fig 11a and b re-spectively)

Overall the model accurately represents the latitudinaldistribution of CO (strongly driven by gradients in emis-sions) it also represents the Southern Hemisphere annualmean but underestimates the high-latitude Northern Hemi-sphere values similar to the multi-model results in Shindellet al (2006) The much higher than observed value close tothe Equator is related to the Bukit Koto Tabang (West Suma-tra) station which being an elevated site (865 m) makes itchallenging for coarse-grid models especially for narrow is-lands The bias could also be related to emission errors al-though the Guam station (at 13 N) does not indicate a strongbias Statistical evaluation is listed in Table 10 We find thatwhile the biases (annual mean and seasonal cycle) are lowerin MERRA the root-mean square difference is larger in thatcase The higher correlation however indicates a better rep-resentation of the surface CO distribution in MERRA Simi-larly the seasonal cycle is quite well represented over all thelatitudes except in the Polar Northern Hemisphere

This is further confirmed by the comparison to the re-trieved CO 500 hPa concentrations by the Measurements ofPollution in The Troposphere (MOPITT v4 Deeter et al2010 Fig 12) We find that all versions tend to overestimatethe CO over Africa in DecemberndashFebruary During summerthe online version tends to reproduce the African maximumbetter

The estimated CO tropospheric lifetime (with respect toOH loss ozonelt100 ppbv) is approximately 15 months inthe MERRA and GEOS5 simulations and 175 in the onlinesimulation similar to the results from Horowitz et al (2003)and Shindell et al (2006) We have also compared (see Sup-plement Fig S5) our tropospheric OH distribution with theSpivakovsky et al (2000) climatology using the Lawrenceet al (2001) diagnostic approach In that case we find thatour OH distribution is in quite good agreement with that cli-matology It is however smaller (20ndash30) in the tropicalmid-troposphere especially in the Southern Hemisphere itis also larger in the Northern mid-latitudes except in the mid-troposphere Based on this analysis the closest OH distribu-tion to Spivakoskyrsquos is provided by the GEOS5 simulation

The tropospheric methane lifetime (reaction with OH onlycomputed as total burden divided by tropospheric loss withthe troposphere defined as the region with ozonelt100 ppbv)ranges between 87 and 98 yr (Table 9) similar to Shin-dell et al (2006) and Horowitz et al (2003) but lower thanMOZART-4 (105 yr E2010 albeit computed slightly dif-ferently) and therefore consistent with the above analysis ofOH This is also consistent with a drier tropical tropospherein the online simulation (Fig 4a) and higher isoprene emis-sions (Table 6)

74 Comparison with total ozone column

The availability of stratospheric chemistry in the online sim-ulation warrants the comparison with the satellite observedtotal ozone column In particular we use here the gridded



OnlineGEOS5MERRA

MISRMODIS

Fig 15 Seasonal cycle of the aerosol optical depth from MODIS and MISR over specific regions

EP-TOMS and OMI (both available athttptomsgsfcnasagov) The data are zonally- and monthly-averaged beforecomparison with the model field (Fig 13) The overall fea-tures (latitudinal distribution and seasonal cycle includingthe Antarctic ozone hole) and interannual variability are wellreproduced In particular even though the model has a lim-ited vertical extent and resolution in the stratosphere thetropical ozone column is quite well reproduced

To further compare with observed values we focus(Fig 14) on comparing the long-term variability in high-latitude spring ozone (March in the Northern Hemisphereand October in the Southern Hemisphere) Because the on-line model is only driven by the observed sea-surface tem-peratures there is no expectation that a single-year in themodel simulation will be directly comparable with obser-vations instead only the mean and standard deviation arerelevant in this case Figure 14 shows that unlike the ver-sion used in the CCMVal-2 simulations (Austin et al 2010)

this updated version has a good representation of the ozonehole (mean and interannual variability) with a limited under-estimate (mean bias isminus50 DU not considering the highlyunusual 2002 conditions) of the mean October Antarcticozone hole Similarly the mean Northern Hemisphere Marchozone distribution is slightly negatively biased (mean bias isminus75 DU) These negative biases are likely due to the coldbias over the polar regions in the online configuration (seeFig 4a) Note however that the model is not quite able toreproduce the Northern Hemisphere dynamical interannualvariability due to its limited representation of the stratosphere(Morgenstern et al 2010)

75 Comparison with aerosol observations

At the regional scale and over land the modeled aerosol op-tical depth (Fig 15) is generally lower than the satellite ob-servations (from the Moderate Resolution Imaging Spectro-radiometer and the Multi-angle Imaging SpectroRadiometer



OnlineGEOS5MERRA

Sulfate EC

OC Ammonium Nitrate

Annual surface layer concentration (microgm3)

Fig 16 Linear correlation between observed (IMPROVE sites black line) and modeled (interpolated to observation sites) annual surfaceconcentrations of aerosols (sulfate top left elemental carbon top right organic carbon bottom left ammonium nitrate bottom right)

all years 2001ndash2010) except over China As discussed inLamarque et al (2011b) none of these simulations includethe impact of water uptake on black carbon optical prop-erties lowering the optical depth associated with this com-pound and partially explaining the negative bias particu-larly over South America where biomass burning is the majorsource of optical depth It is likely that the more importanttropical precipitation in the online simulation significantlycontributes to the underestimation in optical depth

Owing to the availability of a large set of surfaceobservations (time and speciation) we focus our analy-sis on the United States Interagency Monitoring of Pro-tected Visual Environments (IMPROVE Malm et al 2004)dataset (available for download athttpvistaciracolostateeduIMPROVEDatadatahtm) We perform a comparisonof sulfate elemental carbon organic carbon (including sec-ondary organic aerosols SOA) and ammonium nitrate

We first present correlations (Fig 16) between long-termmean (1998ndash2009) observations and model results (interpo-lated to the location of the observing stations) note that theIMPROVE sites are located in remote locations (such as Na-tional Parks) and are therefore representative of the rural en-vironment not urban We find that as discussed in Lamar-que et al (2011b) sulfate is quite well represented by theonline configuration however slightly positively biased Onthe other hand both elemental and organic carbon aerosolsare underestimated (and with large scatter) in all configura-tions

The linear fit parameters (Table 11) indicate that all con-figurations behave similarly except in the case of sulfate Inthat case the slope for the online configuration is closest tothe observations while GEOS5 and MERRA overestimatethe surface values by a factor of 2 It is possible that such dis-parity would be reduced if the SO2 emissions were released



Annual Winter Summer

Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate

Surface layer concentration (microgm3)

Fig 17Probability density function of observed (IMPROVE sites black line) and modeled (interpolated to observation sites) aerosol surfaceconcentration (sulfate top row elemental carbon second row organic carbon third row ammonium nitrate bottom row) The simulationresults shown here are for the online stratosphere-troposphere (red) and the GEOS5 simulation (green) Analysis is shown for annual (leftcolumn) winter (December-January-February center column) and summer (June-July-August right column)

at a specific height instead of the bottom model layer whichis much thinner in the specified dynamics set up (Fig 3)

To further document the behavior of the bulk-aerosolscheme over the United States we present in Fig 17 theprobability density function of the mean annual and seasonal(summer and winter) observed and modeled surface concen-trations Using this diagnostic we find that the modeled sul-fate cannot capture the lowest observed values and instead

peaks at higher values and has a broader distribution in addi-tion the annual mean seems to exhibit a longer tail in the dis-tribution than the observations Furthermore the distributionfunction for elemental carbon is quite well reproduced ex-cept for a higher tendency for small values On the other handit is clear that the model simulations of organic carbon can-not capture the mid-range values especially in the summer-time most likely a representation of the lack of significant



SOA production with the current scheme (Lack et al 2004)Somewhat surprisingly owing to the inherent difficulties inrepresenting ammonium nitrate in a model and to the fairlysimple representation of its formation in the model we findthat ammonium nitrate is quite reasonable with a slightlysmaller proportion for the high concentrations (possibly dueto the model coarse resolution) and higher proportion for thelow concentrations

8 Discussion and conclusions

Using a variety of diagnostics and evaluation datasets (in-cluding satellite aircraft surface measurements and surfacedata) we have demonstrated the capability of CAM-chemin reasonably representing tropospheric and stratosphericchemistry

Based on the simulations discussed here we have foundthat the CO and CH4 lifetimes are in good agreement withpreviously published estimates similarly our OH distribu-tion is in reasonable agreement with the Spivakovsky etal (2000) climatology However our CO distribution in thehigh Northern latitudes is underestimated when comparedto surface aircraft and satellite observations indicating anoverestimate of the CO loss by OH or underestimate of itsemissions or chemical production

Ozone in the troposphere is simulated reasonably wellwith some overestimation of ozone in the upper tropo-spherelower stratosphere region in high northern latitudesin comparison to ozone sondes Even though the model topis limited to 40 km stratospheric composition is acceptableand the polar ozone depletion is reasonably well reproducedin the online configuration All configurations of CAM-chemsuffer from a significant overestimate of summertime surfaceozone over the Eastern United States and Europe On otherhand Western United States sites are quite accurately repre-sented

Aerosol optical depth tends to be underestimated overmost regions when compared to satellite retrievals Addi-tional comparison over the United States indicates an over-estimate of sulfate in the case of MERRA and GEOS5 andan underestimate of elemental and organic carbon in all con-figurations Analysis of the seasonal (summerwinter) andannual probability density functions indicates strong similar-ities between model configurations

Using a variety of statistical measures and especially Tay-lor diagrams we have found that the CAM-chem configu-ration with GEOS5 meteorology provides the best represen-tation of tropospheric chemistry This is particularly clearfor the 500 hPa (best bias and correlation) and 900 hPa (bestbias) regional ozone distribution The increased vertical res-olution in GEOS5 and MERRA (56 levels instead of 26 forthe online meteorology) might play a role in this better per-formance although this hypothesis has not been explicitlytested Based on the ozone budget analysis (and supported

by ozone analysis in the high-latitudes) we have found thatthe MERRA meteorology leads to a stronger ozone flux fromthe stratosphere This is likely associated with the differ-ent assimilation procedures used in MERRA than in GEOS5(Rienecke et al 2011)

When compared against regional climatologies from fieldcampaigns the inclusion of the Neu and Prather (2011) pa-rameterization for wet removal of gas-phase species showslittle impact on 2ndash6 km ozone On the other hand CO isconsistently smaller (by 5ndash10 ppb) Consistent with this themethane lifetime is slightly shorter with the Neu and Pratherscheme Nitric acid and hydrogen peroxide are increasedover the high northern latitudes (TOPSE campaign) Overallour analysis suggests only small differences from this newparameterization

All necessary inputs (model code and datasets) to per-form the simulations described here on a wide varietyof computing platforms and compilers can be found athttpwwwcesmucaredumodelscesm10

Supplementary material related to thisarticle is available online athttpwwwgeosci-model-devnet53692012gmd-5-369-2012-supplementpdf

AcknowledgementsWe would like to acknowledge the help ofM Schultz and M Val Martin with surface ozone measurementsP G H was partially funded under the NSF award 0840825 andEPA Grant 834283 C L H was partially supported by NSFaward 0929282 D K J-F L and F V were partially fundedby the Department of Energy under the SciDAC program Wealso acknowledge the science teams producing the data used formodel evaluation including the NOAA Earth System ResearchLaboratory Global Monitoring Division for surface CO andozone sondes and the World Ozone and Ultraviolet RadiationData Centre (WOUDC) for ozonesondes The CESM project issupported by the National Science Foundation and the Office ofScience (BER) of the US Department of Energy The NationalCenter for Atmospheric Research is operated by the UniversityCorporation for Atmospheric Research under sponsorship of theNational Science Foundation

Edited by A Stenke

References

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Emmons L K Walters S Hess P G Lamarque J-F PfisterG G Fillmore D Granier C Guenther A Kinnison DLaepple T Orlando J Tie X Tyndall G Wiedinmyer CBaughcum S L and Kloster S Description and evaluation ofthe Model for Ozone and Related chemical Tracers version 4(MOZART-4) Geosci Model Dev 3 43ndash67doi105194gmd-3-43-2010 2010

Fiore A M Dentener F J Wild O Cuvelier C Schultz MG Hess P Textor C Schulz M Doherty R M Horowitz IA MacKenzie M G Sanderson D T Shindell D S Steven-son S Szopa R Van Dingenen G Zeng C Atherton DBergmann L W I Bey G Carmichael W J Collins B NDuncan G Faluvegi G Folberth M Gauss S Gong DHauglustaine T Holloway I S A Isaksen D J Jacob J EJonson J W Kaminski T J Keating A Lupu E MarmerV Montanaro R J Park G Pitari K J Pringle J A PyleS Schroeder M G Vivanco P Wind G Wojcik S Wuand Zuber A Multi-Model estimates of intercontinental source-receptor relationships for ozone pollution J Geophys Res 114D04301doi1010292008JD010816 2009

Forster P V Ramaswamy V Artaxo P Berntsen T Betts RFahey D W Haywood J Lean J Lowe D C Myhre GNganga J Prinn R Raga G Schulz M and Van DorlandR Changes in Atmospheric Constituents and in Radiative Forc-ing in Climate Change 2007 The Physical Science Basis Con-tribution of Working Group I to the Fourth Assessment Reportof the Intergovernmental Panel on Climate Change edited bySolomon S Qin D Manning M Chen Z Marquis M Av-eryt K B Tignor M and Miller H L Cambridge UniversityPress Cambridge UK and New York NY USA 2007

Garcia R R Marsh D R Kinnison D E Boville BA and Sassi F Simulation of secular trends in the mid-



dle atmosphere 1950ndash2003 J Geophys Res 112 D09301doi1010292006JD007485 2007

Granier C Guenther A Lamarque J Mieville A MullerJ Olivier J Orlando J Peters J Petron G Tyndall Gand Wallens S POET a database of surface emissions ofo-zone precursors available athttpwwwaerojussieufrprojetACCENTPOETphp (last access August 2008) 2005

Guenther A Hewitt C N Erickson D Fall R Geron CGraedel T Harley P Klinger L Lerdau M McKay WPierce T Scholes B Steinbrecher R Tallamraju R TaylorJ and Zimmerman P A global model of natural volatile or-ganic compound emissions J Geophys Res 100 8873ndash88921995

Guenther A Karl T Harley P Wiedinmyer C Palmer P Iand Geron C Estimates of global terrestrial isoprene emissionsusing MEGAN (Model of Emissions of Gases and Aerosols fromNature) Atmos Chem Phys 6 3181ndash3210doi105194acp-6-3181-2006 2006

Hack J J Caron J M Yeager S G Oleson K W Holland MM Truesdale J E and Rasch P J Simulation of the globalhydrological cycle in the CCSM Community Atmosphere ModelVersion 3 (CAM3) Mean features J Climate 19 2199ndash22212006

Heald C L Henze D K Horowitz L W Feddema J Lamar-que J-F Guenther A Hess P G Vitt F Seinfeld J HGoldstein A H and Fung I Predicted change in global sec-ondary organic aerosol concentrations in response to future cli-mate emissions and land-use change J Geophys Res 113D05211doi1010292007JD009092 2008

Holtslag A A M and Boville B A Local versus nonlocal bound-ary layer diffusion in a global climate model J Climate 6 1825ndash1841 1993

Horowitz L W Walters S Mauzerall D L Emmons L KRasch P J Granier C Tie X X Lamarque J-F Schultz MG Tyndall G S Orlando J J and Brasseur G P A globalsimulation of tropospheric ozone and related tracers Descriptionand evaluation of MOZART version 2 J Geophys Res 1084784doi1010292002JD002853 2003

Hsu J Prather M J and Wild O Diagnosing the stratosphere-to-troposphere flux of ozone in a chemistry transport model JGeophys Res 110 D19305doi1010292005JD006045 2005

Hudman R C Jacob D J Turquety S Leibensperger E MMurray L T Wu S Gilliland A B Avery M Bertram TH Brune W Cohen R C Dibb J E Flocke F M Fried AHolloway J Neuman J A Orville R Perring A Ren XSachse G W Singh H B Swanson A and Wooldridge P JSurface and lightning sources of nitrogen oxides over the UnitedStates Magnitudes chemical evolution and outflow J Geo-phys Res 112 D12S05doi1010292006JD007912 2007

Jacob D J and Winner D A Effect of ClimateChange on Air Quality Atmos Environ 43 51ndash63doi101016jatmosenv20080905 2009

Jakob C and Klein S A A parametrization of cloud and precip-itation overlap effects for use in General Circulation Models QJ Roy Meteorol Soc 126 2525ndash2544 2000

Jonson J E Stohl A Fiore A M Hess P Szopa S WildO Zeng G Dentener F J Lupu A Schultz M G DuncanB N Sudo K Wind P Schulz M Marmer E CuvelierC Keating T Zuber A Valdebenito A Dorokhov V De

Backer H Davies J Chen G H Johnson B Tarasick DW Stubi R Newchurch M J von der Gathen P SteinbrechtW and Claude H A multi-model analysis of vertical ozoneprofiles Atmos Chem Phys 10 5759ndash5783doi105194acp-10-5759-2010 2010

Karcher B and Voigt C Formation of nitric acidwater iceparticles in cirrus clouds Geophys Res Lett 33 L08806doi1010292006GL025927 2006

Kinnison D E Brasseur G P Walters S Garcia R R MarshD A Sassi F Boville B A Harvey L Randall C Em-mons L Lamarque J-F Hess P Orlando J Tyndall GTie X X Randel W Pan L Gettelman A Granier CDiehl T Niemeier U and Simmons A J Sensitivity ofchemical tracers to meteorological parameters in the MOZART-3 chemical transport model J Geophys Res 112 D20302doi1010292006JD007879 2007

Kloster S Feichter J Maier-Reimer E Six K D Stier P andWetzel P DMS cycle in the marine ocean-atmosphere system ndasha global model study Biogeosciences 3 29ndash51doi105194bg-3-29-2006 2006

Lack D A Tie X X Bofinger N D Wiegand A Nand Madronich S Seasonal variability of secondary organicaerosol A global modeling study J Geophys Res 109D03202doi1010292003JD003418 2004

Lamarque J-F and Solomon S Impact of Changes in Climate andHalocarbons on Recent Lower Stratosphere Ozone and Temper-ature Trends J Climate 23 2599ndash2611 2010

Lamarque J-F Kiehl J T Hess P G Collins W D Em-mons L K Ginoux P Luo C and Tie X X Response ofa coupled chemistry-climate model to changes in aerosol emis-sions Global impact on the hydrological cycle and the tropo-spheric burdens of OH ozone and NOx Geophys Res Lett 32L16809doi1010292005GL023419 2005

Lamarque J-F Kinnison D E Hess P G and Vitt F Simu-lated lower stratospheric trends between 1970 and 2005 identi-fying the role of climate and composition changes J GeophysRes 113 D12301doi1010292007JD009277 2008

Lamarque J-F Bond T C Eyring V Granier C Heil AKlimont Z Lee D Liousse C Mieville A Owen BSchultz M G Shindell D Smith S J Stehfest E VanAardenne J Cooper O R Kainuma M Mahowald N Mc-Connell J R Naik V Riahi K and van Vuuren D P His-torical (1850ndash2000) gridded anthropogenic and biomass burningemissions of reactive gases and aerosols methodology and ap-plication Atmos Chem Phys 10 7017ndash7039doi105194acp-10-7017-2010 2010

Lamarque J-F McConnell J R Shindell D T Orlando JJ and Tyndall G S Understanding the drivers for the 20thcentury change of hydrogen peroxide in Antarctic ice-coresGeophys Res Lett 38 L04810doi1010292010GL0459922011a

Lamarque J-F Kyle G P Meinshausen M Riahi K Smith SJ van Vuuren D P Conley A and Vitt F Global and regionalevolution of short-lived radiatively-active gases and aerosols inthe Representative Concentration Pathways Climatic Change109 191ndash212 2011b

Lauritzen P H Ullrich P A and Nair R D Atmospherictransport schemes Desirable properties and a semi-Lagrangianview on finite-volume discretizations in Numerical Techniques



for Global Atmospheric Models Lect Notes Comp Sci 80Springer Berlin 2011

Lawrence M G Jockel P and von Kuhlmann R What does theglobal mean OH concentration tell us Atmos Chem Phys 137ndash49doi105194acp-1-37-2001 2001

Lawrence P J and Chase T N Representing a new MODIS con-sistent land surface in the Community Land Model (CLM 30) JGeophys Res 112 G01023doi1010292006JG000168 2007

Levis S Wiedinmyer C Guenther A and Bonan G CouplingBiogenic VOC Emissions to the Community Land Model Ef-fects of Land Use Change on BVOC emissions J Geophys Res108 4659doi1010292002JD003203 2003

Liao H Adams P J Chung S H Seinfeld J H Mickley LJ and Jacob D J Interactions between tropospheric chemistryand aerosols in a unified general circulation model J GeophysRes 108 4001doi1010292001JD001260 2003

Lin J-T Youn D Liang X-Z and Wuebbles D JGlobal model simulation of summertime US ozone diur-nal cycle and its sensitivity to PBL mixing spatial res-olution and emissions Atmos Environ 42 8470ndash8483doi101016jatmosenv200808012 2008

Logan J A Trends in the vertical distribution of ozone An anal-ysis of ozonesonde data J Geophys Res 99 25553ndash255851994

Lowe D and Mackenzie R Review of polar stratospheric cloudmicrophysics and chemistry J Atmos Solar-Terr Phys 70 13ndash40 2008

Madronich S Photodissociation in the atmosphere 1 Actinic fluxand the effects of ground reflections and clouds J GeophysRes 92 9740ndash9752 1987

Mahowald N Lamarque J-F Tie X X and Wolff E Sea-salt aerosol response to climate change Last Glacial Maximumpreindustrial and doubled carbon dioxide climates J GeophysRes 111 D05303doi1010292005JD006459 2006a

Mahowald N M Muhs D R Levis S Rasch P J YoshiokaM Zender C S and Luo C Change in atmospheric mineralaerosols in response to climate Last glacial period preindustrialmodern and doubled carbon dioxide climates J Geophys Res111 D10202 doi102101102912005JD006653 2006b

Malm W C Schichtel B A Pitchford M L Ashbaugh L Land Eldred R A Spatial and monthly trends in speciated fineparticle concentration in the United States J Geophys Res 109D03306doi1010292003JD003739 2004

McLinden C Olsen S Hannegan B Wild O and PratherM Stratospheric ozone in 3-D models A simple chemistry andthe cross-tropopause flux J Geophys Res 105 14653ndash146652000

Meinshausen M Smith S J Calvin K Daniel J S KainumaM L T Lamarque J-F Matsumoto K Montzka S RaperS Riahi K Thomson A Velders G J M and van VuurenD P The RCP Greenhouse Gas Concentrations and their Exten-sions from 1765 to 2300 Climatic Change 109 213ndash241 2011

Metzger S Dentener F Pandis S and Lelieveld J Gasaerosolpartitioning 1 A computationally efficient model J GeophysRes 107 4312doi1010292001JD001102 2002

Morgenstern O Akiyoshi H H Bekki S Braesicke P Chip-perfield M Gettelman A Hardiman S Lamarque J-F Mi-chou M Pawson S Rozanov E Scinocca J Shibata Kand Smale D Anthropogenic forcing of the Northern Annular

Mode in CCMVal-2 models J Geophys Res 115 D00M03doi1010292009JD013347 2010

Neale R B Richter J and Jochum M The impact of convec-tion on ENSO From a delayed oscillator to a series of events JClimate 21 5904ndash5924 2008

Neale R B Richter J Park S Lauritzen P H Vavrus S JRasch P J and Zhang M The Mean Climate of the Com-munity Atmosphere Model (CAM4) in Forced SST and FullyCoupled Experiments J Climate submitted 2011

Neu J L and Prather M J Toward a more physical repre-sentation of precipitation scavenging in global chemistry mod-els cloud overlap and ice physics and their impact on tropo-spheric ozone Atmos Chem Phys Discuss 11 24413ndash24466doi105194acpd-11-24413-2011 2011

Novelli P C and Masarie K A Atmospheric Carbon Monox-ide Dry Air Mole Fractions from the NOAA ESRL Carbon Cy-cle Cooperative Global Air Sampling Network 1988ndash2009 Ver-sion 2011-10-14 available atftpftpcmdlnoaagovccgcoflaskevent(last access April 2011) 2010

Ohara T Akimoto H Kurokawa J Horii N Yamaji KYan X and Hayasaka T An Asian emission inventory ofanthropogenic emission sources for the period 1980ndash2020 At-mos Chem Phys 7 4419ndash4444doi105194acp-7-4419-20072007

Oleson K W Lawrence D M Bonan G B Flanner M GKluzek E Lawrence P J Levis S Swenson S C ThorntonP E Dai A Decker M Dickinson R Feddema J HealdC L Hoffman F Lamarque J-F Mahowald N Niu G-Y Qian T Randerson J Running S Sakaguchi K SlaterA Stockli R Wang A Yang Z-L and Zeng X Technicaldescription of version 40 of the Community Land Model (CLM)NCAR Technical Note NCARTN-478+STR 257 pp 2010

Pfister G Hess P G Emmons L K Rasch P J and VittF M Impact of the Summer 2004 Alaska Fires on TOAClear-Sky Radiation Fluxes J Geophys Res 113 D02204doi1010292007JD008797 2008

Prather M J Tropospheric O3 from photolysis of O2 GeophysRes Lett 36 L03811doi1010292008GL036851 2009

Price C and Rind D A simple lightning parameterization forcalculating global lightning distributions J Geophys Res 979919ndash9933doi10102992JD00719 1992

Price C Penner J and Prather M NOx from lightning 1 Globaldistribution based on lightning physics J Geophys Res 1025929ndash5941 1997

Randerson J Liu H Flanner M G Chambers S D Jin YHess P G Pfister G Mack M C Treseder K K WelpL R Chapin F S Harden J W Goulden M L Lyons ENeff J C Schuur E A G and Zender C S The Impact ofBoreal Forest Fires on Climate Warming Science 314 1130ndash1132doi101126science1132075 2006

Rasch P J Mahowald N M and Eaton B E Representationsof transport convection and the hydrologic cycle in chemicaltransport models Implications for the modeling of short-livedand soluble species J Geophys Res 102 28127ndash28138 1997

Rasch P J Coleman D B Mahowald N Williamson D L LinS-J Boville B A and Hess P Characteristics of atmospherictransport using three numerical formulations for atmospheric dy-namics in a single GCM framework J Climate 19 2243ndash22662006



Richter J H and Rasch P J Effects of Convective Momen-tum Transport on the Atmospheric Circulation in the Commu-nity Atmosphere Model Version 3 J Climate 21 1487ndash1499doi1011752007JCLI17891 2007

Ridley B Pickering K and Dye J Comments on the parameter-ization of lightning-produced NO in global chemistry-transportmodels Atmos Environ 39 6184ndash6187 2005

Rienecker M M Suarez M J Gelaro R Todling R Bacmeis-ter J Liu E Bosilovich M G Schubert S D Takacs LKim G-K Bloom S Chen J Collins D Conaty A daSilva A Gu W Joiner J Koster R D Lucchesi R MolodA Owens T Pawson S Pegion P Redder C R ReichleR Robertson F R Ruddick A G Sienkiewicz M andWoollen J MERRA NASArsquos Modern-Era Retrospective Anal-ysis for Research and Application J Climate 24 3624ndash3648doi101175JCLI-D-11-000151 2011

Rotman D Atherton C S Bergmann D J Cameron-SmithP J Chuang C C Connell P S Dignon J E Franz AGrant K E Kinnison D E Molenkamp C R ProctorD D and Tannahill J R IMPACT the LLNL 3-D globalatmospheric chemical transport model for the combined tro-posphere and stratosphere Model description and analysis ofozone and other trace gases J Geophys Res 109 D04303doi1010292002JD003155 2004

Rotstayn L D and Lohmann U Tropical rainfall trends and theindirect aerosol effect J Climate 15 2103ndash2116 2002

Sander S P Friedl R R Golden D M Kurylo M J MoortgatG K Wine P H Ravishankara A R Kolb C E Molina MJ Finlayson-Pitts B J Huie R E and Orkin V ChemicalKinetics and Photochemical Data for Use in Atmospheric Studiesndash Evaluation Number 15 JPL Publication 06-2 2006

Sanderson M Collins W Derwent R and Johnson C Sim-ulation of global hydrogen levels using a Lagrangian three-dimensional model J Atmos Chem 46 15ndash28 2003

Sanderson M G Dentener F J Fiore A M Cuvelier CKeating T J Zuber A Atherton C S Bergmann D JDiehl T Doherty R M Duncan B N Hess P HorowitzL W Jacob D J Jonson J-E Kaminski J W Lupu AMacKenzie I A Mancini E Marmer E Park R Pitari GPrather M J Pringle K J Schroeder S Schultz M GShindell D T Szopa S Wild O and Wind P A multi-model source-receptor study of the hemispheric transport and de-position of oxidised nitrogen Geophys Res Lett 35 L17815doi1010292008GL035389 2008

Shindell D T Faluvegi G Stevenson D S Krol M C Em-mons L K Lamarque J F Petron G Dentener F J Elling-son K Schultz M G Wild O Amann M Atherton C SBergmann D J Bey I Butler T Cofala J Collins W JDerwent R G Doherty R M Drevet J Eskes H J FioreA M Gauss M Hauglustaine D A Horowitz L W IsaksenI S A Lawrence M G Montanaro V Muller J F Pitari GPrather M J Pyle J A Rast S Rodriguez J M Sander-son M G Savage N H Strahan S E Sudo K Szopa SUnger N van Noije T P C and Zen G Multi-model simu-lations of carbon monoxide Comparison with observations andprojected near-future changes J Geophys Res 111 D19306doi1010292006JD007100 2006

Shindell D T Chin M Dentener F Doherty R M FaluvegiG Fiore A M Hess P Koch D M MacKenzie I A

Sanderson M G Schultz M G Schulz M Stevenson DS Teich H Textor C Wild O Bergmann D J Bey IBian H Cuvelier C Duncan B N Folberth G Horowitz LW Jonson J Kaminski J W Marmer E Park R PringleK J Schroeder S Szopa S Takemura T Zeng G Keat-ing T J and Zuber A A multi-model assessment of pollu-tion transport to the Arctic Atmos Chem Phys 8 5353ndash5372doi105194acp-8-5353-2008 2008

SPARC CCMVal Report on the Evaluation of Chemistry-ClimateModels edited by Eyring V Shepherd T G and Waugh DW SPARC Repot No 4 WCRP-X WMOTD-No XhttpwwwatmospphysicsutorontocaSPARC 2010

Sparks J P Roberts J M and Monson R K The up-take of gaseous organic nitrogen by leaves A significantglobal nitrogen transfer process Geophys Res Lett 30 2189doi1010292003GL018578 2003

Spivakovsky C M Logan J A Montzka S A Balkanski Y JForeman-Fowler M Jones D B A Horowitz L W Fusco AC Brenninkmeijer C A M Prather M J Wofsy S C andMcElroy M B Three-dimensional climatological distributionof tropospheric OH Update and evaluation J Geophys Res105 8931ndash8980 2000

Stevenson D S Dentener F J Schultz M G Ellingsen Kvan Noije T P C Wild O Zeng G Amann M Ather-ton C S Bell N Bergmann D J Bey I Butler T Co-fala J Collins W J Derwent R G Doherty R M DrevetJ Eskes H J Fiore A M Gauss M Hauglustaine D AHorowitz L W Isaksen I S A Krol M C Lamarque J-F Lawrence M G Montanaro V Muller J-F Pitari GPrather M J Pyle J A Rast S Rodriguez J M Sander-son M G Savage N H Shindell D T Strahan S E SudoK and Szopa S Multi-model ensemble simulations of present-day andnear-future tropospheric ozone J Geophys Res 111D08301doi1010292005JD006338 2005

Tilmes S Lamarque J-F Emmons L K Conley A Schultz MG Saunois M Thouret V Thompson A M Oltmans S JJohnson B and Tarasick D Ozonesonde climatology between1995 and 2009 description evaluation and applications AtmosChem Phys Discuss 11 28747ndash28796doi105194acpd-11-28747-2011 2011

Tie X Brasseur G Emmons L Horowitz L and KinnisonD Effects of aerosols on tropospheric oxidants A global modelstudy J Geophys Res 106 22931ndash22964 2001

Tie X Madronich S Walters S Edwards D P Ginoux P Ma-howald N Zhang R Lou C and Brasseur G Assessment ofthe global impact of aerosols on tropospheric oxidants J Geo-phys Res 110 D03204doi1010292004JD005359 2005

Turnipseed A Huey G Nemitz E Stickel R Higgs J Tan-ner D and Slusher D Sparks J Flocke F and GuentherA Eddy covariance fluxes of peroxyacyl nitrates (PANs) andNOy to a coniferous forest J Geophys Res 111 D09304doi1010292005JD006631 2006

van der Werf G R Randerson J T Giglio L Collatz G JKasibhatla P S and Arellano Jr A F Interannual variabil-ity in global biomass burning emissions from 1997 to 2004 At-mos Chem Phys 6 3423ndash3441doi105194acp-6-3423-20062006

Walcek C J Brost R A Chang J S and Wesely M L SO2sulfate and HNO3 deposition velocities computed using regional



landuse and meteorological data Atmos Environ 20 946ndash9641986

Walmsley J L and Wesely M L Modification of coded param-eterizations of surface resistances to gaseous dry deposition At-mos Environ 30 1181ndash1188 1996

Wesely M L Parameterizations for surface resistance to gaseousdry deposition in regional-scale numerical models Atmos Env-iron 23 1293ndash1304 1989

Wesely M L and Hicks B B A review of the current status ofknowledge on dry deposition Atmos Environ 34 2261ndash22822000

Wiedinmyer C Akagi S K Yokelson R J Emmons L K Al-Saadi J A Orlando J J and Soja A J The Fire INventoryfrom NCAR (FINN) a high resolution global model to estimatethe emissions from open burning Geosci Model Dev 4 625ndash641doi105194gmd-4-625-2011 2011

Wild O and Prather M J Global tropospheric ozone modellingQuantifying errors due to grid resolution J Geophys Res 111D11305 doi1010292005JD006605 2006

Yonemura S Kawashima S and Tsuruta H Carbon monoxidehydrogen and methane uptake by soils in a temperate arable fieldand a forest J Geophys Res 105 14347ndash14362 2000

Zhang G J and McFarlane N A Sensitivity of climate simula-tions to the parameterization of cumulus convection in the Cana-dian Climate Centre general circulation model Atmos Ocean33 407ndash446 1995



Atmosphere13 Model13 version13 413

Dynamics13

Physics13

Chemistry13

Coupler13

Lan13 Land13 Model13

Lan13 Ocean13 Model13

Lan13 Sea-shy‐ice13 Model13

Radiagton13

Analysis13

Fig 1 Schematic representation of the Community Earth System Model The dotted box indicates the features associated with the use ofspecified dynamics (use of meteorological analyses and lack of feedback through radiation)

changes in the atmosphere This allows ldquoslow responsesrdquo(for example in ocean currents and ice extent) within the cli-mate system to occur that are prevented in modes 2 and 3Mode 2 only allows fast atmospheric responses and land re-sponses to occur while mode 3 does not allow any meteoro-logical responses

The availability of a specified dynamics configuration ofCAM-chem offers a number of advantages over traditionalchemistry-transport models (1) It allows for consistent sim-ulations between the online and offline versions (2) it allowsthe offline version to be run in an Earth System frameworkso as to fully exploit other model components In particularit allows an incorporation of the biogeochemical algorithmsin the land components (3) it allows for the radiative algo-rithms incorporated into CAM4 to be fully exploited in theoffline version This allows a calculation of the instantaneousradiative forcing within the offline model version includinga calculation of the instantaneous radiative forcing for spe-cific events (eg forest fires) (Pfister et al 2008 Randersonet al 2006) (4) it allows for the strict conservation of tracermass by accounting for changes in mixing ratio as the watervapor concentration changes within the atmosphere In addi-tion CAM-chem uses a chemical preprocessor that providesextensive flexibility in the definition of the chemical mecha-nism allowing for ease of update and modification

Recent applications of CAM-chem have demonstratedits ability to represent tropospheric (Aghedo et al 2011Lamarque et al 2010 2011a b) and stratospheric (Lamar-que et al 2008 Lamarque and Solomon 2010) conditionsincluding temperature structure and dynamics (Butchart etal 2011) Offline CAM-chem has been used in the Hemi-spheric Transport of Air Pollution (HTAP) assessments(Anenberg et al 2009 Fiore et al 2009 Jonson et al 2010Shindell et al 2008 Sanderson et al 2008)

It is the purpose of this paper to document all these as-pects of CAM-chem along with results from associatedsimulations and their evaluation against observations The

paper is therefore organized as follows in Sect 2 we pro-vide a short introduction to CAM4 In Sect 3 we discussall the chemistry-specific parameterizations of CAM-chemThe implementation of the specified-dynamics version is dis-cussed in Sect 4 Section 5 presents the various chemicalmechanisms used in this study Model simulation setups in-cluding emissions are discussed in Sect 6 while the com-parison to observations is shown in Sect 7 Discussion andconclusions are in Sect 8

2 CAM4 description and definition of CAM-chem

CAM-chem refers to the implementation of atmosphericchemistry in the Community Earth System Model (CESM)All the subroutines responsible for the representation ofchemistry are included in the build of CESM only whenexplicitly requested therefore a user only interested in aclimate simulation (for which the atmospheric composi-tion is specified) is not impacted by the additional codeand additional cost to simulate chemistry The chemistryis fully integrated into the Community Atmosphere Model(Fig 1) meaning that the representation of dynamics (in-cluding transport) and physics (radiation convection andlarge-scale precipitation boundary-layer and diffusion) is thesame whether the model is using computed (online) or spec-ified (specified dynamics dotted box in Fig 1) meteorolog-ical fields CAM-chem is therefore CAM4 with chemistryactivated

In the configurations described in this paper atmosphericchemistry interacts with the climate only through radiationsince no cloud-aerosol interaction is available in CAM4 theimpact on temperature is of course overwritten in the case ofspecified dynamics Also the ocean and sea-ice componentsof the CESM are inactive due to the use of specified sea-surface temperatures and sea-ice distribution On the otherhand the land component is fully active and provides thechemistry with deposition velocities and biogenic emissions













31 Dry deposition





O2 + hv =gt 2O

10-13 10-12 10-11 10-10 10-9 10-8

sec-1

100000

10000

1000

100

010

001

Pres

sure

hPa

CAMCHEM (26L)

WACCM (66L)


10-5 10-4 10-3 10-2

sec-1

100000

10000

1000

100

010

001

O(1D) O(3P)

NO +hv =gt N + O

10-11 10-10 10-9 10-8 10-7 10-6 10-5

sec-1

100000

10000

1000

100

010

001


0001 0010sec-1

100000

10000

1000

100

010

001
















33 Wet deposition







Online26 levels

GEOS5MERRA56 levels




34 Lightning














Table 3Continued



Bulk aerosolspecies




Table 3Continued






36 Photolysis










Table 4Continued







Table 4Continued












ki = 220Eminus11(300T)070f =060


ki = 140Eminus12(300T)070f =060







Table 4Continued











ki = 880Eminus12f = 060


ki = 110Eminus11(300T)f = 060



Table 4Continued









Table 4Continued


ki = 300Eminus11f = 050





Table 4Continued






RadonLead Rate







Table 4Continued







ki = 150Eminus11(300T)190f = 060


ki = 200Eminus12(300T)240f = 060




Table 4Continued



ki = 690Eminus12(300T)290f = 060



























GEOS5 MERRA Online

GEOS5 MERRA Online

GEOS5 MERRA Online









GEOS5 MERRA Online

GEOS5 MERRA Online

GEOS5 MERRA Online













O3 Diff()


O3 Diff(ppb)


O3 Diff(ppb)















Species Sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010



C10H16 biogenic 813 813 813 813 813 813 813 813 813 813 813 813 813 813















Table 7Continued

species sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010




DMS ocean 599 599 599 599 599 599 599 599 599 599 599 599 599 599











61 Emissions





GEOS5MERRAOnline


117120108

Bias099112110

Bias

106121119

Bias









Alert

100200300400500600

Ozo

ne (p

pbv)

250hPa

Ny_Alesund

100200300400500600

250hPa

Edmonton

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

Payerne

100200300400500600

Ozo

ne (p

pbv)

250hPa

Sapporo

100200300400500600

250hPa

Boulder

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

San Cristobal

50100150200250300

Ozo

ne (p

pbv)

100hPa

Lauder

100200300400500600

250hPa

Neumayer

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 2008

Years

0

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008




























































EPTOMS-OMI

Online


























OnlineGEOS5MERRA

MISRMODIS









OnlineGEOS5MERRA

Sulfate EC

OC Ammonium Nitrate










Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate




















Edited by A Stenke

References

































































































































31 Dry deposition





O2 + hv =gt 2O

10-13 10-12 10-11 10-10 10-9 10-8

sec-1

100000

10000

1000

100

010

001

Pres

sure

hPa

CAMCHEM (26L)

WACCM (66L)


10-5 10-4 10-3 10-2

sec-1

100000

10000

1000

100

010

001

O(1D) O(3P)

NO +hv =gt N + O

10-11 10-10 10-9 10-8 10-7 10-6 10-5

sec-1

100000

10000

1000

100

010

001


0001 0010sec-1

100000

10000

1000

100

010

001
















33 Wet deposition







Online26 levels

GEOS5MERRA56 levels




34 Lightning














Table 3Continued



Bulk aerosolspecies




Table 3Continued






36 Photolysis










Table 4Continued







Table 4Continued












ki = 220Eminus11(300T)070f =060


ki = 140Eminus12(300T)070f =060







Table 4Continued











ki = 880Eminus12f = 060


ki = 110Eminus11(300T)f = 060



Table 4Continued









Table 4Continued


ki = 300Eminus11f = 050





Table 4Continued






RadonLead Rate







Table 4Continued







ki = 150Eminus11(300T)190f = 060


ki = 200Eminus12(300T)240f = 060




Table 4Continued



ki = 690Eminus12(300T)290f = 060



























GEOS5 MERRA Online

GEOS5 MERRA Online

GEOS5 MERRA Online









GEOS5 MERRA Online

GEOS5 MERRA Online

GEOS5 MERRA Online













O3 Diff()


O3 Diff(ppb)


O3 Diff(ppb)















Species Sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010



C10H16 biogenic 813 813 813 813 813 813 813 813 813 813 813 813 813 813















Table 7Continued

species sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010




DMS ocean 599 599 599 599 599 599 599 599 599 599 599 599 599 599











61 Emissions





GEOS5MERRAOnline


117120108

Bias099112110

Bias

106121119

Bias









Alert

100200300400500600

Ozo

ne (p

pbv)

250hPa

Ny_Alesund

100200300400500600

250hPa

Edmonton

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

Payerne

100200300400500600

Ozo

ne (p

pbv)

250hPa

Sapporo

100200300400500600

250hPa

Boulder

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

San Cristobal

50100150200250300

Ozo

ne (p

pbv)

100hPa

Lauder

100200300400500600

250hPa

Neumayer

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 2008

Years

0

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008




























































EPTOMS-OMI

Online


























OnlineGEOS5MERRA

MISRMODIS









OnlineGEOS5MERRA

Sulfate EC

OC Ammonium Nitrate










Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate




















Edited by A Stenke

References























































































































O2 + hv =gt 2O

10-13 10-12 10-11 10-10 10-9 10-8

sec-1

100000

10000

1000

100

010

001

Pres

sure

hPa

CAMCHEM (26L)

WACCM (66L)


10-5 10-4 10-3 10-2

sec-1

100000

10000

1000

100

010

001

O(1D) O(3P)

NO +hv =gt N + O

10-11 10-10 10-9 10-8 10-7 10-6 10-5

sec-1

100000

10000

1000

100

010

001


0001 0010sec-1

100000

10000

1000

100

010

001
















33 Wet deposition







Online26 levels

GEOS5MERRA56 levels




34 Lightning














Table 3Continued



Bulk aerosolspecies




Table 3Continued






36 Photolysis










Table 4Continued







Table 4Continued












ki = 220Eminus11(300T)070f =060


ki = 140Eminus12(300T)070f =060







Table 4Continued











ki = 880Eminus12f = 060


ki = 110Eminus11(300T)f = 060



Table 4Continued









Table 4Continued


ki = 300Eminus11f = 050





Table 4Continued






RadonLead Rate







Table 4Continued







ki = 150Eminus11(300T)190f = 060


ki = 200Eminus12(300T)240f = 060




Table 4Continued



ki = 690Eminus12(300T)290f = 060



























GEOS5 MERRA Online

GEOS5 MERRA Online

GEOS5 MERRA Online









GEOS5 MERRA Online

GEOS5 MERRA Online

GEOS5 MERRA Online













O3 Diff()


O3 Diff(ppb)


O3 Diff(ppb)















Species Sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010



C10H16 biogenic 813 813 813 813 813 813 813 813 813 813 813 813 813 813















Table 7Continued

species sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010




DMS ocean 599 599 599 599 599 599 599 599 599 599 599 599 599 599











61 Emissions





GEOS5MERRAOnline


117120108

Bias099112110

Bias

106121119

Bias









Alert

100200300400500600

Ozo

ne (p

pbv)

250hPa

Ny_Alesund

100200300400500600

250hPa

Edmonton

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

Payerne

100200300400500600

Ozo

ne (p

pbv)

250hPa

Sapporo

100200300400500600

250hPa

Boulder

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

San Cristobal

50100150200250300

Ozo

ne (p

pbv)

100hPa

Lauder

100200300400500600

250hPa

Neumayer

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 2008

Years

0

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008




























































EPTOMS-OMI

Online


























OnlineGEOS5MERRA

MISRMODIS









OnlineGEOS5MERRA

Sulfate EC

OC Ammonium Nitrate










Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate




















Edited by A Stenke

References



























































































































33 Wet deposition







Online26 levels

GEOS5MERRA56 levels




34 Lightning














Table 3Continued



Bulk aerosolspecies




Table 3Continued






36 Photolysis










Table 4Continued







Table 4Continued












ki = 220Eminus11(300T)070f =060


ki = 140Eminus12(300T)070f =060







Table 4Continued











ki = 880Eminus12f = 060


ki = 110Eminus11(300T)f = 060



Table 4Continued









Table 4Continued


ki = 300Eminus11f = 050





Table 4Continued






RadonLead Rate







Table 4Continued







ki = 150Eminus11(300T)190f = 060


ki = 200Eminus12(300T)240f = 060




Table 4Continued



ki = 690Eminus12(300T)290f = 060



























GEOS5 MERRA Online

GEOS5 MERRA Online

GEOS5 MERRA Online









GEOS5 MERRA Online

GEOS5 MERRA Online

GEOS5 MERRA Online













O3 Diff()


O3 Diff(ppb)


O3 Diff(ppb)















Species Sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010



C10H16 biogenic 813 813 813 813 813 813 813 813 813 813 813 813 813 813















Table 7Continued

species sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010




DMS ocean 599 599 599 599 599 599 599 599 599 599 599 599 599 599











61 Emissions





GEOS5MERRAOnline


117120108

Bias099112110

Bias

106121119

Bias









Alert

100200300400500600

Ozo

ne (p

pbv)

250hPa

Ny_Alesund

100200300400500600

250hPa

Edmonton

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

Payerne

100200300400500600

Ozo

ne (p

pbv)

250hPa

Sapporo

100200300400500600

250hPa

Boulder

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

San Cristobal

50100150200250300

Ozo

ne (p

pbv)

100hPa

Lauder

100200300400500600

250hPa

Neumayer

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 2008

Years

0

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008




























































EPTOMS-OMI

Online


























OnlineGEOS5MERRA

MISRMODIS









OnlineGEOS5MERRA

Sulfate EC

OC Ammonium Nitrate










Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate




















Edited by A Stenke

References























































































































Online26 levels

GEOS5MERRA56 levels




34 Lightning














Table 3Continued



Bulk aerosolspecies




Table 3Continued






36 Photolysis










Table 4Continued







Table 4Continued












ki = 220Eminus11(300T)070f =060


ki = 140Eminus12(300T)070f =060







Table 4Continued











ki = 880Eminus12f = 060


ki = 110Eminus11(300T)f = 060



Table 4Continued









Table 4Continued


ki = 300Eminus11f = 050





Table 4Continued






RadonLead Rate







Table 4Continued







ki = 150Eminus11(300T)190f = 060


ki = 200Eminus12(300T)240f = 060




Table 4Continued



ki = 690Eminus12(300T)290f = 060



























GEOS5 MERRA Online

GEOS5 MERRA Online

GEOS5 MERRA Online









GEOS5 MERRA Online

GEOS5 MERRA Online

GEOS5 MERRA Online













O3 Diff()


O3 Diff(ppb)


O3 Diff(ppb)















Species Sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010



C10H16 biogenic 813 813 813 813 813 813 813 813 813 813 813 813 813 813















Table 7Continued

species sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010




DMS ocean 599 599 599 599 599 599 599 599 599 599 599 599 599 599











61 Emissions





GEOS5MERRAOnline


117120108

Bias099112110

Bias

106121119

Bias









Alert

100200300400500600

Ozo

ne (p

pbv)

250hPa

Ny_Alesund

100200300400500600

250hPa

Edmonton

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

Payerne

100200300400500600

Ozo

ne (p

pbv)

250hPa

Sapporo

100200300400500600

250hPa

Boulder

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

San Cristobal

50100150200250300

Ozo

ne (p

pbv)

100hPa

Lauder

100200300400500600

250hPa

Neumayer

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 2008

Years

0

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008




























































EPTOMS-OMI

Online


























OnlineGEOS5MERRA

MISRMODIS









OnlineGEOS5MERRA

Sulfate EC

OC Ammonium Nitrate










Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate




















Edited by A Stenke

References





























































































































Table 3Continued



Bulk aerosolspecies




Table 3Continued






36 Photolysis










Table 4Continued







Table 4Continued












ki = 220Eminus11(300T)070f =060


ki = 140Eminus12(300T)070f =060







Table 4Continued











ki = 880Eminus12f = 060


ki = 110Eminus11(300T)f = 060



Table 4Continued









Table 4Continued


ki = 300Eminus11f = 050





Table 4Continued






RadonLead Rate







Table 4Continued







ki = 150Eminus11(300T)190f = 060


ki = 200Eminus12(300T)240f = 060




Table 4Continued



ki = 690Eminus12(300T)290f = 060



























GEOS5 MERRA Online

GEOS5 MERRA Online

GEOS5 MERRA Online









GEOS5 MERRA Online

GEOS5 MERRA Online

GEOS5 MERRA Online













O3 Diff()


O3 Diff(ppb)


O3 Diff(ppb)















Species Sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010



C10H16 biogenic 813 813 813 813 813 813 813 813 813 813 813 813 813 813















Table 7Continued

species sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010




DMS ocean 599 599 599 599 599 599 599 599 599 599 599 599 599 599











61 Emissions





GEOS5MERRAOnline


117120108

Bias099112110

Bias

106121119

Bias









Alert

100200300400500600

Ozo

ne (p

pbv)

250hPa

Ny_Alesund

100200300400500600

250hPa

Edmonton

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

Payerne

100200300400500600

Ozo

ne (p

pbv)

250hPa

Sapporo

100200300400500600

250hPa

Boulder

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

San Cristobal

50100150200250300

Ozo

ne (p

pbv)

100hPa

Lauder

100200300400500600

250hPa

Neumayer

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 2008

Years

0

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008




























































EPTOMS-OMI

Online


























OnlineGEOS5MERRA

MISRMODIS









OnlineGEOS5MERRA

Sulfate EC

OC Ammonium Nitrate










Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate




















Edited by A Stenke

References























































































































Table 3Continued






36 Photolysis










Table 4Continued







Table 4Continued












ki = 220Eminus11(300T)070f =060


ki = 140Eminus12(300T)070f =060







Table 4Continued











ki = 880Eminus12f = 060


ki = 110Eminus11(300T)f = 060



Table 4Continued









Table 4Continued


ki = 300Eminus11f = 050





Table 4Continued






RadonLead Rate







Table 4Continued







ki = 150Eminus11(300T)190f = 060


ki = 200Eminus12(300T)240f = 060




Table 4Continued



ki = 690Eminus12(300T)290f = 060



























GEOS5 MERRA Online

GEOS5 MERRA Online

GEOS5 MERRA Online









GEOS5 MERRA Online

GEOS5 MERRA Online

GEOS5 MERRA Online













O3 Diff()


O3 Diff(ppb)


O3 Diff(ppb)















Species Sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010



C10H16 biogenic 813 813 813 813 813 813 813 813 813 813 813 813 813 813















Table 7Continued

species sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010




DMS ocean 599 599 599 599 599 599 599 599 599 599 599 599 599 599











61 Emissions





GEOS5MERRAOnline


117120108

Bias099112110

Bias

106121119

Bias









Alert

100200300400500600

Ozo

ne (p

pbv)

250hPa

Ny_Alesund

100200300400500600

250hPa

Edmonton

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

Payerne

100200300400500600

Ozo

ne (p

pbv)

250hPa

Sapporo

100200300400500600

250hPa

Boulder

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

San Cristobal

50100150200250300

Ozo

ne (p

pbv)

100hPa

Lauder

100200300400500600

250hPa

Neumayer

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 2008

Years

0

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008




























































EPTOMS-OMI

Online


























OnlineGEOS5MERRA

MISRMODIS









OnlineGEOS5MERRA

Sulfate EC

OC Ammonium Nitrate










Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate




















Edited by A Stenke

References




























































































































Table 4Continued







Table 4Continued












ki = 220Eminus11(300T)070f =060


ki = 140Eminus12(300T)070f =060







Table 4Continued











ki = 880Eminus12f = 060


ki = 110Eminus11(300T)f = 060



Table 4Continued









Table 4Continued


ki = 300Eminus11f = 050





Table 4Continued






RadonLead Rate







Table 4Continued







ki = 150Eminus11(300T)190f = 060


ki = 200Eminus12(300T)240f = 060




Table 4Continued



ki = 690Eminus12(300T)290f = 060



























GEOS5 MERRA Online

GEOS5 MERRA Online

GEOS5 MERRA Online









GEOS5 MERRA Online

GEOS5 MERRA Online

GEOS5 MERRA Online













O3 Diff()


O3 Diff(ppb)


O3 Diff(ppb)















Species Sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010



C10H16 biogenic 813 813 813 813 813 813 813 813 813 813 813 813 813 813















Table 7Continued

species sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010




DMS ocean 599 599 599 599 599 599 599 599 599 599 599 599 599 599











61 Emissions





GEOS5MERRAOnline


117120108

Bias099112110

Bias

106121119

Bias









Alert

100200300400500600

Ozo

ne (p

pbv)

250hPa

Ny_Alesund

100200300400500600

250hPa

Edmonton

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

Payerne

100200300400500600

Ozo

ne (p

pbv)

250hPa

Sapporo

100200300400500600

250hPa

Boulder

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

San Cristobal

50100150200250300

Ozo

ne (p

pbv)

100hPa

Lauder

100200300400500600

250hPa

Neumayer

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 2008

Years

0

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008




























































EPTOMS-OMI

Online


























OnlineGEOS5MERRA

MISRMODIS









OnlineGEOS5MERRA

Sulfate EC

OC Ammonium Nitrate










Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate




















Edited by A Stenke

References























































































































Table 4Continued












ki = 220Eminus11(300T)070f =060


ki = 140Eminus12(300T)070f =060







Table 4Continued











ki = 880Eminus12f = 060


ki = 110Eminus11(300T)f = 060



Table 4Continued









Table 4Continued


ki = 300Eminus11f = 050





Table 4Continued






RadonLead Rate







Table 4Continued







ki = 150Eminus11(300T)190f = 060


ki = 200Eminus12(300T)240f = 060




Table 4Continued



ki = 690Eminus12(300T)290f = 060



























GEOS5 MERRA Online

GEOS5 MERRA Online

GEOS5 MERRA Online









GEOS5 MERRA Online

GEOS5 MERRA Online

GEOS5 MERRA Online













O3 Diff()


O3 Diff(ppb)


O3 Diff(ppb)















Species Sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010



C10H16 biogenic 813 813 813 813 813 813 813 813 813 813 813 813 813 813















Table 7Continued

species sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010




DMS ocean 599 599 599 599 599 599 599 599 599 599 599 599 599 599











61 Emissions





GEOS5MERRAOnline


117120108

Bias099112110

Bias

106121119

Bias









Alert

100200300400500600

Ozo

ne (p

pbv)

250hPa

Ny_Alesund

100200300400500600

250hPa

Edmonton

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

Payerne

100200300400500600

Ozo

ne (p

pbv)

250hPa

Sapporo

100200300400500600

250hPa

Boulder

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

San Cristobal

50100150200250300

Ozo

ne (p

pbv)

100hPa

Lauder

100200300400500600

250hPa

Neumayer

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 2008

Years

0

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008




























































EPTOMS-OMI

Online


























OnlineGEOS5MERRA

MISRMODIS









OnlineGEOS5MERRA

Sulfate EC

OC Ammonium Nitrate










Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate




















Edited by A Stenke

References























































































































Table 4Continued











ki = 880Eminus12f = 060


ki = 110Eminus11(300T)f = 060



Table 4Continued









Table 4Continued


ki = 300Eminus11f = 050





Table 4Continued






RadonLead Rate







Table 4Continued







ki = 150Eminus11(300T)190f = 060


ki = 200Eminus12(300T)240f = 060




Table 4Continued



ki = 690Eminus12(300T)290f = 060



























GEOS5 MERRA Online

GEOS5 MERRA Online

GEOS5 MERRA Online









GEOS5 MERRA Online

GEOS5 MERRA Online

GEOS5 MERRA Online













O3 Diff()


O3 Diff(ppb)


O3 Diff(ppb)















Species Sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010



C10H16 biogenic 813 813 813 813 813 813 813 813 813 813 813 813 813 813















Table 7Continued

species sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010




DMS ocean 599 599 599 599 599 599 599 599 599 599 599 599 599 599











61 Emissions





GEOS5MERRAOnline


117120108

Bias099112110

Bias

106121119

Bias









Alert

100200300400500600

Ozo

ne (p

pbv)

250hPa

Ny_Alesund

100200300400500600

250hPa

Edmonton

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

Payerne

100200300400500600

Ozo

ne (p

pbv)

250hPa

Sapporo

100200300400500600

250hPa

Boulder

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

San Cristobal

50100150200250300

Ozo

ne (p

pbv)

100hPa

Lauder

100200300400500600

250hPa

Neumayer

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 2008

Years

0

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008




























































EPTOMS-OMI

Online


























OnlineGEOS5MERRA

MISRMODIS









OnlineGEOS5MERRA

Sulfate EC

OC Ammonium Nitrate










Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate




















Edited by A Stenke

References























































































































Table 4Continued









Table 4Continued


ki = 300Eminus11f = 050





Table 4Continued






RadonLead Rate







Table 4Continued







ki = 150Eminus11(300T)190f = 060


ki = 200Eminus12(300T)240f = 060




Table 4Continued



ki = 690Eminus12(300T)290f = 060



























GEOS5 MERRA Online

GEOS5 MERRA Online

GEOS5 MERRA Online









GEOS5 MERRA Online

GEOS5 MERRA Online

GEOS5 MERRA Online













O3 Diff()


O3 Diff(ppb)


O3 Diff(ppb)















Species Sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010



C10H16 biogenic 813 813 813 813 813 813 813 813 813 813 813 813 813 813















Table 7Continued

species sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010




DMS ocean 599 599 599 599 599 599 599 599 599 599 599 599 599 599











61 Emissions





GEOS5MERRAOnline


117120108

Bias099112110

Bias

106121119

Bias









Alert

100200300400500600

Ozo

ne (p

pbv)

250hPa

Ny_Alesund

100200300400500600

250hPa

Edmonton

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

Payerne

100200300400500600

Ozo

ne (p

pbv)

250hPa

Sapporo

100200300400500600

250hPa

Boulder

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

San Cristobal

50100150200250300

Ozo

ne (p

pbv)

100hPa

Lauder

100200300400500600

250hPa

Neumayer

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 2008

Years

0

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008




























































EPTOMS-OMI

Online


























OnlineGEOS5MERRA

MISRMODIS









OnlineGEOS5MERRA

Sulfate EC

OC Ammonium Nitrate










Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate




















Edited by A Stenke

References























































































































Table 4Continued


ki = 300Eminus11f = 050





Table 4Continued






RadonLead Rate







Table 4Continued







ki = 150Eminus11(300T)190f = 060


ki = 200Eminus12(300T)240f = 060




Table 4Continued



ki = 690Eminus12(300T)290f = 060



























GEOS5 MERRA Online

GEOS5 MERRA Online

GEOS5 MERRA Online









GEOS5 MERRA Online

GEOS5 MERRA Online

GEOS5 MERRA Online













O3 Diff()


O3 Diff(ppb)


O3 Diff(ppb)















Species Sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010



C10H16 biogenic 813 813 813 813 813 813 813 813 813 813 813 813 813 813















Table 7Continued

species sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010




DMS ocean 599 599 599 599 599 599 599 599 599 599 599 599 599 599











61 Emissions





GEOS5MERRAOnline


117120108

Bias099112110

Bias

106121119

Bias









Alert

100200300400500600

Ozo

ne (p

pbv)

250hPa

Ny_Alesund

100200300400500600

250hPa

Edmonton

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

Payerne

100200300400500600

Ozo

ne (p

pbv)

250hPa

Sapporo

100200300400500600

250hPa

Boulder

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

San Cristobal

50100150200250300

Ozo

ne (p

pbv)

100hPa

Lauder

100200300400500600

250hPa

Neumayer

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 2008

Years

0

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008




























































EPTOMS-OMI

Online


























OnlineGEOS5MERRA

MISRMODIS









OnlineGEOS5MERRA

Sulfate EC

OC Ammonium Nitrate










Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate




















Edited by A Stenke

References























































































































Table 4Continued






RadonLead Rate







Table 4Continued







ki = 150Eminus11(300T)190f = 060


ki = 200Eminus12(300T)240f = 060




Table 4Continued



ki = 690Eminus12(300T)290f = 060



























GEOS5 MERRA Online

GEOS5 MERRA Online

GEOS5 MERRA Online









GEOS5 MERRA Online

GEOS5 MERRA Online

GEOS5 MERRA Online













O3 Diff()


O3 Diff(ppb)


O3 Diff(ppb)















Species Sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010



C10H16 biogenic 813 813 813 813 813 813 813 813 813 813 813 813 813 813















Table 7Continued

species sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010




DMS ocean 599 599 599 599 599 599 599 599 599 599 599 599 599 599











61 Emissions





GEOS5MERRAOnline


117120108

Bias099112110

Bias

106121119

Bias









Alert

100200300400500600

Ozo

ne (p

pbv)

250hPa

Ny_Alesund

100200300400500600

250hPa

Edmonton

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

Payerne

100200300400500600

Ozo

ne (p

pbv)

250hPa

Sapporo

100200300400500600

250hPa

Boulder

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

San Cristobal

50100150200250300

Ozo

ne (p

pbv)

100hPa

Lauder

100200300400500600

250hPa

Neumayer

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 2008

Years

0

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008




























































EPTOMS-OMI

Online


























OnlineGEOS5MERRA

MISRMODIS









OnlineGEOS5MERRA

Sulfate EC

OC Ammonium Nitrate










Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate




















Edited by A Stenke

References























































































































Table 4Continued







ki = 150Eminus11(300T)190f = 060


ki = 200Eminus12(300T)240f = 060




Table 4Continued



ki = 690Eminus12(300T)290f = 060



























GEOS5 MERRA Online

GEOS5 MERRA Online

GEOS5 MERRA Online









GEOS5 MERRA Online

GEOS5 MERRA Online

GEOS5 MERRA Online













O3 Diff()


O3 Diff(ppb)


O3 Diff(ppb)















Species Sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010



C10H16 biogenic 813 813 813 813 813 813 813 813 813 813 813 813 813 813















Table 7Continued

species sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010




DMS ocean 599 599 599 599 599 599 599 599 599 599 599 599 599 599











61 Emissions





GEOS5MERRAOnline


117120108

Bias099112110

Bias

106121119

Bias









Alert

100200300400500600

Ozo

ne (p

pbv)

250hPa

Ny_Alesund

100200300400500600

250hPa

Edmonton

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

Payerne

100200300400500600

Ozo

ne (p

pbv)

250hPa

Sapporo

100200300400500600

250hPa

Boulder

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

San Cristobal

50100150200250300

Ozo

ne (p

pbv)

100hPa

Lauder

100200300400500600

250hPa

Neumayer

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 2008

Years

0

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008




























































EPTOMS-OMI

Online


























OnlineGEOS5MERRA

MISRMODIS









OnlineGEOS5MERRA

Sulfate EC

OC Ammonium Nitrate










Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate




















Edited by A Stenke

References























































































































Table 4Continued



ki = 690Eminus12(300T)290f = 060



























GEOS5 MERRA Online

GEOS5 MERRA Online

GEOS5 MERRA Online









GEOS5 MERRA Online

GEOS5 MERRA Online

GEOS5 MERRA Online













O3 Diff()


O3 Diff(ppb)


O3 Diff(ppb)















Species Sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010



C10H16 biogenic 813 813 813 813 813 813 813 813 813 813 813 813 813 813















Table 7Continued

species sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010




DMS ocean 599 599 599 599 599 599 599 599 599 599 599 599 599 599











61 Emissions





GEOS5MERRAOnline


117120108

Bias099112110

Bias

106121119

Bias









Alert

100200300400500600

Ozo

ne (p

pbv)

250hPa

Ny_Alesund

100200300400500600

250hPa

Edmonton

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

Payerne

100200300400500600

Ozo

ne (p

pbv)

250hPa

Sapporo

100200300400500600

250hPa

Boulder

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

San Cristobal

50100150200250300

Ozo

ne (p

pbv)

100hPa

Lauder

100200300400500600

250hPa

Neumayer

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 2008

Years

0

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008




























































EPTOMS-OMI

Online


























OnlineGEOS5MERRA

MISRMODIS









OnlineGEOS5MERRA

Sulfate EC

OC Ammonium Nitrate










Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate




















Edited by A Stenke

References






































































































































GEOS5 MERRA Online

GEOS5 MERRA Online

GEOS5 MERRA Online









GEOS5 MERRA Online

GEOS5 MERRA Online

GEOS5 MERRA Online













O3 Diff()


O3 Diff(ppb)


O3 Diff(ppb)















Species Sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010



C10H16 biogenic 813 813 813 813 813 813 813 813 813 813 813 813 813 813















Table 7Continued

species sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010




DMS ocean 599 599 599 599 599 599 599 599 599 599 599 599 599 599











61 Emissions





GEOS5MERRAOnline


117120108

Bias099112110

Bias

106121119

Bias









Alert

100200300400500600

Ozo

ne (p

pbv)

250hPa

Ny_Alesund

100200300400500600

250hPa

Edmonton

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

Payerne

100200300400500600

Ozo

ne (p

pbv)

250hPa

Sapporo

100200300400500600

250hPa

Boulder

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

San Cristobal

50100150200250300

Ozo

ne (p

pbv)

100hPa

Lauder

100200300400500600

250hPa

Neumayer

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 2008

Years

0

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008




























































EPTOMS-OMI

Online


























OnlineGEOS5MERRA

MISRMODIS









OnlineGEOS5MERRA

Sulfate EC

OC Ammonium Nitrate










Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate




















Edited by A Stenke

References























































































































GEOS5 MERRA Online

GEOS5 MERRA Online

GEOS5 MERRA Online













O3 Diff()


O3 Diff(ppb)


O3 Diff(ppb)















Species Sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010



C10H16 biogenic 813 813 813 813 813 813 813 813 813 813 813 813 813 813















Table 7Continued

species sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010




DMS ocean 599 599 599 599 599 599 599 599 599 599 599 599 599 599











61 Emissions





GEOS5MERRAOnline


117120108

Bias099112110

Bias

106121119

Bias









Alert

100200300400500600

Ozo

ne (p

pbv)

250hPa

Ny_Alesund

100200300400500600

250hPa

Edmonton

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

Payerne

100200300400500600

Ozo

ne (p

pbv)

250hPa

Sapporo

100200300400500600

250hPa

Boulder

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

San Cristobal

50100150200250300

Ozo

ne (p

pbv)

100hPa

Lauder

100200300400500600

250hPa

Neumayer

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 2008

Years

0

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008




























































EPTOMS-OMI

Online


























OnlineGEOS5MERRA

MISRMODIS









OnlineGEOS5MERRA

Sulfate EC

OC Ammonium Nitrate










Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate




















Edited by A Stenke

References
























































































































O3 Diff()


O3 Diff(ppb)


O3 Diff(ppb)















Species Sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010



C10H16 biogenic 813 813 813 813 813 813 813 813 813 813 813 813 813 813















Table 7Continued

species sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010




DMS ocean 599 599 599 599 599 599 599 599 599 599 599 599 599 599











61 Emissions





GEOS5MERRAOnline


117120108

Bias099112110

Bias

106121119

Bias









Alert

100200300400500600

Ozo

ne (p

pbv)

250hPa

Ny_Alesund

100200300400500600

250hPa

Edmonton

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

Payerne

100200300400500600

Ozo

ne (p

pbv)

250hPa

Sapporo

100200300400500600

250hPa

Boulder

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

San Cristobal

50100150200250300

Ozo

ne (p

pbv)

100hPa

Lauder

100200300400500600

250hPa

Neumayer

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 2008

Years

0

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008




























































EPTOMS-OMI

Online


























OnlineGEOS5MERRA

MISRMODIS









OnlineGEOS5MERRA

Sulfate EC

OC Ammonium Nitrate










Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate




















Edited by A Stenke

References
























































































































Species Sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010



C10H16 biogenic 813 813 813 813 813 813 813 813 813 813 813 813 813 813















Table 7Continued

species sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010




DMS ocean 599 599 599 599 599 599 599 599 599 599 599 599 599 599











61 Emissions





GEOS5MERRAOnline


117120108

Bias099112110

Bias

106121119

Bias









Alert

100200300400500600

Ozo

ne (p

pbv)

250hPa

Ny_Alesund

100200300400500600

250hPa

Edmonton

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

Payerne

100200300400500600

Ozo

ne (p

pbv)

250hPa

Sapporo

100200300400500600

250hPa

Boulder

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

San Cristobal

50100150200250300

Ozo

ne (p

pbv)

100hPa

Lauder

100200300400500600

250hPa

Neumayer

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 2008

Years

0

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008




























































EPTOMS-OMI

Online


























OnlineGEOS5MERRA

MISRMODIS









OnlineGEOS5MERRA

Sulfate EC

OC Ammonium Nitrate










Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate




















Edited by A Stenke

References























































































































Table 7Continued

species sector 1997 1998 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010




DMS ocean 599 599 599 599 599 599 599 599 599 599 599 599 599 599











61 Emissions





GEOS5MERRAOnline


117120108

Bias099112110

Bias

106121119

Bias









Alert

100200300400500600

Ozo

ne (p

pbv)

250hPa

Ny_Alesund

100200300400500600

250hPa

Edmonton

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

Payerne

100200300400500600

Ozo

ne (p

pbv)

250hPa

Sapporo

100200300400500600

250hPa

Boulder

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

San Cristobal

50100150200250300

Ozo

ne (p

pbv)

100hPa

Lauder

100200300400500600

250hPa

Neumayer

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 2008

Years

0

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008




























































EPTOMS-OMI

Online


























OnlineGEOS5MERRA

MISRMODIS









OnlineGEOS5MERRA

Sulfate EC

OC Ammonium Nitrate










Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate




















Edited by A Stenke

References























































































































GEOS5MERRAOnline


117120108

Bias099112110

Bias

106121119

Bias









Alert

100200300400500600

Ozo

ne (p

pbv)

250hPa

Ny_Alesund

100200300400500600

250hPa

Edmonton

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

Payerne

100200300400500600

Ozo

ne (p

pbv)

250hPa

Sapporo

100200300400500600

250hPa

Boulder

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

San Cristobal

50100150200250300

Ozo

ne (p

pbv)

100hPa

Lauder

100200300400500600

250hPa

Neumayer

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 2008

Years

0

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008




























































EPTOMS-OMI

Online


























OnlineGEOS5MERRA

MISRMODIS









OnlineGEOS5MERRA

Sulfate EC

OC Ammonium Nitrate










Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate




















Edited by A Stenke

References























































































































Alert

100200300400500600

Ozo

ne (p

pbv)

250hPa

Ny_Alesund

100200300400500600

250hPa

Edmonton

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

Payerne

100200300400500600

Ozo

ne (p

pbv)

250hPa

Sapporo

100200300400500600

250hPa

Boulder

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 20080

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

1996 2000 2004 20080

20

40

60

80

800hPa

San Cristobal

50100150200250300

Ozo

ne (p

pbv)

100hPa

Lauder

100200300400500600

250hPa

Neumayer

100200300400500600

250hPa

40

60

80

100

Ozo

ne (p

pbv)

500hPa40

60

80

100

500hPa40

60

80

100

500hPa

1996 2000 2004 2008

Years

0

20

40

60

80

Ozo

ne (p

pbv)

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

1996 2000 2004 2008

Years

0

20

40

60

80

800hPa

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

Ozo

ne (p

pbv)

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

600

400

200

100

60

20

60

20

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008

1996 2000 2004 2008 1996 2000 2004 2008 1996 2000 2004 2008




























































EPTOMS-OMI

Online


























OnlineGEOS5MERRA

MISRMODIS









OnlineGEOS5MERRA

Sulfate EC

OC Ammonium Nitrate










Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate




















Edited by A Stenke

References











































































































































































EPTOMS-OMI

Online


























OnlineGEOS5MERRA

MISRMODIS









OnlineGEOS5MERRA

Sulfate EC

OC Ammonium Nitrate










Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate




















Edited by A Stenke

References







































































































































OnlineGEOS5MERRA

MISRMODIS









OnlineGEOS5MERRA

Sulfate EC

OC Ammonium Nitrate










Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate




















Edited by A Stenke

References























































































































OnlineGEOS5MERRA

Sulfate EC

OC Ammonium Nitrate










Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate




















Edited by A Stenke

References
























































































































Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate

Sulfate

EC

OC

Ammonium Nitrate




















Edited by A Stenke

References



































































































































Edited by A Stenke

References






















































































































cam-chem: description and evaluation of interactive ... · conclusions are in sect. 8. 2 cam4...

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